Controlling Pharmaceutical Quality - PQRI · M1 M2 S1 C1 C2 W1 P1 P2 P3 P4 P5 P9 P7 P8 P10 LT LT LT C3 C4 P18 P19 P6 LT P11 P13 P14 P17 P20 P12 P15 P16 LT LT P22 P24 S3 S4 W2 M3 M4

Richard D. Braatz

Novartis-MIT Center for Continuous Manufacturing

MIT Center for Biomedical Innovation

Department of Chemical Engineering

Controlling Pharmaceutical Quality

2 | Novartis/MIT, All Rights Reserved

Background on QbD approach

Process development involves:

• Define the target product profile

• Identify the critical quality attributes (CQAs)

• Select an appropriate manufacturing strategy

• Implement a control strategy

This talk is on controlling quality in integrated pharmaceutical and biologics manufacturing


Why Integrated Manufacturing?

Reduce contact between

biology/chemistry & personnel

Continuous operation has

the potential to

• Increase product quality

• Increase yields

• Enable new drug product

formulations (e.g., thin films)

• Reduce scale-up risks

• Reduce footprint


Outline

Control Systems for Integrated Continuous Operations

Relationship with Design Space

Application to Biologics Manufacturing


Integrated Control Strategy

for Continuous Manufacturing

Tight integration of continuous operations can result in disturbances propagating downstream, unless their effects are suppressed by an integrated control strategy

The strategy must optimize the overall plant operation instead of only isolated units (i.e., need plantwide control)

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Plantwide Control of Continuous Manufacturing

Challenges

• Many connected unit operations

• Very fast to slow processes

• Multi-purpose plants with short development time

• Alignment with regulatory requirements

Approach adapted from the chemical industry

• Employs systematic and modular design of plantwide control strategies for continuous manufacturing facilities

• Experimentally demonstrated on continuous pilot plant

R. Lakerveld, B. Benyahia, P.L. Heider, H. Zhang, A. Wolfe, C. Testa, S. Ogden, D.R. Hersey, S. Mascia, J.M.B. Evans, R.D.

Braatz, and P.I. Barton. The application of an automated control strategy for an integrated continuous pharmaceutical pilot plant.

Organic Process Research & Development, in press.

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A continuous pilot plant

S. Mascia, P.L. Heider, H. Zhang, R. Lakerveld, B. Benyahia, P.I. Barton, R.D. Braatz, C.L. Cooney, J.M.B. Evans, T.F. Jamison,

K.F. Jensen, A.S. Myerson, and B.L. Trout. End-to-end continuous manufacturing of pharmaceuticals: Integrated synthesis,

purification, and final dosage formation. Angewandte Chemie, 52(47):12359-12363, 2013


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2I E API


• First-principles dynamic models were built for each unit operation (UO) as they were developed

• Models were validated and then placed into a plant-wide simulation

• Plant simulation used to design UO & plantwide control strategy


Model-based Design of a Plant-wide Control Strategy

2. Optimizing control

objectives Level 1 – Total I/O

Level 4 – Detailed

Control tasks

(CQA, CPP)1

Level 2 – Intermediates

1. Stabilizing control

objectives (local)

Structure of plant-wide

control strategy

Level 3 – Recycles

Control tasks

(CQA, CPP)2

Control tasks

(CQA, CPP)3

Control tasks classified into

optimizing and stabilizing

Hierarchical decomposition

• Reduces complexity

• Exploits separation of time scales


Parametric Sensitivities Used to Evaluate Relationships Between CPPs and CQAs

0 0 0, , ,ft t t x t t x - State variables

- Input variables

- Output variables (CQAs)

for all feedback control loops

0

1( ) ( ) ( ) ,

( ) ( )

t

MV p D

I

SP CV

du t K t d

dt

t y y t

( ) ( ), ( ), , ,

( ) ( ), ( ), ,

( ), ( ), , 0,

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dt

y t g x t u t p t

h x t u t p t

( )

( )

( )

x t

u t

y t

p - Parameters (PPs)

Use sensitivities (Si,j) to identify

causal relations CPPs-CQAs:

