CONTINUED PROCESS VERIFICATION AN INDUSTRY POSITION …

Continued Process Verification : An industry position paper with example plan©BioPhorum Operations Group Ltd | April 2020 1

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CONTINUED PROCESS VERIFICATION AN INDUSTRY

POSITION PAPER WITH EXAMPLE PLAN

©BioPhorum Operations Group Ltd | April 2020 Continued Process Verification : An industry position paper with example plan 2

ContributorsThe following people were lead contributors to the content of this document, writing sections, editing and liaising with colleagues to ensure that the messages it contains are representative of current thinking across the biopharmaceutical industry. This document is a consensus view of a model CPV Plan, but it does not represent fully, the internal policies of the contributing companies.

AbbVie Mike Doremus

AstraZeneca Cynthia Ball

Baxter Joerg Gampfer

Bayer John Grunkemeier

Gallus Madeline Roche

Genzyme Lada Laenan

AstraZeneca Ranjit Deschmukh

Genentech/Roche Mark Smith

Merck Beth Junker

Novartis Christelle Pradines

GSK Dan Baker

Lonza Rajesh Beri

Merck Julia O’Neill

Novartis Abe Germansderfer

Pfizer Jeff Fleming

Regeneron Jenny McNay

Pfizer Eric Hamann Paul McCormac

Regeneron Rajesh Ahuja

Shire Bert Frohlich

Additionally, excellent editorial support and constructive criticism was provided by:

The work was facilitated by Darren Whitman and Robin Payne of BioPhorum Operations Group.

Though this paper is issued under copyright, © 2014, Biophorum Operations Group, it is intended to be readily accessed

across the industry, free of charge and can be accessed from the BioPhorum website at the following address:

www.biophorum.com/download/cvp-case-study-interactive-version/

When citing this paper, please use the following form:

BioPhorum, 2014, Continued Process Verification: An Industry Position Paper with Example Plan

© 2014, Biophorum Operations Group

Redesigned in line with new brand guidelines April 2020

https://www.biophorum.com/cpv-continued-process-verification-case-study/


SUMMARY

Executive summaryThis paper is a response to US Food and Drug Administration (FDA) 2011 process validation guidance on Stage 3, ‘Process Validation: General Principles and Practices’5. It describes the approach commonly referred to as ‘Continued Process Verification’ (CPV). As one might expect, manufacturers in the biopharmaceutical sector all wish to respond to this guidance appropriately. A group of 20+ companies felt it would be valuable to work on this topic together, using the facilitation services of the BioPhorum Operations Group (BPOG) (www.biophorum.com). This paper is one of the results of the collaborative effort. It is written as a consensus view of an acceptable CPV program, but it does not fully represent the internal policies of the contributing companies. It is a basis upon which to build and share knowledge further across the industry. The authors believe this is one of the first comprehensive papers on this topic.

The paper seeks to provide practical developments on the themes: what is CPV, why is it

important, and how might it be implemented. It offers some specific recommendations on the

content of a CPV Plan, along with associated rationale. These recommendations are based on

a typical cell culture production process for making a fictitious monoclonal antibody product,

described in the ‘A-Mab Case Study’3. Consequently, not all of the details contained in this

paper are going to apply directly to actual products or processes. The authors recognize that

the A-Mab Case Study represents only one industry archetype, and that there are a number

of others that are important. However, the concepts and principles upon which the content

of this paper was derived should help with CPV implementation for a real product. Some of

the complications of implementation are addressed, with recommended approaches, but the

issue of information technology (IT) systems is not dealt with directly here. The case for IT

systems, their design and introduction, is the subject of other collaborative efforts facilitated

by BPOG and some of the results of that work may be published in the future.


CPV is fundamentally a formal means by which a

commercial manufacturing process is monitored to

ensure product quality. It encompasses a written plan for

monitoring a licensed biopharmaceutical manufacturing

process, as well as regular reporting and actions based

on the results of monitoring the process. CPV reporting

provides a basis from which to improve process

understanding, risk assessment, the control strategy

(CS) [9], and ultimately the process itself. In general, the

nature and extent of CPV is aligned with the outcomes of

process qualification. Whilst a CPV Plan is likely to include

data related to Batch Release (BR), and so may be useful

in supporting BR decisions, CPV operates independently

from the BR process and is not expected to have any

impact on batches that have been previously released.

Adopting or building on an existing system of monitoring

manufacturing process performance means more data

will be collected over the lifetime of the product. CPV

execution may involve examination of existing process

control measurements and improved methods for data

tracking and analysis. Enhanced monitoring of process

performance provides the opportunity to identify and

control sources of variation and hence improve process

robustness, offering the major benefit of reliable supply to

the market.

One of the main technical issues to resolve when

implementing CPV relates to the quantity of data required

before product commercialization. In a sense, CPV

complements the ‘Quality by Design’ (QbD) framework

that manufacturers have developed to license and

commercialize the product, though a CPV Plan may be

constrained to data available in manufacturing. It should

be noted that not all products will have a QbD framework

but all need a CPV Plan. Also, at the time of commercial

product introduction, there is unlikely to be a statistically

robust set of data at the scale of commercial manufacture.

To manage this situation in practice, it is recommended

that short term control criteria are set initially, based on

prior process experience and including data acquired at

the laboratory and clinical scales of manufacture. This

initial period of production would then be used to establish

longer term criteria that are more statistically appropriate.

The implementation and ongoing execution of a CPV

Plan is likely to require additional effort, beyond what is

typically needed to prepare for the Annual Product Review

(APR), because significant amounts of additional data

are collected and analyzed to improve understanding of

process variability. However, it is likely that the benefits

accruing from the improved information available for

process improvement will outweigh any additional costs.

The actual additional cost depends on the amount of data

to be analysed which in turn depends on the outcomes

of quality risk assessments that define data collection

scope and frequency. The frequency of collection depends

on several factors, including: whether production is

campaigned or continuous; the extent of variability

apparent in the process; whether risks to product quality

(and thus product disposition) and process consistency are

sufficiently mitigated, and the intended use of the reported

data (for example, use in continuous improvement may

mean collecting and analyzing certain data on a daily

basis).

Given the importance of CPV in both compliance and

process improvement terms, the authors encourage

executives to read and share this paper with their

colleagues. The authors also welcome any comments or

questions arising which can be submitted via the following

email address: [email protected].


ContentsContents ............................................................................................................................................................................................................ 5

1.0 Purpose ................................................................................................................................................................................................ 7

2.0 Scope .................................................................................................................................................................................................... 9

3.0 Roles and responsibilities..............................................................................................................................................................10

4.0 CPV plan references .....................................................................................................................................................................12

5.0 Product and process description ................................................................................................................................................13

5.1 Brief description of the general approach used in the A-Mab case study ................................................................... 14

5.2 Parameters to be included in CPV .......................................................................................................................................... 15

5.3 Upstream process overview ..................................................................................................................................................... 16

5.4 Downstream process overview ............................................................................................................................................... 17

5.5 Identification of CQAs and acceptance ranges ................................................................................................................... 18

5.6 Process parameter characterization ...................................................................................................................................... 20

5.7 Control strategy CQAs and CPPS ........................................................................................................................................... 22

6.0 Developing a monitoring strategy ..............................................................................................................................................23

6.1 Rationale and background ........................................................................................................................................................ 23

6.2 Hypothetical scenarios and planned process changes ...................................................................................................... 24

7.0 CPV plan recommendations for the A-Mab process ..............................................................................................................28

7.1 Step 1, seed culture expansion in disposable vessels – CPV recommendations ....................................................... 29

7.2 Step 2, Seed Culture Expansion in Bioreactors – CPV Recommendations ................................................................ 30

7.3 Step 3, Production Culture Bioreactor – CPV Recommendations ............................................................................... 31

7.4 Step 4, Clarification (centrifugation and depth filtration) – CPV recommendations .............................................. 35

7.5 Step 5, Protein A Chromatography – CPV recommendations ........................................................................................ 36

7.6 Step 6, Low pH treatment – CPV recommendations ......................................................................................................... 37

7.7 Step 7, Cation Exchange Chromatography (CEX) – CPV recommendations ............................................................. 39

7.8 Step 8, Anion Exchange Chromatography (AEX) – CPV recommendations ............................................................... 40

7.9 Step 9, Small Virus Retentive Filtration (SVRF) – CPV recommendations ................................................................. 42

7.10 Step 10, Ultrafiltration and Diafiltration (UF/DF) – CPV recommendations ............................................................. 43

7.11 Step 11, Final Filtration and Freezing of BDS – CPV recommendations ..................................................................... 45

7.12 Bulk Drug Substance Lot Data – CPV recommendations ................................................................................................. 47

8.0 Frequency and scope of CPV analysis .......................................................................................................................................49

8.1 Scope of CPV Analysis .............................................................................................................................................................. 49

8.2 Frequency of Analysis ................................................................................................................................................................. 50

9.0 Establishing control limits ...........................................................................................................................................................51


10.0 Example CPV execution plan for drug substance ...................................................................................................................53

10.1 Step 1, Seed culture expansion in disposable vessels – CPV variables ....................................................................... 57

10.2 Step 2, Seed culture expansion in bioreactors – CPV variables .................................................................................... 58

10.3 Step 3, Production culture bioreactor – CPV variables .................................................................................................... 59

10.4 Step 4, centrifugation and depth filtration – CPV variables ........................................................................................... 62

10.5 Step 5, Protein A chromatography – CPV variables .......................................................................................................... 63

10.6 Step 6, Low pH treatment – CPV variables .......................................................................................................................... 64

10.7 Step 7, Cation exchange chromatography – CPV variables ............................................................................................ 65

10.8 Step 8, Anion exchange chromatography – CPV variables .............................................................................................. 66

10.9 Step 9, Small virus retentive filtration – CPV variables ................................................................................................... 68

10.10 Step 10, Ultrafiltration and diafiltration – CPV variables ................................................................................................ 68

10.11 Step 11, Final filtration/Bulk fill and freezing of BDS – CPV variables ........................................................................ 70

10.12 CPV monitoring of bulk drug substance lot data ................................................................................................................ 71

11.0 CPV sampling plan ..........................................................................................................................................................................73

11.1 Template for specific process steps ...................................................................................................................................... 76

12.0 How data will be analyzed ...........................................................................................................................................................80

12.1 Identifying software .................................................................................................................................................................... 80

12.2 Description of tools to trend and evaluate data ................................................................................................................. 81

12.3 Process capability index ............................................................................................................................................................ 82

12.4 Control charts ............................................................................................................................................................................... 84

12.5 Multivariate data analysis ......................................................................................................................................................... 86

12.6 Responses to shifts and trends ................................................................................................................................................. 87

12.7 Establishing initial limits ............................................................................................................................................................ 88

12.8 Establishing long-term limits .................................................................................................................................................... 88

12.9 Finding signals of special cause variation ............................................................................................................................. 89

13.0 Change management ....................................................................................................................................................................90

14.0 Dataverification ..............................................................................................................................................................................93

Discretionary elements of a CPV program .............................................................................................................................................95

References ......................................................................................................................................................................................................96

Glossary ...........................................................................................................................................................................................................97


1.0

PurposeThis document is written with the aim of providing a technical, non-binding, industry consensus response to regulatory guidance. It is not in itself guidance. The objective of this paper is to provide:

(1) an example of key portions of a Continued Process Verification (CPV) plan for a biologics process; (2) relevant industry thinking on CPV plan development and implementation.

This document is different from others on this subject1, 2

because it is specific to a biologics manufacturing process

and provides a comprehensive case-study lifecycle

view that leverages antibody manufacturing process

development, as described in the A-Mab Quality-by-

Design case study3. It is worth the reader being familiar

with the A-Mab case study and perhaps having a copy

available for reference. It should be recognised that

the monoclonal antibody process is just one archetype

in the industry, though it is a useful one upon which to

demonstrate principles, as it is known to many.

The example of a CPV plan shown in this paper describes

how to meet expectations5 for routine monitoring

of critical process parameters (CPPs), critical quality

attributes (CQAs), key process attributes (KPAs) and key

process parameters (KPPs) to demonstrate the state of

control over the manufacturing process. N.B. at the time

of writing, the European Medicines Agency (EMA) draft

guidance on Process Validation is out for consultation,

referring to KPAs as 'performance indicators'. The thought

processes and examples presented in this document

are backed by biotech industry experience with, subject

matter expertise in process monitoring for monoclonal

antibody and similar manufacturing processes.

Furthermore, this document describes the thought

processes that determine the content for a CPV plan. The

plan serves as the procedure governing document for the

implementation and maintenance of CPV for a licensed

manufacturing process. Various parts of the plan are

described in the following sections of the document, as

noted in Table 1.1 overleaf:


General topic section Section

number/title

Description

Manufacturing Process 5 Summary of the A-Mab manufacturing process and the A-Mab product description.

6 Selection of the process monitoring sampling plan backed by the process validation Phase I and II

data and the updated risk assessment.

7 The rationale for classification of quality-linked process parameters summarized in the A-Mab

case study is reviewed and summarized in the table that presents process performance

consistency and robustness. Rationale for what to include in CPV is provided, based on a

review and analysis of quality-linked process parameters from the A-Mab case study that

affect process performance, consistency and robustness.

Verification process 8 The frequency of CPV data analysis and trend review is discussed. The concept of an initial

or short-term CPV phase is introduced, where sufficient process experience is collected

to establish the manufacturing control limits for the process attributes identified during

validation. A subsequent phase of CPV implementation; that of steady state or long-term

process monitoring is also discussed.

9 Statistical and general methods for establishing CPV trend limits are presented.

10 The summary of the monitored attributes and parameters within the scope of the CPV program

are presented. The monitoring method and periodicity associated with specific attributes and

parame ters are also specified.

11 The sampling plan derivation with tabulated examples.

12 Aspects of data analysis and evaluation of results are discussed in this section. The emphasis is on

the possible outcomes of routine monitoring.

13 Change management and the impact of CPV on this process.

14 The specific need for data verification.

15 Elements of CPV that are considered discretionary.

Table 1.1: Plan parts referenced by section number


2.0

ScopeConsistent with the FDA’s 2011 Process Validation guidance document5 describing three stages of the product lifecycle, CPV implementation discussed in this paper is limited to Stage 3, commercial manufacture of a drug substance, following process design (Stage 1) and process qualification and qualification of the equipment and the facility (PQ, Stage 2, see FDA 2011 Guidance Stage 24) 12.

Note: Whilst this paper focuses on the drug substance

manufacturing process, CPV should be applied all areas of

Operations including formulation, fill and finish.

The application of the principles discussed in this

document BioPhorum is initiating collaborative work,

specifically focused for new products relies on product

and process development on CPV for established, licensed

(or legacy) products and the and characterization studies

(Stage 1) to define the scope resulting recommendations

may be published in the future. of the CPV program. This

document is based on the CS An ISPE group produced an

article covering this broadened presented in the A-Mab

bioprocess development case scope in 201224; here we

believe we address a reduced study and is primarily

focused on the commercialization of scope in greater

detail, providing deeper development of a a new product.

However, the proposed approach for CPV model CPV

Plan. implementation is also applicable to legacy products

where quality attributes and parameters for monitoring

can be determined based on a combination of process

knowledge and historical performance data.

BioPhorum is initiating collaborative work, specifically

focused on CPV for established, licensed (or legacy)

products and the resulting recommendations may be

published in the future. An ISPE group produced an article

covering this broadened scope in 201224; here we believe

we address a reduced scope in greater detail, providing

deeper development of a model CPV Plan.


3.0

Roles and responsibilitiesThe roles and responsibilities suggested here as examples, are based upon a typical organizational structure of a biopharmaceutical manufacturing company (Table 3.1.).

Several primary functional areas have important

responsibilities required to successfully execute the CPV

program. These areas are: Development, Validation,

Operations, Quality Control, Quality Engineering and

Quality Assurance. Operations, a function which may also

be known as Technical Operations, is assumed to contain

Manufacturing as well as Manufacturing Science and

Technology personnel. Mathematical sciences or

non-clinical statistics support is of paramount importance

in achieving sound data interpretation. Each functional

area has responsibility for specific activities, as shown in

Table 3.1.

Outputs of the CPV program can be used by the

Regulatory Affairs and Quality organizations for annual

agency updates, such as the Annual Product Review (APR)

and Product Quality Review (PQR). Terminology for each

function may vary by organization.

Note: The responsibilities for continued process monitoring

should be clearly defined within the organization and recorded

in the CPV Plan. Responsibilities can be tailored to a specific

organizational structure, given its maturity and size.


Table 3.1: Roles and Responsibilities for a CPV Program:

Functional area Responsibility

Management • Ensure that adequate resources are available to carry out the CPV program and to regularly

perform a review of CPV plan summaries or reports.

Development • Provide documentation that defines current process knowledge, quality attributes, process

parameters and elements of the overall CS that forms the basis for the CPV program.

• Provide documented scientific justification for parameters, limits, ranges and elements of the

CS, based upon development studies or other prior knowledge.

• Provide technical input to develop response actions, including input in prioritization of

continuous improvement activities.

• Consider application of CPV outcomes to new processes in development.

Validation/ Quality functions • Provide internal advice on current validation principles and ensure that validation protocols,

interim and final reports meet applicable standards.

• Participate in cross-functional teams to review production and QC data as part of the CPV

program.

• Review the data, pursue appropriate investigations and make decisions on how to proceed.

• May generate CPV plans and summary reports.

• Review and approve CPV plan, CPV reports and any changes to the CPV plan.

Operations/Manufacturing

Science and Technology

(N.B. It is not unusual for a Manufacturing Science

and Technology function to be independent

of Operations and Quality organisations. An

alternative arrangement may be

reporting into Process Sciences.)

• Own the manufacturing process and take responsibility to ensure that it is maintained in a

state of control throughout the product lifecycle in manufacturing.

• Ensure that all required production and process data are collected as part of executing the

CPV plan for the product.

• Performs continued process monitoring activities, including collecting, entering, verifying,

reviewing and analyzing process data.

• Generate control charts and document CPV analysis for process data.

• Regularly participate in cross-functional teams in order to review production and QC data

as part of the CPV program.

• Maintain the process commercial master batch production and control records up to date,

capturing continuous improvements resulting from CPV in documentation as necessary.

Quality Control • Perform quality control testing and document results that are used in CPV evaluations.

• Perform continued process monitoring activities, including collecting, entering, verifying, reviewing

and analyzing QC data.

• Generate control charts and document CPV analysis for QC data.

• Participate in cross-functional teams to review production and QC data as part of the CPV program.

Quality Engineering/Mathematical Sciences/

Non Clinical Statistics

• Provide internal advice on statistical analyses needed to successfully complete CPV activities.

• Act as a Subject Matter Expert (SME) and train personnel in other groups on statistical data

analysis techniques used in CPV.

• Provide internal advice on how to develop the data collection plan and help select suitable

statistical methods and procedures that are used to measure and evaluate the process

stability and capability.

• Generate procedures that define the way statistical tools and approaches are to be used in

routine process monitoring.

• Provide guidance on how to set control limits and define and interpret signal criteria.

Quality Assurance • Review and approve CPV plans and reports.

• Review and approve the list of attributes and parameters to be monitored, and control

chart limits.

• Participate in cross-functional data review to review production and QC data as part of

the CPV program.

• Review CPV reports and establish where signals require formal non-conformance

investigations.

• Coordinate assembly of CPV program reports.


4.0

CPV plan references The following references are expected to be created in the quality management system and are important when constructing a CPV plan, providing background and critical internal interpretation of regulatory guidance. They should be referenced accurately in a CPV Plan document. Note, a CPV Plan is expected to be product and process specific. It may be advantageous to develop corporate policies and this forms the basis for some of the list of references that follows.

• Quality Policy, Manual or Master Plan on CPV

• Company Standard/Guideline for CPV

(requirements for CPV, for e.g. timing, relationship

to APRs, etc)

• SOP on CPV (Definitions, Abbreviations, responses

to deviations, report generation, etc)

• SOP on Statistical Methods for trending, statistical

analysis and identifying special cause variations

• Template for CPV Plan

• Template for CPV Charts & Graphs

• Template for CPV Report

• Manufacturing process description

• Control Strategy for the process (version number)

• Process risk assessment (version number)

• Applicable Risk assessment(s) (version number)

providing basis for rationale of CPV monitoring

selection

• Previous annual product report(s) if available,

otherwise consider evidence for a similar product*.

Technical references relevant to the detailed sections of

this paper are provided in section 16. References 1 to 9 are

recommended as initial texts when creating or updating a

CPV plan.

*The authors recognize that the plan illustrated in this paper

is written largely with CPV for new products in mind and that

there would not be APRs available at the point of product

licensure. This bullet point is included as a reminder that

historic APRs would provide data for the creation of a CPV plan

where established, licensed or legacy products are concerned.

©BioPhorum Operations Group Ltd | April 2020 Continued Process Verification : An industry position paper with example plan 13BPOG Continued Process Verification: An Industry Position Paper With Example Plan – Page 13

5.0

Product and process description The A-Mab case study describes a model Quality by Design (QbD) approach for development of a monoclonal antibody (A-Mab)3,6. Considering the FDA process validation guideline5, the case study includes work covered during Stage 1 (Process Design) but does not include information on Stage 2, Process Performance Qualification (PPQ)5.

In preparing this CPV example plan, it is assumed that

Stage 2, was completed successfully for the A-Mab

process. The plan described applies to Stage 3 of the

process validation lifecycle.

Note: Whilst a QbD approach could be said to provide was

completed successfully for the A-Mab process. The plan

advantages in terms of process understanding, it is not an

described applies to Stage 3 of the process validation lifecycle.

approach that has to be applied. However, it is necessary to

have a CPV Plan for each product, even if a QbD approach has

not been applied.


PPQ CPV CPV

TTP QTTP CQA CCP

EQ

ProvenAcceptable

Ranges(Design Space)

Covered in A-Map Study

Short-termPlan

Long-termPlanDevelopment of Control Strategy

PV Stage 2 PV Stage 3PV Stage 1

Figure 5.1.2. Process flow of a QbD based product development according to ICH Q8, 9, 10, 11 and FDA PV guideline January 2011.

Principles outlined in the ICH guidelines Q8, Q9, Q10 and

Q117-9, 22 provide the basis for the methodology used for

this case study, even though Q11 was published after the

A-Mab case study.

One principle of a QbD approach is to develop a Target

Product Profile (TPP). As a natural extension of a TPP, a

Quality Target Product Profile (QTPP) is built to describe

quality characteristics (attributes) of the drug product.

The process of systematic development follows a general

roadmap that includes the following steps:

• Identification of Quality Attributes (QA)

based on a QTPP;

• Risk Evaluation to identify CQAs;

• Upstream/ downstream/ drug substance and

product development;

• Risk based approaches and potentially,

multivariate analyses25 (see Section 12.5 for a

description of multivariate analysis), to classify

process parameters and other variables linked

to product quality (e.g. identification of Critical

Process Parameters, CPPs);

5.1 Brief description of the general approach used in the A-Mab case study

• Univariate and multivariate approaches to define

Proven Acceptable Range (PARs) or limits;

• Rational approach to define a CS that reflects

product/ process knowledge and risk mitigation;

• Process (and Equipment) Performance

Qualification to verify the CS established in Stage 1

of development.

• Facility design qualification of Stage 25.

In creating this CPV plan it is assumed that all deliverables

up to establishment of a CS and PQ are available based on

the work described in the A-Mab study (see Figure 5.1.2 /

green boxes). For the A-Mab process, it is assumed that

PPQ was completed successfully, after investigating and

resolving deviations.

PPQ and Equipment Qualification (EQ) are part of Stage

2 and are therefore presumed to have been completed

before Stage 3 where CPV guidance applies. They are a

pre-requisite for Stage 3 CPV. See guidance for Industry5.


5.2 Parameters to be included in CPVAll types of parameters should be considered for inclusion

in CPV. Typically those included will be weighted more in

favor of CPPs and WC-CPPs because of their importance

to the control strategy, but non-critical “key” and general

parameters should not be overlooked if they are indicative

of process performance and/or measurably impact process

variation. Parameters to be included should be based on

the current understanding of the manufacturing process

and may be subject to change over time.

Parameter types described in A-Mab study are as follows: (1) Critical Process Parameter (CPP) and (2) Well-

Controlled Critical Process Parameter (WC-CPP): CPPs

and WC-CPPs are process parameters whose variability

impact a critical quality attribute and should be monitored

or controlled to ensure the process achieves the required

product quality.

• A WC-CPP has a lower risk of falling outside the

specified limits.

• A CPP has a higher risk of falling outside the

specified limits.

The assessment of risk is based on a combination of factors

that include severity of impact to quality, equipment

design considerations, process control capability

and complexity, the size and reliability of the proven

acceptable range and/or design space, ability to detect/

measure a parameter deviation, etc.

