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The Mean versus Individual Values: A Key to Developing a Risk Based Quality System

2. Outline. Goals and development of a quality systemHurdles to achieving an effective quality systemAmbiguities in stability evaluationMethod validation and transferDefinition of design space. 3. Goals and development of a quality system. Goal: to help assure safe and effective therapies and va

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The Mean versus Individual Values: A Key to Developing a Risk Based Quality System

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    1. The Mean versus Individual Values: A Key to Developing a Risk Based Quality System Tim Schofield GlaxoSmithKline 2009 Non-Clinical Biostatistics Conference Harvard School of Public Health, Boston, MA October 23, 2009

    2. 2 Outline Goals and development of a quality system Hurdles to achieving an effective quality system Ambiguities in stability evaluation Method validation and transfer Definition of design space

    3. 3 Goals and development of a quality system Goal: to help assure safe and effective therapies and vaccines for patients Achieved through strategic product development and appropriate bridging of manufacturing to development experience Development and maintenance of key analytical methods Coordinated product development Preclinical and clinical studies that reveal the boundaries of critical quality attributes which forecast patient safety and efficacy Establishment of appropriate specifications (explicit protection) and control limits (implicit protection) Vigilance to important product characteristics including stability Requires a vision towards information management throughout the product lifecycle

    4. 4 Hurdles to achieving an effective quality system Historical modes of development and operation are difficult to change in an aggressive competitive environment In the interest of efficiency over effectiveness, many departments operate in silos Basic research, nonclinical development, and clinical development Process research, formulation development, and analytical development Regulatory guidelines and expectations engender a disincentive for collecting valuable information USP General Notices FDA OOS Guidance

    5. 5 Hurdles to achieving an effective quality system (cont.)

    6. 6

    7. 7 Shelf-life determination versus stability monitoring ICH shelf-life determination aims at the mean product profile Ambiguities in stability evaluation

    8. 8 Shelf-life determination versus stability monitoring (cont.) One approach to mitigating risk of a post licensure stability OOS – establish shelf-life based on protecting individual stability measurements Historical practice in some pharma companies WCBP Strategy Forum on Stability – solution to post licensure stability OOS was offered as a “late breaking” presentation An option offered in the approach being developed by the PQRI Stability Shelf Life Working Group

    9. 9 Shelf-life determination versus stability monitoring (cont.) Solutions which warrant individual stability measurements do not contribute to the quality of pharmaceuticals or vaccines Controlling stability measurements protects the “stability unit” rather than the customer Under some strategies this would force a company to set excessively wide specifications which might increase customer risk Holding individual stability measurements to specifications creates a disincentive for collecting data Contrary to QbD which advocates for data collection to facilitate process understanding and control

    10. 10 Shelf-life determination versus stability monitoring (cont.) More valuable to work on stability study design and approaches which appropriately model product stability Mixed effects modeling rather than the ICH poolability strategy The ICH approach creates a disincentive for increasing the number of batches (increased power to detect a difference in slopes/intercepts) Fixed power approach by Ruberg & Stegeman (1991) A random batches approach can be instituted with more development batches and/or as part of continuous development throughout the product lifecycle Bayesian approaches can be utilized to leverage prior knowledge Incorporates appropriate modeling of intra- and inter-assay variability

    11. 11 Method validation and transfer The classical approach to method validation and transfer has been to assess performance characteristics of the method Mean accuracy and intermediate precision An equivalence approach has been proposed to demonstrate conformance to acceptance criteria Transfer - H1: |lab1-lab2| < ?? USP <1033> - H1: |Relative bias| < ? Studies are not sufficiently powered to apply this approach to intermediate precision USP <1033> proposes using variance component analysis to establish assay format Like stability, method performance can be re-evaluated after adequate experience has been achieved

    12. 12 Method validation and transfer (cont.) The classical approach ignores the combined impact of bias and variability Boulanger et.al. suggest the use of a ?-expectation tolerance interval to warrant future results

    13. 13 Method validation and transfer (cont.) Comparison of “equivalence” versus “tolerance” approaches For a fixed ?/? and study size there is a significantly greater risk of failing the study using the ?-expectation TI approach versus the equivalence approach Transfer of Methods Supporting Biologics and Vaccines (Liu, R.,et.al. 2009) “However the criteria on individual results cannot be the same as the criteria on a mean of many results.” (Boulanger, B., et.al. 2009) ? would have to be larger than ? to harmonize the risks – but this impacts the customer risk of receiving a bad result (under ?)

    14. 14 “Design Space: the design space is the established range of process parameters that has been demonstrated to provide assurance of quality.” - emphasis mine - “Formal Experimental Design: a structured, organized method for determining the relationship between factors (Xs) affecting a process and the output of that process (Y). Also known as “Design of Experiments”.”

    15. 15 Definition of design space (cont.) ICH Q8(R2) shows design space as the intersection of a specification with the response surface associating process factors with a quality attribute

    16. 16 Definition of design space (cont.) Peterson, et.al. (2009) have suggested a Bayesian multivariate approach to definition of design space to assure quality From this design space can be defined as the region with suitable probability (reliability) of “meeting specification”

    17. 17 Definition of design space (cont.) What do we mean by “meeting specification” What is the “experimental unit”?

    18. 18 Regulatory challenge The process design space is a part of the NDA/BLA Should describe the batch process and the impact of changes on overall process variability Should be flexible to changes in the measurement system – which impacts measurement variability Technology changes Assay format changes Measurement variability should be the subject of the process control strategy rather than the development of design space Specification development Definition of design space (cont.)

    19. 19 Summary Many areas of CMC development and commercial QC are impacted by the issue of whether decisions are made from individual measurements or from averages Agreeing to averages opens up opportunities for strategic design and analysis of key development experiments, as well as important quality initiatives (QbD) Statisticians provide not only design and analysis expertise, but also strategic thinking regarding rational goals in ensuring quality of pharmaceutical products

    20. 20 References Guidance for Industry: Investigating Out-of-Specification (OOS) Test Results for Pharmaceutical Production, U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), October 2006, Pharmaceutical CGMPs Ruberg, S.J., Stegeman, J.W., 1991. Pooling data for stability studies: testing the equality of batch degradation slopes. Biometrics 47, 1059–1069. Bruno Boulanger, Eric Rozet, Francois Moonen, Serge Rudaz, Philippe Hubert, A risk-based analysis of the AAPS conference report on quantitative bioanalytical methods validation and implementation, Journal of Chromatography B, 877 (2009) 2235–2243 Rong Liu, Timothy L. Schofield, and Jason J.Z. Liao, Transfer of Methods Supporting Biologics and Vaccines, accepted by Statistics in Biopharmaceutical Research, 2009. USP <1033> Bioassay Validation, Pharmacopeial Forum, March/April, 2009. ICH Q8(R2), Pharmaceutical Development, Current Step 4 version, August 2009 Peterson, J. J., A Posterior Predictive Approach to Multiple Response Surface Optimization, Journal of Quality Technology, 2004, 36, 139-153. Peterson, J. J. A Bayesian Approach to the ICH Q8 Definition of Design Space, Journal of Biopharmaceutical Statistics, 2008, 18, 959-975. Stockdale, G. and Cheng, A. , Finding Design Space and a Reliable Operating Region using a Multivariate Bayesian Approach with Experimental Design, Quality Technology and Quantitative Management, 2009, (online at the QTQM web site).

    21. 21 Acknowledgements The PQRI Stability Shelf Life Working Group Stan Altan (J&J) Bruno Boulanger (UCB) Jinglin Zhong (FDA) John Peterson (GSK) Seth Clark (Merck)

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