Chemical Risk Assessment in Absence of Adequate Toxicological Data

Transcription

1 Chemical Risk Assessment in Absence of Adequate Toxicological Data Mark Cronin School of Pharmacy and Chemistry Liverpool John Moores University England

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3 The Problem Risk Analytical chemistry can identify compounds at increasingly low quantities Over 5,000 food packaging ink compounds in use with no data To determine the safety of chemicals Risk = Exposure x Hazard

4 The Problem No Data Hazard identification usually requires the use of animal testing Adequate toxicity data are available for comparatively few chemicals Public or business concern may require very rapid risk assessment for chemicals for which no data exist or are available

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6 Making Safety Decisions Without Adequate Data Incident crisis management Product development Screening new lead compounds Designing out toxicity Classification and Labelling Prioritisation for testing Dealing with legislation EU: REACH, Cosmetic Regulation, 3Rs US: EPA PMN, KidSafe Canada: DSL Japan: CSCL

7 The Question Is exposure low enough to be of no concern? If so, no further action If not, further risk assessment may be required...

8 The Question Is exposure low enough to be of no concern? If so, no further action If not, further risk assessment may be required... Current Solutions for Risk Assessment Computational methods What do we know about the chemical? What can we predict from the structure? What do we know about similar chemicals?

9 In Silico / Computational Toxicology Includes Databases of existing information Category formation (grouping) read-across Structure-Activity Relationships (SAR) Quantitative Structure-Activity Relationships (QSAR) Expert Systems Threshold of Toxicological Concern (TTC) Bioinformatics Chemoinformatics Biokinetics (PBPK)

10 Models to Predict Toxicity

11 Software to Predict Toxicity

12 ILSI-EU Expert Group on Risk Assessment in the Absence of Adequate Toxicological Information With a remit... To determine the scope of the problem and identify solutions To draw on expertise from toxicologists and in silico modellers To develop a decision tree to allow for risk assessment to be made without adequate toxicological information

13 Problem Formulation Risk assessment required due to incidental contamination of food products or acquisition of new knowledge The problem formulation should ensure that all relevant concerns have been addressed the assessment will yield a scientifically sound and credible risk characterisation Methods should be defined and guidance provided

14 The Proposed Solution: A Decision Tree Exposure Assessment Hazard Identification Hazard Characterisation Risk Characterisation

15 The Proposed Solution: A Decision Tree Exposure Assessment Is there evidence for exposure to the chemical?

16 The Proposed Solution: A Decision Tree Exposure Assessment YES Is there evidence for exposure to the chemical? NO Collect and screen relevant information No safety concern Hazard Identification YES Genotoxic through DNA reactivity? NO Carcinogenic potency prediction: TD 50 Chronic toxicity prediction: (N)LO(A)EL

17 The Proposed Solution: A Decision Tree Exposure Assessment Is there evidence for exposure to the chemical? YES NO Collect and screen relevant information No safety concern Hazard Identification YES Genotoxic through DNA reactivity? NO Carcinogenic potency prediction: TD 50 Chronic toxicity prediction: (N)LO(A)EL Hazard Characterisation Identification of the most relevant data

18 The Proposed Solution: A Decision Tree Exposure Assessment Is there evidence for exposure to the chemical? YES NO Collect and screen relevant information No safety concern Hazard Identification YES Genotoxic through DNA reactivity? NO Carcinogenic potency prediction: TD 50 Chronic toxicity prediction: (N)LO(A)EL Hazard Characterisation Identification of the most relevant data Risk Characterisation Margin of Exposure Results and Conclusion

19 Is There Evidence for Exposure to the Substance? Identity of the substance Intake(s) of potentially contaminated foods Actual or anticipated occurrence of the substance in relevant foods Populations potentially affected Duration of exposure Other potential sources of exposure

20 Is There Evidence for Exposure to the Substance? Identity of the substance Intake(s) of potentially contaminated foods Actual or anticipated occurrence of the substance in relevant foods Populations potentially affected Duration of exposure Other potential sources of exposure Evidence for exposure requires continuing on the Decision Tree

21 Hazard Identification: Structure Characterisation and Identification of Similar Compounds A single defined chemical structure is required Mixtures can be assessed but components must be identified Similar compounds should be identified Freely available tools: e.g. OECD QSAR Toolbox Grouping and category formation allow for readacross

