1. Modeling Approaches René Crevel

2.  Modeling approaches, including the hypoallergenicity model and the Bindslev- Jensen et al allergen model.  Data requirements and underlying assumptions  Interpreting the results of applying models

3. Food allergen risk management: the challenge  Protecting allergic consumers while Minimising the effects on their quality of life Maintaining economic operation of food manufacturing

4. Food allergen risk management: how to meet the challenge  Label where the allergen is present (usually where it is an ingredient) OR  Ensure residual allergen content of product is low enough to be harmless (to vast majority of allergic consumers)

5. How to determine harmless level (minimum eliciting dose) ApproachUsefulness in allergen risk assessment Comment Case reportsLimitedEstablish hazard, usually no description of population Controlled challenge studies GoodPopulation can be described better Dose distribution modelling GoodBased on challenge study results; uses all data

6. Hypoallergenicity approach  Unofficial standard for designating infant formulae based on cows’ milk as hypoallergenic [Kleinman RE, Bahna S, Powell GF, Sampson HA (1991) Use of infant formulas in infants with cow milk allergy - A review and recommendations. Pediatr.Allergy Immunol. 4: 146-155].  Statistics based on binomial theorem: Upper confidence limit (95% significance, 1-sided) Number of participants required...with no reactions...with 1 reaction 90% non-reactors2946 95% non-reactors5993 99% non-reactors299473

7. Using data generated by hypoallergenicity approach  Protecting 90, 95 or 99% of the allergic population is not sufficient for the food industry  How can we improve this level of protection? Apply safety factors to LOAEL or NOAEL?  Arbitrary  Level of protection not defined Model dose distribution of minimum eliciting doses?  Can define level of protection for any residual allergen level  Can apply safety factor to calculated MEDs [lower 95% confidence interval]

8. Does modeling work?  We asked: Could we fit a curve to the distribution of mimimum eliciting doses from challenge studies? Could that curve be used to predict the number of reactions likely to occur as a result of exposure to a specified amount of inadvertent allergen?

9. Clinical data and mathematical models Proportion of reactions (in clinical study) Dose (mg protein) 100% 50% 10% Experimental range Extrapolation ED 50 ED 10

10. What is the impact of the choice of model on the predicted MEDs? Good clinical data were available for egg, milk and peanut. We fitted the data using the following statistical distributions and calculated ED10s and ED1s for each: Linear extrapolation from LOAEL to zero dose LogNormal model Weibull model LogLogistic model

11. Illustration of curve fits obtained [using data from Wensing et al (2002) on roasted peanuts]

12. Differences in ED10s and ED1s between studies and models

13. Impact of model choice - summary  For the ED10 values (in experimental zone), the differences between studies is greater than between models ê focus on standardising protocols and consistent patient selection criteria  For the ED1 values, differences between models are larger (and increase as we move further from the experimental zone ) ê to use the approach, we need to know which model fits closest with reality (validation)

14. Key assumptions underlying the values generated by the model  The participants in a controlled challenge study are a representative sample of the whole allergic population  Allergic people eat the same foods as non- allergics (except for the allergenic food)  The distribution of allergic reactivity is steady at the population level  Responses to a given dose of allergen are similar in the clinic to those experienced outside

15. Risk assessment Hazard characterisation Dose-response modelling Reaction severity Clinical data Sensitivity of study population Epidemiology No. of allergic consumers No. of consumers Proportion of allergic consumers Clinical diagnosis Self diagnosis No. of reactions Data capture issues Food allergy registries Tendency to report Data required for validation and application of modeling approach Residual allergen levels Production sequencing Distribution of allergen Serving size Cleaning regimes Variation in residual allergen over batches Consumer exposure Bioavailability

16. Summary and conclusions  The modeling approach complements clinical studies to establish minimum eliciting doses and relies on the data generated  It permits a more complete use of those data  It is also more transparent, allowing a more informed discussion of risk management objectives by all stakeholders HOWEVER  It requires validation before it can be fully operational

17. Thank you for your attention