• Direction and order of magnitude

• Guide selection of automated control loops

• Determined from process simulation (could use DOE)

,i

i j

j

yS

p


Example Sensitivity Results: Level 1: Total Inputs & Outputs


Sensitivities Used to Evaluate Dynamic I/O Relationships, to Assess Controllability and Disturbance Propagation

0

0.2

0.4

0.6

0.8

1

0 50 100 150

No

rmal

ize

d S

en

siti

vity

Time / h

API dosage

Impurity content

Model-based sensitivity of two final product quality variables

with respect to feed flow rate of reactant


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Met all purity

specs in Summer

2012


Outline





Comparing Design Space and Feedback Control (both are consistent with QbD principles)

Design-space methods:

• Control strategy based on operation

within a fixed parameter space

• Applicable to each continuous

process unit operation

• More complicated to apply to an

entire continuous pharmaceutical

manufacturing plant

Feedback methods:

• Control strategy based on

feedback to a “parameter space”

• Easier to scale up

• Design space does not need to

be exhaustively validated a priori

• Necessary for continuous

manufacturing


There are Multiple Strategies for Ensuring That a Particular CQA Spec is Satisfied

1. Direct measurement of the CQA

2. Prediction of the CQA based on a first-principles

model that is fed measurements of related variables

and is running in parallel with operations

3. Prediction of the CQA based on an empirical or semi-

empirical model (e.g., response surface map, PLS

model) that is fed measurements of other variables

4. Operation of the CPPs to lie within a design space, that

is, some specified set shown in offline studies to provide

assurance

A.S. Myerson, M. Krumme, M. Nasr, H. Thomas, and R.D. Braatz. Control systems engineering in

continuous pharmaceutical manufacturing. Organic Process Research & Development, in press.

Can use

in closed-

loop

feedback

control

strategy

Open-

loop

strategy

The smaller the number, the easier to justify real-time release


Outline




Product manufacturing:

Allston, MA USA

Product QC: Haverhill, UK

Fill/finish: Waterford, IE

Cerezyme patients distributed worldwide

Manufacturing Biologic Drugs Today

18

Towards Biomanufacturing on Demand (BioMOD)

Design Requirements Patient

19

BioMOD capabilities

• Enable flexible methodologies for genetic engineering/modification of microbial strains to synthesize multiple and wide-ranging protein-based therapeutics

• Develop flexible & portable device platforms for manufacturing multiple biologics with high purity, efficacy, and potency, at the point-of-care, in short timeframes (<24 hours), when specific needs arise

• Include end-to-end manufacturing chain (including downstream processing) within a microfluidics-based platform

• Focus on currently approved therapeutics by FDA (i.e., no drug discovery)

Column Tanks

1 2

Downstream

Affinity Chromatography

Sterile Media

Yeast Inoculum

Crude Product Hold Tanks

Upstream

1 2 1 2

Polishing Membrane #1 Tanks

Polishing Membrane #1


Waste Waste Waste


UF/DF Membrane

Waste

Concentration

Identity

Purity

pH, DO, T

Potency

Safety

Sterility

Analytics

Purified Product for Quality Testing

Multivariate

Model

OK for Release

Not OK

☐

Final

Product

On-line Reactor Control

Perfusion

Fill

Elute

Real-Time Process

Data

Integrated and Scalable Cyto-Technology

(InSCyT) Biomanufacturing Platform

20

Rationale for Pichia pastoris as

a Microbial Host for Biosimilar Products

Advantages from a regulatory perspective

• Many products on market or in late-stage development

(including one Phase I target)

• Reduced risk for viral contamination

• Human-like post-translational modifications (folding, glycosylation, etc.)

