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. 210-09.11.1995, S.11604 (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, 26-27 October 2000.
• Kennedy, R.H. et al. Eye banking and screening for Creutzfeldt-Jakob disease. Arch Ophthalmol. 2001. 119:721-6.
Risk Analysis
A comprehensive, structured approach to dealing with risk
• Assessment
• Management
• Communication
Elements of Risk Assessment
• Hazard identificationSource(s) of risk and quantitative description
of the adverse effect
• Exposure assessmentLevel and duration of exposure to the risk
• Hazard characterizationMechanisms and dose-response relationship
• Risk characterizationProbability 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 2:333-337. Oct.-Dec. 1996.
Age-specific mortality rates: CDC. National Vital Statistics Reports. 47(19), 1999.
Population estimates: U.S. Bureau of the Census, P25-1127, 1995.
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, &
PopulationDynamics
Recovery / Processing
Therapeutic Application
s
DonorScreening
TissueRecovery
TissueProcessing
TransplantProcedures
Risk Characterization
RiskFactors
Population Characteristics
Exposure AssessmentRisk
Characterization
Model Parameters: Assumptions
Parameter Model Minimum Median Maximum
Proportion of missed cases Risk Characterization 0.005 0.01 0.10
Asymptomatic Incubation Period Risk Characterization 5 years 10 years 40 years
Symptomatic Period Risk Characterization 0.2 years 0.5 years 1.5 years
Actual vs. Observed Prevalence Risk Characterization 0.8 0.9 1.0
Donors per Year Exposure Assessment 25,000
Transplant Items per Donor Exposure Assessment 1 10 75
Transplant Items per Recipient Exposure Assessment 1 2 10
Medical History Exposure Assessment 0.60 0.75 0.85
Transfer by Cross-contamination Exposure Assessment 0.0001 0.001 0.01
Processing Reduction Exposure Assessment 0.01 0.9 0.99
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
Model Mean Annual Exposures
90th Percentiles
History (75%), commingling 8.39 1.97 – 20.52
History (75%), no commingling
1.71 0.95 – 2.77
History (100%), commingling 5.47 1.39 – 13.14
History (100%), no commingling
1.39 0.79 – 2.21
Model Mean Annual Exposures
Percent Reduction in Risk
Percent of Donors Excluded
Exclude donors over age 60 1.09 77.5 – 90.4 55.8
Exclude donors over age 65 2.65 56.1 – 78.4 44.0
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