Best practices to develop artificial intelligence models for predicting multilevel effects in Adverse Outcome Pathways (AOP)
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1 Best practices to develop artificial intelligence models for predicting multilevel effects in Adverse Outcome Pathways (AOP) Altox Ltda Senior Data Scientist, Toxicology and Drug Discovery Specialist
2 2 Outline The AOP-based in silico model overview Transparency vs mechanistic interpretability vs predictivity AOP-based in silico model Framework How to ensure accuracy and mechanistic interpretability? Chemical Representation / Description STR continuous x categorical Prediction of MIE and KEs Model validation and development Chemical Space and Coverage Final remarks
3 3 Adverse outcome pathways (AOP) framework An AOP is an analytical construct that describes a sequential chain of causally linked events at different levels of biological organization that lead to an adverse health or ecotoxicological effect (OECD, 2012). A proposed AOP-based in silico model Concept (Altox) An in silico framework used to identify chemicals that can activate the associated modular AOP components (MIE/KE) and based in these individual multilevel predictions, balanced by adjustments, relationships and weights, to predict an adverse outcome.
4 4 Transparency vs mechanistic interpretability vs predictivity Frequently discussed assumptions: Global statistical models Accuracy Transparency Mechanistic interpretability Alert-based models Accuracy Transparency Mechanistic interpretability OECD - Organisation for Economic Co-operation and Development. Report of the expert consultation on scientific and regulatory evaluation of organic chemistry mechanism-based structural alerts. Series on Testing and Assessment, No. 120 PART 1, Available in:
5 5 How to ensure accuracy and mechanistic interpretability? chemical representation 1 Transparency vs mechanistic interpretability vs predictivity Artificial intelligence model 2 3 STR Probability Mapping
6 Structure-toxicity relationship (STR) the fragments more related to the absence/decrease of toxicity (green) or presence/increase (red) 6 1 Categorical Predict ed Out com e/assay Predict ed class (Confidence) STR Cont ribut ion M apping Local Lym ph Node Assay (LLNA, OECD ) Deep Learning cat egorical m odel im plem ented with hybrid descriptors (ECFP6 fingerprint and physicochem ical propert ies: MW, TPSA, logk ow, logd) Sensit izer (+ ) (78.4%) 2 Continuous parathion
7 7 Structure-toxicity relationship (STR) parathion 1 Reduce the Toxicity parathion methyl 2
8 Statistical models with probability map Alert model How to ensure accuracy and mechanistic interpretability? Detecting mitigating factors (steric, electronic, and detoxifying) by statistical models with a visual probability mapping Theory 1 Examples: 2 OECD - Organisation for Economic Co-operation and Development. Report of the expert consultation on scientific and regulatory evaluation of organic chemistry mechanism-based structural alerts for the Identification of DNA binding chemicals. Series on Testing and Assessment, No. 120 PART 1, Available in: 8
9 9 AOP-based in silico model Develop and Validate individual models 1 OECD Principles Structural Alert DPRA KeratinoSens H-CLAT LLNA Human and Combined dataset 2 key event relationships (KERs) integrating in chemico, in vitro, ex vivo and in vivo data in the in silico models integrating all multilevel predictions balancing predictivity, key events relationships and WoE adjustments, and to predict an adverse outcome Benchmark Key event Models X AOP X Combined dataset (GMTP, LLNA and human data) 6971 Chemicals AOP-based algorithm =
10 Benchmark - Combined dataset (GMTP, LLNA and human data) Model Dataset Specificity Sensitivity Accuracy Coverage Alert analysis : 1A 69: 1B % DPRA % DPRA AD % KeratinoSens % KeratinoSens AD % h-clat % h-clat AD % LLNA % LLNA AD % Human Skin % Human Skin AD % Combined dataset (GMTP, LLNA and human data) * % Combined dataset (GMTP, LLNA and human data) AD * % AOP-based prediction % 6971 Chemicals Measures of goodness-offit, robustness and predictivity (External Validation)
11 Benchmark - Combined dataset (GMTP, LLNA and human data) Chemicals 0,9 0,8 0,7 0,6 Measures of goodness-offit, robustness and predictivity (External Validation) 0,5 0,4 0,3 0,2 0,1 0 Balanced Accuracy Specificity Sensitivity Coverage Alert analysis DPRA KeratinoSens h-clat LLNA Human Skin AOP-based prediction
12 OECD Principles of (Q)SAR Validation for regulatory purposes 1. A defined endpoint; 2. An unambiguous algorithm; 4. Appropriate measures of goodness-of-fit, robustness and predictivity; 3. A defined domain of applicability; 5. A mechanistic interpretation, if possible. 12
13 1 Prehaptens and prohaptens - Activation of weak or non-sensitizing substances into sensitizers
14 Chemical and Toxicological Space for Skin Sensitization Grouped by Similarity neighbors and Endpoint 14 Diversity? Data DPRA 195 KeratinoSens 190 h-clat 161 LLNA 997 Human Skin 389 Combined dataset (GMTP, LLNA and human data) * 6971
15 Chemical and Toxicological Space for Skin Sensitization Grouped by Similar neighbors and Endpoint * * 15
16 Chemical and Toxicological Space for Skin Sensitization Grouped by Similar neighbors and Endpoint 16
17 Chemical and Toxicological Space for Skin Sensitization Grouped by Similar neighbors and Endpoint 17
18 Final Remarks A logical framework balancing transparency, mechanistic interpretability and predictivity in a sequential chain of causally linked events at different levels; To assess key event relationships (KERs) integrating in chemico, in vitro, ex vivo and in vivo data in the in silico models; To design new regulatory decision trees based in predictive Integrated Approaches to Testing and Assessment (IATA) containing in silico models ;
19 Thank you for your attention! 19
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