Developing a Roadmap to Implement Quality by Design

Page 1 of 64

Roadmap to Quality by Design

Developing a Roadmap to

Implement Quality by Design

Barry Gujral

Director, Quality Engineering

Peter Amanatides

Vice President, QA and QC

Noven Pharmaceuticals, Inc.

Miami, Fl 33186

Page 2 of 64

Agenda

Roadmap for Quality by Design

The Roadmap for Quality by Design is a detailed plan or explanation

to guide you in setting standards or determining a course of action

This Presentation includes:

A) Formulation Optimization

B) Process Optimization

C) Design Space and Control Strategy

We have included two case studies that we completed at Noven

Pharmaceuticals, Inc. for Formulation Optimization and Process

Optimization during Development Stage

Page 3 of 64

Introduction

Noven Pharmaceuticals, Inc. is a specialty

pharmaceutical company engaged in the research,

development, manufacture, marketing and sale of

prescription pharmaceutical products

We are engaged in mainly three types of products:

Therapeutics

Hormone

ADHD

The subsequent slides summarize the status of products

marketed, approved and/or under advanced

development by Noven

Page 4 of 64

Noven Key Products

Vivelle-Dot®

(estradiol transdermal system)

World’s Smallest Transdermal Estrogen Patch (U.S. & Canada)

Marketed by Novogyne Pharmaceuticals

CombiPatch®

(estradiol/norethindrone acetate)

Combination Estrogen/Progestogen Patch (U.S.)

Marketed by Novogyne Pharmaceuticals

Estradot®

(estradiol transdermal system)

World's Smallest Transdermal Estrogen Patch (Europe & elsewhere)

Marketed by Novartis

Estalis®

(estradiol/norethindrone acetate)

Combination Estrogen/Progestogen Patch (Europe & elsewhere)

Marketed by Novartis

Page 5 of 64

Noven Key Products (Continued)

Stavzor®

(valproic acid)

Delayed Release Capsules (U.S.)

Marketed by Noven Therapeutics, LLC

Pexeva®

(paroxetine mesylate)

Selective Serotonin Reuptake Inhibitor (SSRI)


Lithobid®

(lithium carbonate)

Extended Release Lithium (U.S.)


Daytrana®

(methylphenidate transdermal system)

Once-daily Transdermal Methylphenidate Patch (U.S.)

Marketed by Shire plc

Fentanyl Transdermal System

(fentanyl)

Extended Release Fentanyl Patch (U.S.), Marketed by Apotex Corp

http://www.stavzor.com/index.php

Page 6 of 64

Introduction

Transdermal therapeutic systems are designed to provide controlled

continuous delivery of drugs via the skin to the systemic circulation

They reduce various side effects like painful delivery of the drugs

and the first pass metabolism of the drug occurred by other means

of drug delivery systems

Many drugs which can be injected directly into the blood stream via

skin have been formulated

The main advantages of this system are that there is controlled

release of the drug and the medication is painless

The drug is mainly delivered to the skin with the help of a

transdermal patch which adheres to the skin

Page 7 of 64

Introduction

Transdermal Technology is used when:

The patient has intolerable side effects (including constipation) and who

is unable to take oral medication

The pain control might be improved by reliable administration. This

might be useful in patients with cognitive impairment or those who for

other reasons are not able to self-medicate with their analgesia.

Used in combination with other enhancement strategies to produce

synergistic effects

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Formulation Optimization

Steps to follow for Formulation Optimization:

– Select the critical inputs of designed molecule

– Select critical outputs

– Develop Design of Experiment (DOE) between inputs and

outputs

– Develop mathematical modeling between outputs and inputs

– Optimize formulation based upon analysis of DOE

– Perform Risk Analysis based upon Monte Carlo Simulations

– Analyze Sensitivity Studies

Page 9 of 64


What is Mathematical Modeling?

Mathematical modeling is the process of creating a mathematical

representation of some phenomenon in order to gain a better understanding

of that phenomenon

It is a process that attempts to match observation with symbolic statement

The success of a mathematical model depends upon how easily it can be

used and how accurate its predictions are

How to Build a Mathematical Model?

Identify the Problem

State the Assumptions

Identify the Important Variables and Constants and determine how they

relate to each other

Develop an equation that expresses the relationships between the variables

and constants

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How to verify a Mathematical Model?

Is the information produced reasonable?

Are the assumptions made during the development of the model reasonable?

Are there any factors that were not considered and could affect the outcome?

