Modeling Approaches

René Crevel

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

Food allergen risk management:the challenge

Protecting allergic consumers while

Minimising the effects on their quality of

life

Maintaining economic operation of food

manufacturing

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)

How to determine harmless level(minimum eliciting dose)

Approach Usefulness in allergen risk assessment

Comment

Case reports Limited Establish hazard, usually no description of population

Controlled challenge studies

Good Population can be described better

Dose distribution modelling

Good Based on challenge study results; uses all data

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-reactors 29 46

95% non-reactors 59 93

99% non-reactors 299 473

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]

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?

Clinical data and mathematical models

Proportion of reactions (in clinical study)

Dose(mg protein)

100%

50%

10%

Experimental rangeExtrapolation

ED50ED10

What is the impact of the choiceof 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

Model: Actual Log-Linear LogLogistic

LogNormal Weibull

Cu

mu

lati

ve

pe

rce

nta

ge

of

res

po

ns

es

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Dose of Protein (mg)

0.01 0.1 1 10 100 1000 10000

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

0.0001

0.001

0.01

0.1

1

10

100

1000

Bock May Wensing Bock May Wensing

mg

pro

tein

LOAEL LogNormal Weibull LogLogistic Linear

ED10 ED1

Differences in ED10s and ED1s between studies and models

Impact of model choice- summary

For the ED10 values (in experimental zone), the differences between studies is greater than between models 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)

to use the approach, we need to know which model fits closest with reality (validation)

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

Risk assessment

Hazardcharacterisation

Dose-responsemodelling

Reactionseverity Clinical data

Sensitivityof study

population

Epidemiology

No. of allergicconsumers

No. ofconsumers

Proportion ofallergic consumers

Clinicaldiagnosis

Selfdiagnosis

No. of reactions

Data captureissues

Food allergy registries

Tendencyto report

Data required for validation and application of modeling approach

Residual allergen levels

Productionsequencing

Distributionof allergen

Servingsize

Cleaningregimes

Variation inresidual allergen

over batches

Consumerexposure

Bioavailability

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

Thank you for your attention