Visualization of Study Data to Evaluate UDU Capability. Presented at IFPAC 28 Jan 2015 Thomas Parks, Jeff Hofer, Adam Rauk (Inventive Clinical, LLC)

Transcription

1 Visualization of Study Data to Evaluate UDU Capability Presented at IFPAC 28 Jan 2015 Thomas Parks, Jeff Hofer, Adam Rauk (Inventive Clinical, LLC) 1

2 Motivation Increased expectations to demonstrate acceptable UDU The ISPE BUCU Group has provided a framework to address these expectations in two papers published in the Journal of Pharmaceutical Innovation Recommendation for the Assessment of Blend and Content Uniformity: Modifications to Withdrawn FDA Draft Stratified Sampling Guidance, T Garcia, et al., ISSN , DOI /s Assessment of Blend and Content Uniformity. Technical Discussion of Sampling Plans and Application of ASTM E2709/E2810, J Bergum, et al., ISSN , DOI /s z A level of conservativeness relative to USP <905> is recommended and shown using operating characteristic (OC) curves for a given sample plan and level of confidence/coverage derived from tables of acceptance criteria 2

3 Example OC Curves Demonstration of Conservativeness True These OC curves change based on the true batch mean! 3

4 Example ASTM E2810 Acceptance Table Acceptance Ranges This table changes based on the true batch mean, sample plan, and confidence/coverage 4

5 Question How do we summarize a product s capability to meet complex criteria, such as that of ASTM E2810, from multiple Stage 1 studies in a readily understandable way? 5

6 Challenge Create a way to summarize development UDU study data that Includes individual studies and provides an overall summary for a given product Enables more informed risk assessments as a product moves from development into process validation and routine manufacture Allows for comparison between products, platforms, and scales Is readily understandable/interpretable 6

7 Desired Attributes KISS Seamlessly adjust for studies of varying size Visualize the amount of uncertainty in the results Leverage prior knowledge where appropriate Apply Bayesian methods to estimate credible intervals for variability estimates Some estimates using traditional random effects resulted in poor estimates. Bayesian methods provide significantly improved estimation in these cases. Determine reasonable priors to use for variability estimates 7

8 Factors Impacting the Ability of a Batch to Pass the Different UDU Criteria Batch Mean Location to Location Variability Within Location Variability Note Weight variability, concentration variability, and assay variability are not explicitly called out but they contribute to the above elements 8

9 Monte Carlo Simulation to Obtain 95% Limit Contours Properties Varied True batch mean True location to location standard deviation True within location standard deviation Estimates Probability of passing the various criteria USP <905>, ASTM 90/95 20x3/40x3 (example Process Validation (Stage 2)), and ASTM 50/95 10/30 (example Routine Release (Stage 3)) Summarize Show contours of constant 95% probability of passing a given criteria on a plot of within location versus between location variability 9

10 95% Limit Contours for Passing USP, Example Process Validation (90/95)*, and Example Routine Release (50/95)* Plots were created displaying the contours with 95% probability of meeting the specific criteria for different combinations of True Batch Mean (96, 97, 98, 99, 100) True Between Location Standard Deviation True Within Location Standard Deviation The plot with the worst case mean for the actual product depicts the most restrictive contours for that product. *ASTM E2709/E

11 95% Pass Contours for Mean=100 & Credible Intervals for Line Segment Creation Within Location SD 6 5 Upper 95% 3 Credible Limit 4 USP PV (example) Routine (example) 2 Median 1 Estimate Upper 95% Credible Limit Between Location SD 11

12 Quality Statements When the line endpoint lies inclusively within the contours, there is >95% probability that the true standard deviation is small enough for the batch mean that the corresponding criteria can be met >95% of the time When a point estimate falls directly on the line, there is a 50% probability that the corresponding criteria will be met 95% of the time 12

13 True SD Within Locations Product #1 Plot of Contours for 95% Pass Percentage for Different Criteria using Worst Case Batch Mean versus 95% Credible Limits for Between and Within Location Variability True SD Between Locations USP PV (example ASTM 90/95, 20x3/40x3) Routine (example ASTM 50/95, 10x1/30x1) 13

14 True SD Within Locations Product #2 Plot of Contours for 95% Pass Percentage for Different Criteria using Worst Case Batch Mean versus 95% Credible Limits for Between and Within Location Variability USP PV (example ASTM 90/95, 20x3/40x3) Routine (example ASTM 50/95, 10x1/30x1) A long line indicates a very small study not an issue True SD Between Locations 14

15 Conclusion Given that expectations for demonstrating acceptable UDU have tightened, it is necessary to assure products can meet these heightened expectations Bayesian methods provide an excellent analysis option to summarize development data Visualization of product capability is useful for assessing risk as a product transitions to manufacturing with process validation (Stage 2) and routine release (Stage 3) 15