How do the results compare with real data?

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Why Mathematical Modeling?

Mathematical modeling is the art of translating problems from an

application area into tractable mathematical formulations whose

theoretical and numerical analysis provides insight, answers, and

guidance useful for the originating application

Mathematical Modeling is an interdisciplinary subject bringing

together mathematicians and specialists in other fields to:

– Improve existing products

– Develop better products

– Predict the behavior of certain products

– Future Predictions

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This Section includes:

Quality by Design

– Objectives

– Factors Impacting

Case Study #1

Optimal Parameters

Risk Analysis

Thinking Outside the Box

Conclusions

Page 13 of 64

Quality by Design

Objectives of Quality by Design:

Product and process characteristics important to desired

performance must be derived from a combination of prior knowledge

and experimental assessment during product development

From this knowledge and data, a multivariate model linking product

and process measurements and desired attributes may be

constructed

Clinical study would then be viewed as confirmatory performance

testing of the model

Page 14 of 64

Quality by Design

Goals of Case Study # 1:

a) To get a maximum value of Total Delivered Amount

(TDA)

b) To get a minimum value of Jmax/Jmin

We need to get a single scenario where we meet both

the conditions mentioned above

Page 15 of 64

Case Study # 1

BUILDING OF DOE MODEL

Factors

Acrylic as Adhesive

DPG ( Dipropylene Glycol) as Solvent and Enhancer

K-30 (Kollidon) as excipient

E2 is API

Outputs

Total Delivered Amount ( mdg/cm2)

J maximum (Maximum Flux in mcg/cm2/hr)

J minimum (Minimum Flux in Mcg/cm2/hr)

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Total Delivered

AmountDF SS

MS

(variance)F p

Total 16 42081 2630.0500

Constant 1 41590 41589.5000

Total Corrected 15 491.2900 32.7529

Regression 8 391.1500 48.8941 3.4178 0.0610

Residual 7 100.1400 14.3057

Lack of Fit

(Model Error)7 -- -- -- --

Pure Error

(Replicate Error)0 -- --

N = 16 Q2 = 0.3180 Cond. no. = 1.0667

DF = 7 R2 = 0.7960 Y-miss = 0.0000

Comp. = 1 R2 Adj. = 0.5630 RSD = 3.7823

ANOVA Table for TDA

Page 17 of 64

ANOVA table for Flux J Max

Jmax DF SS MS (variance) F p SD

Total 16 11.37020 0.7106390

Constant 1 11.2058 11.2058000

Total Corrected 15 0.164469 0.01096460 0.104712

Regression 10 0.150152 0.01501520 5.24401 0.041000 0.122537

Residual 5 0.014317 0.00286331 0.053510

Lack of Fit

(Model Error)5 -- -- -- -- --

Pure Error

(Replicate Error)0 -- -- --

N = 16 Q2 = 0.4870000 Cond. no. = 1.06700

DF = 5 R2 = 0.9130000 Y-miss = 0.00000

Comp. = 2 R2 Adj. = 0.7390000 RSD = 0.05351

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ANOVA Table for Flux J Min

Jmin DF SS MS (variance) F p SD

Total 16 3.7207700 0.2325480

Constant 1 3.5603000 3.5603000

Total Corrected 15 0.16047100 0.0106981 0.1034320

Regression 10 0.15244800 0.0152448 9.5000500 0.0110000 0.1234700

Residual 5 0.00802400 0.0016047 0.0400590

Lack of Fit 5 -- -- -- -- --

(Model Error)

Pure Error 0 -- -- --

(Replicate Error)

N = 16 Q2 = 0.7130000 Cond. no. = 1.0670000

DF = 5 R2 = 0.9500000 Y-miss = 0.0000000

Comp. = 2 R2 Adj. = 0.8500000 RSD = 0.0400600

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-5

-4

-3

-2

-1

0

1

2

3

4

5

Acr

DP

G

K-3

0

E2

Acr*

DP

G

Acr*

K-3

0

Acr*

E2

K-3

0*E

2

mdg/c

m2

Scaled & Centered Coefficients for Total Delivered Amount

N=16 R2=0.796 R2 Adj.=0.563

DF=7 Q2=0.318 RSD=3.7823 Conf. lev.=0.95

Investigation: Puchun Data Model 7152009 (PLS, comp.=1)