Technical benefits

• Genetically stable organism

• High-density cultivation (culture volume >70% biomass)

• High yields of secreted proteins (up to ~15 g/L)

• Limited host cell protein (HCP) profile (eases burden on downstream)

• Amenable to lyophilization

21

Column Tanks

1 2

Downstream


Sterile Media

Yeast Inoculum


Upstream

1 2 1 2




Waste Waste Waste


UF/DF Membrane

Waste

Concentration

Identity

Purity

pH, DO, T

Potency

Safety

Sterility

Analytics


Multivariate

Model

OK for Release

Not OK

☐

Final

Product


Perfusion

Fill

Elute

Real-Time Process

Data



On-line quality

analytics

22

Recall Quality-by-Design Approach

Process development involves:

• Define the target

product profile

• Identify the

critical quality

attributes (CQAs)

• Select an

appropriate

manufacturing

strategy

• Implement a

control strategy

23

Plant-wide Control Approach

• Characteristics of InSCyT

– Many connected unit operations

– Many discrete operations

– Multi-product plant

– Alignment with regulatory

requirements

• QbD approach adapted from chemical industry

– Employing systematic and modular design of plantwide

control strategies for production-scale manufacturing facilities

– Using numerical algorithms that can handle discrete operations

and multiple products

Column Tanks 1 2

Downstream


Sterile Media

Yeast Inoculum


Upstream

1 2 1 2




Waste Waste Waste


UF/DF Membrane

Waste

Concentration

Identity

Purity

pH, DO, T

Potency

Safety

Sterility

Analytics


Multivariate

Model

OK for Release

Not OK

☐

Final Product


Perfusion

Fill

Elute

Real-Time Process

Data

24 R. Lakerveld, B. Benyahia, P.L. Heider, H. Zhang, A. Wolfe, C. Testa, S. Ogden, D.R. Hersey, S. Mascia, J.M.B. Evans, R.D. Braatz, and P.I. Barton. The application of an automated control strategy for an integrated continuous pharmaceutical pilot plant. Organic Process Research & Development, in press.

Application to Biologic Drug Production

• Build first-principles

dynamic models for

each unit operation (UO)

• Design control system

for each UO to meet

“local” material attributes

• Evaluate performance in

simulations and propose

design modifications as needed

• Implement and verify the control system for each UO

• Design and verify plantwide control system to ensure

that the CQAs are met 25



dynamic models for



for each UO to meet








Column Tanks

1 2

Downstream


Sterile Media

Yeast Inoculum


Upstream

1 2 1 2




Waste Waste Waste


UF/DF Membrane

Waste

Concentration

Identity

Purity

pH, DO, T

Potency

Safety

Sterility

Analytics


Multivariate

Model

OK for Release

Not OK

☐

Final

Product


Perfusion

Fill

Elute

Real-Time Process

Data



27

Local “UO” Control for Bioreactor Unit Operations

28

29

Local “UO” Control: Microscale Controlled Cell Culture

K.S. Lee et al., Lab on a Chip, 11, 1730-1739 (2011)

OD

600 (

Cell C

on

ce

ntr

ati

on

)

POD 3

POD 2

POD 1

POD 0

Outgrowth Transition Production

Time, h

Reproducible Microbioreactor Cultivations

30

K.S. Lee et al., Lab on a Chip, 11, 1730-1739 (2011)

Local “UO” Control: Microscale Controlled Cell Culture

31



dynamic models for



for each UO to meet








Acknowledgments (Novartis-MIT)

Paul I. Barton

Bernhardt L. Trout

Allan T. Myerson

Klavs F. Jensen

Timothy F. Jamison

Richard Lakerveld (Delft)

Brahim Benyahia (Loughborough)

33

Acknowledgments (Bioman)

Chris Love

Rajeev Ram

Anthony Sinskey

Timothy Lu

Michael Strano

Jongyoon Han

James Leung

GK Raju

Stacy Springs

Paul Barone

Kerry Love

Philippe Mouriere

Nate Cosper

William Hancock

Lisa Bradbury

Ralf Kuriyel

Russell Newton

Susan Dexter

Steve Cramer

Pankaj Karande

34

Documents

Controlling Pharmaceutical Quality - PQRI · M1 M2 S1 C1 C2 W1 P1 P2 P3 P4 P5 P9 P7 P8 P10 LT LT LT C3 C4 P18 P19 P6 LT P11 P13 P14 P17 P20 P12 P15 P16 LT LT P22 P24 S3 S4 W2 M3 M4