1. Risk-Based Sourcing for Bovine Materials in FDA-regulated Medical Products: Introduction and General Considerations FDA TSE Advisory Committee Meeting Holiday Inn Silver Spring MD 12 February 2004 David M. Asher, MD Laboratory of Bacterial, Parasitic and Unconventional Agents Division of Emerging & Transfusion-Transmitted Diseases Office of Blood Research and Review Center for Biologics Evaluation and Research United States Food and Drug Administration e-mail address: asher@cber.fda.gov

6. Risk-Based Sourcing for Bovine Materials in FDA- regulated Medical Products: Introduction and General Considerations Recent TSE-related events with implications for safety of FDA-regulated products  Presumptive transfusion-transmitted vCJD  Cow with BSE in Washington State Human blood/tissues and related products Products containing or manufactured with bovine materials  Risk analysis and TSEs  Existing safeguards (“firewalls”), enhancements  Possible additional safeguards for injectable biologics and similar products: discussion points

7. Risk Analysis for Products: Conventional Definition Risk Assessment  (Paradigm of Natl Acad Sci USA /Natl Res Council report 1983) 1.Identify hazard 2.Characterize hazard (dose-response) 3.Estimate probable exposure to hazard 4.Characterize risk (from estimated probable exposure and dose-response) Risk Management (based on assessment) Risk Communication

8. Risk Assessment for Products: General Elements of Microbiological Risk Source of raw materials (contamination) Manufacturing process  Reduction of contamination (ability to inactivate or remove contaminant)  (? Concentration/increase of contaminating agent) End-use (frequency, amounts, routes)

9. “Effective” Exposure to TSE Agent (Kimberlin) : An Exposure Sufficient to Infect a Recipient l Dose of agent (infectivity/volume x total volume) l Route of exposure (CNS>intravenous>im/subcut >> oral) Host susceptibility  Species barrier (variable, not absolute)  ? Agent “virulence” (? BSE > scrapie)

10. Quantitative Risk Assessment Expresses risk as an overall probability l Tries to represent the complexity of real situations as “scenarios” (called System or Fault Tree or Failure Modes-and-Effects Analysis: multiply individual probabilities of a series of system failures leading to an adverse event) l Requires collection of adequate, accurate, quantitative data to be reliable l Allows for variability and uncertainty (best expresses probabilities as distributions, not single-point estimates) l Identifies variables with the greatest impact on the risk estimates (Sensitivity Analysis)

11. Quantitative Risk Assessment: Dealing with Variability and Uncertainty (EC DG SANCO) l Describe all assumptions and constraints. l Represent parameters as probability distributions rather than as single-point- estimate values. l Perform Sensitivity Analysis to demonstrate the influence of assumptions used in the risk assessment on the final risk estimate.  (Answers question: “What if we are wrong about an assumption?”)

12. Advantages and Weaknesses of Quantitative Risk Assessment Transparency  Assumptions and methods clearly articulated  Failure scenarios available for independent review: elements of risk less likely to be overlooked l Requires collection of adequate, accurate, quantitative data to be reliable  For unprecedented hazards no actuarial information available to estimate probabilities  Absent precedents, probabilities for each failure step are estimated at least partially subjectively l Expressing subjective probabilities numerically implies unjustified level of confidence Assumes no surprises

13. PhRMA BSE Risk Model (Bader & al. 1998) and Uncertainty Bader F et al. Assessment of risk of bovine spongiform encephalopathy in pharmaceutical products. Biopharm 1998;11(1):20-31 and 11(3):18-30 ProbabilitySources of Uncertainty Cow infected (herd, age)Quality of surveillance, inspection etc. Batch contaminated (tissue, slaughter technique) High-infectivity tissue cross contamination, etc. Human infectivity per tissueSpecies barrier estimate Human infectivity per batch after processing Limited process validation data, questionable relevance Human infection per doseUniformity of susceptibility Human infections per courseUniformity of susceptibility

14. Problems with All Risk Assessments Experts may disagree about assumptions.  Elements of risk  Probabilities Models assume no surprises. Bias is unavoidable.  General bias  Non-independent probability estimates  (Experts may be optimists or pessimists.)  Bad-news bias (Probability of a dramatic [recent] adverse event happening again is overestimated.)  Others  Expert bias  Contractor bias (“piper’s” bias): seek a desired outcome  Manager bias (shop the experts): seek a desired outcome  Calibration bias (unrealistically narrow confidence estimates)

15. Measures to Reduce Risk of Product Contamination. 1. Source materials free of contaminants History of low risk  Materials of animal origin: certificates  Materials of human origin: donor questionnaire, postmortem history Test for contamination  Agent detection (e.g., NAT for HCV, HIV)  Surrogate (e.g., antibodies to HBV, HBV, HCV)  Problem  No accessible and validated antemortem test for TSE of bovines or humans Ideal solution  Replace with lower risk material of non-human, non- animal origin:  Often not feasible, especially for established product

16. Measures to Reduce Risk of Product Contamination. 2. Manufacturing Process: Robust Elimination of Potential Contaminants Eliminate agents in starting material  Inactivation (preferred)  Removal Prevent cross contamination (“downstream” contamination)  Cleaning  Disinfection  Discard any residual removed agent safely Validate  Effect (single steps; for “orthogonal” multi-step should verify additivity)  Relevance to actual production

17. Possible “Rules” for Decisions in Risk Management (Morgan MG. Scientific American 1993) Utility-based decision rules  Perform risk-benefit analysis.  Maximize net benefit.  (Accept a remote risk to achieve a substantial benefit.) Technology-based decision rules  Use best available technology.  (Protect vulnerable populations from remote risks, even at great cost.)

18. Regulatory Safeguards against BSE: “Firewalls” and Recent FDA Enhancements ( )  Import prohibitions (1989 USDA)  Surveillance of cattle for BSE (1990 USDA)  Feed ban: most mammalian proteins prohibited in ruminant feed (1997 FDA)  SRM removal from beef  30 mo carcasses, no mechanically recovered meat (2004 USDA)  BSE response plans ([1990] 1996 USDA, [1998] 2001 FDA) ----------------------------------------------------------------------------------------  BSE-free sourcing guidances for bovine materials in biologics, drugs, and devices ([1991] 1993 FDA) ---------------------------------------------------------------------------------------- “Downer” and dead cows, SRM  30 mo prohibited in FDA- regulated foods (including dietary supplements), cosmetics = USDA regulations [IFR pending] Enhanced ruminant feed ban [IFR pending]  Additional prohibited proteins   Mammalian blood  Poultry litter  Plate waste  Dedicated non-ruminant feed: equipment/facilities/production lines

19. Possible Additional Safeguards to Consider for Bovine Materials Used in or to Manufacture Injectable Biologic Products ? “Select” herds of cattle  Fully traceable  Certified (how?) never fed (or otherwise exposed to?) prohibited proteins  BSE surveillance—active, adequate (how evaluated?) ? “Select” individual cattle and tissues  Young (  30 mo—how old?)  (Not feasible for cows  fetuses for fetal bovine serum)  SRM removed < 30 mo (  what age?)  PrP Sc testing? (  what age?; need validated tests)