USP1220 for Analytical Lifecycle: Its Application for Bioassay

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Pierre Lebrun | Director Statistics| Pharmalex| USP <1220> panel

Prepared for CASSSNLABMarch 2021

Contents

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Introduction: Quality by Design ?

The ATP: Analytical Target Profile

Stage 1: Development and Characterization

Stage 2: Performance Qualification

Stage 3: Continued Performance Verification

Q&A

RQbD egulatory Framework

Quality by Design (QbD) vs. Quality by Testing (QbT)

Increased knowledge

Science based Assurance of quality

Design Space (DS)

QbD for Analytical Technology

The Application of Quality by Design to Analitycal MethodsPhil Borman, Phil Nethercote, Marion Chatfield, Duncan Thompson, Keith Truman

Introduction

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USP <1220> is a new chapter that presents an alternate framework for analytical procedures that holistically incorporates all of the events that take place over the procedure life cycle that are designed to demonstrate that a procedure is, and remains, fit for the intended purpose.

It is to be understood as an umbrella or companion for− Transfer of Analytical Procedures <1224>− Validation and Verifications of Compendial Procedures <1225><1226>− Statistical Tools for Procedure Validation <1210>

Not a replacement for Development, Validation and Analysis of Biological Assay <1032><1033>, <1034>, but may give some nice working frameworks around them

… and harmonization is searched with− ICHQ2 (in review)− ICHQ12− ICHQ14 (in review)

Know your target : the Analytical Target Profile (ATP)

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Start with the end in mind: what’s the objective ?To identify or quantify a biological activity. This is the measurement of interest.− Classical process is (QbTesting)

• 1) Develop• 2) Use during product development activities• 3) ? Define objectives for performances or use common ones (e.g. 80-120%)? • 4) Verify performance

- i.e. the “validation”, seen as a “final” & regulatory oriented• 5) Don’t mention the impact of results uncertainty to the process team

− “Life cycle” process is (QbDesign)• 1) Define an ATP with meaningful specifications, e.g. related to product• 2) Develop the assay, refine the assay to see if the ATP is achievable (e.g. reportable value)• 3) Qualification and control strategy. Go to 1) if this does not fly !• 4) With more product understanding (e.g. safety, efficacy), refine the ATP

The ATP needs some clues on (minimal) assay performances should be gathered from the beginning… do this for your own results quality, not for the regulatory bodies!Q

ualit

yby

Des

ign

(QbD

) vs.

Qua

lity

by T

estin

g(Q

bT)

Examples of ATP

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A “classical” ATP“The procedure must be able to accurately measure product relative potency in a range from 50% to 150%, in the vaccine, in the presence of Drug Product matrix, with an accuracy = 100% ± 5% and an intermediate precision ≤ 25% for the reportable value”

A “total error“ ATP“The procedure must be able to quantify Drug Substance impurities in a range from 0.1 µg/mL 25 µg/mL in all intermediates, in the presence of matrix and impurities x, y, z, so that the distribution of the total analytical error of the reportable value falls within the total allowable analytical error range of ±30%”

Obviously, it is mandatory to gather information of what is an acceptable assay range and bias/precision/total error.Assay can still be refined in later stage defining an assay replication strategy (replication for the reportable value)… or just restart the cycle if necessary

The Analytical Procedure Life Cycle

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The validation is a set of activities from development to routine, and not just one single exerciseFully inspired from FDA process validation guidance (2011)

Life Cycle objectives

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The Quality by Design− Product quality and performance achieved and assured by design of effective

and efficient manufacturing processes− Product specifications based on mechanistic understanding of how formulation

and process factors impact product performance− Ability for continuous improvement and assurance of quality

Risk reduction over the whole life cycle !

Life Cycle objectives

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The Analytical Quality by Design− Product Results quality and performance achieved and assured by design of

effective and efficient manufacturing processes bioassays− Bioassay acceptance criteria Product specifications based on mechanistic

understanding of how formulation and process bioassays factors impact results quality product performance

− Ability for continuous improvement and assurance of quality

Risk reduction over the whole life cycle !

Risk management

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In aQBD, risk management is central

What is risk? − The uncertainty of the performance of a bioassay, as quantified by a probabilistic statement

and measures of possible outcome consequencesAn important concept for the ICH Q8-Q11 Guidance− ICH Q8 – The word ‘risk’ or ‘risk-based’ occurs 35 times− ICH Q9 - The word ‘risk’ or ‘risk-based’ occurs 290 times− ICH Q10 - The word ‘risk’ or ‘risk-based’ occurs 34 times− ICH Q11 - The word ‘risk’ or ‘risk-based’ occurs 52 times

Risk management

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Several risk management tools can be used through the validation activities− FMEA, Ishikawa diagrams, etc.− Think business risk (e.g. too many assay failure) and product related-risks (e.g. safety of

accepting a wrong batch after release testing)Probability/Severity

Minor Medium Major

Improbable

Occasional

Frequent

Unit or Operation Procedure parameter Hazard Accuracy Precision

Sample Prep Pre-dilution level Pipetting error

Hold Time Time before analysis Sample/Reagent stability

Plate coating … … … …

Measurement process Time of incubation Reach proper level of binding

… … … … …

These maps are reviewed during the life cycle !

Stage 1

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Development and characterizationUnderstanding effects of assay parameters generally require modelingDesign of Experiments is the recommended way

From early risk assessement (criticality analysis)− Define procedure chararectistics (Quality Attributes) to follow

• Can be a bit more than the ATP- QAs can be the reportable value or individual measurements (e.g. Potency, Titer) - Or a surrogate attribute (e.g. OD, dose-response fit, etc.)

