Role of Statistics in Pharmaceutical Development Using Quality-by-Design Approach – an FDA Perspective Chi-wan Chen, Ph.D. Christine Moore, Ph.D. Office.

Role of Statistics in Pharmaceutical

Development Using Quality-by-Design Approach – an

FDA Perspective

Chi-wan Chen, Ph.D.Christine Moore, Ph.D.

Office of New Drug Quality AssessmentCDER/FDA

FDA/Industry Statistics WorkshopWashington D.C.

September 27-29, 2006

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Outline

FDA initiatives for quality Pharmaceutical CGMPs for the 21st Century ONDQA’s PQAS The desired state Quality by design (QbD) and design space (ICH

Q8) Application of statistical tools in QbD

Design of experiments Model building & evaluation Statistical process control

FDA CMC Pilot Program Concluding remarks

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21st Century Initiatives

Pharmaceutical CGMPs for the 21st Century – a risk-based approach (9/04) http://www.fda.gov/cder/gmp/gmp2004/GMP_finalreport2004.htm

ONDQA White Paper on Pharmaceutical Quality Assessment System (PQAS) http://www.fda.gov/cder/gmp/gmp2004/ondc_reorg.htm

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The Desired State(Janet Woodcock, October 2005)

A maximally efficient, agile, flexible pharmaceutical manufacturing sector that reliably produces high-quality drug products without extensive regulatory oversightA mutual goal of

industry, society, and regulator

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FDA’s Initiative on Quality by Design

In a Quality-by-Design system: The product is designed to meet patient

requirements The process is designed to consistently meet

product critical quality attributes The impact of formulation components and

process parameters on product quality is understood

Critical sources of process variability are identified and controlled

The process is continually monitored and updated to assure consistent quality over time

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Qualityby

Design

FDA’s view on QbD, Moheb Nasr, 2006

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Design Space (ICH Q8)

Definition: The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality

Working within the design space is not considered as a change. Movement out of the design space is considered to be a change and would normally initiate a regulatory post-approval change process.

Design space is proposed by the applicant and is subject to regulatory assessment and approval

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Current vs. QbD Approach to Pharmaceutical Development

Current Approach QbD Approach Quality assured by testing and inspection

Quality built into product & process by design, based on scientific understanding

Data intensive submission – disjointed information without “big picture”

Knowledge rich submission – showing product knowledge & process understanding

Specifications based on batch history

Specifications based on product performance requirements

“Frozen process,” discouraging changes

Flexible process within design space, allowing continuous improvement

Focus on reproducibility – often avoiding or ignoring variation

Focus on robustness – understanding and controlling variation

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Pharmaceutical Development & Product Lifecycle

Candidate Selection

Product Design & Development

Process Design & Development

Manufacturing Development

ProductApproval

Continuous Improvement

Design of Experiments

(DOE)

Model BuildingAnd Evaluation

Process Design & Development:Initial ScopingProcess CharacterizationProcess OptimizationProcess Robustness

Statistical Tool

Product Design & Development:Initial ScopingProduct CharacterizationProduct Optimization

Manufacturing Development and Continuous Improvement:

Develop Control SystemsScale-up PredictionTracking and trending

StatisticalProcess Control

Pharmaceutical Development & Product

Lifecycle

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Process Terminology

Process StepInput Materials Output Materials

(Product or Intermediate)

InputProcess

Parameters

MeasuredParameters or Attributes

Control Model

Design Space

Critical Quality Attributes

Process Measurementsand Controls

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Design Space Determination

First-principles approach combination of experimental data and

mechanistic knowledge of chemistry, physics, and engineering to model and predict performance

Statistically designed experiments (DOEs) efficient method for determining impact of

multiple parameters and their interactions Scale-up correlation

a semi-empirical approach to translate operating conditions between different scales or pieces of equipment

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Design of Experiments (DOE)

Structured, organized method for determining the relationship between factors affecting a process and the response of that process

Application of DOEs: Scope out initial formulation or process design Optimize product or process Determine design space, including multivariate

relationships

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DOE Methodology(1) Choose experimental design (e.g., full factorial, d-optimal)

(2) Conduct randomized experiments

(4) Create multidimensional surface model (for optimization or control)

(3) Analyze data

Experiment

Factor A Factor B Factor C

1 + - -2 - + -3 + + +4 + - +

A

BC

www.minitab.com

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Models for process development Kinetic models – rates of reaction or degradation Transport models – movement and mixing of mass or

heat Models for manufacturing development

Computational fluid dynamics Scale-up correlations

Models for process monitoring or control Chemometric models Control models

All models require verification through statistical analysis

Model Building & Evaluation - Examples

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Chemometrics is the science of relating measurements made on a chemical system or process to the state of the system via application of mathematical or statistical methods (ICS definition)

Aspects of chemometric analysis: Empirical method Relates multivariate data to single or multiple

responses Utilizes multiple linear regressions

Applicable to any multivariate data: Spectroscopic data Manufacturing data

Model Building & Evaluation - Chemometrics

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Statistical Process Control - Definitions

Statistical process control (SPC) is the application of statistical methods to identify and control the special cause of variation in a process.

Common cause variation – random fluctuation of response caused by unknown factors

Special cause variation – non-random variation caused by a specific factor

Upper Control Limit

Lower Control Limit

Target

Upper Specification Limit

Lower Specification Limit

Special cause variation?

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*Percent out of specification beyond the high risk specification limit.

σ3

)SLX(minCpk

2.28%20.7

15.9%10.33

0.135%31

0.003%41.33

051.7

062

Expected Avg. OOS%*|X - SL|Cpk

2.28%20.7

15.9%10.33

0.135%31

0.003%41.33

051.7

062

Expected Avg. OOS%*|X - SL|Cpk

Industry Practice is to consider processes with Cpk below 1.33 as “not capable” of meeting

specifications.

Cpk = 1.33 Cpk = 0.33

Process Capability Index (Cpk)

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Quality by Design & Statistics

Statistical analysis has multiple roles in the Quality by Design approach Statistically designed experiments (DOEs) Model building & evaluation Statistical process control Sampling plans (not discussed here)

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CMC Pilot Program

Objectives: to provide an opportunity for participating firms to submit CMC information based on QbD FDA to implement Q8, Q9, PAT, PQAS

Timeframe: began in fall 2005; to end in spring 2008 Goal: 12 original or supplemental NDAs Status: 1 approved; 3 under review; 7 to be submitted Submission criteria

More relevant scientific information demonstrating use of QbD approach, product knowledge and process understanding, risk assessment, control strategy

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CMC Pilot - Application of QbD

All pilot NDAs to date contained some elements of QbD, including use of appropriate statistical tools

DOEs for formulation or process optimization (i.e., determining target conditions)

DOEs for determining ranges of design space Multivariate chemometric analysis for in-line/at-line

measurement using such technology as near-infrared Statistical data presentation and usefulness

Concise summary data acceptable for submission and review

Generally used by reviewers to understand how optimization or design space was determined

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Concluding Remarks

Successful implementation of QbD will require multi-disciplinary and multi-functional teams Development, manufacturing, quality personnel Engineers, analysts, chemists, industrial

pharmacists & statisticians working together FDA’s CMC Pilot Program provides an

opportunity for applicants to share their QbD approaches and associated statistical tools

FDA looks forward to working with industry to facilitate the implementation of QbD