(3) Key Process Parameter (KPP): An adjustable

parameter (variable) of the process that ensures

operational reliability when maintained within a

narrow range. A key process parameter does not affect

critical product quality attributes but rather impacts

process consistency.

(4) General Process Parameter (GPP): An adjustable

parameter (variable) of the process that does not have

a meaningful impact on product quality or process

performance.

Typically the parameters included in CPV will be weighted

more in favor of CPP and WC-CPP because of their

importance to the control strategy. But, non-critical “key”

and general parameters should not be overlooked as

they may be indicative of process performance and/or

measurably impact process variation. Definitions of A-Mab

terms used to define categories of process parameter are

provided in a Glossary at the end of this document.

Note: Throughout this paper the A-Mab classification of

process parameters is used for consistency with the structure

of that case study, but it must be recognised this is not the

only scheme used in the industry; a situation arising in part

no standard approach is recommended by the regulators.

Consistency with ICH Q8 and Q11, where definitions exist

seems prudent. A recent informal communication by FDA/

EMA counseled against using “key parameter” for describing

lower levels of criticality in formal submissions and stated that:

‘all parameters potentially impacting product quality should be

classified as critical process parameters’23. The use of KPPs in

internal systems and documentation seems not to contravene

this statement.

In general, it is the responsibility of the biopharm company

to establish a categorization and nomenclature fitting with

their development approach and risk evaluation tools. The

company’s approach should be clearly explained and followed

over the life cycle of the product.


5.3 Upstream process overviewThe upstream commercial manufacturing process for A-Mab comprises 4 steps and is summarized below and in Figure 5.4.

The A-Mab cell culture process uses a proprietary, chemically defined, basal medium formulation. The medium is essentially

protein free with recombinant human insulin (1 mg/mL) being the only protein component added. The growth medium also

contains 1 g/L pluronic and 50 nM methotrexate, which are added up to the N-2 seed bioreactor. The N-1 and production

bioreactor steps do not contain methotrexate.

Figure 5.4: Upstream process flow diagram. [Adapted from A-Mab case study, Page 62 (Figure 3.1)]

ThawWorking Cell Bank

Seed Culture Expansionin disposable shake flasks

and/or bags

Seed Culture Expansionin fixed stirred tank reactors

N-1 Seed Culture Bioreactor3000L WV

Production Bioreactor15,000 L WV

HarvestCentrifugation & Depth Filtration

Clarified bulk

Step 4

Step 3

Step 2

Step 1

Nutrient feed

Glucose feeds

Seedmaintenance

Seedmaintenance

Seed cultures are expanded through multiple passagesby increasing the volume and/or number of disposableculture vessels. Seed cultures may be maintained foradditional culture passages or used to generateadditional inoculums trains.

Additional seed expansion in fixed stirred tankbioreactors. Cultures obtained from Step 1 areexpanded to increase the volume of culture to meet thetarget initial cell density for the production bioreactor.

Production bioreactor is inoculated with the seedculture prepared in Step 2 to achieve an initial ViableCell Concentration (VCC) and is cultivated atcontrolled conditions for temperature, pH anddissolved oxygen (DO). A bolus addition of nutrientfeed (NF-1) and multiple discrete glucose feeds areused to maintain the glucose concentration at > 1.0g/L. Antifoam solution is used for foam control ofthe agitated mixture. VCC, culture viability andresidual glucose concentration are monitoredperiodically. The fermentation reaction produces amixture containing the A-Mab drug substance.

Cultures are clarified by a primary continuouscentrifugation step using a disk-stack centrifuge toremove the bulk of suspended cells and cell debris.A secondary clarification using a depth filtrationsystem removes remnant solids and smaller debris toprovide a clarified bulk solution of A-Mab.


5.4 Downstream process overviewThe downstream manufacturing process for A-Mab comprises 7 steps which are summarized in Figure 5.5.

The downstream process captures A-Mab from the clarified bulk and purifies the antibody by a combination

of chromatography unit operations11. Also included in the process are two orthogonal steps dedicated to virus

inactivation and removal. The antibody is formulated through an Ultra-Filtration/Dia-Filtration (UF/DF) step

to a composition and concentration suitable for drug product manufacturing. The formulated product is 0.2 μm

filtered, filled into the appropriate storage containers and stored frozen.

Figure 5.5: Downstream process flow diagram. [Adapted from A-Mab case study, Pages 113 (Figure 4.1) and 114 (Table 4.1)]

Step 5Protein A Affinity Chromatography

Step 6Low pH Incubation

Step 7Cation Exchange Chromatography

Step 8Anion Exchange Chromatography

Step 9Small Virus Retentive Filtration

Step 11Final Filtration, Fill and Freeze

Clarified Purpose

Purpose of step

• Capture monoclonal antibody from clarified harvest liquid• Removal of process-related impurities (HCP, DNA and small molecules)

Step 10Formulation:

Ultrafiltration and Diafiltration A

A-Mab drug substance

• Inactivate enveloped viruses that are potentially present in therapeutic protein products derived from mammalian cell culture

• Reduce aggregate to acceptable levels for drug substance• Reduce HCP to acceptable levels for subsequent processing by AEX chromatography

• Remove HCP, DNA, Protein A and endotoxins to levels that meet drug substance acceptance criteria• Virus removal

• Removal of small parvoviruses such as minute virus of mice (MVM) and larger viruses such as murine leukemia virus (MuLV), potentially present in product derived from mammalian cell culture

• Formulation and concentration of mAb to drug substance specifications (e.g. 75 g A-Mab/L)

• Sterilize filtration and dispensing for drug substance storage


5.5 Identification of CQAs and acceptance rangesTable 5.6.1 provides the QTPP of the A-Mab drug product, as defined in the A-Mab case study. The QTPP describes

quality characteristics (attributes) that the drug product should possess in order to reproducibly deliver the therapeutic

benefit promised in the label. Attributes in the red box are determined during Drug Substance (DS) manufacturing.

Therefore, these attributes guide determination of DS CQAs22 relevant for establishing a CPV strategy.

Table 5.6.1: QTPP for A-Mab (reference 3, Page 180). DS relevant product attributes are marked with a red box

Product attribute Target

Dosage form Liquid, single use

Protein content per vial 500mg

Dose 10mg/kg

Concentration 25mg/mL

Mode of administration IV, diluted with isotonic saline or dextrose

Viscosity Acceptable for manufacturing, storage and delivery without the use of special devices (for example, less

than 10 cP at room temperature

Container 20R type 1 borosilicate glass vials, fluro-resin laminated stopper

Shelf life ≥ 2 years at 2–8°C

Compatibility with manufacturing process Minimum 14 days at 25°C and subsequent 2 years at 2–8°C, soluble at higher concentration

during UF/DF

Biocompatibility Acceptable toleration on infusion

Degradants and impurites Below safety threshold, or qualified

Pharmacopeial compliance Meets pharmacopoeial requirements for parental dosage forms, colorless to slightly yellow, practically

free of visible particles and meets USP criteria for sub-visiable particles

Aggregate 0–5%

Fucose conent 2–13%

Galactosylation (%G1+%G2) 10–40%

HCP 0-100 ng/mg

The DS QAs related to the QTPP are identified as summarized in Table 5.6.2. Criticality Analysis was performed using a risk

ranking approach (as in ICH Q98) and CQAs were identified as attributes of high or very high risk regarding their potential

impact on patient safety.


The product quality attributes and the points where they are impacted in the A-Mab drug substance process are

summarized in the Table 5.6.2 below.

Table 5.6.2: A-Mab drug substance Product Quality Attributes and the points where they are impacted in the process (see A-Mab Case Study (3), Section 2.3.2,

Page 29). BDS is Bulk Drug Substance, DP Drug Product and IPC in-process control.

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5.6 Process parameter characterizationIn reviewing the A-Mab process information while preparing the CPV plan, members of BioPhorum team questioned

thecompleteness of the CPPs, KPPs and Key Process Attributes (KPAs) identified in the case study. Specifically, it was

felt two steps of the downstream process (step 10 UF/DF, and step 11 final filtration and freezing of the Bulk Drug

Substance, BDS) were not addressed in sufficient detail in the case study for the purpose of developing a CPV Plan, so

typical characterization outcomes for these steps were assumed and CPPs, KPPs and KPAs were identified based on

that characterization10. In addition, two more KPPs and KPAs were identified for process steps 3 and 7, based on typical

outcomes for similar monoclonal antibody processes. The following table summarizes all CPPs, in-process quality attributes

(IPQAs), KPPs and KPAs identified for the process in preparation for CPV.

Figure 5.7: Critical and key process parameters and key process attributes identified during process characterization. Lists were amended during

planning for CPV (bold entries)

Process Step Critical Process Parameters In-process Controls Key Process Parameters Key Process Attributes

Step 1: Seed culture

expansion in disposable

shake flasks and/ or bags

None None Temperature,

Culture duration,

Initial VCC/ split ratio

VCC (viable cell conc), Culture

viability

Step 2: Seed culture

expansion in fixed stirred

tank reactors

None None Temperature,

pH, Dissolved oxygen,

Culture duration,

Initial VCC/ split ratio

VCC,

Culture viability

Step 3: Production

bioreactor 15,000l wv

Temperature,

pH,

Max partial pressure of CO2

(pCO2),

Culture duration,

Medium Osmolality

Bioburden,

Mycoplasma,

MMV and AVA

(murine minute virus and

adventitious viral agents)

Antifoam conc.,

Time of nutrient feed,

Volume of nutrient feed,

Time of glucose feed,

Volume of glucose feed,

Dissolved oxygen

Product yield (titer),

Viability at harvest,

Turbidity at harvest,

Peak VCC, Remnant glucose

concentration

Step 4: Harvest

centrifugation & depth

filtration

None None Flow rate, Pressure,

Duration of clarification

Step yield,

Turbidity


Figure 5.7: Critical and key process parameters and key process attributes identified during process characterization. Lists were amended during planning for CPV

(bold entries)

Process Step Critical Process Parameters In-process Controls Key Process Parameters Key Process Attributes

Step 5: Protein a affinity

chromatography

Protein load ratio,

Elution buffer pH

Bioburden,

Endotoxin

End collection,

Step duration

Step yield

Step 6: Low ph incubation pH,

Time,

Temperature

Bioburden,

Endotoxin

Quantity of acid added

Step 7: Cation exchange

chromatography

Protein load ratio,

Wash conductivity,

Elution pH,

Elution stop collect

Bioburden,

Endotoxin

Step duration Step yield,

Eluate volume

Step 8: Anion exchange

chromatography

Equilibration/ Wash1

buffer conductivity,

Protein load ratio,

Load conductivity,

Load pH,

Flow rate

Bioburden,

Endotoxin

Step duration Step yield

Step 9: Small virus

retentive filtration

Operating pressure,

Filtration volume

Bioburden,

Endotoxin

None Step yield

Step 10: formulation:

ultrafiltration and

diafiltraion

Number of

dia-volumes,

pH,

Step processing time,

Protein conc. prior to fill

Bioburden,

Endotoxin

Protein conc. prior to

Diafiltration,

Recirculation flow rate

Step yield,

Permeate flow rate

Step 11: final filtration, fill

and freeze

None Bioburden,

Endotoxin

Filtration volume,

Filtration time,

Maximum (inlet) pressure

Bulk fill step yield


5.7 Control strategy CQAs and CPPS Risk-based criticality assessment, along with process

characterization studies, allows a CS to be established

which is subsequently verified during PPQ. Table 5.7

summarizes the CS established for the A-Mab upstream

and downstream process steps for A-Mab production.

The CS consists of CPPs and WC-CPPs, KPPs, KPAs and

IPQAs. The CS should ensure required product quality and

a consistent and robust process.

Here, CPPs must be controlled within limits and in-process

controls (specifically microbial and viral safety) must be

within specified ranges to ensure drug safety and efficacy.

Although KPPs and KPAs have been shown not to impact

product quality, they are included in the CS because

their monitoring and control ensures that the process

is operated in a consistent and predictable manner. The

control of KPPs and KPAs also ensures that commercial

success criteria such as cycle time and yield are met.

Product quality and safety are ensured by controlling all

quality-linked process parameters (CPPs and WCCPPs)

within the limits. Process consistency is ensured by

controlling KPPs within established limits and by

monitoring relevant process attributes.


6.0

Developing a monitoring strategy

6.1 Rationale and background CPV is a formal activity enabling the detection of variation in the manufacturing process that might have an impact on the product quality or process consistency. CPV should evaluate whether the process consistently delivers product with acceptable QAs and continues to operate robustly, within the validated state. It should also identify any new sources of variability in the process that may have arisen since the initial Stage 2 PQ was performed. For this case study it has been determined that PPQ batches will be included in CPV data collection and analysis; indeed, all appropriate batches should be considered. CPV efforts should, where appropriate, also focus on areas that have proved challenging or may have shifted since the initial validation. A risk based approach to process monitoring should be used to direct these efforts. For products with a legacy history, a defined time period or number of batches should be set to determine how much of the historical experience will be considered. The assessment interval chosen should be sufficient to establish a solid production history and also reflect the frequency of production. For example a product that is produced frequently may permit a shorter time period to be used relative to a product that is produced infrequently.

In general, the points in the process to be monitored

during CPV should be comparable to, but not necessarily

include all of those selected during the initial validation.

If limited data results are available at the time of PPQ

completion, prior to execution of the CPV plan, a short

term sampling plan may be established to continue

sampling based on the PPQ protocol until sufficient data

results are gathered in preparation for CPV. Additional

considerations that influence the determination of which

points in the process are monitored during a CPV exercise

are summarized below.

(1) The final classification of attributes should be revisited.

(2) The process risk assessment, which is typically

performed prior to the initial PPQ, should be revisited

and updated to develop the CPV plan. The revised risk

assessment should reflect learning obtained during

PPQ, any additional laboratory process characterization

information, and key findings from historical

manufacturing experience. In revisiting the process

risk assessment prior to commercial manufacture, late

stage clinical manufacturing knowledge is particularly

important. Levels of risk, and indeed the range of risks,

that apply in the manufacturing environment might be

quite different to those anticipated from the early stage

development environment.

(3) The control strategy should be updated as necessary

and hence the CPV Plan.

The selection of points in the manufacturing process

that are to be monitored for CPV purposes may be

either a subset of those selected during PPQ or include

additional monitoring points beyond those included in

the initial PPQ to reflect new learning obtained since

the initial validation was conducted.

This includes but is not limited to:

• New CS elements

• Process elements that have proved challenging

but may not have been covered during the initial

process validation

• Changed or additional analytical capabilities,

including the availability of online data

collection systems and improvements in assay

or instrument capabilities

• If a parameter has been shown to have good

control and consistency, it may not be necessary to

continue monitoring this parameter in subsequent

CPV evaluations.


(4) CPPs, WC-CPPs, KPPs and GPPs should be clearly

specified. These parameters and established release

specifications, additional product characterization

testing, and KPAs should be appropriately considered

during CPV. Any changes since the initial validation

should be explained and justified.

(5) All changes implemented should be assessed in

the context of potential impact on process validation.

Process changes which may have occurred after the

PPQ, such as vendor initiated change in a raw material,

should be handled a change control process including

but not limited to data trending and risk assessments,

to determine if the change has any impact on process

performance and/ or product quality. These changes

may potentially require additional testing beyond that

performed as part of PPQ to ensure full characterization.

Such testing may be incorporated as part of CPV or may

be handled separately as part of the company’s change

control process, depending on the nature of the change

and the potential for product impact.

(6) Appropriate regulatory reporting of CPV outcomes,

such as inclusion in the Annual Product Review (APR),

must be made for any conclusions related to process

assessment conducted during CPV. The CPV reports

should be consistent with regulatory reporting

standards, so that CPV charts may be copied and pasted

directly into the regulatory submissions or included

as an attachment. The regulatory submissions then

provide context and unify the information presented in

the attached CPV reports.

(7) Other elements of Good Manufacturing Practice

(GMP) applicable to biopharmaceutical production

operations are assumed to be handled by appropriate

quality systems and are therefore outside the scope

of this document, and will not be discussed further

in the context of process validation. In particular,

acceptable microbial control is a critical element for any

biopharmaceutical process and is typically demonstrated

via initial validation efforts and then monitored as part of

routine operations.

6.2 Hypothetical scenarios and planned process changes

Five hypothetical scenarios and planned changes are

provided below to illustrate how the CPV monitoring

plan might be affected by events encountered during

commercialization of a product such as A-Mab. In this

example it is assumed that the PPQ campaign proceeded

smoothly and that the expected results were achieved.

6.2 Hypothetical scenarios and planned process changesFive hypothetical scenarios and planned changes are provided below to illustrate how the CPV monitoring plan might be affected by events encountered during commercialization of a product such as A-Mab. In this example it is assumed that the PPQ campaign proceeded smoothly and that the expected results were achieved. In particular, CPPs, WC-CPPs, KPPs and GPP are defined and achievable and the process CS is appropriately established. The process CS is assumed to include input raw material controls, procedural controls, process parameter controls and monitoring, in-process testing, and product specification testing (see Figure 5.1.1). These scenarios are accounted for in the CPV plan:

Scenario 1: Supplier change notification - culture medium change.

A supplier converted to a new process to manufacture a cell

culture medium ingredient that may alter its performance

in the A-Mab process without impacting the material

procurement specifications. No intentional changes to

composition, test requirements or certificate of analysis

were made. The following justification for the change was

provided:

(1) Improved control of temperature during blending reduces

potential for degradation of the heat labile components;

(2) Equipment cleaning will use robust validated cycles to

reduce ingredient carryover risks;

(3) Equipment is located in an Animal Origin Free area to

reduce cross contamination risks.


The following strategy was employed to introduce the revised

cell culture medium:

• Determining process and quality impact for the material

change through the change control process was electively

agreed to by process experts and quality representatives

via verification testing of culture performance and the

ability to operate within the established parameters and

attributes;

• A study was thus completed in the small-scale model

from thaw through the production bioreactor to provide

additional process characterization data and establish

confidence in expectations of process control when the

new material lot is introduced into the commercial scale

process;

• Minor but statistically significant differences for KPPs

normal operating ranges and attributes (e.g. VCC, and cell

density, titer and turbidity at the end of the bioreactor

production) were identified at small scale;

• Medium qualification attributes should be assessed in

the change control evaluation to determine if/ how these

attributes may be impacted. The supplier was requested

to demonstrate if a detectable mean shift in any of their

output tests could be identified with respect to their

change.

Small scale production bioreactor material was purified

downstream. No structural modifications to the protein,

or shifts in CQAs were observed. Based on the outcome

of the small scale studies, a comparison should be made to

evaluate the product quality obtained at full scale, to verify

that no unexpected quality change has occurred and to

provide further verification of process control ranges and

performance outcomes.

A CPV plan is expected to take account of this type of

scenario, providing the internal policies and procedures upon

which decisions related to changes in process verification

should be based. The change described in this scenario can be

addressed through the change management system and does

not require additional sampling in the CPV plan, as routine

sampling is already in place to monitor the upstream cell

growth impact of this scenario (Tables 7.1, 7.2, 7.3 and 10.1,

10.2, 10.3). Potential downstream impact could be included

in the monitoring plan, e.g. the KPAs of inlet pressure to

depth filters and duration of the broth clarification, which are

suggested as optional items for CPV in Tables 7.4, 10.4.

Note: Attributes should only be considered optional after their

impact on the process has been risk assessed and any lack of

monitoring fully justified.

Scenario 2: High Protein A leachate observed in chromatography eluate, Step 5.

A PPQ batch contained 123 mg of protein A/g A-Mab in the

Protein A pool, which exceeded the control limit for this

process-related impurity. Investigation revealed that:

• Protein A ligand released from the chromatography resin

(‘Resin A’ from Supplier A) and entered the process stream

during product elution. R&D and Supplier A confirmed

that elevated amounts of Protein A can leach from the

bead surface during an initial elution after extended

resin storage, even when storing under recommended

conditions;

• Extended storage can cause increased Protein A leaching

in the next use cycle. The resin storage time of more

than 12 months between the last clinical manufacturing

batch and first PPQ batch was longer than previously

experienced and was not represented in small scale trials

used to establish PPQ limits;

• In-process testing of the Protein A clearance will be

performed to further demonstrate downstream process

capability of control of this product quality attribute (AEX

Table 7.8, 10.8);

• The level measured in the Protein A step eluate for the

batch implicated by this scenario was orders of magnitude

below the impurity safety limit for final drug product.

Also, at full scale in the affected PPQ batch, downstream

clearance of Protein A below the detectable level was

demonstrated which is consistent with small-scale

observations that the subsequent chromatography steps

are capable of removing Protein A (The possibility that

limits or controls on extended storage time, conditions,

and/ or resin treatments may need to be considered if

data indicates the clearance capability of the process is

not sufficiently high enough for the reader’s situation).

An additional Design of Experiments (DOE) study was

conducted after PPQ to determine the potential for Protein

A leaching relative to storage time, resin age (use cycles) and

storage conditions. Spiking study confirmation of clearance

capabilities in the downstream process steps was achieved

and is discussed in the amended CS revision completed

after the PPQ experience, where the new CPPs to control

clearance are clearly identified. Within CPV, results will

be monitored to detect any departures from the expected

behavior observed during development; monitoring tools

such as ‘tool wear charts’ or ‘residuals charts’ may be useful,

and consultation with a statistician is recommended. These

tools are mentioned again in Section 12.4.


Scenario 3: High elution volume from CEX, Step 7.

Because of resin capacity limitations, elution of the

product stream through the CEX resin requires processing

a batch in multiple portions (sub-batches). The column

eluate streams are then pooled. With one PPQ batch,

an unexpected additional volume of buffer solution was

required to complete the elution of A-Mab from the CEX

column resin for one sub-batch. The prior wash of process

impurities from Cation Exchange (CEX) Chromatography

resin proceeded without incident but there was a delay

while the additional elution buffer was prepared (during

which the product loaded column was idle) before

proceeding to complete the product elution operation to

recover all the A-Mab from the resin.

• No impact on A-Mab quality was detected, which

involved a deviation for a KPA (elution buffer

volume);

• The investigation did not reveal a definitive root

cause. Performance of the flow meter was not

implicated as a cause of the unusual observation

from review of GPPs and instrument calibration

checks;

• Flow channeling through the resin was the initial

suspected cause, but no similar observation was

made during earlier or later PPQ sub-batches;

• Delay in starting the elution operation may have

played a role, but this could not be confirmed

because it had not been specifically studied, nor did

delays after load prior to elution occur in historical

small-scale studies;

• Similar incidents have not been observed with

other A-Mab batches at any scale studied; A

change in the buffer (e.g. conductivity which is not

a CPP, or pH which is a CPP) as a result of the delay

has not been conclusively eliminated as a cause,

but no deviation associated with the buffer was

apparent from careful scrutiny of the batch record

(BRc) and interview with process operators.

Investigation of elution buffer stability data is also

suggested. If insufficient hold time and buffer attribute

data exists to determine the potential for buffer stability

to be a contributing cause, this may be pursued as an

independent study, rather than including buffer chemical

stability in the CPV Plan. Tracking of buffer volume used

to elute A-Mab from the CEX column is included in the

CPV recommendations for this step (see Tables 7.7,

10.7) because it has demonstrated variability and there

is a theoretical potential for increased aggregates with

extended processing time (not observed in any studies as

of yet) that may result from the need for additional elution

to recover A-Mab from the CEX resin.

Scenario 4: UF/DF measurements exceeded action limits.

During preparation of one PPQ batch, the starting UF/

DF concentration measurements did not meet the PPQ

control limits and step yield was above the expected PPQ

range. The starting UF/DF concentration has not been

classified as a KPA in the A-Mab case study.

• A change prior to PPQ revised the in-process UV

absorbance (A280) test method, which led to an

apparent upward shift in yield results. While a

bridging study was conducted to determine the

suitability of the revised test method, evaluation

of the change did not consider the impact to the

limits used during PPQ that were calculated based

on earlier experience. Limits in place during

PPQ were based on measurements from the

previous version of the method used for in-process

monitoring.

• Change control improved the accuracy of the

measurement and also removed a bias error

when compared to the final bulk drug substance

concentration which uses a different method

performed in the QC release testing laboratory.

• The implemented change in the test method

involved improvements to both the precision and

accuracy of the in-process measurement system;

there has been no change to the UF/DF process.

Analytical SME’s decided it would be inappropriate

to compare new results to a set of limits based

on data measured using a different/ altered

procedure, or simply adjust previous results for a

fixed bias correction (due to potential proportional

variance, see section 12.4).

• The corrective action being implemented will

supersede the original PPQ limits with new CPV

limits calculated using data from the revised test

method procedure.

No monitoring recommendations for CPV are proposed

as a result of this scenario. Care should be taken not to

include data generated prior to the method change in

calculating long-term limits.


Scenario 5: Environmental monitoring during Bulk Drug Substance (BDS) fill, Step 11.