MODDE 7 - 7/15/2009 3:46:26 PM

Coefficients Plot

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Coefficients Plot

-5

-4

-3

-2

-1

0

1

2

3

4

5

Acr

DP

G

K-3

0

E2

Acr*

DP

G

Acr*

K-3

0

Acr*

E2

DP

G*K

-30

DP

G*E

2

K-3

0*E

2

mdg/c

m2

Scaled & Centered Coefficients for Total Delivered Amount

N=16 R2=0.847 R2 Adj.=0.540

DF=5 Q2=0.093 RSD=3.88 Conf. lev.=0.95

-0.12

-0.11

-0.10

-0.09

-0.08

-0.07

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

-0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Acr

DP

G

K-3

0

E2

Acr*

DP

G

Acr*

K-3

0

Acr*

E2

DP

G*K

-30

DP

G*E

2

K-3

0*E

2

mcg/c

m2/h

r

Scaled & Centered Coefficients for Jmax

N=16 R2=0.913 R2 Adj.=0.739

DF=5 Q2=0.487 RSD=0.05351 Conf. lev.=0.95

-0.08

-0.07

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

-0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

Acr

DP

G

K-3

0

E2

Acr*

DP

G

Acr*

K-3

0

Acr*

E2

DP

G*K

-30

DP

G*E

2

K-3

0*E

2

mcg/c

m2/h

r

Scaled & Centered Coefficients for Jmin

N=16 R2=0.950 R2 Adj.=0.850

DF=5 Q2=0.713 RSD=0.04006 Conf. lev.=0.95


MODDE 8 - 7/30/2009 11:36:25 AM

Page 21 of 64

Total Delivered Amount


Contour Plot

MODDE 7 - 7/15/2009 3:53:04 PM

K-30 = 4

E2 = 1.5

Contour Plot

Page 22 of 64

Contour Plot

Jmax Total Delivered Amount

Jmin


Contour Plot

MODDE 8 - 7/30/2009 12:16:51 PM

K-30 = 4.9999

E2 = 1.5683

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45

46

47

48

49

50

51

52

53

54

55

56

57

7 8 9 10

Tota

l Deliv

ere

d

Am

ount

Acryli c

45

46

47

48

49

50

51

52

53

54

55

56

57

6.00 6.50 7.00 7.50 8.00

DPG

45

46

47

48

49

50

51

52

53

54

55

56

57

3.00 3.50 4.00 4.50 5.00

K-30


Prediction Plot

MODDE 7 - 7/15/2009 4:09:13 PM

N=16 DF=7 Conf. lev.=0.95

Acr yl i c = 8. 5

DPG = 7

K- 30 = 4

E2 = 1. 5

Prediction Plot

Page 24 of 64


Acrylic DPG K-30 E2Total Delivered

Amount

10.0000 6.5197 4.9697 1.4000 42.9323

10.0000 6.2857 5.0000 1.4001 42.7571

7.0000 6.0000 3.082 1.5910 43.4493

9.8217 6.9127 5.0000 1.4000 43.7177

10.0000 7.7544 5.0000 1.4000 43.1519

7.0000 6.0000 3.0000 1.6000 42.7225

10.0000 6.0000 5.0000 1.4000 42.675

9.9972 6.0081 4.9944 1.4007 42.7347

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Acrylic DPG K-30 E2 Total Delivered Amount

7.0001 7.9619 4.8923 1.4387 59.1820

9.8772 8.0000 3.0389 1.5877 51.8096

7.0002 8.0000 4.9773 1.4528 59.1989

9.8410 6.9101 4.9334 1.6000 50.7456

7.0000 7.2000 4.9996 1.4000 57.2777

7.0035 7.9777 5.0000 1.6000 57.0198

7.0000 7.4876 4.8339 1.4000 58.1701

7.0050 7.9020 4.1025 1.4000 59.1841

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Acrylic DPG K-30 E2

Total

Delivered

Amount

Lower Upper

7.6000 7.0000 5.0000 1.4200 53.7899 49.1910 58.3890

10.000 7.0000 3.4000 1.4000 49.0842 44.0450 54.1240

7.0000 7.0000 3.0000 1.6000 46.1188 40.2030 52.0350

10.0000 7.0000 5.0000 1.5600 49.0682 44.0290 54.1080

9.1000 7.0000 4.2000 1.5000 49.9987 47.5430 52.4550

7.0000 6.0000 3.0000 1.6000 42.7225 36.0150 49.4300

10.0000 6.0000 5.0000 1.4000 42.6750 35.9670 49.3830

10.0000 7.6000 5.0000 1.4000 43.1090 36.8960 49.3220

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Acrylic DPG K-30 E2Total Delivered

