Risk Assessments: Models for Estimating the Risk of Transmitting TSE by Human Tissue Intended for Transplantation Rolf E. Taffs, Ph.D. Center for Biologics Evaluation and Research U.S. Food and Drug Administration

Background (1) BfAr.M, (Fed. Inst. for Med. Supplies and Medicinal Products), Public notice regarding approval and registration of medicinal products. Protection against medicinal risks, Grade II. Federal Publicity No , S (25 September 1995). Bader F. et al. Pharmaceutical Research and manufacturers of America (PhRMA) BSE committee. Assessment of risk of bovine spongiform encephalopathy in pharmaceutical products. Biopharm 1998, 11 (1)20-31, and 11(3):18-30.

Background (2) EC Health and Consumer Protection Directorate-General, Directorate C - Scientific Opinions. First Report on the Harmonisation of Risk Assessment Procedures Part 1: Report of the Scientific Steering Committee's Working Group on Harmonisation of Risk Assessment Procedures in the Scientific Committees Advising the EC in the Area of Human and Environmental Health, October Kennedy, R.H. et al. Eye banking and screening for Creutzfeldt-Jakob disease. Arch Ophthalmol :721-6.

Risk Analysis A comprehensive, structured approach to dealing with risk Assessment Management Communication

Elements of Risk Assessment Hazard identification Source(s) of risk and quantitative description of the adverse effect Exposure assessment Level and duration of exposure to the risk Hazard characterization Mechanisms and dose-response relationship Risk characterization Probability of occurrence, severity of adverse effects, attendant uncertainties, sensitivity analysis (also called importance analysis)

Objectives of Risk Modeling Quantitate the relative contributions of parameters in the model Identify critical elements for additional research in order to improve the model Provide accurate information for making regulatory decisions

Objectives of Sensitivity (“Importance”) Analysis To evaluate effects of changes in risk models by varying model parameters such as the underlying assumptions about CJD and vCJD in the donor pool - Used as a tool to examine the assumptions, variability, and uncertainty of the model and their effect on risk estimation

Components of CJD Risk Assessment Models  Prevalence of CJD in the donor pool  Donor availability and utilization  Sources of uncertainty in the model  Potential impact of infection ­ diagnosed cases of CJD and vCJD ­ undiagnosed symptomatic cases ­ asymptomatic cases

Data Inputs for the Model population by age age-specific all-cause deaths age-specific CJD deaths age-specific tissue donations rational assumptions regarding screening, processing, and cross-contamination

Examples of Information Sources CJD Incidence in the U.S.: Holman et al. Emerging Infectious Diseases 2: Oct.-Dec Age-specific mortality rates: CDC. National Vital Statistics Reports. 47(19), Population estimates: U.S. Bureau of the Census, P , Cornea donors: EBAA Report, 1998

Age Distributions of Sporadic CJD

Cornea Donor Age Distribution

Model Variables (1) symptomatic cases diagnosed but missed by current screening procedures symptomatic cases prior to diagnosis asymptomatic cases (incubation period) disease prevalence specificity of additional donor screening procedures

Model Variables (2) extent of decontamination of tissues effect of cross-contamination or commingling during processing steps batch size numbers of donors, recipients, and grafts used in a given transplant procedure

CJD, Donor, & Population Dynamics Recovery / Processing Therapeutic Applications Donor Screening Tissue Recovery Tissue Processing Transplant Procedures Risk Characterization Risk Factors Population Characteristics Exposure Assessment Risk Characterization

Model Parameters: Assumptions

CJD Risk: Model Comparisons Probability distributions for number of exposures per year under different model assumptions (based on 25,000 donors) Medical history (75% reviewed) Commingling No commingling Medical history (100% reviewed) Commingling No commingling

Likelihood of Exposure (Model 1)

Likelihood of Exposure (Model 2)

Likelihood of Exposure (Model 3)

Likelihood of Exposure (Model 4)

Comparative Models: Summary Table

Sensitivity Analysis Comparisons under different model assumptions Medical history (100%) Commingling No commingling

Sensitivity Analysis (Model 3)

Sensitivity Analysis (Model 4)

Data Gaps More data are needed on: the amount of CJD agent that is present in or could contaminate tissues during procurement from a CJD- infected donor. the progression of CJD and the infectivity of different tissues during the course of the disease. the extent of reduction of CJD agent that might occur during processing of tissues from a CJD-infected donor. donor utilization and allograft implantation practices for different tissues. the extent of cross-contamination by instruments or equipment that might occur during processing.

Conclusions Probabilistic risk assessment allows detailed examination of the likelihood of exposure under differing model assumptions. Different tissues and processing methods have unique models and must be assessed separately. Cross-contamination is a major driver in the CJD risk model. Other parameters such as number of transplanted tissues are also significant drivers in the model. Additional data are needed to provide accurate estimates of exposure to infected tissues.

Acknowledgements David M. Asher Steven A. Anderson