During environmentally controlled open system filling

of one BDS batch, the routine sentinel plates indicated

environmental monitoring (EM) bioburden was above the

PPQ action limits. Investigation determined that:

• Based on organism identification, the likely source

was skin flora shed by an operator who conducted

the final filtration and filling of the BDS;

• The bioburden samples of each post-filtration

product container (for the PPQ) met the

acceptance criteria with results of 0 CFU/ 10 mL.

This confirmed that the 0.2 μ m filtered BDS was

not impacted and the routine criteria were met for

batch release (BR);

• Following the filling operation, BDS is frozen

within 24 hrs, and once thawed, the material is

pooled, mixed, and sampled for bioburden prior

to sterile filtration when initiating drug product

manufacturing;

• Corrective and preventive actions have been

implemented, including a review of personnel

practices, skills and training, and changes to

operating procedures to alert operators to

use appropriate practices when working in the

controlled filling environment.

No additional monitoring recommendations for CPV

are proposed as a result of this scenario because, even

with this incident, no impact to the BDS was found and

corrective actions have been implemented to prevent

its recurrence. Routine monitoring is sufficient. No

addition to the enhanced monitoring plan is needed

because it is not reasonable to expect from a single

incident that there will be variability in bioburden results

due to the processing of this step. Note: Whilst attributes

and parameters that are included in a CPV Plan are likely

to include some that are relevant to BR, a CPV program

is expected to operate independently of BR processes and

procedures. Analysis of data within the CPV program is

not expected to have an impact on product that has been

previously released. The release of batches compares batch

quality and performance to a specific set of pre-determined

specifications and other measures. In contrast, the focus

of CPV is to reveal trends and sources of variation in

batch quality and performance that already fall within the

predetermined criteria for BR.


7.0

CPV plan recommendations for the A-Mab process This section describes, for each of the A-Mab process steps, what to include in the CPV plan and the justification for its conclusion. This justification is primarily based on process knowledge and process experience. A table is provided for each step to summarize the recommendations for CPV. Discretionary items are also included that may be needed in a CPV program depending on the assurance of process understanding or that provide additional depth to the monitoring plan.

No recommendation for including in-process product

pool hold times in CPV is proposed, because the hold

times were validated as part of the basis for controls

within the Master BRc. In the event that a hold time is

exceeded this one-off event would trigger a deviation

within the Quality System, under which impact to

product quality would be determined.

In the steps with elution of product from resin beds

(i.e. steps 5 and 7), several resin loading/ elution cycles

are used to process each batch. No controls have been

identified for resin regeneration operations in either of

these steps. For these steps, concurrent validation of

the resin use lifetime includes periodic sample testing of

appropriate quality attributes for continued verification

of packed resin effectiveness during its use lifetime.

Effectiveness of resin regeneration conditions is included

in the ongoing resin use validations. Therefore monitoring

of CQAs for this purpose need not be included in the CPV

plan. Continued monitoring, and further verification of

effective process controls, should be considered for CPV

when resin use lifetime monitoring ceases, if further data

are needed for understanding of impurity clearance.

No recommendation for including in-process hold times

in CPV is proposed because ongoing study of hold times

during commercial manufacturing is conducted using a

separate hold time qualification study.

Steps that have in-process quality attributes related to

microbial control (bioburden, endotoxin) are sampled and

tested as routine in-process controls. The nature of test

results in this case (approximately 0 cfu/ sample, and ≤

Limit of Quantification, LOQ, respectively) do not permit

meaningful Statistical Process Control (SPC) analysis in

CPV. QC microbiology laboratory review of these results

against action and alert limits will provide appropriate

monitoring for drift in microbial control of the process and

management of deviations, so monitoring, data analysis

and any response to bioburden and endotoxin results are

not included under this CPV plan.

Note: It could be seen as best practice that the quality system

for bioburden and endotoxin monitoring and the CPV system

are connected, so that any deviations would be reflected in

CPV Reports.

Statistical criteria that may be applied to analyses of data

are discussed in section 12.

The A-Mab case study did not identify any critical raw

materials or address CS or risk assessment for input

material controls. However, as a result of a hypothetical

culture medium change described in section 6, one

monitoring recommendation related to material variability

is provided as a recommendation for the CPV plan.

Additional monitoring of materials used in the bulk drug

formulation is also included as an option.


7.1 Step 1, seed culture expansion in disposable vessels – CPV recommendations

Table 7.1. Step 1 CPV recommendations

Variable Class CQAS

impacted

CPV recommendation &

justification

Determination method

and/or source

Type of data expected/

analytical approach

VCC

(each passage end)KPA

— Include, to verify process

consistency

Routine batch documentation for each

passage

Discrete value,

univariate

Optional elements to include in CPV

Initial VCC/split ratio

(each passage)KPP

— Optional, to verify process

consistency

Calculation from routine batch

documentation for each passage, ratio of

passage ending cell density over initial cell

density of next passage.

Discrete value,

multivariate

Culture duration

(each passage)KPP


consistency


passage.

Discrete value,

univariate

Culture viability


Optional, to verify process

consistency


passage.

Discrete value,

multivariate

The process risk assessment established that steps 1 and 2

of the A-Mab process do not entail risk of impact to product

quality in the production bioreactor because no product

is accumulated at these stages. Specifications for raw

materials, such as cell banks and media components, assure

use of the intended genetic cell line to produce A-Mab and

control introduction of endotoxins which could affect cell

metabolism.

CPV for this step should focus on process consistency and

obtaining sufficient data to calculate long-term control

limits (see Sections 9.0 and 10 for further discussion and

examples of control limits, and Section 12 for information

on the statistical basis for control limits. which account for

normal process variability. As stated in the A-Mab case

study and demonstrated in the PPQ, BR procedures, SOPs,

automated process controls and use of alarms all ensure

the seed expansion steps are routinely monitored and

operated within established limits. Therefore, monitoring

of non-critical parameters in this step such as temperature,

pH, and dissolved oxygen need not be included in the CPV

plan. This is shown in Table 7.1 below.

Environmental Monitoring (EM) is routinely performed

for open (under appropriate ISO classified conditions)

process manipulations (including use of Rodac and settling

plates) to demonstrate microbial control and the existing

QC laboratory program is established for reporting results

and assessing trends. Therefore inclusion of this EM

monitoring plan in the CPV plan is unnecessary. As noted

previously, these systems need to connect as it would

be best practice to ensure deviations are present in CPV

Reports.


7.2 Step 2, Seed Culture Expansion in Bioreactors – CPV RecommendationsAs noted in section 7.1 for step 1, cell growth is complex and it is difficult to comprehensively define or predict all sources

of variability. Expansion culture conditions may impact cell biology which in turn can impact product quality during product

expression. CPV for this step should focus on process consistency and obtaining sufficient data to resolve long-term control

limits which account for normal process variability.

Inclusion of EM in the CPV plan is unnecessary because an existing QC program is established for reporting and trending of

EM results.


Variable Class CQAS

impacted


justification


and/or source


analytical approach

VCC



consistency


passage

Discrete value,

univariate


Initial VCC/split ratio

(each passage)KPP


consistency

Calculation from routine batch

documentation for each passage, ratio of

passage ending cell density over initial cell

density of next passage.

Discrete value,

multivariate

Culture duration

(each passage)KPP


consistency


passage.

Discrete value,

univariate

Culture viability


Optional, to verify process

consistency


passage.

Discrete value,

multivariate


7.3 Step 3, Production Culture Bioreactor – CPV RecommendationsThe process risk assessment established the BDS

CQAs that may be impacted by this step. As stated in

the A-Mab case study and demonstrated in PPQ, BR

procedures, other SOPs, automated controls, and an

alarm system all ensure the production step is routinely

monitored and operated within established limits for

many of the related parameters.

Turbidity at harvest (a KPA known to vary in response

to bioreactor culture conditions) is not included in the

CPV plan because of the confidence that centrifugation

and depth filtration can accommodate variability in

the harvest material (low differential pressure across

depth filters). However, if there is a filter change, or the

medium component change introduced between PPQ

and commercial manufacturing indicates a shift in other

monitored variables for this step or process performance

of the next step, establishing control limits for turbidity at

the end of the production bioreactor should be added to

the CPV plan.

The CPPs for medium osmolality and culture duration

are included in the CPV plan. For these CPPs, a large

tolerance for variation has been shown in development

during process characterization studies. Maximum pCO2,

bioreactor temperature and bioreactor pH are other

identified CPPs to be included in the CPV plan.

At the time this protocol is initiated, the PLS model

is classified as a KPA; it is a predictor of A-Mab

oligosaccharide structure CQAs and acidic variants. Model

input parameters of temperature and pH, and model

input attributes of titer, VCC, and viability are separately

included in the initial monitoring while the bioreactor

model is qualified.

Remnant glucose concentration is not included in the CPV

plan because it is assumed to be a fixed value CPP which

triggers additions of glucose feed. However, as an attribute

of the culture, it is measured daily and when the glucose

concentration drops below a particular level, a discrete

volume (assumed to be a fixed KPP) of a glucose solution

is added as a bolus to ensure the glucose concentration

remains ≥ 1.0 g/L. A fixed volume of nutrient feed is added

at a defined time under automation and routine batch

document controls, therefore trending of the KPPs nutrient

feed volume and timing of nutrient feed does not provide

value because they are not subject to random variation.

Harvest attributes of titer, viability, and culture duration

are also included for trend monitoring to verify process

outcome consistency.

The Partial Least Squares (PLS) multivariate model

generated during process characterization in the A-Mab

case study [3, Section 3.10, Page 108]25, includes other

CPPs (e.g. dissolved oxygen, pressure, gas addition rates)

and KPAs (e.g. VCC, viability), noted in Table 7.3.

The CPV plan may optionally include selected KPPs and

KPAs to provide additional measurements of robust

process consistency and to obtain sufficient data to

resolve long-term control limits that account for normal

process variability. Two suggested discrete KPAs, peak

VCC and culture viability at harvest, are optional in

Table 7.3 for Step 3. Other KPAs (glucose and lactate

concentrations) are also included as part of the PLS model

described in the A-Mab case study.

In-process quality attributes for this step, namely

bioburden, Murine Minute Virus (MMV), mycoplasma,

and Adventitious Viral Agents (AVA) are controlled

as routine in-process specifications linked to drug

substance BR. Their binary pass/ fail nature does not

permit meaningful SPC trend monitoring, and does

not provide prospective warning of pending batch

failures. These routine control measures are sufficient

for maintaining the process in its validated state and

deviations detected will trigger investigations for out of

control situations/events.

Regarding the productivity of the production culture step,

whether to include the Antibody-Dependent Cellular

Cytotoxicity (ADCC) bioassay in the CPV plan or not,

is an interesting and somewhat complicated question.

ADCC is correlated with afucosylation in vitro. Thus

measurement of potency by ADCC is an indicator for this

quality attribute that might impact Fc effector function.

However, this bioassay is not qualified to test crude

production bioreactor material just prior to harvest due to

broth interference. Fundamentally, that would not prevent

reliable results that correlate with the potency of the

purified material. But, confirmation of functional activity

is relevant to the finished dosage form since it is the drug

product that is provided to the patient. So, monitoring of

bioreactor harvest for potency is not recommended since

ADCC activity has a drug product release specification for

CPV trending and is a stability indicating assay included

in routine stability testing protocols [derived from A-Mab

case study section 6.4.2, Page 247].


The CS categorized the antifoam ingredient to be a

critical raw material (CRM), probably because it is a

process residual CQA. However, there is no particular

critical material attribute (CMA) that requires enhanced

monitoring. The addition of antifoam varies as needed up

to a maximum 100 mg/L concentration (section 5). As a

single antifoam lot is used for multiple bioreactor batches,

lot change points in the batch genealogy will be traceable

to correlate with any process shifts in trends for this step,

the clarification step, or the Protein A chromatography

step (steps 3, 4 and 5 in the process). Because only one

lot of antifoam was introduced in the PPQ, three BDS

batches during the initial CPV period, which employ

different antifoam lots in the upstream process, are tested

to provide evidence of robust clearance of the process

residual. Due to the low turnover in antifoam lots, routine

but periodic batches being tested for stability may also

be selected for this extra testing in BDS, i.e. at ‘time zero’.

This does not suggest that clearance of antifoam is a

stability indicating attribute.

The oligosaccharide profile (a CQA) is solely influenced

by the production bioreactor. Input material and

procedural controls are in place to ensure the quality of

raw materials and the cell line. Control of step 3 CPPs

(temperature, pH, dissolved carbon dioxide, culture

duration, and medium osmolarity) within their limits

ensures consistent glycosylation. No process clearance

or further glycan modification occurs in downstream

processing, and the oligosaccharide profile is not regarded

as stability indicating. Routine testing is not part of

the drug substance lot release specification based on

the development process design history, process risk

assesments, CS, and PPQ. The risk that exists is that no

process clearance or further modification is expected in

downstream processing. An oligosaccharide profiling

method utilizing Capillary Electrophoresis-Laser Induced

Fluorescence (CE-LIF) was developed and qualified for

characterization of the oligosaccharide profile. There

is also an in vitro cell-based bioassay qualified to enable

collection of biological activity data related to ADCC

functions, as a means to assess Fc-oligosaccharide

structure-function relationships.

CPV trend monitoring of afucosylated and galactosylated

glycans in the bioreactor for step 3 is recommended to

build confidence in process consistency (see Table 7.12,

10.12). Note that sialylation, high mannose content (also

afucosylated) and non-glycosylated heavy chain were

also determined to be CQAs but recommended only as

optional elements to include in CPV monitoring (see Table

7.12, 10.12). The frequency of lifecycle monitoring of

glycans will be reviewed and adjusted based on trends.

Characterization of the oligosaccharide profile will be

conducted to confirm comparability when needed to

support process changes [derived from A-Mab case study

section 6.4.5, Page 250 and section 6.6.1, Page 251-253].

The mechanism and conditions conducive to formation

of deamidated isoforms are widely known and well

understood. This knowledge, in conjunction with the

level of risk associated with the quality attribute in the

post-PPQ risk assessment, negates the need for in-process

CPV testing. Process control includes testing with a

routine CEX HPLC method at lot release, of both drug

substance and drug product, to confirm the identity of

A-Mab, monitor charge heterogeneity and detect shifts

in deamidated isoforms [derived from A-Mab case study

section 6.6.4, Page 259]. The method separates the main

charged isoforms of A-Mab that are considered to be

product-related substances as defined in ICH Q6B. The

resulting chromatographic profile is specific to A-Mab and

unambiguously distinguishes it from other monoclonal

antibodies. The spectrum of isoforms contained in the

reference chromatogram for A-Mab represents acidic and

basic isoforms. The chromatogram is inspected to ensure

a consistent profile with the reference standard and the

absence of any new peaks. A quantitative definition of

new peaks is included in the CEX test method. Charged

isoforms of A-Mab do not increase when stored at

recommended conditions; therefore, the attribute is not

monitored on stability [derived from A-Mab case study

section 6.4.2, Page 247].

The KPA of titer (yield) is included in CPV for trend

monitoring for process consistency.



Variable Class CQAS impacted CPV recommendation &

justification


and/or source


analytical approach

Culture durationCPP

Aggregates, glycosylated

glycans, HCP, DNA; can

also impact turbidity at

harvest, yield variation

Include, to establish

SPC capability and large

tolerance for variation

Routine batch documentation Discrete value,

univariate

Maximum

(dissolved) pCO2CPP

Glycosylated glycans,

deamidated isoforms;

also product yield

Include, to establish SPC

capability and correlate with

in-vitro cell age (IVCA)


univariate

(Bioreactor)

temperatureCPP


deamidated isoforms

Include, to demonstrate

appropriate range is

established

Routine batch documentation Continuous datastream,

univariate

(Bioreactor) pHCPP


deamidated isoforms

Include, to demonstrate that

appropriate monitoring and

automated adjustments are

established


univariate

Afucosylated

glycansCQA


consistency

Will require non-routine test,

record results in Laboratory

Information Management

System (LIMS)

Discrete value,

univariate

Galactosylated

glycansCQA


consistency


record results in LIMS

Discrete value,

univariate

PLS model

employing pH,

DO, temperature,

pressure, gas

rates, weight, VCC,

viability, titer,

glucose, lactate

KPAIsoforms, variants, DNA,

monomer, aggregates,

HCP oligosaccharides

Include, to verify process

consistency


multivariate

Product yield (titer

at harvest)KPA


consistency

QC ELISA results in LIMS Discrete value,

univariate


Table 7.3. Step 3 CPV recommendations (continued)


justification


and/or source


analytical approach

Antifoam lotCMA

Residual antifoam C Include, to track lot

changes. Test clearance at

BDS for 3 different lots

Routine batch document

genealogy

Qualitative text/label,

univariate

(Medium) osmolalityCPP


deamidated isoforms

Include; large tolerance for

variation has been shown.

Monitor by exception a


univariate

Mannose contentCQA

— Optional, to verify

process consistency



Discrete value,

univariate

Sialic acid contentCQA


process consistency



Discrete value,

univariate

Non-glycosylated

heavy chainCQA


process consistency



Discrete value,

univariate

Time of glucose feeds

(hrs since inoculation)KPP


process consistency


univariate

Peak VCCKPA


process consistency


univariate

(Culture) viability

at harvestKPA


process consistency


multivariate

Key: a: The term “monitor by exception” means that reported data outside of established alert or action limits will be reported as incident(s); for CPV, a review of

reported incidents will examine the occurrence of any events outside of established limits and determine the collective impact of these events.


7.4 Step 4, Clarification (centrifugation and depth filtration) – CPV recommendationsThe process risk assessment established that the clarification step is unlikely to impact product quality. Monitoring for CPV

is limited to process consistency for the purpose of examining data to establish long-term control limits that account for

normal process variability (per statistical confidence criteria stated in section 12). Therefore, the KPA of yield is included in

CPV for trend monitoring as a process performance indicator. One KPA, turbidity of filtrate, is also recommended to confirm

process consistency following the culture medium change (see section 6.2, scenario 1).

The PPQ demonstrated that BR procedures, SOPs, automated process controls and alarming ensure the centrifuge and

filtration step are routinely monitored and operate within established limits. Temperature, centrifuge feed rate and rpm, and

filter flow rate are not CPPs and are tightly controlled engineering or fixed design parameters that are not subject to random

variation and therefore do not merit inclusion in CPV.

Evaluation of a change in the manufacturing method of the culture medium used upstream (see Section 6, Scenario 1) could

include additional monitoring of downstream KPAs of inlet pressure to depth filters and duration of the broth clarification,

which are noted in the Table 7.4 as optional items for CPV. The small scale model evaluation of new lots of the medium

material showed that product quality attributes of Host Cell Protein (HCP), DNA, and product structural characteristics are

not impacted, so monitoring of these KPAs is not included in the CPV recommendations. The decisions not to include these

KPAs in CPV could be re-examined pending the results of the change management evaluation of the culture medium change.



justification


and/or source


analytical approach

Turbidity (of filtrate)KPA


deamidated isoforms

Include, to confirm process

consistency following medium

change

Non-routine testing needed Discrete value,

univariate

Step yield

(product in filtrate)KPP


consistency

QC ELISA test results in LIMS Discrete value,

univariate


Duration of broth

clarificationKPP

— Optional, to confirm process


change

Routine batch documentation,

elapsed time from start of

harvest (opening of bioreactor

bottom valve) to end of

filtration (closing or filtrate

vessel inlet valve)

Discrete value,

univariate

Inlet Pressure to

filtersKPP

— Optional, to confirm process


change

Routine batch documentation Continuous datastream

or discrete value,

univariate


7.5 Step 5, Protein A Chromatography – CPV recommendationsThe process risk assessment established that protein

A purification may impact product quality (aggregate;

charge variants; leached Protein A; clearance of HCP,

DNA, and methotrexate) and links to performance of

chromatography steps 7 and 8 (CEX and AEX). Platform

and prior process knowledge negate the need for specific

process studies except as noted below for HCP and

leached Protein A.

CPV for step 5 (the first of the downstream DS process

steps) should focus on process consistency to obtain

sufficient data to establish long-term control limits that

account for normal process variability (see section 12 for

statistical confidence criteria). Variables recommended

for inclusion in the CPV plan are shown in the Table 7.5

below, including CPPs identified for this step (protein load

ratio and elution buffer pH). Elution buffer pH is closely

controlled by batch procedure and buffer is not released

for use if pH is out of range. This variable is included in the

CPV plan to monitor the extent of buffer pH variability

incorporated in the HCP model prediction. Step duration,

a KPP, is included in the CPV recommendations to

establish capability on processing time for this step.

The key process attribute of yield is included in

the CPV recommendations for trend monitoring of

process consistency.

CPV need not include monitoring of flow rate through

the resin, nor the end collection point (column volume or

A280 absorbance) for the eluate because the PPQ verified

the expected control and minimal variation for these key

parameters. Operating temperature and other GPPs

were shown in characterization studies to not impact

product quality or process consistency when controlled

within easily achieved design ranges. The automated

continuous process controls and alarm system, as well

as BR sequencing and SOPs, ensure the step is routinely

monitored and operated within its established limits.

A linkage model study was proposed in the A-Mab case

study to examine HCP levels at different points within

the process (after each chromatography step – steps 5, 7

and 8). This is included in the CPV plan and involves non-

routine analysis to provide data on measured HCP levels

at the final point in the process covered by the multivariate

model (after AEX chromatography, step 8) against the

predicted outcome.

Storage of the Protein A resin (section 6, scenario 2) is

expected to potentially introduce a variable amount of

leached Protein A into the product stream. This will be

monitored via residual protein A (leached from the resin)

testing and trending of the results to establish process

capability for controlling this CQA.

Process control deviations for this step should evaluate

the case-by-case potential impact on these attributes

(and viral clearance), as process streams continue

further downstream for purification. For deviation

investigations, it may be appropriate to review the risk

assessment justification for any low risk CPPs. Note that

the process has high Impurity Safety Factor (ISF) (for a

definition, see A-Mab Case Study3 Section 4.10.3, Page

167) clearance (>5x104) for all process related impurities

for normal processing.




justification


and/or source


analytical approach

Protein load (ratio)

(in HCP model), each

sub-batch

CPPHCP, DNA, process

impurities


capability


calculated using packed resin

volume

Discrete value,

univariate

Elution buffer pH (in

HCP model)CPP

HCP, DNA, process

impurities


consistency


univariate

Residual Protein A in

eluate poolCQA

Protein A Include, to establish SPC

capability

Non-routine testing needed,

results recorded in LIMIS

Discrete value,

univariate

Step durationKPP

— Include, to verify that

process can capably control

this CQA


elapsed time from closing of

vessel inlet valve to eluate

pooling is completed

Discrete value,

univariate

Step yieldKPA

— Include, to confirm process

consistency


calculation using in-process

A280 test result

Discrete value,

multivariate likely

to exhibit normal

distribution

7.6 Step 6, Low pH treatment – CPV recommendationsThe process risk assessment established that the low pH

treatment step for viral inactivation impacts two product

CQAs (aggregate and viral inactivation). There is no claim

for removal of process related impurities (HCP, DNA,

methotrexate or leached Protein A) but some incidental

reduction in these impurities may be achieved in this

step, which includes precipitation and downstream filter

clearance. In general, CPV for this step should focus on

obtaining sufficient data to resolve long-term control

limits related to viral inactivation, so CPPs for inactivation

time and pH should be included in CPV. The viral safety

risk CQA (inactivation of particular AVA) for the A-Mab

process has been validated in the small scale model during

stage 1 process validation. Inactivation time and pH are

readily controlled within desired limits for the process

as shown by PPQ. However, inclusion of both these

parameters in CPV is recommended, because they are

manually controlled and susceptible to variation within

their PARs. One additional test, for aggregates, is also

recommended for CPV to establish process capability for

this CQA.

CPV need not include inactivation temperature and

agitation mixing because PPQ verified the expected

control and minimal variation for these key parameters.

BR sequencing, automated process controls and the alarm

system will ensure the step is routinely monitored and

operated within its established limits for these parameters.

The limit for maximum protein concentration in the

Protein A pool is bound by the pH inactivation step

requirements, but trending of the protein concentration

is not recommended as the information will provide little

benefit in process understanding. However, a related

optional inclusion for CPV is to trend the amount of acid

added, to ensure the A-Mab process does not drift or shift

toward the edge of the qualified conditions of the platform

process without this being recognised.


Step yield is not included as a CPV recommendation because yield is not expected to be impacted at this point in the

process and variability around the expected 100% has been associated with measurement uncertainty rather than process

variability, therefore it does not merit CPV trending or monitoring. The basis for yield for the next step (7, CEX) begins from

the eluate pool of the previous step (5, Protein A).


