2010 teva process drift ben-anat

8/17/2019 2010 TEVA Process Drift Ben-Anat

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© Teva Pharmaceutical USA.

Generic R&D – CONFIDENTIAL

Inna Ben-Anat, Senior Engineer,Research and Development, Teva Pharmaceuticals USA

PQRI-FDA Workshop on Process DriftDecember 1-3, 2010

Process Drift Affects Specifications and Shelf-Life


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The Boiling Frog Story…

“The boiling frog story is awidespread anecdote describing a

frog slowly being boiled alive. Thepremise is that if a frog is placed inboiling water, it will jump out, but if

it is placed in cold water that is

slowly heated, it will not perceivethe danger and will be cooked todeath…”


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The Boiling Frog Story…

“The boiling frog story …”

“Process Drift” is the change of “normal process behavior” over time

http://www.google.com/imgres?imgurl=http://www.vaniercollege.qc.ca/international-education/images/vefa/question-mark-red.png&imgrefurl=http://www.vaniercollege.qc.ca/international-education/projects/vefa/resources/mdg.html&usg=__fiWACsUXAGSspLfy5fipQjltwSs=&h=300&w=300&sz=7&hl=en&start=12&sig2=H6brt7LDGHnKK76X6HiAOQ&zoom=1&tbnid=MWyQHgAnywFrVM:&tbnh=116&tbnw=116&ei=ILCTTIa4E4SclgfQnIynCg&prev=/images%3Fq%3Dquestion%2Bmark%26um%3D1%26hl%3Den%26tbs%3Disch:1&um=1&itbs=1http://www.google.com/imgres?imgurl=http://www.payrollcheck.co.uk/graphics/warning-sign.gif&imgrefurl=http://www.payrollcheck.co.uk/scam.htm&usg=__yxc7spYAjWy_iu9TbN3Y0xIbtZM=&h=216&w=245&sz=4&hl=en&start=2&sig2=Shbrwz0qC1puQ_BlNRNlYQ&zoom=1&tbnid=rE76B2orThNzcM:&tbnh=97&tbnw=110&ei=7K-TTNHTAsGBlAfJxN2mCg&prev=/images%3Fq%3Dwarning%2Bsign%26um%3D1%26hl%3Den%26tbs%3Disch:1&um=1&itbs=1


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Presentation Outline

Process Drift: Potential Causes

Statistical Tools for Monitoring and Detecting ProcessDrift

Shelf-Life Re-Evaluation Illustration

Process Drift Affects Dissolution Dissolution Test Specifications

Potential root-causes for Dissolution change

Detecting and Monitoring tools and techniques

QbD, PAT and Specifications Setting

Summary and Conclusions


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Process Drift: What Is Causing It?

Raw materials: physical and chemical properties vendors change

Manufacturing equipment: upgraded/downgraded machinery

equipment models/outputs

Operators: knowledge gain and lost recourses availability

Environment

Drift within measurement equipment

Analytical Methods...and many more factors potentially changing the process conditions…continuously


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Process Drift: How Can We Monitor andDetect It?

Control Charts

Data Modeling/DOE

Statistical Tools for Monitoring and Trending

Capability Analysis


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Process Drift: How Can We Monitor andDetect It?

Distribution ANOVA (Analysis Of Variance)

Regression Analysis


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Shelf Life Re-Evaluation

Shelf-Life of a product might be re-evaluated throughoutproduct life-cycle

24 months Shelf-Life is commonly defined based on 12

weeks Accelerated Conditions stability data of thesubmission batch

A longer Shelf-Life might be considered based on actualobtained stability data

“Statistical design andanalysis of stability

studies”

by Shein-Chung Chow


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Product Shelf-Life Determination

Definition of Shelf-Life (ICH Q1A): “The time period duringwhich a drug product is expected to remain within the

approved Shelf-Life specification, provided that it isstored under the conditions defined on the container

label”


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Product Shelf-Life Prediction(ICH Q1A(R2))

Shelf-Life will be determined based on stability data evaluation ofminimum three batches

The degree of variability of individual batches affects the confidencethat a future production batch will remain within specification

throughout its Shelf-Life If the batch to batch variability is small, it is advantageous to

combine the data into one overall estimate – slope and interceptequivalence test

If it is inappropriate to combine data from several batches, prediction is

based on the batch with shortest Shelf-Life

Shelf-Life will be determined based on the time at which the 95%confidence limit for the mean curve (CI) intersects the acceptancecriteria


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Calculation example for Shelf-LifeRe-evaluation: pooled lots data