− Define assay / procedure parameters• Which one are likely to play a role on bias• Which one need to be strictly controlled to ensure uncertainty is under control

− From early data, think about your control strategy (e.g. SST, variance of replicates)• Create designs that allow such assessment during development

Stage 1

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Design of experiments− Out of the scope of this presentation

DoE are powerful tools that can be run sequentially:− Screening studies to select the most relevant parameters− Optimization studies to ensure bias and uncertainties are as low as possible− Robustness studies to demonstrate results are not affected by small by classical changes in

the assay (Run, Reagent, Antibody lots, etc.)DoE fully in line with life-cycle strategy

Stage 1

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Assume two significant operating conditions have been chosen during a 16 XP Plackett & Burman screening, amongst

− Capture Antibody− Biotin− Amount Enzyme− Volume− Incubation Time− Number of Cleanings

Provide knowledge of factor influence

Do not rely on the p-value

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Statistical significance alone is useless !

Do you really know what is the interpretation of a p-value ?

When a statistical test is not significant can you really accept the null hypothesis ?

Are your interpretation(s) of a confidence interval of a parameter (e.g. the mean) correct ?

What is effectively the aim of your studies ?

As the American Statistical Association officially reminded in March 2016….

Stage 1

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Take care to p-value !!

− Statistical significance alone is useless !− Rely on effect size instead, compared to spec (even an arbitrary spec would do the trick)

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Slope p-value: 0.45 Slope p-value: <0.0001

Significant (with respect to spec) Not significant (with respect to spec)

Specification(e.g. max tolerated)

Stage 1

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Change assay operating condition to flatten the profile

CQAs must be not sensitive to slight changes in operating conditions and batch variations

Method Operable Design Region (MODR)

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Parallel with ICH Q8 Design Space:"the multidimensional combination andinteraction of input variables andprocess parameters that have beendemonstrated to provide assurance ofquality."

Res

pons

e

Factor

LowerSpecification

prediction95% Prediction Interval

Experimental domain

MODR is a sub-region of theexperimental domain where theobjectives are met with a definedminimal probability.

MODR

On average only

MDR is optional, but if you want to report it, make sure it proposes guarantee !!

Optimized Robust assay : Mean response vs future resultsHere, Dosing Range > 440, on average in the past….. or guaranteed in the future !

mean based DS Risk based

MODR p=0.7

p(DR>440)

mean responses = there is about 50% of chance that my response is, say, 460 (at optimum)

Dosing Range > 440 ?

MODR

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MODR

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Where do you prefer to live?

… if you know the risk and are able to accept them, feel free to live on the edge of the cliff

Think about noise factors !OperatorRun / PlateLot of reagent

Stage 1

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Consider how the assay is used to make important decision− Release of a product, stability testing, etc.− How to give guarantees when not everything is under control, yet

Know your margins of error !− E.g. Using indecision zones− Often simpler to define “working specifications” from assay uncertainty than to provide an

interval on each single measurement done.

Result

Lower spec

5%5%

Upper spec

Working specifications

Stage 2: Analytical Procedure Performance Qualification

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Verify if the reportable value meet the ATPAssess quality of fit (e.g. 4PL) only for informationQuality of fit is not part of the ATP− Quality of results is !

ATP can be about precision and bias, on a rangeATP can be about total error, on a range

Assuming a required Precision better than 15%

Lower and Upper LOQ are here derived from this specification

Dosing range [10-320] Avg. Precision 6.5%

Acceptancelimits

Prediction/ToleranceInterval (~2*CV)

SystematicError/Bias

Statistical risk: Uncertainty of performance vs performance (sampling)

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Bioassay tested 3 times

Estimates: Mean = 0.1, SD= 1You’re NOT confident to claimthis bioassay will be reliable

in the future

Bioassay tested 30 times

Estimates: Mean = 0.1, SD= 1You’re confident to claim

this bioassay will be reliablein the future

The difference is only linked to the uncertainty of estimates

Stage 2

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If early performance indicators are not satisfying the ATP

− Envisage a more robust replication strategy• Need early indicator of Run, Plate/Gel / operator / equipment variabilities

− Go back to Stage 1…

Stage 3 – Continued Procedure Performance Verification

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• Analytical Control Strategy (ACS) start in Stage 1 !!• ACS: ongoing assurance is gained during routine analyses that

the bioassay remains in a state of control• E.g. Control samples, replication

It allows controlling the risks and keep the quality constant over time.You maintain your initial claim and monitor it with appropriate levels of risk.

Stage 3

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If too many issues occur during the assay routine…− E.g. SST out of range, non parallelism of 4PL curves, trends during monitoring, etc.

… It is necessary to restart the Life Cycle !

Should not occur with an appropriate Stage 1 !− This is aQbD !

Thanks all attendees !

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Take Home Message:

Validation is a full process that starts from development to routineInclude variability in experimentsExplore a large range of value of bioassays parametersMake smart experiments: DoEsModel your Bioassay AND its uncertaintyTotal Analyical Error is the only way to assess the very results you will get

Ackowledgements

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The USP <1220> Panel− Kimber Barnett − Amir Malek− Christopher Burgess − Greg Martin − Jaime Marach− Joachim Ermer− Phil Borman − Samuel Gardner − Horacio Pappa− Steven L. Walfish− Yun (Jenny) Wang− Ken Freebern

Pharmalex colleagues thinking about aQbDsince a long, long time− Bruno Boulanger− Eric Rozet