AmountJmax Jmin

7.0000 8.0000 4.9969 1.5674 58.2026 0.9875 0.5107

7.0000 7.8706 3.0002 1.4001 58.1281 0.9426 0.5544

7.0000 8.0000 3.9362 1.6000 54.5638 0.9824 0.4190

7.0000 8.0000 4.7917 1.5716 57.5983 0.9866 0.4955

7.0000 7.9998 5.0000 1.5658 58.2167 0.9868 0.5118

7.0000 8.0000 5.0000 1.5677 58.2099 0.9877 0.5107

7.0025 8.0000 4.9999 1.5683 58.2010 0.9878 0.5103

7.0107 7.9992 5.0000 1.5701 58.1695 0.988 0.5093

Page 28 of 64


Acrylic DPG K-30 E2TDA mdg/

cm2

Jmax mcg/

cm2/hr

Jmin mcg/

cm2/hrJmax/Jmin

7.0000 8.0000 4.9969 1.5674 58.2026 0.9875 0.5107 1.9336

7.0000 7.8706 3.0002 1.4001 58.1281 0.9426 0.5544 1.7002

7.0000 8.0000 3.9362 1.6000 54.5638 0.9824 0.4190 2.3446

7.0000 8.0000 4.7917 1.5716 57.5983 0.9866 0.4955 1.9911

7.0000 7.9998 5.0000 1.5658 58.2167 0.9868 0.5118 1.9281

7.0000 8.0000 5.0000 1.5677 58.2099 0.9877 0.5107 1.9340

7.0025 8.0000 4.9999 1.5683 58.2010 0.9878 0.5103 1.9357

7.0107 7.9992 5.0000 1.5701 58.1695 0.9880 0.5093 1.9399

Optimized

Formulation

With

Maximum

value of TDA

and minimum

value of

Jmax/jmin

Page 29 of 64

Risk Analysis

What is Risk?

The uncertainty regarding a situation indicates risk,

which is the possibility of loss, damage, or any other

undesirable event

Low risk strategies translate to a high probability of

success, profit, or some form of gain

Risk Analysis is one of the essentials of ProcessAnalytical Technology (PAT) being adopted in thePharmaceutical Industry as per the new guidance of theFDA

Under PAT, the quality is designed into aPharmaceutical product, rather than established bytesting of a finished batch

Page 30 of 64

Risk Analysis

Process Analytical Technology (PAT)

According to the FDA, “Process Analytical

Technology is a system for designing, analyzing,

and controlling manufacturing through timely

measurements (i.e., during processing) of critical

quality and performance attributes of raw and in-

process materials and processes with the goal of

ensuring final product quality.”

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Risk Analysis

The 2004 risk-based regulatory approaches

under Process Analytical Technology (PAT)

recognize the level of scientific understanding of

formulations and manufacturing process factors

affecting the product and quality performance

and the capability of process control strategies

to prevent and mitigate the risk of producing a

poor quality pharmaceutical product.

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Risk Analysis

This is a basis to understanding the relationship among

various critical process and formulation factors and

developing effective risk mitigation strategies

A desired goal of PAT is to design and develop

processes that will ensure a predefined quality at the end

of the manufacturing process

Page 33 of 64

Risk Analysis

The objective of PAT is to improve understanding andcontrol the manufacturing process, which is consistentwith our current drug quality system: quality cannot betested into products, it should be built in or it should beby design

The increased emphasis of PAT on building quality intoproducts allows us to have more focus on multi-factorialrelationship among raw materials, manufacturingprocesses, and their impact on quality

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Risk Analysis

The traditional spreadsheet analysis of quality outcomesdoes not give any probability information on quality. InPAT, the critical product quality variables are identifiedfrom the historical data

The simulation model provides probability data foralmost all possible outcomes by analyzing the statisticsof simulations . The probability of quality in the finalproduct can be predicted with confidence

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Risk Analysis

All critical sources of variability are identified and

explained

The variability is managed by the process and product

quality attributes which can be accurately and reliably

predicted over the design space established for the

materials used, process parameters and manufacturing

conditions

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Benefits of Risk Analysis

Enhanced patient confidence in decision making on

pharmaceutical quality

Promotes more effective use of regulatory agency

and industry resources

Establishes a systematic, well-informed and

thorough method of decision making which leads to

greater transparency and predictability

Increased knowledge of exposure to risk

Fosters quality by design, continuous improvement

and new technology introduction, which generally

leads to enhanced product quality

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Scope of Risk Analysis

This guideline provides a framework that may be applied

to all aspects of pharmaceutical quality including GMP

and submission/review processes throughout the

lifecycle of drug substances (API) and drug (medicinal)

products, biological and vaccine products, and the use of

excipients and packaging materials

This guideline is not intended to apply to risk

management used in a pharmacovigilance setting

involving safety and efficacy

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Guiding Principles

The evaluation of the risk should ultimately link back tothe potential risk to the patient