All the batches can bepooled together for one

overall Shelf-Life estimate:slope and intercept

equivalence p-value is largerthan 0.25

Poolability test: Slope and Intercept equivalence model

Intercept p-value

Slope p-value

All the data presented in thisexample is fictitious in sake

of illustrating the calculationprocedure


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Calculation example for Shelf-LifeRe-evaluation: pooled lots data

Shelf-Life determination based on 95% confidence limit forthe mean curve

Recommended Shelf-Life is 34 months


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Calculation example for Shelf-LifeRe-evaluation: based on the „worst‟ lot data

Shelf-Life determination based on one lot with shortest Shelf-Life(not required in this case)

Recommended Shelf-Life is20 months based on the lot

with shortest Shelf-Lifeprediction


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Process Drift Affects Specifications:Dissolution Test Example

Dissolution/Drug Release Rate is one of the most critical

quality attributes in product development

Used as a quality control tool to ensure batch –to –batch

consistency of predicted performance


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Dissolution Specifications Setting –Current Practice Deficiency

Specifications are set to fit the available, often limiteddata

May not be discriminative or sensitive enough to reflectthe ‘true’ product quality and performance

Absolute Q values based on Mean and do not reflectStandard Deviation

Based on empirical approach – lack of product andprocess understanding

OOS results’ Root Cause is unknown

Variability source is unknown


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Process Drift May Affect Specifications:Dissolution Test Example

Dissolution is one of the most critical quality attributes that can bepotentially affected by process drift

Possible root-causes:

Specifications setting deficiency (previous slide)

Excipients

API

Equipment

Processing parameters

Packaging

Analytical Method

Statistical tools and instrumentation can be utilized in order tomonitor and detect process drift impact on dissolution


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Statistical Tools

Control Charts


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Dissolution AVG: Grouped by manufacturing year

Dissolution Range (Max-Min): Grouped by manufacturing year

Year X Year Y Year Z



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© Teva Pharmaceutical USA.Generic R&D – CONFIDENTIAL


Dissolution AVG: Grouped by manufacturing year


Date Tested??

Lab Error??


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Statistical Tools ANOVA

Is Year Ysignificantlydifferent??


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Statistical Tools

Regression and Trendanalysis

Is Year Zstability trendsignificantlydifferent??


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The FT4 Powder Rheometer ( ) is a universalpowder tester that can measure dynamic flow, shear and bulk

properties of powders and granules:

Instrumentation

http://www.freemantech.co.uk/


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Lot C

Lot D

Lot B

FT4 Powder Rheometer Granulation Testing

T o t a l E n e r g y ( m J )

Lot A

FT-4 powder tester is discriminative enough toobserve the granulation differences and correlate

it with dissolution values


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Specifications Setting –QbD Approach

Drug Development process understanding and control:

Design Specifications early in development-target product profile Root Cause analysis

Gauge R&R to identify source of variability

Specifications as continuous "real time" assurance of product quality,

should evolve during development as process knowledge andexperience increases

Life Cycle Management – data collection and trending using statisticaltools

Raw MaterialsProperties

Effect Of Formulation onmanufacturing process

Effect Of Manufacturing Processon critical quality attributes


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In Process Testing – PAT

PAT – Process monitoring tools needed to analyze criticalattributes: on-line monitoring (NIR, FBRM)

Detecting errors or process deviations and correcting

them while the product is being made Continuous Improvement and control of design space

Impact on Specifications Setting: Quality control testing

will evolve from testing against a discrete specification(pass/fail) to real-time comparison of process/productsignatures against a reference


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In Process Testing – PAT


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Summary

Don‟t become a "boiled frog": be proactive – look fortrends, be a detective – explore your data

"Traditional" Specifications based on process

experience; "QbD" specifications leverage processunderstanding: increased ability to control the processand eliminate the drift in the future

Specifications as continuous "real time" assurance ofproduct quality: life cycle management, data collectionand trending using statistical tools


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References

ICH Q6A – Specifications: Test Procedures and Acceptance Criteriafor New Drug Product

ICH Q8 – Pharmaceutical Development

ICH Q9 – Quality Risk Management

ICH Q10 – Pharmaceutical Quality System

ICH Q1A (R2) – Stability Testing of New Drug Substances andProducts

ICH Q1E – Evaluation of Stability Data – Comments

„Establishing Drug release/Dissolution Specifications – QBD Approach‟, Moheb M. Nasr, Ph.D., ACPS, 2005


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2010 teva process drift ben-anat

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