1

Quality by Design Considerations for Analytical Procedures

and Process Control

Moheb M. Nasr, Ph.D.ONDQA/CDER/FDA

IFPAC 2009Baltimore, MD

January 26, 2009

2

Outline

Background on FDA Initiatives and Quality by Design (QbD)

Applying QbD Principles to Analytical Methods and Process Controls

Considerations for Multivariate Models

Update on PAT Implementation

Concluding Comments

3

Background on FDA Initiatives: “Pharmaceutical Quality for the 21st Century”

In 2002, FDA identified a series of ongoing problems and issues in pharmaceutical manufacturing using traditional approachesInternal and external assessment found:

Pharmaceutical manufacturing highly regulated compared to food, chemical, etcCost of quality compliance very highProcess efficiency and effectiveness were low – high waste and reworkLevel of technology lower than comparable industriesReasons for manufacturing failures were not understood

4

Outreach and collaboration with industryRevised regulationsPharmaceutical inspectorate Change the CMC review processImplement quality systems internallyIntroduce new manufacturing science into regulatory paradigmHarmonize modern quality concepts internationally (ICH Q’s)

“Pharmaceutical Quality for the 21st Century” Final Report (2004)

5

Timeline of QbD Related Activities

21st Century Initiative Report

Critical Path Initiative

ICH Q8 Finalized

ICH Q9 Finalized

ONDQA CMC Pilot Program

PAT Guidance

2004 2005 2006

OGD QbR Announced

Quality Systems

Guidance Finalized

2007 2008

ICH Q10 Finalized

ICH Q8R Finalized

2009

OBP Pilot

Program

ICH Q11 (Concept Paper)

6

QbD Definition

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.(ICH Q8(R))

FDA View of QbD (Drug Product)

Define desired product performance

upfront;identify product CQAs

Design formulation and process to meet product CQAs

Understand impact of material attributes and process parameters on

product CQAs

Identify and control sources of variability

in material and process

Continually monitor and update

process to assure consistent quality

Risk assessment and risk control

Product & process design and development

Qualityby

Design

8

QbD - The Bottom Line

Systematic approach to pharmaceutical development using:

Modern scientific and quality risk management (QRM) principlesQuality control strategies based on product and process understanding

Sharing development and manufacturing information with regulatorsRegulatory decisions based on scientific and QRM principles

9

A Quality by Design provides:Higher level of assurance of product qualityCost saving and efficiency for industryMore efficient regulatory oversight

QbD provides continued assurance of quality

Throughout product lifecycleThroughout product supply chain

Benefits of QbD

10

Why Quality by Design?

Quality cannot be “tested into”a productQuality cannot be “inspected into”a productQuality should be “built in” or “designed into” the process and product

11

If quality cannot be tested into the product, what is the role of analytical methods in QbD?

12

Analytical Methods in a QbD System

Collect in-process information for timely control decisionsMonitor and trend process parameters

Adjust process before failures occur

Monitor product qualityQuality is not determined solely by product specification

Provide data to better understand the process

Use data for continual improvement

Confirm success of process changesCan use in-process methods or sampling

13

Process Analytical Technology (PAT) a system for designing, analyzing, and controlling manufacturing through timely measurements of critical quality and performance attributes of raw and in-process materials and processes with the goal of ensuring final product qualityManufacturers implementation strategy may vary (development, monitoring/analyzing, and/or control)

PAT and QbD share similar goals for pharmaceutical manufacturing

Process understandingProcess controlRisk based decisions

FDA’s View of Process Analytical Technologies

Example QbD Approach for Drug Product (Q8(R1))

Quality target product profile

Determine critical quality attributes (CQAs)

Link raw material attributes and process parameters to CQAs and perform risk assessment

Develop a design space

Design and implement a control strategy

Manage product lifecycle, including continual improvement

Product profile

CQAs

Risk assessment

Design space

Control strategy

ContinualImprovement

Example QbD Approach for Analytical Methods

Determine what to measure and where/when to measure itSelect appropriate technique for desired measurement and/or characterizationAssess risk of parameter and sample variation on method resultsDevelop a robust method, examining potential multi-variate interactionsEnsure suitable quality systems are in place for method and system suitability Monitor method performance; update as needed or as analytical technology evolves

Target Measurement

Select Technique

Risk assessment

MethodDevelopment

Control strategy

ContinualImprovement

Current Status of Applying QbD Principles to Analytical Methods

Continual Improvement

Control Strategy

Method Development

Risk Assessment

Select Technique

Target Measurement

In-Process Methods (e.g., NIR)

Traditional Methods(e.g., HPLC, KF)

Well established Well established

Well established

Well established

Evolving

Evolving

Evolving

Evolving

Evolving

Not practiced

Target Measurement

Select Technique

Risk assessment

MethodDevelopment

Control strategy

ContinualImprovement

Well established

Well established

17

“Method Understanding”Understand how variation in input parameters affect analytical resultsExamine multivariate relationships

Across instrument, sample and method parameters

Employ mechanistic understandingBased on chemical, biochemical and physical knowledge

Incorporate prior knowledge of techniques and methodsApplicable to traditional and in-process analytical methods

18

Multivariate Analytical MethodDevelopment and Validation

ICH Q2(R1) is mostly applicable to multivariate methods

SpecificityLinearity RangeAccuracyPrecisionDetection LimitQuantitation LimitRobustness Model MaintenanceRepresentative Sample

Calibration Model

19

Different Types of Multivariate Models

Identification methodsDifferentiate between other compounds or productInclude variability between multiple lots

Quantitative methodsUsed for assay or concentration measurementsCalibration based on a reference methodStandard error cannot be lower than reference method

Rate of change methodsSometimes used for end-point determination (e.g., blending, drying)Non-calibration method, based on change of varianceProbe location can be critical (e.g., scale-up)

20

Considerations for Multivariate Model Development

Include as many sources of variability as possibleUnderstand robustness of model

The lowest error is not always the best model!Data preprocessing type should have a scientific/physical basisAvoid over-fitting the model

Validate using independent data setExamine internal vs. external fit of data

21

Maintaining and Updating Calibration

Process changes or drifts can introduce new sources of variabilityEvaluate consistency with calibration model (e.g., residual error of fit)Investigate cause of outliersAs needed, add to model any spectra representing new acceptable variationUpdate Calibration Models

Appropriateness of model continually evaluatedModel recalibrated as needed

22

QbD and PAT Progress

Several applications have been submitted and approved using in-process monitoring and/or controlA few applications have been submitted and approved utilizing Real Time Release Testing (PAT) Others in progress at different stages of development (IND)

23

Challenges in Implementing PAT and QbD

Not all concepts for implementing PAT have been refined

How much information should be submitted regarding model verification?What information is required regarding model maintenance and update?What do specifications look like for RTRT approach?

Further work will provide clarity and best practices (ICH IWG)ONDQA is willing to work closely with applicants prior to submission and during the review process

24

Concluding RemarksAnalytical techniques and methods are an essential part of QbD

Right analysis at the right timeBased on science and risk

QbD can offer more flexibility, but requiresHigh degree of process, product and analytical method understanding Robust quality systems

Implementation of PAT must be based on good science and best practices (IFPAC)FDA is willing to work with industry to implement these “new” concepts