Variable Class CQAs

impacted

CPV Recommendation &

Justification

Determination Method and/

or source


Analytical approach

pH (during inactivation)CPP

AVA,

aggregates


consistency


integrated average of online pH

values during inactivation time

Continuous datastream,

univariate

Post-inactivation

aggregatesCQA

— Include, to establish SPC

capability

Non-routine testing needed,

results recorded in LIMS

Discrete, multivariate


(Inactivation) timeCPP

AVA Optional, to establish SPC

capability


calculate from completion

of acid addition to start of

titration

Discrete value, univariate

Quantity of acid addedKPP

— Optional, to establish SPC

capability


change in supply vessel weight




7.7 Step 7, Cation Exchange Chromatography (CEX) – CPV recommendationsThe process risk assessment established that the CEX

step primarily impacts two product CQAs, aggregate

and residual HCP. Characterization studies showed that

DNA and protein A clearance were not impacted by this

step, and there is no claim of viral clearance for this step.

Trending of CPPs identified as potentially impacting

these CQAs are included as recommendations for CPV.

CQAs for HCP (as supporting evidence for the linkage

model prediction) and aggregate are included in CPV

to establish process capability, and the KPA of yield

is included in CPV for trend monitoring as a process

performance indicator.

Univariate monitoring is not required for flow rate

through the resin, elution buffer pH, load buffer pH,

wash buffer pH, re-equilibration buffer pH, eluate

volume, nor the starting or end collection point

(A280/A320) for the eluate because PPQ verified

the expected control and minimal variation for

these key parameters. BR sequencing, automated

process controls, and the alarm system will ensure

the step is routinely monitored and operated within

its established limits for the independent parameters.

Development studies concluded a wide operating

temperature range had no impact on product quality

or performance/ process consistency, so monitoring

of temperature beyond that routinely done for each

batch is not included in CPV recommendations.

CEX eluate volume (each sub-batch) is included in CPV

to determine a higher confidence range of the normal

variation due to the special cause event that occurred

during PPQ (see section 6.2). Data obtained will be used to

show process consistency with respect to this parameter.

The basis for yield for this step begins from the step 5

eluate pool.

Variable Class CQAS impacted

CPV recommendation & justification

Determination method and/or source

Type of data expected/ analytical approach

Protein load (ratio) (in

HCP model)CPP

Aggregates,

HCP


capability


calculation using packed resin

volume

Discrete value, multivariate

Wash conductivity (in

HCP model)CPP

HCP Include, to confirm process

consistency

Routine batch documentation Discrete value, univariate

Elution pHCPP

HCP, DNA,

Protein A,

aggregates


consistency


univariate

Aggregates in CEX eluate

poolCQA

– Include, to verify process

performance

Will require non-routine in-

process test, results recorded

in LIMS



7.8 Step 8, Anion Exchange Chromatography (AEX) – CPV recommendationsThe process risk assessment established that the AEX

step impacts several CQAs (viral clearance, aggregate,

endotoxin, and clearance of protein A, charge variants,

HCP, DNA, and methotrexate). Trending of three CPPs

identified as potentially impacting these CQAs (protein

load ratio, equilibration buffer conductivity and load pH)

are included as recommendations for CPV.

Monitoring of other CQAs impacted by this step is not

recommended because trending for HCP and Protein A are

sufficient to represent the performance and establish the

capability of this step. Since the step 5, 7, 8 linkage model

(see Section 12) is for predicting an impurity CQA (residual

HCP), the output of the model is classified as a KPA, and

is also included for monitoring against CPV control limits

(not BR acceptance criteria).

Monitoring of step duration (a KPP) is suggested as an

optional inclusion for measuring process capability.

Inclusion of other process parameters including flow

rate (a CPP) and KPPs such as starting or end collection

UV for the eluate, or pH of the prepared equilibration/

wash 1 buffer are not suggested because PPQ verified

the expected control and minimal variation for these

parameters. BR sequencing, automated process controls,

and the alarm system will ensure the step is routinely

monitored and operated within its established limits for

these independent parameters. Development studies

concluded that a wide protein concentration range had

no impact on product quality or performance/process

consistency, so trending of protein concentration is also

not included in CPV recommendations.

Monitoring of step yield will serve as an indicator of any

drift in process control for this step.






HCP content in CEX

eluate poolCQA

– Include, to verify process

performance

Will require non-routine

in-process test, record results

in LIMS


CEX eluate volume (each

sub-batch)KPA

– Include, to confirm process

consistency

Routine batch documentation Discrete value, univariate

Step yieldKPA

– Include, to confirm SPC

capability


calculation using field A280

test result



Step durationKPP

– Optional, to confirm SPC

capability

Routine batch documentation;

elapsed time from end of step 6

(vessel inlet valve closes)

Discrete value,

univariate







Protein load (ratio)CPP

HCP, viral

clearance


capability


calculation using packed resin

volume


Load conductivityCPP

Viral

clearance


consistency

Routine sample and test for

buffer use


Load pH (in HCP model)CPP

HCP, viral

clearance


consistency


sample test


Equilibrium/ Wash 1

buffer conductivity (in

HCP model)

CPPHCP, viral

clearance


performance

Routine sample and test for

buffer use


Linkage model output

for HCP content in AEX

eluate (predicted)

KPA– Include as outcome of HCP

linkage model, to demonstrate

understanding of HCP

clearance through multiple

processing steps

Calculated from six variable

terms logged in batch

documents for step 5,7,8


HCP content in AEX

eluate (measured)CQA

– Verify model of HCP clearance

through multiple processing

steps

Non-routine test, results

recorded in LIMS


Residual Protein A in

eluateCQA

– Include, to confirm process

consistency



in LIMS


Step yieldKPA


capability



test result



Step durationKPP

– Optional, to confirm SPC

capability

Routine batch documentation;

elapsed time from end of step 7

(vessel inlet valve closes)

until product elution completed

in step 8




7.9 Step 9, Small Virus Retentive Filtration (SVRF) – CPV recommendationsSVRF is a physical size separation step that is critical for

viral clearance. Filter function is confirmed after each batch

by standardized integrity testing. Introduction of leachate

from the filter is minimized by a routine pre-use rinse of the

filter with a validated quantity of AEX elution buffer.

Operating pressure is a WC-CPP recommended for

including in the CPV plan. Some variation in pressure has

been observed; trending of pressure data will increase

predictability and confidence in knowledge of the natural

variation and what, if any, impact this variation may have

on process consistency. Correlation of operating pressure

with filtration volume (the other CPP to be monitored)

and protein concentration will also ensure consistent

viral Log Reduction Value (LRV) and serve as a basis for

future process improvements/change controls. Small-

scale studies have shown that the likely variation in these

parameters does not represent a BR risk for product safety

or quality. Including filtration load volume in CPV provides

an alternate measure of process consistency, given its

impact on processing time for this step. Verification of

the filter integrity testing was included in PPQ and will be

included upon completion of the filtration of every process

batch. Re-filtration has also been validated and details

are registered in regulatory licenses. Detectable impact

of re-filtration is a decrease in the measured protein

concentration due to dilution by a hold-up recovery flush

after filtration to optimize step yield. Incidents of failed

filter integrity and/ or when re-filtration is performed are

tracked with the change control system as incidents and

are trended as part of Annual Product Review, so will not

be included in CPV.

Step yield is not included as a CPV recommendation

because yield is not expected to be impacted by this step.

Yield after step 10 will include step 9.

Rinse or processing flow rate through the filter and the

flush volume used are not recommended for inclusion

in CPV, because PPQ demonstrated tight control and

minimal variation of these KPPs. Although these variables

are manually controlled, the BR instructions and sequence

will ensure the step is routinely monitored and operated

within its established limits.

Variable Class CQAs impacted

CPV recommendation & Justification


Type of data expected/ Analytical approach

Operating (inlet) pressureCPP

Viral

clearance


consistency

From online data acquisition,

plot results with range of

acceptable standard profiles.

Correlate these data with

filtration volume and protein

concentration.


univariate

Filtration (load) volumeCPP

Viral

clearance


consistency


vessel weight change from pre-

rinse tare to filled weight



7.10 Step 10, Ultrafiltration and Diafiltration (UF/DF) – CPV recommendationsThe A-Mab case study does not provide sufficient detail

to trace CPV rationale back to a CS, risk assessment, or

process development for this step. Therefore, six well

known process parameters typical for the operations of a

UF/DF step are considered for CPV:

• Trans-Membrane Pressure (TMP);

• Temperature of the product containing stream;

• Permeate and recirculation flow rates;

• Number of dia-volumes to complete the

buffer exchange;

• Product concentration (prior to and after

buffer exchange);

• Step processing time.

Platform knowledge was leveraged to define an initial

membrane life limit controlled via batch documentation

and equipment logbooks. A specific membrane lifetime

monitoring protocol is expected to be in place alongside

the CPV plan, to verify filter performance.

Flow rates are response variables that automatically

adjust to maintain a fixed TMP set point (pressure

controlled operation). Because flow rate profiles tend

to vary over the re-use lifetime of the membranes,

an optional choice for CPV includes monitoring of

permeate and recirculation flow rates via a trajectory

profile of the continuous dynamic data. Reference

standard profiles (3SD tunnels, i.e. control charts with + 3

standard deviation acceptability limits) will be shown for

comparison (sourced from the initial use cycles for the

membrane and from the PPQ batches).

It is assumed that PPQ demonstrated that BR procedures,

automated process controls, and alarming ensure

the UF/DF step is routinely monitored and operated

within established limits characterized in small scale

development DOE studies. Therefore temperature and

TMP are dismissed as non-CPPs and are not recommended

for inclusion in CPV.

The number of dia-volumes needed to complete the buffer

exchange, pH of the AEX eluate solution to be processed

in this step, and UF/DF processing time are additional

CPPs to include in CPV, because of their potential impact

on product concentration and dia-volumes (affects

osmolality), the potential formation of aggregate (from

lengthy processing time and/or incorrect pH), and because

process validation has not yet provided sufficient data to

demonstrate process capability for these parameters.

It is assumed that a risk assessment established that

the UF/DF step has potential to affect various product

quality attributes. Variation in protein concentration

prior to BDS freeze and fill (step 11, post-filtration)

may affect downstream drug product manufacturing

controls/ capability, so trending of this CQA is included

in CPV recommendations. These data will also provide

evidence for any correlation with other variables (e.g.

dia-volumes needed for buffer exchange). Optionally, CPV

may include selected product CQAs, chosen because of

knowledge that they may reveal the impact of variability

in the process or provide useful information about process

capability. The product solution identity, composition, and

aggregation could be altered by either post-diafiltration

pH or osmolality (or by a trace contaminant in compendial

grade raw material), so inclusion of these CQAs should

be considered for CPV. The A-Mab case study CS

established that impurity clearance capability for residual

methotrexate is very high and does not require further

verification. The genealogy link between the culture media

used in the upstream process batches (impurity clearance

of antifoam and methotrexate) and the UF/DF membrane

lot will be logged in the enterprise resource planning

system for use in investigations.

Protein concentration prior to diafiltration is suggested as

an option for inclusion in CPV, to provide data linking this

in-process CQA measure to the final protein concentration

at completion of this step. Another suggested option is to

trend aggregates in the final retentate with the intent of

providing additional data to trend this step for ability to

control formation of this process-related impurity.


Diafiltration and final formulation buffer ingredients are

excipients of the drug product and therefore are critical

raw materials. Supplier quality management includes

specifications for material purity, content of particular

elemental impurities, and endotoxin from inspection of

Certificate of Assay (CoA) summaries and/or in-house

verification testing. Reserve samples and CoA summaries

for each excipient lot are preserved until drug product

expiration to enable investigations as needed. Although

not specifically identified here, an un-named Critical

Material Attribute (CMA) for ‘excipient 1’ may require

characterization of the variability of ‘attribute A’ during

CPV as a risk mitigation action for the CS, so this has been

included as an option for CPV.

The key process attribute of yield is included in CPV for

trend monitoring as a process performance indicator.

Normalized Water Permeability (NWP) and average

filtrate flux are monitored and verified by a membrane

lifetime protocol and new membrane installation

SOP. Sampling for lifetime monitoring, verification,

and potentially extension of the number of reuses is

managed under this separate protocol and is therefore

not considered here for CPV.





UF/DF processing timeCPP

Aggregates Include, to verify process

capability


elapsed time from UF start until

defined UF end

Discrete, univariate

Number of dia-volumesCPP

Product con-

centration

and several

others

Include, to establish process

consistency


include in batch documentation


UF/DF retentate final pHCPP

Aggregates Include, to establish process

consistency

Routine batch documentation Discrete, univariate

Protein concentration

prior to BDS fill stepCPP

Protein

conc. of BDS

Include, to establish process

consistency


recording of field A280 test

results


Yield (final retentate)KPA


capability



test result



Excipient ”1” Attribute

“A”CMA

– Optional, to examine variability

of materials used

Released by compendia testing

or COA, results recorded in

LIMS


SEC aggregates in final

retentateCQA

– Optional, to confirm

consistency of mixing and foam

control



in LIMS


Protein concentration

prior to dia-filtrationKPP

– Optional, to establish process

consistency


recording of field A280 test

results


Recirculation flow rateKPP


consistency




univariate

Permeate flow rateKPA

Optional, to establish process

consistency




univariate



7.11 Step 11, Final Filtration and Freezing of BDS – CPV recommendationsFinal filtration performed while filling sterile containers

provides assurance of microbial control of the drug

substance intermediate, but is otherwise unlikely to

impact product quality. No provisions are assumed for a

validated re-filtration option. Filter function is confirmed

after each batch by standardized filter integrity testing.

Introduction of filter leachates are minimized by process

design and leachable studies, which include a pre-use

rinse of the filter with a qualified fixed amount of final

formulation buffer.

The KPA of yield was chosen for monitoring this step in

CPV, because trend monitoring of yield will provide a good

process performance indicator.

Although it is not a CPP, maximum inlet pressure (filter

pressure) is known to exhibit product specific batch

variation from platform process knowledge and so is

suggested as an optional inclusion in CPV. Filtration

volume is another KPP that would be a reasonable

optional choice for CPV, providing a different measure

for assessing processing capability. Correlation of filter

pressure with filtration volume, protein concentration,

and filtration time are other optional considerations that

could be included in the CPV plan, to characterize normal

performance and variation for the A-Mab process for

future predictability.


The BDS freezing rate profile and container seal integrity

has been validated at commercial scale and the freezing

time and conditions are well controlled and documented

to support any investigations. The freezing equipment

is included in the periodic validation maintenance and

instrument preventative maintenance programs. CPV

monitoring is not proposed for several related operating

variables because PPQ demonstrated tight control and

minimal variation of these variables, including: bulk

mixing after UF/DF and during the fill, the fixed flow rate

through the filter, the flush volume used, verification

of the filter integrity testing and product intermediate

freezing temperature. BR sequence and instructions,

automation monitoring and alarm systems will ensure

the step is routinely monitored and operated within its

established limits. The time the intermediate is stored

frozen prior to shipment for drug product manufacturing,

could be considered as a means of identifying any potential

correlation with data from the stability program, but this is

not included in the CPV recommendations here.

EM is a supporting quality system subject to periodic

monitoring, so it is not included in CPV recommendations,

despite a related incident report for this step (see Section

6, scenario 5).


Variable Class CQAs

impacted


justification

Determination Method and/

or source


Analytical approach

Bulk Fill step yieldKPA

– Include, to establish process

consistency


determination using field A280

test result



Filtration volumeKPP

– Optional, to verify process

capability




Maximum (inlet) pressureKPP


consistency


max. inlet pressure from online

data acquisition during fill


Filtration time

documentation, elapsed

time

KPP– Optional, to define normal

range

Routine batch Discrete value, univariate


7.12 Bulk Drug Substance Lot Data – CPV recommendationsThe QC group ensures each drug substance batch

meets specifications for lot release to drug product

manufacturing. Some specifications, such as the identity

attribute of consistency with reference standard and

inspection for new peaks, are not amendable to trend

analysis for CPV. The QC microbiology laboratory

monitors all drug substance lot data for endotoxin and

bioburden against alert and action limits to provide

appropriate monitoring of the process and management of

microbial control deviations.

In the A-Mab case study, routine BR specifications

proposed for the drug substance were intentionally

minimized to show a potential application of QbD

development for process validation Stage 1. Endotoxin

testing, a BDS and drug product release requirement is

reviewed as per the QC laboratory SOP, with SPC based

alert limits which are assessed for suitability during each

annual product review. For the CPV plan, additional

BDS CQAs were selected for continued verification and

enhanced monitoring, to demonstrate consistency over a

longer period during which more process variation may be

observed. Content of various oligosaccharide structures

were selected as high risk CQA examples from the A-Mab

case study (refer to section 7.3 and 10.3). Control limits

are set inside the claimed acceptable range (see section

5) based on statistical analysis of data to provide early

warning during trend monitoring.

Some additional process and product related impurity

parameters are included in the CPV plan for this process

step. For example, monitoring of antifoam C rather than

methotrexate clearance was chosen. Antifoam additions

vary batch-to-batch to control foam and clearance is

combined over steps 4 and 5. In contrast, methotrexate is

a fixed addition prior to the N-1 seed bioreactor, resulting

in significant dilution as the process scales up to 15,000L

and a high log reduction factor was demonstrated for the

Protein A step 5 alone. HCP is not included for CPV at

BDS because it is monitored for CPV at step 8 (AEX), as

both a special sample test with a control limit well inside

the 0 to 100 ng/mg acceptable range (based on the similar

X-mAb process) and via the multivariate model for linked

chromatography parameters25. No particular deamidated

isoforms (which incidentally, were not designated as

CQAs) or other charge variants are required for CPV

monitoring. The routine drug substance specification

confirmation of A-Mab identity includes a CEX HPLC

method which separates isoforms (product-related

substances as defined by ICH Q6B) and both a consistent

profile with the reference standard and absence of new

peaks are part of the acceptance criteria, so this would not

be included in the plan.

It is recommended for CPV, that the routine lot release

Size Exclusion Chromatography (SEC) results for percent

monomer and aggregates be trended and long-term

control (alert) limits defined within their release and

stability specifications. The data for these parameters

will not form a normal distribution, so control will involve

QC review of the results against action and alert limits.

Note that samples for SEC testing are collected from

the product intermediate prior to bulk freezing and the

effect of freezing, storage, and shipping conditions on

aggregation should be considered for inclusion in CPV as

inputs to the DP process.

Additional CQAs are listed here as optional for inclusion

in the CPV plan. Those CQAs that should perhaps be

included in trending more often than annually for the

Annual Product Review, as a result of relatively frequent

manufacture, should be included in CPV. Content of

three oligosaccharide structures (sialic acid, mannose

and non-glycosylated heavy chain) and two process

impurities (DNA and methotrexate) were selected as

options for CQAs to be added to CPV trending. As noted

earlier, trending of results for two other oligosaccharide

structures will be done at step 3. Methotrexate is a raw

material used in steps 1 and 2 and there are no specific

controls for its removal but since there is a high safety

clearance limit for this residual process impurity, testing

for it in the BDS is optional.

Shipping of the drug substance has been validated.

Monitoring of in-shipment time and maximum

temperature during shipment is routinely verified to be

within qualified limits. Trending of shipping conditions

should be considered for monitoring of the shipping

process, though these parameters are considered out of

scope for Stage 3 CPV, so they are not included in the plan.







Monomer (by SEC)CQA

– Include, to establish SPC LIMS results from routine

testing of BDS


Aggregates (by SEC)CQA


testing of BDS


Galactose contentCQA


testing of BDS

Discrete value

AfucosylationCQA


testing of BDS

Discrete value


DNACQA

Include, to establish SPC LIMS results from routine

testing of BDS

Discrete value

Methotrexate and/or

antifoam CCQA


testing of BDS

Discrete value

Sialic acid contentCQA


testing of BDS

Discrete value

Mannose contentCQA


testing of BDS

Discrete value

Non-glycosylated heavy

chainCQA


testing of BDS

Discrete value


8.0

Frequency and scope of CPV analysis

CPV analysis commences with commercial production, following successful completion of the PPQ batches. The start of data collection and analysis begins with the first representative commercial batches produced at the commercial scale facility. Due to potential scale and facility differences, as well as modifications in the process control or adjustments to test methods prior to PPQ, CPV monitoring will not include data from clinical batches, though experience gained in these project phases are likely to help in assigning initial control limits. As a result, the amount of directly relevant data available to set appropriate monitoring limits will be limited at this point. This poses a problem, at least until significant quantities of data have been gathered.

8.1 Scope of CPV Analysis To address the problem of limited data when commercial

production starts, it is recommended that CPV analysis

is performed in two phases, the initial CPV phase and the

long-term CPV phase.

Phase 1: Initial CPV Phase

The initial CPV phase is considered pre-SPC and provides

the ability to analyze process performance based on

a limited data set to gain understanding of the normal

process variability in the commercial facility. This phase

should include enough batches to provide data to reflect

the range of potential variability and allow statistical

process ranges to be established. During this phase, charts

are run using the specifications based on PPQ, clinical and

process characterization information. Data collected will

be used to identify possible trends and to demonstrate

that the process remains in a state of control. For A-Mab,

the initial CPV phase will continue until at least 30 batches

have been produced (this is assuming one upstream cell

culture batch feeds one downstream purification batch).

It is worth noting that, though 30 batches are suggested

as the minimum number to form a representative data set,

this should not be regarded as a ‘magic number’. Many

introductory statistical texts cite 30 as a reasonable

start for independent data that fit approximately the

description of a Normal distribution. But, the actual

sample size needed to establish variation with a good level

of confidence could involve a larger number of batches.

It is recommended that a statistician is consulted in the

context of a particular data set.

At the conclusion of the initial CPV phase, alert limits for

the monitored parameters should be established where

applicable, if they do not already exist, or to justify the

alert limits that have been set. Additionally, the risk

assessment performed following completion of the PPQ

batches should be reviewed to determine whether the

additional process experience has changed the risk score

for the monitored parameters. Trends in process related

non-conformances should also be included in the review

of the risk assessment, and this should involve considering

whether parameters not originally included in the plan

for the initial CPV phase ought to be added. Should there

be an increase or decrease in risk for the monitored

parameters, or a noted non-conformance trend for a

parameter which was not previously monitored for CPV

analysis, the plan may be revised to reflect the updated

process understanding and risk analysis prior to initiation

of the long-term CPV phase.


Phase 2: Long-term CPV Phase

The long-term CPV phase is the statistical process control

phase. This phase has the following objectives:

• Ongoing verification of the process over the

lifetime of the product to demonstrate the process

remains in control;

• Identify trends which may be within the normal

process variability, but indicate a potential to trend

outside the alert limits;

• Continue to build understanding of the sources of

variability in the process and their impact.

Section 10 provides detail proposing how monitored items

fit into the plans for short-term and long-term CPV.

8.2 Frequency of AnalysisA documented analysis and conclusion as to whether

the process remains in a state of control (a CPV Report)

may be performed based on the production schedule.

For example, the CPV plan might include the following

conditions for a particular product like A-Mab:

• Campaign (< 10 batches) – Minimally at the

conclusion of the campaign;

• Campaign (> 10 batches) – Minimally every 10

batches, and at the conclusion of the campaign, or

at a predetermined time interval (e.g. quarterly);

• Continuous – Minimally every 10 batches, or at a

predetermined time interval;

• A frequency preference of every 10 batches

has been selected to enable trend identification

via typical tests for special causes of variation

in control charts. Note that analysis will be

performed as described in section 9 per the

requirements of the phase of CPV analysis.

Frequency of documented analysis and conclusion

may be increased when greater than desired

process variability is noted or if conclusions are

needed to support product disposition.

It is important to note that these statements are given as

an example for a product like A-Mab, being manufactured

at a frequency of the order of two to ten batches a month.

Even so, formal CPV Reports are only likely to be created

up to four times a year. For products where the frequency

of batch manufacture is low e.g. once a year, it wouldn’t

make sense to have more than one CPV Report a year.


Figure 9.1. A Mab case study example indicating different limits during monitoring of certain process parameter

9.0

Establishing control limits

Note: Control limits (for parameters and attributes selected for the CPV Plan) need to be established initially. However, they are likely to be re-established at some point and this may require change control (see Section 13). This section focuses on the principles involved in establishing initial and long-term control limits, in preparation for the example of a CPV execution plan (Section 10). More detailed, mathematical considerations are covered in Section 12.

To initially establish control limits a documented

business process should be in place to address

collecting, analysing, reporting and storing of data for

the process at the manufacturing scale. Additionally,

data generated during development, scale up, as well

as small scale data, can be used to set control limits.

By evaluating process performance, the initial control

limits would help provide an early indication of a lack of

control in the process for certain process parameters or

quality attributes, by establishing the anticipated range

of expected variation. During the evaluation process,

such indications may need timely intervention to drive

process consistency. Initial control limits in a CPV plan

should not be interpreted as acceptance limits (i.e. a

specification for the product).