The extent of the risk management process should becommensurate with the level of risk associated with thedecision

A more robust data set will lead to lower uncertainty

It is essential to have a clear delineation of the riskquestion

Risk management should be an iterative process

People who apply risk management should have theappropriate training, skills and experience

The risk management process should be appropriatelydocumented and verifiable

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Risk Management

Defining specifically the risk management problem orquestion, including the assumptions leading to thequestion

Assembling background information and data on thehazard, harm or human health impact relevant to theassessment

Defining how the assessment information andconclusions will be used by the decision makers

Identifying the necessary resources, members of theteam who have the appropriate expertise, with the leaderclearly identified

Asking the right risk assessment question(s)

Stating clearly the assumptions in the risk assessment

Assessing the quality and sufficiency of relevant data

Specifying a timeline and deliverables for the riskassessment

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Risk Assessment

What can go wrong?

What is the likelihood (probability) it would go wrong?

What are the consequences?

Risk analysis is a systematic use of information toidentify specific sources of harm (hazards) and toestimate the risk

Risk evaluation compares the estimated risk againstgiven risk criteria using a quantitative or qualitative scaleto determine the significance of the risk

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Risk Control

Risk control describes the actions of implementing riskmanagement decisions.

– What can be done to mitigate and reduce risks?

– What options for controlling risks are available?

– What are the impacts of current risk managementdecisions on future options for risk management?

Risk mitigation focuses on a reduction of severity ofharm

Risk reduction focuses on the reduction of probabilitiesof occurrence of harm and detection of harm

Risk acceptance is a decision to accept risk, i.e., noadditional risk control activities are necessary at thattime

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Risk Communication

Risk communication is the exchange or sharing of

information about risk and risk management between the

decision maker and other stakeholders. The information

can relate to the existence, nature, form, probability,

severity, acceptability, treatment, detectability or other

aspects of risks to quality

The communication among stakeholders concerning

quality risk management decisions can be made through

existing channels

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Risk Monitoring and Review

All risk management processes are dynamic/iterative.

Quality risk management when applied should benefit

from new knowledge with each decision cycle and used

to enhance future decisions allowing for continuous

improvement

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Current Risk Management Tools

Process mapping

Preliminary Hazard Analysis (PHA)

Hazard Analysis of Critical Control Points (HACCP)

Hazard Operability Analysis (HAZOP)

Fault tree analysis (FTA)

Failure Mode Effects Analysis (FMEA)

Failure Mode, Effects and Criticality Analysis (FMECA)

Risk Ranking and Filtering

Informal Risk Management

Taguchi, variation risk management method

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Supporting Statistical Tools

Design of experiments (DOE)

Process Capability Analysis

Control charts:

Acceptance control charts

Shewhart control charts

Accumulative sum charts

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Monte Carlo Simulations - A New Tool

Use of Monte Carlo Simulations as a new tool in Risk

Analysis for:

Risk Analysis in Formulation Optimization of

Transdermal Products

Risk Analysis in Quality of Pharmaceuticals-Content

Uniformity

Risk Analysis in Building Capacity Models

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Risk Analysis

Page 48 of 64

Sensitivity Analysis

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Thinking Outside the Box

We can get total Delivered

amount outside the box by

changing value of Acrylic

We can get total Delivered

amount outside the box by

changing value of DPG

Acrylic DPG K-30 E2 TDA

5.6055 8 5 1.6 60.0000

Acrylic DPG K-30 E2 TDA

7 8.8530 5 1.6 60.0000

The Total Delivered Amount (TDA) of 60.00 mdg/cm2 is outside

the box of our DOE. The Sensitivity Analysis of Monte Carlo

Simulation indicates that DPG and Acrylic are the most important

drivers of TDA. We can change the value of either Acrylic to 6 or

DPG to 9 to achieve TDA of 60.00 mdg/cm2.