22 Predicting Properties Through Analogy to Similar Chemicals NH 2 Target Chemical NH 2 NH 2 Mutagenic O CH3 O CH 3 Unknown Activity H 3 C O NH 2 Mutagenic H 3 C O Mutagenic

23 NH 2 Target Chemical NH 2 NH 2 Mutagenic O CH3 NH 2 O CH 3 Predicted Activity: Mutagenic H 3 C O H 3 C O Mutagenic Mutagenic Rationale

24 Assessing Evidence of Genotoxic Potential Through DNA Reactivity Identification of genotoxic fragments in molecules Software exists Freely available: ToxTree, OECD QSAR Toolbox, OncoLogic, AMBIT Commercial: Derek Nexus, LeadScope Identify genotoxic fragments in both the substance of interest and the analogues Including metabolites and degradates, if available Compound may / may not have an alert

25 Fragments Associated with Binding to DNA

26 Identification of Genotoxic Fragments is an Important Decision Point

27 If a Genotoxic Fragment is Found in the Molecule A non-threshold approach may have to be applied This requires the quantitative prediction of carcinogenic potency e.g. TD 50 QSAR TD 50 model e.g. OncoLogic, Simulation Plus, TOPKAT etc Grouping and read-across OECD QSAR Toolbox, Toxmatch, AMBIT

28 If a Genotoxic Fragment is NOT Found in the Molecule Prediction of chronic toxicity lowest observed (adverse) effect level (LO(A)EL) QSAR TOPKAT, Lazar, Molcode Grouping OECD QSAR Toolbox

29 Hazard Characterisation Qualitative information (e.g. mechanistic, target organ) and quantitative values (e.g. LOAELs, TD 50 ) are listed and described in detail The main information to be collected: Types of predictions (QSAR, read-across) and models used Information on model validity and domain of applicability Endpoints (organ-specific, mechanistic) Reference points (e.g. LOAEL, NOAEL, TD 50 ) Species (animal species, human) Time/duration consideration (chronic, short-term) Quality of toxicological data found on analogues

30 Risk Characterisation: Margin of Exposure (MoE) MoE = Toxicological Values Exposure Estimate Usually, the greater the MoE, the lower the concern MoE is to be interpreted on a case-by-case basis Guidance is required

31 Guidance on How to Analyse Some Basic Elements to be Covered by a MoE Quality of data Dealing with inter and intra -species differences Dose response Exposure duration Route-to-route extrapolation Toxicokinetic information

32 Identifying Uncertainty Characterisation of the uncertainty involved in a specific risk assessment is an important element to be communicated to decision-makers/risk managers Uncertainty may arise from in silico methods Many uncertainties can be quantified A qualitative evaluation of all uncertainties will be undertaken to evaluate: their potential impact on the overall uncertainty of the assessment confidence associated with the assessment

33 Conclusions A framework for providing information for risk assessment when no toxicological information is available For emergency uses and priority setting Much use is made of in silico prediction and estimated exposure to understand a Margin of Exposure Many uncertainties involved in this process are similar to classic risk assessment Level of protection and conservatism A report and publication is planned for late 2012

34 Acknowledgements ILSI-EU Expert Group on risk assessment in the absence of adequate toxicological information: Benoît SCHILTER (Chair) Nestlé CH Romualdo BENIGNI Italian National Institute for Health IT Alan BOOBIS Imperial College London UK Andrew COCKBURN University of Newcastle UK Mark CRONIN Liverpool John Moores University UK Elena LO PIPARO Nestlé CH Sandeep MODI Unilever UK Hervé NORDMANN Ajinomoto Europe CH Anette THIEL DSM CH Andrew WORTH European Commission - Joint Research Centre IT Alessandro CHIODINI ILSI Europe BE

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36 Mixtures Evidence suggesting that compounds of the mixture could act similarly or interact would require assessing them as a group. Methods are available for conducting assessments of combined exposures to multiple chemicals. Dose-additivity is the default assumption Doses of the relevant components of the mixture are added after being multiplied by a scaling factor that accounts for differences in their toxicological potencies. For individual chemicals of a mixture (Q)SAR and read across tools may provide valuable information The Point of Departure Index (PODI) method can be applied using predicted toxicological values such as LOAELs to account for the differences in toxic potencies of the individual chemicals. A MoE approach can be used for the mixture of interest.