Through the initial control limits evaluation, the

strategy for process control should be identified and

applicable limits established based on process and

measurement system capability. The Process Capability

Index (Cpk) and Process Performance Index (Ppk)

provide useful indicators of the level of control likely to

be achievable for the process.


The means of establishing SPC limits for an initial period

and for the long-term, is described in detail in Section

12.4. Additionally, an SPC chart showing different limits:

upper and lower control limits (UCL, LCL), being the

most commonly used and arguably important for many

data sets, is presented in the Figure 9-1. If insufficient

data are available, either with a new process or after a

major process/assay change, initial, temporary limits

may be proposed, based on available development,

initial small scale data and process knowledge. If so,

small scale models should be appropriately developed

and qualified in order to guarantee the scale process is

representative and predictable.

Statistical and scientific rationale should pre-determine

what data set is required. Once sufficient at-scale data are

available long-term SPC limits can be established.

Understanding which elements (e.g. raw materials,

operators, facility etc.) contribute to common-cause

variation may depend on the relevance and knowledge

of the specific process, and will help to set relevant

and appropriate SPC limits. This requires the inclusion

of sufficient data (initially determined or statistically

relevant) to capture long-term common-cause variation.

Factors that may lead to variation include for example:

pack-to-pack variation in chromatography columns,

measurement system recalibrations, raw material lot-to-

lot variability, etc.

The calculation of control limits depends on an assumption

that data is normally distributed and each datum point is

independent. This may not always be the case, and data

transformation can be helpful in making data meet the

assumptions of normality. Process knowledge may help

in transforming data to a more SPC-amenable form. For

example when a known and codified relationship exists

between process parameters and QAs, normalizing the

data (taking into account the available process knowledge)

can lead to a more relevant and reliable value for trending.

Data distribution should be considered when selecting

analysis tools. The method of reporting each data set

should be defined and approved in applicable GMP

documentation. All excursions outside approved

documentation should be further investigated, justified

and documented in appropriate GMP documentation.

When more data are available, calculated SPC limits

can be identified. The SPC limits should be periodically

reviewed to capture process variability and be brought

into line with any new regulatory or quality guidance or

additional CPV Plan requirements. Established SPC limits

should be reviewed in light of process changes to confirm

their continuing validity and may be adjusted in response

to generation of additional data. The process monitoring

procedure, as well as process capability review, should

be established in applicable documentation (e.g. the

CPV Plan). With a given frequency of analysis, further

statistical examination is required to determine if the

results suggest a potential impact on the product.

This is described further in Section 10. Multiple data

sources and applicable analysis should be organized and

integrated in appropriate process data analysis tools.

Subsequent statistical tools should be appropriate for the

data to be analyzed (see Section 12).


SECTION 10.0

10.0

Example CPV execution plan for drug substance After completion of the PPQ, continued process verification should demonstrate that the process is in control. In the words of the FDA guidance from 2011, it should offer: ‘assurance that the process is reasonably protected against sources of variability that could affect production output, cause supply problems, and negatively affect public health’5. A monitoring and trending program for the A-Mab drug substance process parameters and attributes is outlined in this section, but the reader should view the discussion and content of the tables as recommendations and ensure that the parameters and values they use are appropriate for their product and process. It is applicable for both initial and long-term monitoring of the drug substance manufacturing process. Selection of variables for monitoring is based on information and rationale in Sections 5 and 7.

Note: This plan sub-section is neither a minimalistic nor

comprehensive listing of variables expected for CPV

monitoring. Rather we attempt to maintain a reasonable

consistency with the A-Mab case study to provide an example

of likely CPV variables associated with a product launch

(where in this case, understanding of variability is not evident

immediately after completing a platform-based Process

Validation (PV) Stage 2 PPQ with only two commercial scale

batches of the particular A-Mab molecule). This is meant to

demonstrate reliable process control and ability to detect

process drift. Commercial scale process data for legacy

processes would likely be available and may justify a smaller

set of CPV monitored variables. It is emphasized that this is

an untested example package for consideration, not general

guidance or proven best practice approach.

Continued assurance of consistent process performance

and identification of potential out of trend results is

achieved by applying SPC rules and capability analysis

(Ppk) as discussed in Section 12. CPV datasets enable

process capability predictions with higher confidence,

deepen process understanding, and improve process

robustness by increasing the likelihood of detecting

sources of process variability before they cause batch

failures. The CS is updated based upon reviews of related

risk assessments, as a part of assessing accumulated CPV

data in summary CPV Reports. CPV should be integrated

into the organization’s development process and quality

system. A CPV Master Plan may be used across a

corporation, to guide development of product specific CPV

procedures including the incorporation of outputs from

Stage 1 and Stage 2 (e.g., CQA, CPP). CPV output (from the

executed plan) will be documented and summarized at a

frequency defined by the plan. Figure 10.1 is a schematic

showing the continuity of review in the product lifecycle.


Figure 10.1: Continued review as part of the product lifecycle.

Process UnderstandingCPP/CQA’s

Risk Assessment ReviewProcess Knowledge Report

Process AnalysisInitial Process Performance

Evaluation Acceptance & ReleaseOngoing Process Monitoring

CpK Statistics DatabaseAnnual Product Review

ContinuousQuality

Monitoringand Feedback

Process Control Strategy

Batch Record DataSpecifications

ContinuousProcess

ImprovementChange

ManagementDocumentation

ProductQuality

12

34


The A-Mab case study (and assumed post-PPQ CS)

contains information on the linkage between product

quality attributes and CPPs across the eleven process

steps. These relationships and the relationships between

KPPs and KPAs are specific to each step. Therefore, this

section is divided into tables for each (Steps 1 through

11, plus BDS) in order to list process parameters and

attributes to be monitored to verify process control over

the entire lifetime of manufacturing the drug substance.

Non-routine sampling and specific data gathering will

augment routine sampling and data recording to generate

the data for trending and monitoring under this CPV plan.

For the tables presented in this section, CPV process

variables and their classification are listed in Columns

A and B, as recommended earlier in Section 7. Column

C includes information on any data treatment required

before graphing to monitor trends. ‘Unadjusted’ raw data

are measurement results (source data) that are directly

charted. ‘Converted’ data indicate that monitoring the

process variable involves treatment of measured results

and either combining with other process data (e.g. yield is

a ratio of combined raw data) or standardizing to match

the intent of control limits (e.g. weight measurements

converted to volume or converting totalized flow

through volume to column volumes). Raw (or converted)

data that is mathematically ‘transformed’ is a third type

of data treatment that may be required before charting

for trend monitoring.

Column D specifies the recommended SPC tool for

monitoring performance trends against control limits.

The tool listed is selected based on subject matter expert

experience with the process development history. The

chosen tool provides a means to visually review the data

and may be revised when the nature of the data is better

understood. The options included in the plan include:

‘individual run chart’ for data without initial control limits;

‘individual measurement chart‘ (a control chart) for data

that can be plotted with an expected fixed mean and

proposed control limits; ‘EWMA chart’ (Exponentially

Weighted Moving Average control chart) (see Section

12 for application); ‘3SD tunnel’ (a control chart with

+3 standard deviation control limits) for data that has a

dynamically changing mean during the batch processing

time (such as a VCC profile, UV chromatogram, or UF/DF

flow rate); or ‘exception flag’ which uses routine process

monitoring for process parameters and reporting of any

out of range result (exception). Any custom correlations

that are developed during CPV would also be shown in this

column (e.g. VCC versus dCO2, or step processing time

versus step yield).

Column E identifies plan monitoring requirements for

an initial short-term CPV period of manufacturing which

follows completion of PPQ in order to obtain sufficient

data to set long-term control limits (unless limits already

exist with sufficient confidence and understanding of the

expected long-term normal variation). This period is based

on a minimum number of independent batch experiences,

e.g. 30 as mentioned in Section 8.1 (with a reminder to

consider that raw material lot impact experience may

lag process lot experience), or achieving a target Ppk for

the variable’s range of control. Proposed initial control

limits to use when starting CPV baseline monitoring are

given in column F and are based on assumed PPQ criteria

for A-Mab, or a fictitious control range proposed after

completing the process validation Stage 2 effort. See

Section 9 for more information on establishing initial

control limits.

Note: Due to various strategies for combining batches in

manufacturing, 30 completed batches may not necessarily

be sourced from 30 independent vial thaws; or use 30

uniquely prepared lots of the involved solutions; or employ

30 different raw material lots (which could be sub-lots

of fewer supplier bulk lots); or may not produce 30 drug

substance or drug product batches. Awareness and

tracking of different lot counts for different variables is

important information during CPV.


Column G is dedicated to the CPV plan after the initial

baseline period ends and the frequency of sampling and/

or trending is subject to long-term monitoring during

commercial manufacturing. At this future point in the CPV

lifetime, it is assumed that sizeable specific commercial

manufacturing experience and knowledge of the A-Mab

process exists and sources of variation are understood

well enough to support a reduced frequency of testing or

review of data trends with lowered risk for undetected

process drift. In some cases it may be possible to gain

enough confidence in the behavior of certain parameters

and their relationship to the process, that they may be

removed from the CPV Plan and only reconsidered for

enhanced monitoring to evaluate future process changes.

In column H, ‘Initial limits’ is an abbreviated placeholder

term for documenting the dates that particular life-time

control limits apply which would be documented as

‘Range1’. This information would be populated after the

initial baseline monitoring is complete and short-term

control limits are superseded by long-term limits. Long-

term limits and ‘Range2’ are included in the early tables

to demonstrate the historical nature of CPV lifecycle

management. Control limits may change at a given time

for a particular justified reason (such as appearance of

very long-term variation or change factors), and past data

profiles should not necessarily be assessed (or displayed)

against more recent control limits. However, the ability to

review historical data ranges along with changes in more

recent predecessor control limits can enhance process

understanding over the product lifespan, especially if these

changes are associated with a set of diagnosed root causes.

Collection of data may at some point provide sufficient

demonstration of control of a variable that it may be

removed from the long-term monitoring plan, or that the

frequency with which a particular variable is monitored

can be reduced to an occasional (audit) basis. Examples

of variables that might be removed from long-term

monitoring include CPPs that have been identified as

being well-controlled (WC-CPP). Initial (short-term)

monitoring data may verify that the expected control is

routinely achieved, and that there are select CQAs for

which monitoring data shows little variability. Responding

to signals in the data in this way allows adjustment of the

long-term monitoring plan to tailor it for monitoring those

elements of the process most likely to exhibit variability

and hence need the greatest attention.

Besides time-based risks to maintaining the validated state

of process control, the other type of risk that requires

verification and monitoring involves change-based risks.

These assumed known ‘for-cause’ events are shown in

column I and new SME knowledge can be added as it

is gained. These changes (e.g. critical raw material lot

changes or process improvements) may have an impact

that extends beyond the change implementation and may

make previous data and trend characteristics obsolete and

invalidate previous short-term or long-term control limits.

When available, collected monitoring data should be

provided with the resolution recorded in its raw data form,

rather than reflecting any rounding to the significant figures

included in the control limits. This enables more accurate

statistical analysis and determination of capability.

Note: Situations that would result in duplicating information

across a table are occasionally presented with alternative

proposals to offer the reader different options to consider for

CPV. Various charting options are presented as examples and

different life-time plans shown for the variables. Rationale

would be subject to SME justification for each individual

variable, and as not shown below, may actually result in the

same monitoring tool and life-time monitoring plan.

Note: Since the contents of column H are subject to more

frequent updates than the other plan elements, the reader

could consider migrating or referencing the column H lifetime

control limits for each variable (as they become available) in a

separate document for efficient review and approval of revised

ranges to maintain both the historical control ranges with

current control ranges.


Table 10.1. Step 1 CPV variables

10.1 Step 1, Seed culture expansion in disposable vessels – CPV variablesThe CPV plan for process variables in this step are shown in

the following table and align with the rationale in Section 7.

The qualification of new cell banks is beyond the scope of

this CPV plan and subject to change control management

by registered regulatory agreements. Monitoring and

verification of the commercial scale impact of a change in a

medium component presented earlier are included in this

example. In this example, it was assumed the split ratio

for this step did not have PPQ acceptance criteria (VCC, %

viability, and duration ranges employed in PPQ) nor was

there sufficient process data from the A-Mab working cell

bank to adopt initial CPV control limits.

A B C D E F G H I

Variable Class Data treatment

prior to

analysis

Monitoring

tool

Initial baseline

monitoring

(short-term)

Initial

baseline

control

limits

(short-

term)

Periodic

monitoring

(time/cycle-

based)

Lifetime

control

limits

(Long-

term)

For cause

monitoring

(change-based)

Viable Cell Conc., each

passage endKPA

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

0.7 to 2.8

x 106 (vc/

mL)

Every batch LT range1

TBD (from

date X

to Y) LT

range2

(from date

Y to Z)

Cell bank or

growth medium

changes

Optional elements

Initial VCC split ratio,

each passageKPP

Converted

(ratio)

Individual Run

chart

Every batch until

long-term limits

set

Character-

ize (No

PPQ limits)

Once annually LT Period1

Range1

TBD LT

Period2

Range2

Cell bank or

growth medium

changes

Culture duration, each

passageKPP

Raw data Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

3 to 4

(days)

While PpK <

1.0, Otherwise

not required

LT range

TBD

(dates:

TBD)

Cell bank or

growth medium

changes

Culture viability, each

passage endKPA

Converted

(ratio)

EWMA chart Every batch until

long-term limits

set

88 to 98

(%)

not required LT range

TBD (from

date X

to Y)

Cell bank or

growth medium

changes



10.2 Step 2, Seed culture expansion in bioreactors – CPV variablesThe CPV plan for process variables in this step is shown

in the following table and follows the rationale in Section

7. Monitoring and verification of the commercial scale

impact of a change in a medium component presented

earlier is included in this example. It was assumed the

cell culture split ratio for this step had sufficient data for

the expected bioreactor expansion performance to adopt

initial control limits.

A B C D E F G H I


prior to

analysis

Monitoring

tool

Initial baseline

monitoring

(short-term)

Initial

baseline

control

limits

(short-

term)

Periodic

monitoring

(time/cycle-

based)

Lifetime

control

limits

(Long-

term)

For cause

monitoring

(change-based)

Viable Cell Conc., each

passage endKPA

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

3.9 to 6.0 x

106

(vc/mL)

Every batch LT range

TBD

(from date

X to Y)

Cell bank or

growth medium

changes

Optional elements

Initial VCC split ratio,

each passageKPP

Converted

(ratio)

Individual Run

chart

Every batch until

long-term limits

set

3.0 to 4.1 Once annually LT range

TBD

(from date

X to Y)

Cell bank or

growth medium

changes

Culture duration, each

passageKPP

Unadjusted (x.x

resolution)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

3 to 5

(days)

While PpK

< 1.0,

Otherwise not

required

LT range

TBD

(from date

X to Y)

Cell bank or

growth medium

changes

Culture viability, each

passage endKPA

Converted

(ratio)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

90 to 99

(%)

not required LT range

TBD

(from date

X to Y)

Cell bank or

growth medium

changes



10.3 Step 3, Production culture bioreactor – CPV variablesTo be consistent with the status of the A-mab case

study when this plan is initiated, the option of creating a

time-dependent multivariate PLS (partial least squares)

bioreactor model is a CPV objective, based on previous

successful experiences25. The A-mab case study [3,

Section 3.10, Page 107-109] describes a principle

components bioreactor model as a predictor of acceptable

oligosaccharide structure and acidic variant CQAs. The

parameter inputs to the model include temperature and

pH, and attribute inputs to the model include titer, VCC,

and viability. All these variables are included individually

for CPV monitoring while the bioreactor model is qualified.

The output of the model for each batch is classified as a

KPA. The potential added value in using the model is in

ensuring internal correlations among different variables

are considered. In the future, the values generated from

this model may provide a multivariate output for trend

monitoring that is predictive of process performance, with

its own alert and action limits.

A fixed volume of nutrient feed is added at a

defined time under automation and routine batch

document controls, therefore trending the amount

and timing of the nutrient feed addition does

not provide value because they are not subject

to random variation. Production cultures are

harvested within an acceptable duration based on

viability and titer considerations.

Temperature and pH are continuously feedback

controlled to set points, during the 16 to 18 day

culture but measured values are dynamic over that

time period. Therefore, 3SD tunnels (the range

defined by the mean + 3 standard deviations) for the

parameter profiles will be developed during the initial

CPV period, to generate an expected ’conduit’ for

results when tracking consistent control of the CPP.

A B C D E F G H I


prior to

analysis

Monitoring

tool

Initial baseline

monitoring

(short-term)

Initial

baseline

control

limits

(short-

term)

Periodic

monitoring

(time/cycle-

based)

Lifetime

control

limits

(Long-

term)

For cause

monitoring

(change-based)

Culture durationCPP

Unadjusted

(x.x resolution)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

16 to 18

(days)


TBD

(dates:

TBD)

Change in cell

bank, culture

medium,

or process

setpoint

Maximum pCO2CPP

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch

until long-term

limits set, also

correlate w/

IVCA

45 to 140

(mmHg)


TBD

(dates:

TBD)

Change in cell

bank, culture

medium,

or process

setpoint



A B C D E F G H I

Variable Class Data

treatment

prior to

analysis

Monitoring

tool

Initial baseline

monitoring

(short-term)

Initial

baseline

control limits

(short-term)

Periodic

monitoring

(time/cycle-

based)

Lifetime

control

limits

(Long-term)

For cause

monitoring

(change-based)

Culture durationCPP

Unadjusted

(x.x

resolution)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

16 to 18

(days)


TBD (dates:

TBD)

Change in cell

bank, culture

medium,

or process

setpoint

Bioreactor pHCPP

Unadjusted

(raw data)

3SD tunnel Every batch until

long-term limits

set for reference

6.75 to 6.95

(-log [H+])

Not required,

included in

PLS model

See PLS

model, LT

range TBD

(dates:

TBD)

Change in cell

bank, culture

medium,

or process

setpoint

Afucosylated glycansCQA

Unadjusted

(raw results)

Individual chart

with long-term

3-sigma limits

Required

for model

qualification only

5 to 10 (%) Once annually

(time 0

of annual

stability

batch)

LT range 5

to 10 Initial

to current

date

Change in cell

bank, culture

medium,

or process

setpoint

Galactosylated glycansCQA

Unadjusted

(raw results)

Individual chart

with long-term

3-sigma limits

Required

for model

qualification only

15 to 35 (%) Once annually

(time 0

of annual

stability

batch)

LT range 15

to 35 Initial

to current

date

Change in cell

bank, culture

medium,

or process

setpoint

PLS model employing

pH, DO, temperature,

pressure, gas rates,

weight, VCC, viability,

titer, glucose, lactate

KPAConverted &

transformed

Custom

PLS model

PCA t1

Every batch until

model is > 95%

predictive

Trajectory

versus time

± 3 StDev


TBD

(dates: TBD

Change in cell

bank,

culture medium,

or

process

setpoint

Product yield (titer) at

HarvestKPA

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

and PpK >1.0

4.0 to 5.5

(g/L)

Not required,

included in

PLS model

See PLS

model,

LT range

TBD

(dates:

TBD)

Change in cell

bank,

culture medium,

or

process

setpoint



A B C D E F G H I


prior to

analysis

Monitoring

tool

Initial baseline

monitoring

(short-term)

Initial

baseline

control

limits

(short-term)

Periodic

monitoring

(time/cycle-

based)

Lifetime

control

limits

(Long-

term)

For cause

monitoring

(change-

based)

Optional elements

Antifoam lotCMA

Unadjusted

(raw data)

Exception

Flag (new lot)

Test BDS

clearance

for 3 different

lots

< LOD Not required < LOD Change in

material

ID or supplier

Medium

osmolalityCPP

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Track OOR

Exception flags

365 to 435

(mOsm)

Track OOR

Exception

flags

375 to 425

mOsm

(dates:

current)

Change in

medium prep

Mannose

contentCQA

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Required

for model

qualification only

5 to 8

(%)

Not required

at this time

LT range

TBD

(dates:

TBD)

Changes in cell

bank

or step 3

setpoints

Sialic acid

contentCQA

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Required

for model

qualification only

NMT 1.6

(%)

Not required

at this time

LT range

TBD

(dates:

TBD)

Changes in cell

bank

or step 3

setpoints

Non-glycosylated

heavy chainCQA

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Required

for model

qualification only

0 to 2.4

(%)

Not required

at this time

LT range

TBD

(dates:

TBD)

Changes in cell

bank

or step 3

setpoints

Time of glucose feeds

(hrs since inoculationKPP

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

Feed 1: a to

b hrs

Feed 2: c to

d hrs

Feed 3: e to

f hrs

Not required

at this time

LT range

TBD

(dates:

TBD)

Change in cell

bank,

culture

medium, or

process

setpoint

Peak VCC

(Viable Cell Conc.) KPA

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch until

PLS qualified

20 to 30 x

106

(vc/mL)

Not required,

included in

PLS model

See PLS

model,

LT range

TBD

(dates:

TBD)

Change in cell

bank,

culture

medium, or

process

setpoint

Culture viability

at HarvestKPA

Converted

(ratio)

Individual chart

with long-term

3-sigma limits

Every batch until

PLS qualified

40 to 61

(%)

Not required,

included in

PLS model

See PLS

model,

LT range

TBD

(dates:

TBD

Change in cell

bank,

culture

medium, or

process

setpoint



10.4 Step 4, centrifugation and depth filtration – CPV variablesFor Step 4, Critical Raw Materials (CRM) that impact

the step include the working cell bank, upstream growth

medium components, and depth filters. Changes to these

items would require a period of enhanced monitoring

of filtrate turbidity and step yield, to demonstrate that

the process can attenuate upstream process variability

prior to purification. If turbidity or the duration of depth

filtration shifts upward, monitoring the inlet pressure

parameter or the attribute of differential pressure across

the filters may need to be added to CPV.

A B C D E F G H I


prior to

analysis

Monitoring

tool

Initial baseline

monitoring

(short-term)

Initial

baseline

control

limits

(short-term)

Periodic

monitoring

(time/cycle-

based)

Lifetime

control

limits

(Long-

term)

For cause

monitoring

(change-based)

Turbidity

of filtrateKPA

Unadjusted

(raw data)

Individual

chart with

long-term

3-sigma limits

Every batch until

long-term limits

set

< 2

(NTU)

Not required LT range

TBD

(dates:

TBD)

CRM or process

change to this

or previous

step

Step yield

FiltrateKPA

Converted

(ratio)

Individual

Run chart

Every batch until

long-term limits

set

Characterize

(No PPQ

limits)


TBD

(dates:

TBD)

CRM or process

change

to this or

previous step

Optional elements

Duration of Broth

ClarificationKPP

Converted

(end minus

start time)

Individual

Run chart

Every batch until

long-term limits

set

Characterize

(No PPQ

limits)

Once annually LT range

TBD

(dates:

TBD)

Upstream

scale-up,

Centrifuge feed

or flow

rate setpoint

changes

Inlet pressure,

depth filtersKPP

Unadjusted

(raw data)

Individual

chart with

long-term

3-sigma limits

Not required None Not required ± 3 StDev

of most

recent 30

batches

(pre-

change)

Upstream

scale-up,

filter changes,

Centrifuge feed,

or filter flow

rate setpoint

changes, shifts

in turbidity or

filtration time.


10.5 Step 5, Protein A chromatography – CPV variablesInitial control limits for load ratio and elution buffer pH

are assumed, based on a simulated normal range within

the DOE PAR provided in the A-Mab case study. Likewise,

the short-term residual protein A control limits are based

on the outcome of a fictional study done after PPQ at

small scale, which examined the capacity for high cycle

count AEX resin to clear protein A (spiking study) and,

characterization of potential Protein A leachate from both

high and low use cycle counts with respect to resin storage

time. Note that elution buffer pH data is included while

the limits for the HCP model are generated.