Outside the boxOutside the box

Outside the box

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Conclusions Total Delivered Amount lies between 38.18 and 58.91 mdg/cm2 by

the combinations of 16 runs under Full Factorial Design at two levels

Our Model indicates that these are not the only factors that are impacting the final Response, the interactions between the factors are also very crucial

The important interactions are: Acrylic and E2; Acrylic and K30: Acrylic and DPG

Prediction indicates that Optimum Results may be obtained by Keeping: Acrylic=7.0000; DPG=7.8706; K30=3.0002 and E2=1.4001

Sensitivity Analysis indicates that Acrylic and DPG are the important drivers of Total Delivery Amount

Risk Analysis predicts Total Delivery amount close to 51 mdg/cm2 with 100% confidence interval

We can calculate Total Delivery amount outside the box too

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Process Optimization

Steps to follow for Process Optimization:

– Select critical parameters of raw materials and manufacturing

process

– Develop a screening and d-optimal models of outputs with

critical parameters of raw materials and manufacturing

processes

– Optimize the output parameters

– Analyze risk assessment

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This Section Includes:

Case Study # 2

Interactions

Contour Plot

DOE results

Within Box

Outside the Box

Recommendations

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-0.3

-0.2

-0.1

-0.0

0.1

0.2

0.3In

l

Web

Inl*W

eb

%

Unscaled Coefficients for Residual Solvent

N=11 R2=0.932 R2 Adj.=0.903

DF=7 Q2=0.867 RSD=0.06682 Conf. lev.=0.95

-6

-5

-4

-3

-2

-1

0

1

2

Inl

Web

Inl*W

eb

%

Unscaled Coefficients for Average Drug Loss

N=11 R2=0.556 R2 Adj.=0.366

DF=7 Q2=-0.388 RSD=1.825 Conf. lev.=0.95

Investigation: Amphetamine 03152010-3 (MLR)

MODDE 8 - 3/17/2010 5:19:19 PM

Page 54 of 64

Residual Solvent Average Drug Loss

Investigation: Amphetamine 03152010-3 (MLR)

Contour Plot

MODDE 8 - 3/17/2010 5:17:15 PM

Temperature = 74


Page 55 of 64


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Process Optimization (Within Box)

Inlet Air

VelocityWeb Speed

Residual

Solvent

Average

Drug Loss

4.1200 1.0000 0.3388 14.9800

3.7000 1.4788 0.8613 10.4418

4.6000 1.0000 0.2732 15.8968

3.7000 1.4393 0.8230 10.7496

3.7000 1.1778 0.5690 12.7905

3.7000 1.4788 0.8613 10.4418

4.6000 1.0000 0.2733 15.8960

4.6000 1.0000 0.2732 15.8968

Based upon our DOE, we are not able to achieve a scenario

where Residual Solvent is less than 0.3 and Drug Loss is

less than 15. Residual Solvent and Drug Loss are inversely

related.

Page 57 of 64

Process Optimization (Outside the Box)

Inlet Air

VelocityWeb Speed

Residual

Solvent

Average

Drug Loss

3.1000 0.7878 0.2944 14.6326

3.1008 1.3509 0.7820 10.3899

3.1000 0.7891 0.2955 14.6228

4.0860 1.5669 0.9326 10.3931

3.1001 1.3475 0.7791 10.4144

3.1000 1.3447 0.7768 10.4347

3.1000 0.7814 0.2889 14.6806

3.1000 0.7889 0.2954 14.6241

Optimization outside the box by adding systematic variability

Page 58 of 64

Design Space and Control Strategy

The Role of Design Space Development

in Quality by Design

Process development results in the definition and approval of a “Control Space” within the universe of possibilities about a process called a “Knowledge Space.” The approved manufacturing process can be operated within the Control Space to produce material that meets the required specifications for identity, potency, quality, etc.

As the product matures in its life cycle, scale-up, economic and/or other factors can require changes in the control scheme for the process, moving it from Control Space 1 to a new Control Space 2

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Conclusions

The Process Analytical Technology (PAT) initiative of the FDA provides risk based manufacturing of Pharmaceuticals

Under PAT, the risk is inversely proportional to Process Understanding

Monte Carlo Simulations helps to quantify the risk

Once the risks involved to make a bad Pharmaceutical product are quantified, we can use other statistical tools like Solver to reduce those risks

The Design space will be different for different unit operations. Design Space for each unit operation can be optimized using statistical tools

The Control Strategy will be specific to equipments being used in manufacturing and monitoring

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Developing a Roadmap to Implement Quality by Design