Quality by Design Considerations for Analytical Procedures and Process Control

Transcription

1 Quality by Design Considerations for Analytical Procedures and Process Control Moheb M. Nasr, Ph.D. ONDQA/CDER/FDA IFPAC 2009 Baltimore, MD January 26,

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 2

3 Background on FDA Initiatives: Pharmaceutical Quality for the 21 st Century In 2002, FDA identified a series of ongoing problems and issues in pharmaceutical manufacturing using traditional approaches Internal and external assessment found: Pharmaceutical manufacturing highly regulated compared to food, chemical, etc Cost of quality compliance very high Process efficiency and effectiveness were low high waste and rework Level of technology lower than comparable industries Reasons for manufacturing failures were not understood 3

4 Pharmaceutical Quality for the 21st Century Final Report (2004) Outreach and collaboration with industry Revised regulations Pharmaceutical inspectorate Change the CMC review process Implement quality systems internally Introduce new manufacturing science into regulatory paradigm Harmonize modern quality concepts internationally (ICH Q s) 4

5 Timeline of QbD Related Activities 5 OGD QbR Announced ONDQA CMC Pilot Program 21 st Century Initiative Report Critical Path Initiative OBP Pilot Program Quality Systems Guidance Finalized ICH Q8 Finalized ICH Q9 Finalized ICH Q11 (Concept Paper) PAT Guidance ICH Q10 Finalized ICH Q8R Finalized

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)) 6

7 FDA View of QbD (Drug Product) Product & process design and development Quality by Design Define desired product performance upfront; identify product CQAs Design formulation and process to meet product CQAs Continually monitor and update process to assure consistent quality Identify and control sources of variability in material and process Understand impact of material attributes and process parameters on product CQAs Risk assessment and risk control

8 QbD - The Bottom Line Systematic approach to pharmaceutical development using: Modern scientific and quality risk management (QRM) principles Quality control strategies based on product and process understanding Sharing development and manufacturing information with regulators Regulatory decisions based on scientific and QRM principles 8

9 Benefits of QbD A Quality by Design provides: Higher level of assurance of product quality Cost saving and efficiency for industry More efficient regulatory oversight QbD provides continued assurance of quality Throughout product lifecycle Throughout product supply chain 9

10 Why Quality by Design? Quality cannot be tested into a product Quality cannot be inspected into a product Quality should be built in or designed into the process and product 10

11 If quality cannot be tested into the product, what is the role of analytical methods in QbD? 11

12 Analytical Methods in a QbD System Collect in-process information for timely control decisions Monitor and trend process parameters Adjust process before failures occur Monitor product quality Quality is not determined solely by product specification Provide data to better understand the process Use data for continual improvement Confirm success of process changes Can use in-process methods or sampling 12

13 FDA s View of Process Analytical Technologies 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 quality Manufacturers implementation strategy may vary (development, monitoring/analyzing, and/or control) PAT and QbD share similar goals for pharmaceutical manufacturing Process understanding Process control Risk based decisions 13

14 Example QbD Approach for Drug Product (Q8(R1)) Product profile CQAs Risk assessment Design space Control strategy Continual Improvement 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

15 Example QbD Approach for Analytical Methods Target Measurement Select Technique Risk assessment Method Development Control strategy Continual Improvement Determine what to measure and where/when to measure it Select appropriate technique for desired measurement and/or characterization Assess risk of parameter and sample variation on method results Develop a robust method, examining potential multi-variate interactions Ensure suitable quality systems are in place for method and system suitability Monitor method performance; update as needed or as analytical technology evolves

16 Current Status of Applying QbD Principles to Analytical Methods Target Measurement Select Technique Target Measurement Select Technique Traditional Methods (e.g., HPLC, KF) Well established Well established In-Process Methods (e.g., NIR) Well established Well established Risk assessment Risk Assessment Evolving Evolving Method Development Method Development Well established Evolving Control strategy Control Strategy Well established Evolving Continual Improvement Continual Improvement Not practiced Evolving

17 Method Understanding Understand how variation in input parameters affect analytical results Examine multivariate relationships Across instrument, sample and method parameters Employ mechanistic understanding Based on chemical, biochemical and physical knowledge Incorporate prior knowledge of techniques and methods Applicable to traditional and in-process analytical methods 17

18 Multivariate Analytical Method Development and Validation ICH Q2(R1) is mostly applicable to multivariate methods Specificity Linearity Calibration Model Range Accuracy Precision Detection Limit Quantitation Limit Robustness Model Maintenance Representative Sample 18

19 Different Types of Multivariate Models Identification methods Differentiate between other compounds or product Include variability between multiple lots Quantitative methods Used for assay or concentration measurements Calibration based on a reference method Standard error cannot be lower than reference method Rate of change methods Sometimes used for end-point determination (e.g., blending, drying) Non-calibration method, based on change of variance Probe location can be critical (e.g., scale-up) 19

20 Considerations for Multivariate Model Development Include as many sources of variability as possible Understand robustness of model The lowest error is not always the best model! Data preprocessing type should have a scientific/physical basis Avoid over-fitting the model Validate using independent data set Examine internal vs. external fit of data 20

21 Maintaining and Updating Calibration Process changes or drifts can introduce new sources of variability Evaluate consistency with calibration model (e.g., residual error of fit) Investigate cause of outliers As needed, add to model any spectra representing new acceptable variation Update Calibration Models Appropriateness of model continually evaluated Model recalibrated as needed 21

22 QbD and PAT Progress Several applications have been submitted and approved using inprocess monitoring and/or control A few applications have been submitted and approved utilizing Real Time Release Testing (PAT) Others in progress at different stages of development (IND) 22

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 23

24 Concluding Remarks Analytical techniques and methods are an essential part of QbD Right analysis at the right time Based on science and risk QbD can offer more flexibility, but requires High 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 24