A B C D E F G H I


prior to

analysis

Monitoring

tool

Initial baseline

monitoring

(short-term)

Initial

baseline

control

limits

(short-term)

Periodic

monitoring

(time/cycle-

based)

Lifetime

control

limits

(Long-

term)

For cause

monitoring

(change-

based)

Protein Load Ratio

(in HCP model)

Each sub-batch

CPPConverted

(ratio)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

15 to 40

(g A-mAb/L

resin)


TBD

(dates:

TBD)

Resin, or

process

change to this

or previous

step

Elution buffer

pH (in HCP model)CPP

Unadjusted

(x.xx resolution)

Individual chart

with long-term

3-sigma limits

Every batch until

HCP model

limits set

3.4 to 3.8

(-log [H+])

Track OOR

Exception

flags

LT range

3.4 to 3.8

(dates:

initial to

current)

Buffer

formulation

scale-up

Residual Protein A

in eluate poolCQA

Unadjusted

(raw data)

w/ upper limit,

& correlation

vs. storage age

Every batch until

long-term limits

set

≤ 1234

(ng/mg

A-mAb)

Per resin/

column

lifetime

protocol

LT range

TBD

(dates:

TBD)

First two

cycles after

extended

storage

(≥ 3 months)

Step durationKPP

Converted

(elapsed)

Individual

Run chart

Every batch until

long-term limits

set

Characterize

(No PPQ

limits)


TBD

(dates:

TBD

Scale

increases

Step yieldKPA

Converted

(ratio)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

68 to 88

(%)


TBD

(dates:

TBD)

Reset range

for process

change to

this or

previous step

x.xx


10.6 Step 6, Low pH treatment – CPV variablesThe viral safety risk CQA (inactivation of particular AVA)

for the A-Mab process has been validated in the small scale

model during Stage 1 process validation. Initial control

limits for inactivation time and pH are assumed, based on

a fictitious normal range within the DOE PAR provided in

the A-Mab case study. 3SD tunnel monitoring of pH during

inactivation ensures the parameter remains in range

throughout the inactivation time and it is not monitored

as a single point measurement. Aggregate results are

assumed to include a sum of all quantifiable non-monomer

SEC peaks (dimer, trimer, etc). Yield is not expected to

be impacted by this step and variability around 100% has

been associated with measurement uncertainty, therefore

it does not merit CPV trending or monitoring. The basis of

yield for the next step (7, CEX) begins from the eluate pool

of the previous step (5, Protein A).


A B C D E F G H I


prior to

analysis

Monitoring

tool

Initial baseline

monitoring

(short-term)

Initial

baseline

control

limits

(short-term)

Periodic

monitoring

(time/cycle-

based)

Lifetime

control

limits

(Long-

term)

For cause

monitoring

(change-

based)

pH during

inactivationCPP

Unadjusted

(raw data)


long-term tunnel

set

3.4 to 3.8

(-log [H+])


TBD

(dates:

TBD)

Scale

increases

Post inactivation

aggregatesCQA

Converted

(additive)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

end and PpK

>1.3

≤ 3.0

(%)


TBD

(dates:

TBD

Process

change

to this or

previous step

Optional elements

Inactivation

timeCPP

Converted

(elapsed)

Individual chart

with long-term

3-sigma limits

Every batch

until a

long-term sigma

set

80 to 120

(minutes)

Track OOR

Exception

flags

LT range

TBD

(dates:

TBD)

No known risk

events

Quantity of

acid addedKPP

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch

until a

long-term sigma

set

Characterize

(No PPQ

limits)


TBD

(dates:

TBD)

Scale

increases



10.7 Step 7, Cation exchange chromatography – CPV variablesInitial control limits provided in the table below are

assumed, based on a fictitious normal range within

the DOE PAR provided in the A-Mab case study. Step

duration has a batch document control limit based on

platform experience, but for A-Mab CPV a typical range

will be determined for historical reference. Aggregate

results are assumed to include all quantifiable non-

monomer SEC peaks (dimer, trimer, etc).

A B C D E F G H I


prior to

analysis

Monitoring

tool

Initial baseline

monitoring

(short-term)

Initial

baseline

control

limits

(short-term)

Periodic

monitoring

(time/cycle-

based)

Lifetime

control

limits

(Long-

term)

For cause

monitoring

(change-

based)

Protein Load Ratio

(in HCP model)CPP

Converted

(ratio)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

15 to 25

(g A-mAb/L

resin)


TBD

(dates:

TBD)

Process

change

to this or

previous step

Wash Conductivity

(in HCP model)CPP

Unadjusted

(x.x resolution)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

4 to 6

(mS/cm)

Track OOR

Exception

flags

LT range

TBD

(dates:

TBD)

Buffer

formulation

scale-up

Elution pHCPP

Unadjusted

(x.xx resolution)


long-term tunnel

set

5.9 to 6.1

(-log [H+])


TBD

(dates:

TBD)

Process

change to

this or

previous step

Aggregates

in CEX eluate poolCQA

Converted

(additive)

Individual chart

with long-term

3-sigma limits

Every batch until

long term limits

set

and PpK >1.3

≤ 1.0

(%)

Per resin/

column

lifetime

protocol

LT range

TBD

(dates:

TBD)

Process

change to

this or

previous step

HCP content

in CEX eluate poolCQA

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch

until a

long-term sigma

set

≤ 130

(ppm)

Per resin/

column

lifetime

protocol

LT range

TBD

(dates:

TBD)

Process

change to

this or

previous step

CEX eluate volume

(each sub-batch)KPA

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

3.7 to 4.7

(CV)

Not Required LT range

TBD

(dates:

TBD)

Column pack

or repack

Step yieldKPA

Converted

(ratio)

Individual chart

with long-term

3-sigma limits

Every batch

until a

long-term sigma

set

83 to 97

(%)


TBD

(dates:

TBD)

Process

change to step

Optional elements

Step durationKPP

Converted

(elapsed

Individual

Run chart

Every batch until

long-term limits

set

Characterize

(No PPQ

limits)


TBD

(dates:

TBD)

Process

change to step

x.xx


10.8 Step 8, Anion exchange chromatography – CPV variablesInitial control limits provided in the table below are

assumed, based on a fictitious normal range within a

DOE PAR provided in the A-Mab case study. The CEX

eluate is adjusted to a target pH and conductivity (CPPs)

before loading for flow-through mode chromatography.

The fictitious HCP clearance linkage model for the three

chromatography steps (Protein A, CEX, and AEX) is

included here for monitoring the trend in the algorithm

output, using the 6 parameters (two from each step) for

a given batch.


A B C D E F G H I


prior to

analysis

Monitoring

tool

Initial baseline

monitoring

(short-term)

Initial

baseline

control

limits

(short-

term)

Periodic

monitoring

(time/cycle-

based)

Lifetime

control

limits

(Long-

term)

For cause

monitoring

(change-based)

Protein

Load Ratio CPP

Converted

(ratio)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

100 to 200

(g A-mAb/L

resin)


TBD

(dates:

TBD)

Process change

to this or

previous step

Load ConductivityCPP

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

4.0 to 7.0

(mS/cm)


TBD

(dates:

TBD)

Process change

to this or

previous step

Load pH

(in HCP model) CPP

Unadjusted

(x.xx resolution)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

7.4 to 7.7

(-log [H+])

Track OOR

Exception

flags

LT range

TBD

(dates:

TBD

Process change

to

previous step

Equilibration/ Wash 1

Buffer Conductivity (in

HCP model)

CPPUnadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

2.0 to 3.2

(mS/cm)

Track OOR

Exception

flags

LT range

TBD

(dates:

TBD)

Buffer

formulation

scale-up

Linkage model

output for HCP

(predicted)

KPATransformed

A-mAb case

study

model equation

6

(Pg 158)

Individual chart

with long-term

3-sigma limits

Every batch until

AEX

eluate HCP

is predictive with

95% confidence

99.5%

prediction

interval for

mean

of HCP

output


TBD

(dates:

TBD)

Re-qualify

model for

process

changes in

ProA, CEX, or

AEX steps

x.xx



A B C D E F G H I

Variable Class Data

treatment

prior to

analysis

Monitoring

tool

Initial baseline

monitoring

(short-term)

Initial

baseline

control

limits

(short-term)

Periodic

monitoring

(time/cycle-

based)

Lifetime

control

limits

(Long-

term)

For cause

monitoring

(change-based)

HCP content

in AEX eluate

(measured)

CQAUnadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch

until a

long-term sigma

set

≤ 15

(ng/mg)

Per resin

column

Lifetime

protocol

LT range

TBD

(dates:

TBD)

Process

change to any

chromatography

step

Residual Protein A

in eluateCQA

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Not Required ≤ 10

(ng/mg

A-mAb)

Per resin

column

Lifetime

program

LT range

TBD

(dates:

TBD

Align monitoring

with

ProA process

changes

Step yieldCQA

Converted

(ratio)

Individual chart

with long-term

3-sigma limits

Every batch

until a

long-term sigma

set

≥ 87

(%)

Trend every

batch vs. long-

term limits

LT range

TBD

(dates:

TBD)

Reset range for

Process change

to step

KPAUnadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

Feed 1: a to

b hrs

Feed 2: c to

d hrs

Feed 3: e to

f hrs

Not required

at this time

LT range

TBD

(dates:

TBD)

Change in cell

bank, culture

medium,

or process

setpoint

Optional elements

KPPConverted

(elapsed)

Individual

Run chart

Every batch until

long-term limits

set

Characterize

(No PPQ

limits)

Not

Required

LT range

TBD

(dates:

TBD)

Reset range for

Process change

to step


10.9 Step 9, Small virus retentive filtration – CPV variables3SD tunnel monitoring of the operating inlet pressure

ensures the parameter (and filtration flow rate) remains

consistent throughout the filtration; it is not monitored as

a single point measurement. Initial control limits provided

in the table below are assumed based on a fictitious normal

range within a DOE PAR, provided in the A-Mab case

study. Platform knowledge of SVRF operations indicates

this step does not impact yield, therefore consideration is

limited to any future qualification of re-filtration.


A B C D E F G H I


prior to

analysis

Monitoring

tool

Initial baseline

monitoring

(short-term)

Initial

baseline

control

limits

(short-

term)

Periodic

monitoring

(time/cycle-

based)

Lifetime

control

limits

(Long-

term)

For cause

monitoring

(change-based)

Operating

Inlet pressureCPP

Unadjusted

(raw data)


long-term limits

set

Tunnel

derived

from

PPQ and

platform

batches at

same scale

Trend every

batch

vs. long-term

limits

LT range

TBD

(dates:

TBD)

Process change

to this or

previous step

Filtration load

volumeCPP

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch

until a

long-term sigma

set

640 to

1040

(L)


TBD

(dates:

TBD)

No known risk

events

10.10 Step 10, Ultrafiltration and diafiltration – CPV variablesTo address the risk that future incoming buffer component

lots test within specification, but outside a typical

experience to cause an undesired or unpredicted variation

not controlled by the process, we propose monitoring a

trend of the CoA reported attribute for at least 30 batches

from each approved supplier (manufacturer/ packager/

distributor/ vendor) as an optional item (see ‘Excipient 1’ in

Table 10.10). If the actual range for this material attribute

does not exhibit a Ppk > 1.3 against its ICH Q3D limit,

further monitoring and control actions may be necessary.

The initial control limits provided, are based on fictitious

normal ranges to serve as early warning triggers. Buffer

pH, osmolality, and conductivity are controlled (and

adjusted as necessary), before the solutions are released

for use in this process step. The A-Mab case study

includes IPC tests for pH and osmolality in drug product

manufacturing, but does not include BDS specifications

for these attributes. It will be assumed, based on the case

study information, that the process stream after UF/DF

(rather than after Step 11) is subject to probe monitoring

for pH (PAT), to show performance prior to final filtration,

as an input to the drug product CPV effort (which occurs

after BDS storage, shipping, and thawing).

TMP is a design parameter with a fixed setpoint even as

membrane use cycles increase. The permeate flow output

attribute varies and the input retentate flow parameter

responds to achieve and maintain the TMP setpoint.



A B C D E F G H I


prior to

analysis

Monitoring

tool

Initial baseline

monitoring

(short-term)

Initial

baseline

control

limits

(short-term)

Periodic

monitoring

(time/cycle-

based)

Lifetime

control

limits

(Long-

term)

For cause

monitoring

(change-

based)

UF/DF

processing timeCPP

Converted

(elapsed)

Individual

Run chart

Every batch until

long-term limits

set

≤ 7

(hours)

Trend every

batch

vs. long-term

limits

LT range

TBD

(dates:

TBD)

Scale or

process

change to step

Number of

dia-volumesCPP

Converted

(ratio)

Individual

Run chart

Every batch until

long-term limits

set

9.5 t 10.4

(DV)


TBD

(dates:

TBD)

Buffer

formulation

or protein

conc. changes

UF/DF retentate

final pHCPP

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

5.2 to 5.4

(-log [H+])


TBD

(dates:

TBD)

Step 10 or 11

process or

raw material

changes

Protein Conc. A280

Prior to BDS fill stepCPP

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

65 to 85

(mg/mL)

Trend every

batch

vs. long-term

limits

LT range

TBD

(dates:

TBD)

Process

change

to this step

Product yield,

Final RetentateKPA

Converted

(ratio)

Individual chart

with long-term

3-sigma limits

Every batch

until a

long-term sigma

set

Per

membrane

lifetime

program

Trend every

batch

vs. long-term

limits

LT range

TBD

(dates:

TBD)

When

extending

re-use cycles

Optional elements

Excipient1

AttributeACMA

Unadjusted

(raw data)

Individual

Run chart

Each new lot

until

30 lots tested

Characterize

(No PPQ

limits)


TBD

(dates:

TBD

Qualify new

suppliers,

RM or CoA

changes

SEC aggregates in

final retentateCQA

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

≤ 1.4

(%)


TBD

(dates:

TBD)

Process

change to

this step,

extensions of

membrane

lifetime

Protein Conc. A280

Prior to diafiltrationKPP

Unadjusted

(raw data

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

40 to 60

(mg/mL)


TBD

(dates:

TBD)

No known risk

events

Recirculation

flow rate KPP

Unadjusted

(raw data)


long-term limits

set

Profile

within

tunnel limits


TBD

(dates:

TBD)

When

extending

re-use cycles

Permeate

flow rateKPA

Unadjusted

(raw data)


long-term limits

set

Profile

within

tunnel limits

Verify

when new

membranes

installed

LT range

TBD

(dates:

TBD)

When

extending

re-use cycles


10.11 Step 11, Final filtration/Bulk fill and freezing of BDS – CPV variablesThe initial control limits provided in the next table are

assumed, based on a fictitious normal range and PPQ

consistency limits, or in the case of yield, an assumed limit

from fictitious platform knowledge.


A B C D E F G H I


prior to analysis

Monitoring

tool

Initial baseline

monitoring

(short-term)

Initial

baseline

control

limits

(short-

term)

Periodic

monitoring

(time/cycle-

based)

Lifetime

control

limits

(Long-

term)

For cause

monitoring

(change-based)

Bulk Fill

Step yieldKPA

Converted

(ratio)

Individual chart

with long-term

3-sigma limits

Every batch

until a

long-term sigma

set

≥ 98

(%)


TBD

(dates:

TBD)

No known risk

events

Optional elements

Filtration

volumeKPP

Unadjusted

(raw data)

Individual

Run chart

Every batch

until a

long-term sigma

set

200 to 300

(L)

Trend every

batch

vs. long-term

limits

LT range

TBD

(dates:

TBD)

Reset range for

scale or

process change

to this

or previous step

Maximum

Inlet pressureKPP

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch

until a

long-term sigma

set

≤ 2

(psig)


TBD

(dates:

TBD)

No known risk

events

Filtration

timeKPP

Converted

(elapsed)

Individual

Run chart

Every batch until

long-term limits

set

≤ 3

(hours)

Trend every

batch

vs. long-term

limits

LT range

TBD

(dates:

TBD)

Scale or process

change to step



10.12 CPV monitoring of bulk drug substance lot dataAs with the process steps, BR depends on a rational assessment of lot data. This is shown in the next table.

A B C D E F G H I


prior to

analysis

Monitoring

tool

Initial baseline

monitoring

(short-term)

Initial

baseline

control

limits

(short-

term)

Periodic

monitoring

(time/cycle-

based)

Lifetime

control

limits

(Long-

term)

For cause

monitoring

(change-based)

Monomer

by SECCQA

Peak

integration

(raw data

Individual chart

with long-term

3-sigma limits

Every batch

until a

long-term sigma

set

NLT 98

(%)

Trend every

batch

vs. long-term

limits

LT range

TBD

(dates:

TBD

Per BDS

stability

monitoring

protocol

Aggregates

by SECCQA

Converted

(additive)

Individual chart

with long-term

3-sigma limits

Every batch

until a

long-term sigma

set

NMT 2

(%)

Trend every

batch

vs. long-term

limits

LT range

TBD

(dates:

TBD)

Per BDS

stability

monitoring

protocol

Galactosylated

GlycansCQA

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

and PpK >1.0

15 to 35

(%)

Not required,

Not

stability

indicating

LT range

TBD

(dates:

TBD

Confirm

comparability

for

changes in cell

bank or

step 3 setpoints

Afucosylated

GlycansCQA

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Every batch until

long-term limits

set

and PpK >1.0

5 to 10

(%)

Not required,

Not

stability

indicating

LT range

TBD

(dates:

TBD)

Confirm

comparability

for

changes in cell

bank or

step 3 setpoints



A B C D E F G H I


prior to

analysis

Monitoring

tool

Initial baseline

monitoring

(short-term)

Initial

baseline

control

limits

(short-

term)

Periodic

monitoring

(time/cycle-

based)

Lifetime

control

limits

(Long-

term)

For cause

monitoring

(change-based)

Optional elements

DNACQA

Unadjusted

(raw data)

Individual

Run chart

Every batch for

30 BDS batches

None

detected

Per column

resin Lifetime

protocols

LT range

TBD

(dates:

TBD)

Per column

resin Lifetime

protocols

Methotrexate and/or

Antifoam CCQA

Unadjusted

(raw data)

Individual

Run chart

Every batch for

30 BDS batches

None

detected

Per column

resin Lifetime

protocols

LT range

TBD

(dates:

TBD)

One BDS batch

for

3 raw material

lots

Sialic acid

ContentCQA

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Not required

at this time

NMT 1.6

(%)

Not required,

Not

stability

indicating

LT range

TBD

(dates:

TBD

Confirm

comparability

for

cell bank

or process

changes

Mannose

ContentCQA

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Not required

at this time

5 to 8

(%)

Not required,

Not

stability

indicating

LT range

TBD

(dates:

TBD)

Confirm

comparability

for

cell bank

or process

changes

Non-glycosylated

heavy chainCQA

Unadjusted

(raw data)

Individual chart

with long-term

3-sigma limits

Not required

at this time

0 to 2.4

(%)

Not required,

Not

stability

indicating

LT range

TBD

(dates:

TBD

Confirm

comparability

for

changes in cell

bank or

step 3 setpoints


11.0

CPV sampling plan A sampling plan makes it clear which samples need to be taken to meet the data acquisition requirements of CPV. This section shows how this might be done giving examples and using templates. A sampling plan matrix is presented in Table 11.0.1. Please note, this table is not intended to be complete, or entirely consistent with information contained previously. It is an example of a visual tool that can usefully summarize an otherwise complex plan, making it easier to develop and communicate.

Considerations for non-routine sampling

and testing include:

• Sample plan for routine monitoring, baseline

monitoring, time-based periodic monitoring, or

special event / change based monitoring;

• Sample frequency (e.g. every day during a reactor

run, every batch, or every 5 batches, etc);

• Specific sampling location within the process.

Provide specific and clear instructions for collection

of the required sample, e.g. BRc, BRc step number,

sampling device such as manual sample valve or

automated sample valve. It is critical to ensure that

samples collected at the selected sampling points

are representative of the drug substance or process

intermediates;

• Sample container and container size (e.g.

polypropylene tubes);

• Sample volume, number of aliquots, and retains;

• Sample labelling (may be driven by site SOP);

• Sample storage temperature and transport

conditions;

• Tests to be performed (including any additional

sampling due to assay needs (e.g. a blank solution

as reference for the test);

• Testing acceptance criteria.


Table 11.0.1. Sampling Plan Matrix

Sampling plan for process attributes

On-floor

tests

QC

Micro

Product quality testing

Ret

ain

A2

80

co

nc

pH

Co

nd

uct

ivit

y

Bio

bu

rden

En

do

toxo

n

RP

-HP

LC

CIE

F

CG

E

SEC

HC

P

Pro

du

ct c

on

c.

Bio

assa

y

DN

A

Affi

nit

y lig

and

Met

ho

trex

ate

An

tifo

am

Tru

nca

ted

imp

uri

ty

Pro

du

ct v

aria

nt

Vol req'd for assay

Storage temp <-40 °C

Testing offsite

Testing onsite

Method SOP

Stage 1: Cell Expansion

After last open manipulation prior

to transfer to production stage

Stage 2: Product Expression

EOR prior to harvest

Stage 3: Clarification

Centrate

Clarified filtered broth •

Step 4: Affinity chromatography

Load post hold period

Load flow-through

Elution pool • • • ο ο ο ο ο ο

Strip flow thru

Symbols are used to indicate which sampling is relevant to each A-Mab process step so the intention of sampling is clear:

• (Routine test), ο (CPV test), ∆ (Retain), (Stability), (Reuse Lifetime Performance), (Cleaning Verification)


Table 11.0.1. Sampling Plan Matrix

Sampling plan for process attributes

On-floor

tests

QC

Micro

Product quality testing

Ret

ain

A2

80

co

nc

pH

Co

nd

uct

ivit

y

Bio

bu

rden

En

do

toxo

n

RP

-HP

LC

CIE

F

CG

E

SEC

HC

P

pro

du

ct c

on

c.

Bio

assa

y

DN

A

Affi

nit

y lig

and

Met

ho

trex

ate

An

tifo

am

Tru

nca

ted

imp

uri

ty

Pro

du

ct v

aria

nt

Step 5: CEX chromatography


Load flow-through

Elution pool • • • ο ο

Strip flow thru

Step 6: IEX chromatography


Load flow-through

Elution pool • • • ο ο ο

Strip flow thru

Step 9: UFDF

Load pool post hold period

Permeate during concentration

Retentate pool post diafiltration • • ο

Step 10: BDS filtration and Freezing

Post hold period, prior to filter

BDS sample prior to freeze • • • • • • •

Symbols are used to indicate which sampling is relevant to each A-Mab process step so the intention of sampling is clear:

• (Routine test), ο (CPV test), ∆ (Retain), (Stability), (Reuse Lifetime Performance), (Cleaning Verification)


11.1 Template for specific process steps For each sampling activity, it can be helpful to have a breakout template which shows what needs to occur (thus a symbol on Table 11.0.1 may become a template table in itself. Examples of templates for sample collection and testing are presented (Table 11.1.1 and 11.1.2). The rows represent the considerations for sampling and testing and suggested alternatives. The yellow highlighted cells in Table 11.1.1 are the choices for the peak viable cell concentration tested at Step 3 in the process (Production Culture Bioreactor. In Table 11.1.2, the yellow highlighted cells relate to aggregates tested at process Step 7, CEX. It is worth noting that, though justification for the choices made is not given fully in this example, justification would be expected as part of the CPV plan.

Note: Whilst a sampling template may be regarded as good practice, it is not mandatory. Readers may feel it is worth creating such

plans for CPV as a priority and extending the activity to elements of process monitoring not included in CPV on a risk managed basis.

Table 11.1.1. Template for Sampling and Testing (CPP). Completed example is for Step 3, Production Culture Bioreactor

Options

Process StepStep 3: Production

Culture Bioreactor

Variable Peak Viable Cell

Conc.

Classification

(Product Quality

Attribute Or

Process Parameter)

CQA CPP KPP KPA

Assay MethodCapacitance Probe,

Per Sop

Sample Plan Routine Monitoring Baseline MonitoringTime-Based

Periodic Monitoring

Special Event Or

Change Based

Monitoring

Sample Frequency

Multiple Times In A

Batch (E.G., Every

Day For Bioreactor)

Once Per Batch Every X Batches


Table 11.1.1. Template for Sampling and Testing (CPP). Completed example is for Step 3, Production Culture Bioreactor (continued)

Options

Sample Collection Sample Location Bioreactor

Sampling DeviceManual Sample

Valve

Automated Sample

Valve

Automated

Sampling DeviceIn-Line Sensor

Container Materials

Of Construction

(Moc)

Pp Tube, SterileNo Sample - On-

Line Instrument

Container Size 10 Ml N/A

Sample Volume 5 Ml

Sample Replicates No 2

Sample Retain No Yes

Sample Handling No AliquotedPrepared For

Shipping

Sample Labeling Driven By Sop N/A

Sample Storage Temperature Ambient 2-8 C -20 C -70 C N/A

Location Manufacturing Qc Lab Development Lab Sample Control N/A

Sample

TransportationTransport Yes (Per Sop) No

Assay Testing Who Manufacturing Qc Dept Development Dept External LabIn-Situ (On-Line

Sensor)

Reference/Blank Reference Blank Solution Control No


Table 11.1.2. Template for Sampling and Testing (CQA). Completed example is for Step 7, Cation Exchange Chromatography (CEX)

Options

Process StepStep 7: Cation

Exchange Chrom.

