Integrated probabilistic risk assessment

1. Integrated probabilisticrisk assessment Bas Bokkers National Institute for Public Health and the Environment (RIVM) – the Netherlands

2. Deterministic risk assessment -Variability extreme consumer sensitive subpopulations -Uncertainty limited concentration data interspecies extrapolation Worst-case / conservative approach using point values A deterministic risk assessment does not discriminate between variability and uncertainty

3. Deterministic risk assessment PoD ADI = * * * AF1 AF2 ….. AFi Exposure = consumption concentration * ADI Risk if exposure > ADI or <1 exposure

4. Qualitative: • Exposure > ADI • risk • everyone affected? Conclusions deterministic RA Inconclusive: • Exposure is slightly higher than ADI • “risks cannot be excluded” Remaining question: Quantify the risk: • *Percentage of population affected ? Quantify the uncertainty

5. Probabilistic risk assessment • Variability extreme consumer sensitive subpopulations • Uncertainty limited concentration data interspecies extrapolation Realistic approach using distributions A probabilistic risk assessment can discriminate between variability and uncertainty

6. Integrated probabilistic risk assessment: • Evaluates • - both variability and uncertainty (but separately) • in both exposure assessment • hazard characterization • - in a single (integrated) analysis For instance: Combine variability in exposure with variability in sensitivity Combine uncertainty in concentrations with uncertainty in interspecies differences

7. Probabilistic risk assessment Individual’s dose that would lead to some predefined effect: PoD of iBMD = AF1 AF2 ….. AFi * * * The same individual’s exposure: of = consumption concentration * iEXP iBMD <1 This individual is at risk when his/her iEXP > iBMD or when iEXP No information on the individuals…… but variability distributions can inform iBMD and iEXP distributions

8. Probabilistic risk assessment iBMD <1 An individual is at risk when his/her iEXP > iBMD or when iEXP iBMD distr. = 1 iEXP distr. • * Fraction of the population affected

9. Probabilistic risk assessment PoD of iBMD = AF1 AF2 ….. AFi * * * of = consumption concentration * iEXP Uncertainty distributions can inform uncertainty in iBMD and iEXP distributions

10. PoD distribution BMD distribution PoD distr. iBMD = * * * AF1 AF2 ….. AFi Critical effect size (CES) X% decrease in BW BMD distribution

11. Assessment factors PoD distr iBMD = * * * AF1 AF2 ….. AFi Interspecies Subchronic-to-chronic Subacute-to-chronic based on historical data (BMD ratios)* Sensitivity in whole population: Variability Intraspecies 1 Uncertainty about the variability *see e.g. Bokkers and Slob tox sci 85 & crit rev toxicol 37 Kramer et al. regul toxicol pharm 23 See van der Voet et al. food chem tox 47

12. Integrated probabilistic hazard characterization PoD distr. iBMD = * * * AF1 AF2 ….. AFi = * * * …… Variability and uncertainty in these distributions are analyzed separately

13. Integrated probabilistic risk assessment iBMD <1 An individual is at risk when his/her iEXP > iBMD or when iEXP iBMD distr. = 1 iEXP distr. • * Fraction of the population affected * Uncertainty can be quantified

14. Example of integrated prob. RA output Lower Percentile Upper percentile Prob (% affected & CI) Det 0.0001 no risk (0-0.005) 0.0001 risk (0-0.8) not excl 0.1 risk (0-20) 8 risk (5-20) 10 100 1000 iBMD =1 iEXP

15. Contribution to uncertainty % contribution to uncertainty Guidance to reduce uncertainty in the RA

16. Applied in • European projects • Peer reviewed journals • RA advise to Dutch government Limitations • Not implemented yet: approach for carcinogens • More time-consuming (vs lower tier deterministic RA) • Limited no. of uncertainties incorporated Future challenges • Extend approach for carcinogens • Increase acceptance How…..?

17. All ingredients are available • Dose-response modeling / BMD techniques are available • Empirical AF distributions are available (excl. intraspecies AF) • Probabilistic exposure assessment techniques are available • Integration techniques are available Limited tox or exposure data? Larger uncertainty Incorporated in probabilistic RA And……..

18. Benefits of (integrated) probabilistic RA • Quantification of • Fraction of the population affected • Uncertainty • Risks can be compared • between effects • between substances • Probabilistic approach provides more insight in risk Targeted risk management actions or further research

19. Thank you for your attention

20. Further reading • Bokkers, B. et al (2009). The practicability of the integrated probabilistic risk assessment (IPRA) approach for substances in food. RIVM report 320121001/2009, Bilthoven, the Netherlands. http://www.rivm.nl/bibliotheek/rapporten/320121001.pdf • Bosgra, S. et al (2009). An integrated probabilistic framework for cumulative risk assessment of common mechanism chemicals in food: an example with organophosphorus pesticides. Regul Toxicol Pharmacol 54, 124-33. • Müller, A.K. et al (2009). Probabilistic cumulative risk assessment of anti-androgenic pesticides in food. Food Chem Toxicol 47, 2951-62. • van der Voet, H. and Slob, W. (2007). Integration of probabilistic exposure assessment and probabilistic hazard characterization. Risk Anal 27, 351-71. • Benchmark dose software: www.proast.nl • EFSA (2009) Guidance of the Scientific Committee: use of the benchmark dose approach in risk assessment. The EFSA Journal 1150, 1-72 http://www.efsa.europa.eu/en/scdocs/scdoc/1150.htm bas.bokkers@rivm.nl