1. Infusing Bayesian strategies for Pharmaceutical Manufacturing and Development
JSM 2019
Bill Pikounis, Dwaine Banton, John Oleynick, and Jyh-Ming Shoung
Manufacturing and Applied Statistics
Statistics and Decision Sciences at Janssen R&D

2. R&D and Commercial Manufacturing Statistics
Outline
R&D and Commercial Manufacturing Statistics
Bayesian mindset, concepts, and practice

3. Small Molecule, Large Molecules, Vaccines
Product Development & Manufacturing (e.g. Stages 1 – 12)
Parallel Lifecycles …
Clinical Trials (Phases ) 3

4. Collaboration Scopes: A Sampling
Regulatory Filings and Responses: Specifications, Shelf-Life, Comparability, Investigations
Spectroscopy MVA, PAT & Chemometrics
Process Validation & Performance Qualification
Continuous Manufacturing
Analytical Methods
Robustness, Ruggedness, Precision, Repeatability, Linearity, Measurement, Calibration
DOE for Formulations and Assays

5. Example 1: Stability Batch Profiles
Unbalanced Data
80% of Batches only measured at Release

6. Specifications for Critical Quality Attributes (CQAs)
General Categories of:
Potency
Purity
Identity
as Outcomes

7. Stability Profile Illustration (continued)
“Confidence” translates to “Probability”
Multilevel or Hierarchical terminology replaces “Mixed Effects”

8. Example 2: another Stability Data Set

9. Example 2: Define Out of Specification Event and calculate Probabilities
From last slide (thumbnail):

10. Priors
Beyond Flat Priors…. Instead use Weakly Informative Priors, at least
Natural Boundaries for Outcomes and Parameters
Regularizes the (MCMC) Model Fitting Algorithms
Capture Scientific Knowledge with Countable Plausibilities
represent via probability distributions on parameters

11. Other Applications
Model selecting and building using wAIC and LOO cross validation information criteria for predictive accuracy
Comparability
Sample Size determination of batches and within batches for process performance qualification
Use posteriors from past data to highly inform the new prior

12. Stan programming language and R packages
rstan, rstanarm, brms, …
No more technological barriers

13. Going Forward – “Bayesian First”
Defining Critical Questions -> Events -> Probabilities
Priors
Distributional Forms to match data types
Maximum Entropy, Information Theory, and Uncertainty
Model Specification through Code and Diagrams

14. References
McElreath, R. (2016). Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman & Hall/CRC.
Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
Bob, Carpenter; Andrew, Gelman; Matthew, Hoffman; Daniel, Lee; Ben, Goodrich; Michael, Betancourt; Marcus, Brubaker; Jiqiang, Guo; Peter, Li; Allen, Riddell. (2017). "Stan: A Probabilistic Programming Language". Journal of Statistical Software. 76 (1): 1–32.