Variable Aggregation

Classification

(Product Quality

Attribute Or

Process Parameter)

CQA CPP KPP KPA

Assay Method SEC

Sample Plan Routine Monitoring Baseline MonitoringTime-Based

Periodic Monitoring

Special Event Or

Change Based

Monitoring

Sample Frequency

Multiple Times In A

Batch (E.G., Every

Day For Bioreactor)

Once Per Batch Every X Batches

Sample Collection Sample Location

After Eluate Is Well

Mixed At The Eluate

Collection Tank

Sampling DeviceManual Sample

Valve

Automated Sample

Valve

Automated

Sampling DeviceIn-Line Sensor

Container Moc Pp Tube, SterileNo Sample - On-

Line Instrument

Container Size 10 ml

Sample Volume 5 ml


Table 11.1.2. Template for Sampling and Testing (CQA). Completed example is for Step 7, Cation Exchange Chromatography (CEX)

Options

Sample Collection Sample Replicates No 2

Sample Retain No Yes

Sample Handling No AliquottedPrepared For

Shipping

Sample Labeling Driven By Sop

Sample Storage Temperature Ambient 2-8 C -20 C -70 C N/A

Location Manufacturing Qc Lab Development Lab Sample Control N/A

Sample

TransportationTransport Yes (Per Sop) No

Assay Testing Who Manufacturing Qc Dept Development Dept External LabIn-situ (on-line

sensor)

Reference/Blank Reference Blank Solution Control No


12.0

How data will be analyzed Fundamentally, the aim of setting values for capability criteria and analyzing data against those criteria is to establish a plan that provides sound rationale for decision making. This section provides a basic introduction to the statistical tools likely to be used in support of analytical elements of a CPV plan.

Statistics is a mature though complex mathematical

discipline and it is recommended that specialists are

consulted when applying the approaches described in

this section. A number of references are provided, but

given the maturity of the subject, the authors recognize

this list is far from exhaustive. The focus here is on recent

regulatory guidance and what might be regarded as a few

useful, standard texts.

Note: In this paper we are describing some ways in which data

can be analysed, but these are not intended to be exclusive of

any others. Other analytical methods may be better suited to

particular data sets.

CPV Reports focus on quantitative attributes and

parameters which can be monitored using statistical

process control charts, to identify shifts, trends, and

unusual results. Typical practice is that the attributes and

parameters in a CPV Report include all the quantitative

attributes and parameters which are included in the

APR. CPV Reports can also include additional attributes

or parameters, which are useful for building process

understanding and identifying sources of variation. Whilst

the scope of quantitative attributes included in CPV is

equal to, or broader than, the scope included in APR, the

APR includes additional sections not required for CPV.

The APR will provide comprehensive assessments of

product performance that are only made once a year.

12.1 Identifying softwareSoftware supporting CPV should make it as easy as

possible for CPV reports to be consistent with the APR,

in order to avoid redundant effort by the technical and

quality staff. The charts of quantitative results included in

CPV should meet all requirements of the APR, such that

the CPV charts may be copied and pasted directly into the

APR or included as attachments. The APR would provide

context and tie together the information brought forward

from the CPV Reports.

The CPV analysis may be performed using well established

software such as MINITAB, JMP, Discoverant or Statistica.

The software vendor should provide documentation of

their quality and validation procedures if data is used to

support GMP functions18-20.

‘Homegrown’ analysis routines should be similarly

validated if used to support GMP functions. If specific

calculations outside the base software packages are used

to support GMP functions, they should be validated also.


12.2 Description of tools to trend and evaluate dataTools to trend and assist in the evaluation of CPV data

include types of charts and mathematical treatments.

The rationale and approaches to evaluation should

be documented in a standardized way. The three key

documents are a Risk Assessment justification, CPV

Plan and CPV Report. Process Risk Assessments that

support CPV plan development and implementation

have the important purpose of justifying the scope and

frequency with which CPV reports are required. They

are performed at the start of CPV plan preparation. For

new processes they should draw on the outcomes of

PPQ and it is recommended they also take into account a

Process Failure Mode Effects Analysis (FMEA), which may

have been conducted during Stage 1 process design and

updated after the Stage 2 PPQ experience.

For legacy products, risk assessments should include

the following sets of data: process capability, analysis of

campaign trends, historical causes of discards, customer

complaints and failure investigations.

A CPV Plan should be written with the purpose of

specifying what must be monitored to provide for CPV and

how data should be interpreted. Interpretation should

involve: how data will be collected, transformed, and

evaluated. It is strongly recommended that data driven

rules by which decisions will be made, and the expected

outcomes, are recorded in the Plan. A CPV plan should be

created and issued following the risk assessment and before

the start of Stage 3. As stated previously, the definition of an

initial phase and long-term phase of CPV Plan development

may be helpful. This approach is designed to ensure that

variation in process performance is understood before long-

term control limits are established in the control strategy.

Regular assessments of process performance should be

documented in CPV Reports. The frequency with which

these are created will depend on the assessment of risk

as described previously. CPV Reports should detail

any control alerts, as defined by the CPV Plan, whether

these have led to a formal Quality investigation or not.

Justification supporting the response made to these alerts

should be given, including any outcomes for the control

strategy. Typically, CPV Reports will contain calculations of

process capability, statistical process control (SPC) charts,

any other chosen charts, control limits and alerts arising

from the data.

In combination, these tools help make any changes in

the performance of the process obvious, revealing any

non-random patterns. The following sections expand on

these tools.


12.3 Process capability indexProcess capability assessment evaluates the risk that an

attribute will fail to meet specifications; in other words,

it quantifies the likelihood that an attribute will routinely

meet specifications.

Any assessment of process capability requires the

assumption that the same sources of variation that affected

previous results will continue to affect future results, and

the expected range of variability does not change.

There are many indices that measure process capability,

but two are especially popular: Ppk and Cpk . Both indices

compare the width of the specification range to the width

of the typical variation range. The key difference is that Cpk

uses a short-term estimate of variation, whereas Ppk uses

a long-term estimate of variation [13, Chapter 7]. These

indices take into account the centering of the process

within the specification range, and higher values of either

index indicates higher process capability (or lower risk of

missing specifications).

The short-term estimate of sigma is a best-case value that

represents the minimum variation that could be achieved if

all longer-term sources of variation were eliminated from

the process. The long-term estimate of sigma includes both

the short-term and the longer-term sources of variation.

For many manufacturing processes, these longer-term

sources of variation are an expected part of the total,

routine process variation.

Cpk provides a more optimistic estimate of the potential

process capability if all longer-term sources of variation

are removed; Ppk provides a more realistic estimate of

the process capability that has been achieved in routine

production. For this reason, Ppk is preferred here over Cpk

as an indicator of expected process capability; although

either measure could be used, depending on process

circumstances. Table 12.3 contains guidelines rating the

level of control over the process based on Ppk values.

In this paper, we recommend an initial period in which

control limits are estimated from Stage 1 and Stage 2

and experience with similar processes. Given the nature

of Ppk, it becomes most useful as actual manufacturing

experience develops and robust control limits are

established for the long term.

In cases where longer-term variation exceeds short-term

variation, and the longer-term shifts cannot be tolerated,

Cpk can be used. However, Cpk should not be calculated

until the SPC charts provide evidence that the process is

in a state of statistical control, such that no signals of non-

random variation are present. This means there should be

no evidence of shifts, trends, or results outside of control

limits. This is a prerequisite to calculate a meaningful

Cpk. Ppk can be calculated even when some non-random

signals are present, as long as those signals are considered

to be part of the routine, expected, longer-term variation

inherent to a process.

The index also assumes the data follow a normal

distribution. Transformations are sometimes needed

to enable non-normal data to be analyzed in a rigorous

statistical manner. A common source of non-normal

data is where negative values cannot arise, and the most

frequent values are close to zero. Such data sets may be

termed ‘log-normal’ and taking the logarithm of the data

can transform it into a normal distribution. There is more

discussion on this topic in Section 12.4 that follows, but in

the context of this paper, we refer the reader to Reference

13, recognize there are many other texts on this topic and

recommend consulting a trained statistician.

When reporting process capability, a control chart of the

same data should always be constructed to provide a visual

check that the index is reasonable.

For discussion of frequency and scope of CPV analysis see

Section 8.


Table 12.3. Rating of PpK Index

Note: Ppk values are recognized indicators of process

capability. Although quantitative ranges have been

selected and matched with the potential for improvement

opportunities, these ranges should not replace reasonable

investigation efforts to determine the factors influencing

the Ppk value obtained. The levels presented in Table 12.3

reflect one example of a commonly accepted set of ranges for a

monoclonal antibody manufacturing process.

Other approaches to both assessing process capability as well

as matching this assessment to the potential improvement

opportunity may be more suitable for specific processes.

Ppk Index Rating

>1.33 Limited Opportunity:

Attribute meets specifications with a very high

level of consistency. It may be more valuable

to look for opportunities in other areas of the

process or other processes.

1 - 1.33 Some Opportunity:

Attribute is routinely meeting specifications but

there are indications that it might not always do

so consistently.

0.68 – 1.00 Considerable Opportunity:

Attribute typically meets specifications, but does

not do so to the extent that might be expected.

≤ 0.67 Significant Opportunity:

Attribute/process cannot be expected to

routinely meet specifications.


12.4 Control chartsControl charts consist of a few simple elements:

• Results plotted in time order;

• A centerline, usually at the average of the results;

• Statistical control limits.

There are many varieties of control charts. The type of

chart should be selected based on the type of data to be

plotted [13, Chapter 11]. Most attributes plotted for CPV

are individual continuous measurements.

One important consideration for control charts is the

choice of time order. Results may be plotted in date of

manufacturing order (upstream or downstream) or in

date of test order. Any of these orders can be informative,

since each order highlights sources of variation that occur

in the related process steps. When processes are carried

out in strict first-in, first-out sequence from upstream to

downstream to assay, the time order will be the same and

the choice of time variable has no impact on the charts.

For CPV, generally one time order will be selected and

used; other time orders may be plotted as needed for

investigations and process understanding.

Another important consideration is the method for

calculating control limits. Control limits should always be

set at plus and minus three sigma (standard deviations),

but sigma may be estimated using either a short-term

formula or a long-term formula, the same as for Cpk and

Ppk. For most charts, the long-term estimate of sigma is

recommended; this corresponds to the use of Ppk rather

than Cpk. Control limits based on the long-term estimate

of sigma will encompass the longer-term sources of

variation that are an expected part of total routine process

variation; short-term estimates will generally be narrower,

and may produce false statistical signals when longer-term

variation impacts process results.

Standard Shewhart control charts are simple to set up,

easy to understand and explain, and good at detecting

large shifts quickly. However, the Shewhart charts

are not as good at detecting small shifts (relative to

variation), and do not build any memory of previous

observations. For quick detection of small shifts, EWMA

and Cumulative Sum (CUSUM) control charts are

recommended. These are easy to set up using software,

and have the advantage of using previous observations

to filter noise and detect small shifts more quickly.

However, they are slower than Shewhart charts to detect

large shifts, and are more difficult to explain to shop floor

personnel and business leaders.

Statistical control charts are based on some assumptions

about the process results. When these assumptions

are not met, the probability of false signals can rise

dramatically. The most important assumptions are that the

results are independent over time and that the underlying

results are approximately normally distributed.

In real manufacturing processes, results are rarely

independent over time. Instead, there is some correlation

across sequential results (autocorrelation). This can be a

natural result of operating conditions, such as raw material

lots that are used for several upstream lots in sequence.

The presence of autocorrelation can produce many small

shifts and trends within the control limits; the science

and technology process support and statistical personnel

should document in the CPV Plan whether these types of

shifts and trends will be addressed as signals of unusual

variation, or treated as expected routine variation.

The assumption of normally distributed data is also

important, and should be checked using a histogram,

box plot, and normal quartile plot. Statistical tests for

normality are not generally recommended, since they will

be triggered by other issues in the data, such as occasional

outliers, or the very shifts and trends that the control chart

is intended to identify.

One common violation of the normality assumption is

proportional variance. Proportional variance is variability

that is proportional to the level of results. For example,

measurements of concentration often have higher

variability at higher concentrations, and lower variability

at lower concentrations. When variation is expressed as a

percentage or Relative Standard Deviation, RSD) instead

of a simple standard deviation, this is an indication that the

variance may be proportional, and should be checked.


Figure 12.4.1. Control charts, showing the effect of different scales

Most common SPC tools were designed for results with

constant variance, not proportional variance. When

proportional variance is present, a simple solution is to

transform results from the original scale to a log scale.

Proportional variance on the original scale is stabilized to

constant variance by transforming to the log scale. The

choice of natural or base 10 log scale does not matter,

but one or the other should be used consistently. It is

worth noting that significant data are required to make

transformation a justifiable approach. If there is any doubt

about there being sufficient data, transformation is better

avoided and a statistician should be consulted.

The control charts below illustrate the importance of

finding the appropriate scale for results before identifying

special causes of variation or estimating capability. In

the chart on the left, log-normally distributed data are

treated assuming they are actually normally distributed.

The results are not symmetrically distributed within the

control limits, and there are several results outside the

upper control limit.

These signals would require a response from the process

owners in CPV. In the chart on the right, the same results

are transformed to the log scale. The log transformation

expands the scale at the lower end of the range, and

compresses the scale at the upper end of the range. Now

the results are more symmetric within the limits, and

no results exceed either upper or lower control limits.

Capability indices are best calculated on the transformed

data, using specifications, mean, and sigma in the

transformed scale.

In many situations, the average of a data set is expected

to remain fairly constant over time. However, there are

circumstances when the mean is expected to change

over time, based on process knowledge and experience.

When this is the case, additional tools may be used to

monitor for departures from the expected behavior. These

include “tool wear charts” and other similar charts based

on monitoring the residual result (actual – predicted by a

model). These tools will not be expanded upon further in

this document.

To summarize the recommendations for trending and

evaluating data:

• Start simply – always plot the data;

• Use long-term estimates of sigma for most situations,

both to set the control limits, and to evaluate process

capability (Ppk);

• For some special cases when standard control charts

are not providing satisfactory assessments, consult a

statistician and other process experts. These special

cases include:

– Results with low resolution (few distinct values are

possible within the specification range);

– The original scale does not provide normally

distributed data, and a scale transformation may

be needed;

– EWMA charts or CUSUM charts may be preferred

when it is important to detect relatively small shifts

in data with high variation [14, Chapter 9];

– Statistical methods may not produce meaningful

estimates, for example when a very small number

of results are available, so technically justified

limits must be used either initially or permanently.

An algorithm can be used to support the selection of

control charts (Figure 12.4.2).


Figure 12.4.2. Guide to selecting control charts

12.5 Multivariate data analysisMultivariate Data Analysis (MVDA)25 combines multiple

parameters to provide greater power for detecting

changes in results, and to develop deeper understanding

of the sources of variation in a process. MVDA requires

access to larger bodies of data than univariate approaches.

When MVDA is feasible, it should be considered as a

powerful extension of CPV; it is most effective when it

can be applied to process data in real-time, while there is

still an opportunity to adjust and improve batch results.

MVDA is often used to evaluate and improve within-

batch performance, while CPV is most commonly focused

inter-batch monitoring. CPV using univariate approaches

represents the most common approach within the industry

at the moment; MVDA represents where the industry is

heading in the future.

Plot on a run chart (no limits)

Plot on a run chart (no limits)

MVDACUSUM

EWM

Detecting small shifts in

noisy data?

Many related results

available?

At least five distinct

values?

At least 30 results

available?

Obtain datafor an attribute

Shewartchart

yes no yes no

no yes no yes


12.6 Responses to shifts and trendsCPV is only one part of an overall CS. It is envisaged to

function at a supervisory level and is not typically tied

directly to lot release. There are other basal level control

elements such as alarms and alerts, in-process testing and

release analytics that are designed to be more immediate

controls of product quality. Any result that is Out Of

Specification (OOS) should be investigated under existing

Quality procedures for handling deviations.

CPV is intended to serve as an ‘early warning system’

where process drift can be detected before it can cause an

OOS or failure that could otherwise have product quality

impact. Thus, responses to shifts and trends discovered

during CPV typically include those that remain within

specifications. Investigations or other activities may be

triggered to identify the root cause of the process and/

or quality shift or perturbation; however, closure of the

investigation triggered in this way, is not typically tied to

lot release; unless an ‘out-of-specification’ (OOS) situation

has also occurred.

Shifts and trends that remain within specifications should

be evaluated by trained personnel who are most familiar

with the process and assay; typically floor support

engineers and laboratory scientists. The response to the

shift or trend may be determined by the local engineering

or technology function, with consultation from the

quality, operations, and statistical functions. These

investigations form part of the CPV Plan and in most

cases, a formal quality investigation/deviation will not

be required, as an OOS situation will not normally have

occurred alongside a CPV trend.

A typical path for root cause analysis in response to a

signal includes:

• Establish that the results are valid;

• Check for any indications of inconsistency,

e.g. within a laboratory, during the timeframe

the result was obtained;

• Evaluate any other attributes that typically

correlate with the result, to determine if all

attributes trended together as expected, or if

the particular result was exceptional;

• Walk the process upstream from the sample

point, and collect process performance

data to understand any unusual patterns in

process operations during the timeframe the

result was obtained.

The explanation of within-specification shifts and trends

should be documented in the routine CPV Report. If the

reason for a shift or trend cannot be identified during

CPV, it may be escalated to the status of an official quality

deviation, for further investigation. It may be advantageous

to define a ‘tiered’, risk-based approach linked to anticipated

actions when a shift or trend is observed. This approach

could be developed over time.


12.7 Establishing initial limitsNote: Establishing initial control limits and the choice of

analysis tools is also discussed in Sections 9 and 10. This section

provides further thoughts on their application to CPV, given the

importance of establishing them as part of a CPV Plan.

In general, statistical control limits should be set at the

centerline plus and minus 3 sigma (standard deviation).

Sigma may be estimated from short-term variation [13,

Chapter 11] or long-term variation, using the formula for

standard deviation.

Long-term variation is preferred for two reasons. The

most important is that long-term sigma includes all the

sources of variation that are expected to be inherent to

the process. This provides more realistic control limits,

which will be better able to distinguish between expected

and unusual instances of variation in results. The second

reason is that during initial production, when fewer than

30 results are available for calculating sigma, the long-

term estimate stabilizes more quickly than the short-term

estimate. For independent, normally distributed results,

the long-term and short-term formulas for sigma will

provide very similar values.

During initial production or after a process change, when

fewer than 30 results are available to estimate sigma, it

is recommended that limits are set, based on technical

knowledge of the process. If statistical approaches must be

used to set initial limits, use the long-term sigma formula.

Set temporary limits to be updated once 30 or more results

are available. If longer-term sources of variation occur

over extended timeframes, more than 30 results may be

needed to capture typical long-term variation within sigma

and the control limit values.

12.8 Establishing long-term limitsOnce sufficient process history is established, long-term

control limits should be established against which the

performance of the process can be assessed. Long-term

control limits are sometimes said to be ‘fixed’ meaning

they should not be changed without a sound, recorded

justification. Fixed limits should be based on a minimum

of 30 batches, and should reflect all expected sources

of variation. If there are potential sources of significant

longer term variation, such as a change of raw material lot,

it is important to gather data over a sufficient time period

to account for that variation.

Initial OOS results which are determined to be invalid

may be excluded when setting limits. Examples of invalid

results may include laboratory errors. Such data points

should be excluded from sigma calculations and charts.

However, if the root cause is unknown or representative

of the process or testing method, the data points should be

retained in the sigma calculation and chart.

When a process change or improvement shifts the mean

or changes the variability, control limits should be re-set

based on a minimum of 30 results following the change.


12.9 Finding signals of special cause variationAdditional rules may be applied to a control chart to

increase the sensitivity of detecting shifts and trends

that may remain within the control limits. The Western

Electric15 and Nelson16 rules are implemented in

commercially available SPC software. Note that these

tests were designed on the assumptions of independent

successive results (no autocorrelation) and a normal

distribution. The possibility of false alarms may rise

dramatically when either of these assumptions is violated.

Low-resolution data in particular will generate many false

alarms, and the rules should not be applied for data not

meeting these assumptions. Also, the number of rules

applied should be limited, at least in the initial phase of

CPV, since the probability of false alarms increases with

each additional test applied.

Low resolution results occur frequently in regulated

processes, often because results have been rounded

before they are charted. Whenever possible, use the

unrounded result for SPC charting and estimation of

sigma. It is recommended that the validation procedure

and history of measuring systems is checked for

repeatability and reproducibility. Such data helps

decide whether rounding is appropriate and to what

extent. If in doubt, a statistician should be consulted.

When low resolution data is to be evaluated, the

recommended approach is to plot the data on a run

chart in time order, but avoid setting statistical control

limits. Limits may be set using technical judgment and

process experience17, 18.


13.0

Change management Good change management is critical to getting the greatest possible benefits from a CPV system. Some important general principles of change management are presented here, along with some more specific scenarios related to CPV observations.

Change management is complementary to effective

CPV. Product and process-related changes may impact

the CPV plan (including any CPV limits document that

might exist separately from the plan), and the RA and CS

documents that form the basis for the CPV plan. Changes

to these controlled documents should proceed per the

company’s normal change control procedures, which should

include the description, rationale and justification for all

significant changes. Changes that do not impact monitored

parameters, sampling points or control limits do not require

revision of the CPV plan; however the change control

should consider whether evaluation of normal process

variability for a particular parameter should start again at

the initial CPV phase for analysis. After noting in the change

control documentation, this may be noted in the next CPV

analysis / report.

Process changes or experience with special or common

cause variation may require investigations and a revision

of the CPV execution plan during the lifetime of a process.

Adjustments to the frequency of periodic monitoring, or

a return to collecting baseline data before setting (or re-

setting) future long-term control limits may be necessary

to manage control of changes to reduce variability or

achieve an improved outcome. A deviation which leads

to a corrective or preventative action to begin monitoring

a new variable trend not previously monitored, or to re-

activate monitoring a trend discontinued for CPV may also

need to be added to the plan.

Changes to the control strategy need to consider the

potential impact to the current status of the CPV plan.

Facility, process, equipment, field measurements, or

analytical laboratory method changes (examples of normal

change controls) require a review of the CS, any related

risk assessments, and the current monitoring plan for

the steps being changed as well as the downstream steps

that have linked parameters or attributes. If a process

variable is re-classified in the CS based on new process

understanding, changes to the CPV monitoring plan

may also be needed and any impact to registered details

of regulatory licenses need to be addressed. Below, a

decision flow outlines a process for managing changes

needed due to ongoing CPV monitoring (Figure 10.13).


Figure 13. Decision Flow for changes to CPV Execution Plan for Drug Substance Lifetime. OOT stands for Out-of-Trend, NOR is Normal Operating Range and

PAR is Proven Acceptable Range.

Assessments of CPV trends and CPV plan deviations

are to be documented in CPV reports. This should

indicate actions taken and capture rationale to justify

any recommended CPV plan revisions. These reports

are then used as inputs into a periodic APR / Product

Quality Review package. Assessments may be as simple

as chart status, or include an evaluation into whether

a capability index value demonstrates it is possible to

reduce or stop monitoring a particular parameter or

attribute. If sufficient data has been obtained to calculate

and implement long-term limits with high confidence,

this should be documented in the CPV Report, before

the CPV Plan is changed. An assessment may conclude

that monitoring will continue to a new planned milestone

without change setting new limits. If the capability index

is low, the monitoring plan may need to be changed (e.g. to

obtain data more frequently, or a Corrective Action and

Preventive Action (CAPA), might be considered to improve

control. In any case the decision should be documented in

a CPV Report with its rationale.

In this A-Mab example, for cases where a change would

cancel out capability indices or remove confidence in

continued use of existing control limits, a plan deviation

would be used to document suspending or making these

control limits obsolete. The process performance trend can

then be monitored against a new provisional control range

(justified via the deviation and based on PARs, recent

history, equipment capability, or qualified small scale

model studies). Data collected prior to the change may be

used for comparison to assess the impact of the change.

The following table presents several cases where CPV-

related changes may occur, the impacted documents and

required actions. The change management process is very

similar to the initial setup of the CPV plan, as presented

in preceding sections of this paper. An assessment of the

RA document and its impact on the CS document is always

required, even if not explicitly stated in table 13-1 below.

