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