Unexpected incident reported/impending changenotification communicated

CPV data OOT

Confirm OOT, investigate to determine if a special cause and

excluding data from future natural variation sigma calculation

Review control strategies and risk assessment for actual potentially impacted control parameters and

performance indicators and, if classification rationale is still justified,

or if decision needs to be adjusted

Does Incident/Change indicate setpoints or ranges for any fixed or response variables need additional monitoring, verification, requalification,

resetting, redefinition (setpoints, alert/action limits or NOR/PAR

controls)

Is it completely new? Not addressed

in existing risk assessments or

control strategies yet

Yes, determine sample test/data capture

requirements

Commercial data collection/assessment to establish long term confidence in new understanding, and assurance of control

No. Current status can continue (routine

control or CPV enhanced monitoring as currently in place)

Suspend any existing CPV criteria/limits

and replace with lower confidence control ranges via QMS mechanism

of protocal change management

Generate information

needed to establish initial process understanding

product knowledge (R&D, SSM, supplier data, historical data

analysis)

Yes. Complete new or supplement existing

risk assessment, revise control strategy

No, but it is not in CPV monitoring,

justify adding or not adding to program in

investigation or change impact assessment


Table 13-1. Change Management Examples

Reason for ChangeImpacted

Document(s)Actions

Changes within CPV Plan

Transition from initial control limits to long-term limitsEnough

data available (e.g. n = 30) to establish statistically based

control limits

CPV Plan / Limits

Document

Document (e.g. in a CPV limits document) how statistically based

control limits (CL) were determined. Start routine monitoring with

CL’s and run rules

OOT results (e.g. control limit or run rule violations) due to

newly experienced, but normal, variability (e.g. due to a new

raw material lot)

CPV Limits Document Justify removal of current CPV limits (if applicable), and continue to

collect data until sufficient to recalculate new control limits. During

the extended data collection period, continue to monitor for trends

(e.g. average ± 3 SD), but without run rules.

Shift in mean or a change in variability (e.g. due to a process

improvement)

CPV Limits Document Justify removal of current CPV limits, and reset counter for the

number of runs required to set new control limits for the impacted

attribute / parameter. Continue to monitor for trends (e.g. average

± 3 SD).

Add / Remove control elements based on process knowledge

gained through CPV, e.g. after periodic monitoring. This data

could point to ways to improve the product or optimize the

process. Elements may be deleted (or have reduced sampling

frequency) if their process capability index is high and

variability is well controlled. Elements may be added if a new

source of process variability is discovered.

RA / CS Update to reflect the new knowledge

CPV Plan / Limits

Document

Update. New elements may be monitored (e.g. using average ± 3 SD)

until statistically based control limits can be established (n ≥ 30).

Changes external to CPV Plan

• Add or Remove control elements based on new process

knowledge, e.g. from lab scale studies, CAPA’s, complaints, etc.

• Process changes (e.g. new raw material)

• Change or add manufacturing site

• New equipment (excluding like-for-like changes)

PPQ Perform PPQ run(s) if deemed necessary (consult with QA and

Regulatory Affairs).

RA/ CS See “Add/Remove control elements” above

CPV Plan / Limits

Document

See “Add/Remove control elements” above

New or changed analytical capabilities RA/ CS Only if impacted


14.0

Data verificationThis section describes in general the types of data sources in a CPV reporting system. It goes on to make recommendations as to how data in these systems should be verified and hence how the systems should be validated.

Three major sources of data are used for CPV:

1. Process data e.g. viable cell concentration in the

bioreactor26, offline pH / conductivity measurements

or process volumes which are recorded in either

paper or electronic BRcs.

2. Analytical data generated in QC laboratories which

are typically recorded in a LIMS or Data Historian.

3. Data recorded by inline instruments e.g. pH of

bioreactor, or buffer flow rates for chromatography

operations. These data are archived and managed

by the data historian component of the plant control

system. Examples of data archival systems include

Plant Information (PI_System) by OSISoft and

InfoPlus 21 by Aspentech.

Data recorded on paper-based systems require

transcribing into a secure electronic archiving system (e.g.

a database) so that it can be retrieved in the future for

data analysis, control charting and CPV. The manual data

entry process is prone to human error. Several options

are available to ensure data integrity during the data

transcription process. These are described below.

1. Blinded data entry: Data are independently entered

into the data archival system by two different

operators. The data archival system prompts the

operators if there is a discrepancy noted between

the two separate entries for a process parameter or

attribute so corrections can be made. However there

is a remote possibility that both data entry operators

can make the same mistake.

2. Single data entry and independent verification: The

data are entered from the original data source by

one operator and independently verified by a second

operator using the source documents. This process

is used in many companies, but cannot completely

eliminate errors because the data verifier may not

always catch all data entry errors.

3. SMART Data entry / Error proofing: for a parameter

or attribute being transcribed, the data archival

system can limit the allowable values that can be

entered, thus any errors can be readily pointed out

and corrections can be made, for example:

• Offline pH measurements can be restricted to

values from 0.0 to 14.0;

• Viable cell concentration data can be restricted

to the expected range of values26.

Alternatively, the data archival system can generate a

report of observed minimum and maximum values for

a process parameter or attribute. Any discrepant result

outside of the normally observed range can be readily

detected.

While a SMART Data entry system can detect some errors,

it is also not foolproof. Suppose the observed values for

chromatography step yield vary from 50.2 to 80.4 %. For

a particular batch, if the yield recorded in the source BRc

is 62.6%; this could be wrongly entered as 66.2% which

would still fall within the expected observed values and

may remain undetected.


In summary, there are hidden sources of error in

transcribing data from paper to electronic data archival

systems. It is important to recognize that verification

is critical for some parameters (e.g. CPPs) but may not

be necessary for other process parameters. Therefore

verification depends on purpose and criticality of the

parameter being measured. The intention should to reduce

data entry errors to zero. This may require continuous

improvement and it is important to know the extent to

which any error impacts the process parameter.

Transcribing data from electronic sources e.g. electronic

BRcs or plant information into data archival systems, is

not likely to introduce errors provided the data transfer

system is designed robustly and the ability to transfer

data is validated. For either paper or electronic sources,

the ability to retrieve data from the data archival system

should undergo an initial validation. Similarly if the

data from the data archival system is used routinely

for generating control charts or data tables for process

monitoring and CPV, the procedure for generating

control charts or data tables should be validated or as

recommended in section 12 of this document, established

software should be used. A number of software suppliers

will provide a quality statements regarding the validation

of their statistical software [e.g. 19, 20, 21].

Whether the source data are from paper systems

or electronic systems, a record should be kept (and

continually updated) of the BRc step number or the Tag

ID for electronic sources. It is recommended (although

not an absolute requirement) for the data archival system

to have the ability to record data entry operator ID /

time stamp, any corrections made; i.e. an audit trail of all

entries and changes.

Finally it would be good practice for any process

parameter or quality attribute observed to be out of

controllable range (trend) or for observed shifts and

trends to be independently verified by re-examining the

source data.

Thus in conclusion, it is recommended that robust systems

and procedures are designed and developed in order to

archive data and validate the accuracy of data retrieval in

order to minimize errors during CPV. It is of course, the

responsibility of each individual organization to apply the

recommendations in this section as they see fit.


15.0

Discretionary elements of a CPV program

The Table below shows some elements that may be included within a CPV program based upon the needs and decisions of the individual operating company. The following elements are not considered mandatory for inclusion in CPV, but may provide the operating company with information valuable to the manufacturing of drug substances.

Element Description

Operational or Performance Elements Process performance attributes (i.e., bioreactor titer, column elution volumes) or input parameters linked

to process performance attributes.

Multivariate Data Analysis MVDA, particularly Partial least squares (PLS) regression or Principal component analysis (PCA) may be

useful to identify latent variables within the CPV data set and increase an understanding of the design

space of the drug substance manufacturing process.

Column/Resin/UF Membrane Cleaning and

Performance Lifetime Verification

The full scale verification of column and/or ultra-filtration membrane cleaning and performance, out to

established lifetime limits, may be included within the scope of the operating company’s CPV program.

Table 15.1. Discretionary Elements of CPV


1 Technical Report No. 60. Process Validation: A Lifecycle Approach. PDA, Inc. 2013

2 ISPE PQLI Guidance Series Part 4

3 A-Mab: A case study in Bioprocess Development. CMC Biotech Working Group. 2009

4 FDA Code of Federal Regulations 21 Part 211, Jan. 2013

5 Process Validation: General Principles and Practices, guidance for industry, FDA (CDER, CBER, and CVM), January 2011

6 A-Mab study; Pharmaceutical Quality by Design: Product and Process Development, Understanding, and Control; By Lawrence X. Yu,

Ph. D.; Director for Science; Office of Generic Drugs; Food and Drug Administration

7 ICH Q8 Pharmaceutical Development: http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/

Q8_R1/Step4/Q8_R2_Guideline.pdf

8 ICH Q9 Quality Risk Management: http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2009/09/

WC500002873.pdf

9 ICH Q10 Pharmaceutical Quality System: http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_

guideline/2009/09/WC500002871.pdf

10 Technical Report No. 15. Validation of Tangential Flow Filtration in Biopharmaceutical applications. PDA, Inc. 2009

11 Technical Report No. 14. Validation of column-based chromatography processes for purification of proteins. PDA, Inc. 2008

12 Biopharmaceutical Manufacturing Facilities- Volume 6. ISPE Baseline Pharmaceutical Engineering Guide. June 2004

13 Pena-Rodriguez, ME. Statistical Process Control for the FDA-Regulated Industry, 2013. ASQ Press: Milwaukee, WI

14 Montgomery DC. Introduction to Statistical Quality Control, 2013. 7th edition, Wiley: Hoboken, NJ

15 Western Electric. Statistical Quality Control Handbook 1956, Western Electric Corporation, Indianapolis, IN

16 Nelson, LS. The Shewhart Control Chart – Tests for Special Causes, Journal of Quality Technology 1984; 16: 237-239

17 Woodall, WH. Controversies and Contradictions in Statistical Process Control, Journal of Quality Technology 2000; 32: 341-350

18 Limpert, E, Stahel, WA, Abbt, M, Log-normal Distributions across the Sciences: Keys and Clues. BioScience 2001; Vol. 51 No. 5

19 www.jmp.com/software/qualitystatement.shtml

20 www.minitab.com/en-US/support/documentation/software-validation.aspx?langType=1033

21 www.statsoft.com/services/validation-services/

22 ICH Q11 Development and Manufacture of drug substances (Chemical Entities and Biotechnological/Biological Entities)

23 http://www.ema.europa.eu/docs/en_GB/document_library/Other/2013/08/WC500148215.pdf

24 ISPE Discussion Paper: Applying Continued Process Verification Expectations to New and Existing Products”, D. Bika, P.

Butterell, J. Walsh, K. Epp and J. Barrick, 2012. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&frm=1&source=w

eb&cd=1&cad=rja&ved=0CCkQFjAA&url=https%3A%2F%2Fwww.ispe.org%2Fdiscussion-papers%2Fstage-3-process-

validation.pdf&ei=FIEXU8z1Gurx0wGftoGIBg&usg=AFQjCNFvOl1WjtGX-vPHZjFsPq8n44cI3Q&sig2=QzSLW0X3-

cQvbUnwYDGYDA&bvm=bv.62286460,d.dmQ

25 Guidance for Industry Analytical Procedures and Methods Validation for Drugs and Biologics, Draft, February 2014 http://www.fda.

gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM386366.pdf

26 European Pharmacopoeia, 2.7.29. Nucleated cell count and viability, p233

References


Term Expanation Source (s)

Acceptance criteria Numerical limits, ranges, or other suitable measures for acceptance which the drug substance

or drug product or materials at other stages of their manufacture should meet to conform to the

specification for analytical procedures.

Q6b

Action limits An action limit is an internal (in-house) value used to assess the consistency of the process at less

critical steps. These limits are the responsibility of the manufacturer.

Q6b

Batch Records (BRc) A record of specific identifiers for the batch of material being produced, that includes all activities

required to prepare for production, produce the material and close down the process. It provides

traceability of who did what, when, and the outcomes of those actions, including any observations

on or deviations from the process or anticipated results.

[4]

Batch Release (BR) The process by which the product is tested and results reviewed to ensure product quality under

cGMP regulations and guidelines.

Q8(R2)

BPOG BioPhorum Operations Group, a collaboration of biopharmaceutical companies, seeking to

accelerate the rate at which the industry achieves a lean state.

Bulk Drug Substance (BDS) According to 21CFR207.3(a)(4) this means any substance that is represented for use in a drug

and that, when used in the manufacturing, processing, or packaging of a drug, becomes an active

ingredient or a finished dosage form of the drug, but the term does not include intermediates used

in the synthesis of such substances.

21CFR207.3(a)[4]

Capability of a Process (Ppk) Ability of a process to realise a product that will fulfil the requirements of that product. The

concept of process capability can also be defined in statistical terms via the process performance

index Ppk or the process capability index Cpk (ISO 9000:2005).

Q10

CMC BWG Chemistry, manufacturing and control, biotech working group of the International Society for

Pharmaceutical Engineers (ISPE).

Continued Process

Verification

A formal process that enables the detection of variation in the manufacturing process that might

have an impact on the product. It provides opportunities to proactively control variation and

assure that, during routine production the process remains in a state of control.

[5]

Control Strategy A planned set of controls, derived from current product and process understanding that assures

process performance and product quality. The controls can include parameters and attributes

related to drug substance and drug product materials and components, facility and equipment

operating conditions, in-process controls, finished product specifications, and the associated

methods and frequency of monitoring and control.

Q10

Critical Describes a process step, process condition, test requirement, or other relevant parameter or

item that must be controlled within predetermined criteria to ensure that the API meets its

specification.

Q7

Critical Material Attribute

(CMA)

A material attribute, whose variability has an impact on a critical quality attribute and therefore

should be monitored or controlled to ensure the process produces the desired quality.

Q8(R2)

Critical Process Parameter

(CPP)

A process parameter whose variability has an impact on a critical quality attribute and therefore

should be monitored or controlled to ensure the process produces the desired quality.

Q8(R2)

Critical Quality Attribute

(CQA)

A physical, chemical, biological or microbiological property or characteristic that should be within

an appropriate limit, range, or distribution to ensure the desired product quality.

Q8(R2)

Design Space The multidimensional combination and interaction of input variables (eg, material attributes) and

process parameters that have been demonstrated to provide assurance of quality. Working within

the design space is not considered as a change. Movement out of the design space is considered to

be a change and would normally initiate a regulatory post approval change process. Design space is

proposed by the applicant and is subject to regulatory assessment and approval.

Q8(R2)

Glossary



Detect-ability The ability to discover or determine the existence, presence, or fact of a hazard. Detect-ability is a

component of a Failure Modes Effects Analysis (FMEA).

Q9

Drug product (Dosage form;

Finished product)

A pharmaceutical product type that contains a drug substance, generally in association

with excipients. Drug substance (Bulk material): The drug substance is the material which is

subsequently formulated with excipients to produce the drug product. It can be composed of the

desired product, product-related substances, and product- and process-related impurities. It may

also contain excipients and other components, such as buffers.

Q6b

Failure Modes Effects

Analysis (FMEA)

One of the first systematic techniques for failure analysis. It was developed by reliability engineers

in the 1950s to study problems that might arise from malfunctions of military systems. A FMEA

is often the first step of a system reliability study. It involves reviewing as many components,

assemblies, and subsystems as possible to identify failure modes, and their causes and effects.

For each component, the failure modes and their resulting effects on the rest of the system are

recorded in a specific FMEA worksheet. There are numerous variations of such worksheets.

Q8, IEC 60812 Analysis

techniques for system

reliability—Procedure

for failure mode and

effects analysis (FMEA).

General process parameter

(GPP)

An adjustable parameter (variable) of the process that does not have a critical effect on product

quality or process performance. Ranges for GPPs are established during process development, and

changes to operating ranges will be managed within the quality system.

CMC-BWG

Impurity Any component present in the drug substance or drug product that is not the desired product, a

product-related substance, or an excipient (including added buffer components). It may be either

process- or product-related.

Q6b

In-process quality attributes

(IPQA)

Parameters used in the A-Mab Case Study model of a control strategy, to provide a link between

KPPs and KPAs

[3]

In-Process Control also called

Process Control

Checks performed during production in order to monitor and if necessary to adjust the process

and/or to ensure that the intermediate or API conforms to its specifications.

Q7

In-process test In-process inspection and testing should be performed by monitoring the process or by actual

sample analysis at defined locations and times. The results should conform to established process

parameters or acceptable tolerances. Work instructions should delineate the procedure to follow

and how to use the inspection and test data to control the process.

WHO Portal

http://apps.who.

int/medicinedocs

/en/d/Jh1792e/

20.7.3.3.html

In-process tests Tests which may be performed during the manufacture of either the drug substance or drug

product, rather than as part of the formal battery of tests which are conducted prior to release.

Q6a

Intermediate For biotechnological/ biological products, a material produced during a manufacturing process

that is not the drug substance or the drug product but for which manufacture is critical to the

successful production of the drug substance or the drug product. Generally, an intermediate will

be quantifiable and specifications will be established to determine the successful completion of

the manufacturing step before continuation of the manufacturing process. This includes material

that may undergo further molecular modification or be held for an extended period before further

processing.

Q5c

Key Process Attribute (KPA) An important attribute or output measure of the process used in this paper to maintain consistency

with the language used in the A-MaB case study. N.B. It is important not to confuse a KPA, which is

a measure of process consistency with measures of quality such as CQAs.

N.B. at the time of writing, the European Medicines Agency (EMA) draft guidance on Process

Validation is out for consultation, referring to KPAs as 'performance indicators'.

A-MaB Case Study [3]

Key Process Parameter (KPP) An adjustable parameter (variable) of the process that, when maintained within a narrow range,

ensures optimum process performance. A key process parameter does not meaningfully affect

critical product quality attributes. Ranges for KPPs are established during process development,

and changes to operating ranges will be managed within the quality system. N.B. this category of

parameter is not recognised by the FDA or the EMA for use in formal submissions and reports,

though they do not oppose its use internally. The agencies see all parameters that may have an

impact on CQAs as Critical and hence CPPs [23].

CMC BWG

http://apps.who

20.7.3.3.html



Knowledge Management Systematic approach to acquiring, analyzing, storing, and disseminating information related to

products, manufacturing processes and components.

Q10

Multivariate Analysis MDVA, particularly Partial least squares (PLS) regression or Principal component analysis (PCA) may

be useful to identify latent variables within the CPV data set and increase an understanding of the

design space of the drug substance manufacturing process.

[25]

Normal Operating Range

(NOR)

A defined range, within the Proven Acceptable Range, specified in the manufacturing instructions

as the target and range at which a process parameter is controlled, while producing unit operation

material or final product meeting release criteria and Critical Quality Attributes.

PQRI

Performance Indicators Measurable values used to quantify quality objectives to reflect the performance of an

organisation, process or system, also known as ―performance metrics in some regions.

Q10

Pharmaceutical Quality

System (PQS)

Management system to direct and control a pharmaceutical company with regard to quality. ICH Q10

Plan A detailed description of how something is going to be done. Oxford English

Dictionary online

Potency Potency is the measure of the biological activity using a suitably quantitative biological assay (also

called potency assay or bioassay), based on the attribute of the product which is linked to the

relevant biological properties.

Q6b

Prior product knowledge The accumulated laboratory, nonclinical, and clinical experience for a specific product quality

attribute. This knowledge may also include relevant data from other similar molecules or from the

scientific literature.

CMC BWG

Procedure A written, established way of doing something in the operating environment. Oxford English

Dictionary online

Process Analytical

Technology (PAT)

A system for designing, analyzing, and controlling manufacturing through timely measurements (ie,

during processing) of critical quality and performance attributes of raw and in-process materials and

processes with the goal of ensuring final product quality.

Q8(R2)

Process Control See In-Process Control. Q7

Process Robustness Ability of a process to tolerate variability of materials and changes of the process and equipment

without negative impact on quality.

Q8(R2)

Process-related impurities Impurities that are derived from the manufacturing process. They may be derived from cell

substrates, culture (eg, inducers, antibiotics, or media components), or from downstream

processing (eg, processing reagents or column leachables).

Q6b

Process-related impurities These are impurities that develop from, or are introduced by, the biological or chemical processes

by which the product is made.

Q3B(R2),

Product lifecycle All phases in the life of a product from the initial development through marketing until the

product's discontinuation.

All phases in the life of the product from the initial development through marketing until the

product‘s discontinuation.

Q8(R2)

Q9

Product-related impurities Product-related impurities are molecular variants of the desired product arising from processing

or during storage (eg, certain degradation products) which do not have properties comparable to

those of the desired product with respect to activity, efficacy, and safety.

Q6b

Product-related substances Product-related substances are molecular variants of the desired product which are active and

have no deleterious effect on the safety and efficacy of the drug product. These variants possess

properties comparable to the desired product and are not considered impurities.

Q6b



Protocol Method for carrying out an experiment and/or creating an official record of scientific or

experimental observations. Written GMP Protocols define prospectively the conditions which will

be tested, sample testing plan, and acceptance criteria for results.

Oxford English

Dictionary online

Proven Acceptable Range A characterized range of a process parameter for which operation within this range, while keeping

other parameters constant, will result in producing a material meeting relevant quality criteria.

Q8(R2)

Quality The degree to which a set of inherent properties of a product, system or process fulfils

requirements.

Q9

Quality Attribute (QA) A molecular or product characteristic that is selected for its ability to help indicate the quality

of the product. Collectively, the quality attributes define the adventitious agent safety, purity,

potency, identity, and stability of the product. Specifications measure a selected subset of the

quality attributes.

Q5e

Quality by Design A systematic approach to development that begins with predefined objectives and emphasizes

product and process understanding and process control, based on sound science and quality risk

management.

Q8(R2)

Quality Control (QC) Checking or testing, that specifications are met. Q7

Quality Target Product Profile

(QTPP)

A prospective summary of the quality characteristics of a drug product that ideally will be achieved

to ensure the desired quality, taking into account safety and efficacy of the drug product.

Q8 (R2)

Quality risk management A systematic process for the assessment, control, communication, and review of risks to the quality

of the drug product across the product lifecycle.

Q9

Raw material Raw material is a collective name for substances or components used in the manufacture of the

drug substance or drug product.

Q6b

Reference standards/

materials

In addition to the existing international/national standards, it is usually necessary to create in-

house reference materials.

In-house primary reference material: a primary reference material is an appropriately

characterized material prepared by the manufacturer from a representative lot(s) for the purpose

of biological assay and physicochemical testing of subsequent lots, and against which in-house

working reference material is calibrated.

Q6b

Risk The combination of the probability of occurrence of harm and the severity of that harm (ISO/IEC

Guide 51).

Q9

Risk analysis The estimation of the risk associated with the identified hazards. Q9

Risk assessment A systematic process of organizing information to support a risk decision to be made within a risk

management process. It consists of the identification of hazards and the analysis and evaluation of

risks associated with exposure to those hazards.

Q9

Risk evaluation The comparison of the estimated risk to given risk criteria using a quantitative or qualitative scale

to determine the significance of the risk.

Q9

Severity A measure of the possible consequences of a hazard, which is a component of a Failure Modes

Effects Analysis (FMEA).

Q9

Specification A specification is a list of tests, references to analytical procedures, and appropriate acceptance

criteria with numerical limits, ranges, or other criteria for the tests described, which establishes

the set of criteria to which a drug substance or drug product or materials at other stages of their

manufacture should conform to be considered acceptable for its intended use.

Q6b



Specification - Release The combination of physical, chemical, biological and microbiological tests and acceptance criteria

that determine the suitability of a drug product at the time of its release.

Q1a(R2)

Statistical Process Control

(SPC)

Statistical process control (SPC) is a method of quality control which uses statistical methods.

SPC is applied in order to monitor and control a process. Monitoring and controlling the process

ensures that it operates at its full potential. At its full potential, the process can make as much

conforming product as possible with a minimum (if not an elimination) of waste (rework or

Scrap). SPC can be applied to any process where the "conforming product" (product meeting

specifications) output can be measured. Key tools used in SPC include control charts; a focus on

continuous improvement; and the design of experiments. An example of a process where SPC is

applied is manufacturing lines.

Q8 (R2), [13],

Testing plan A determination as to whether routine monitoring, characterization testing, in process monitoring,

stability testing, or no testing is conducted as a part of the overall control strategy. Extended

testing plans may be put in place to demonstrate that valuable resources can be used more than

once. It may also be necessary to establish additional tests to understand sources of variation

and to demonstrate that changes to the process have addressed sources of variation that are

considered to present appreciable risk to product quality.

CMC-BWG

TPP See Quality Target Product Profile. Q8 (R2)

Viable Cell Concentration

(VCC)

A measure of the number of viable cells per unit volume. [25]

Well Controlled Critical

Process Parameter (WC-CPP)

A process parameter which is controlled by process design and standardized procedures or

automated control systems that ensure it remains within the design space of the process. It is only

likely to vary beyond the design space if there is a failure in the control system and failure modes

for this situation are likely to be mitigated.

CMC-BWG


Permission to useThe contents of this report may be used unaltered as long as the copyright is acknowledged appropriately with correct source citation, as follows “Entity, Author(s), Editor, Title, Location: Year”

DisclaimerThis document represents a consensus view, and as such it does not represent fully the internal policies of the contributing companies.

Neither BioPhorum nor any of the contributing companies accept any liability to any person arising from their use of this document.

The views and opinions contained herein are that of the individual authors and should not be attributed to the authors’ employers.

Documents

CONTINUED PROCESS VERIFICATION AN INDUSTRY POSITION …