Development and application of consensus in silico models for advancing high-throughput toxicological predictions - Collins, Sean P.; Mailloux, Brandon; Kulkarni, Sunil; Gagné, Matthew; Long, Alexandra S.; Barton-Maclaren, Tara S. (Frontiers in Pharmacology, 2024)
Computational toxicology models have been successfully implemented to prioritize and screen chemicals. There are numerous in silico (Q)SAR models for prediction of a range of human-relevant toxicological endpoints, but for a given endpoint and chemical, not all predictions are identical due to differences in their training sets, algorithms, and methodology. This poses an issue for highthroughput screening of a large chemical inventory, as it necessitates several models to cover diverse chemistries, but will then generate data conflicts. To address this challenge, we have developed a consensus modeling strategy to combine predictions obtained from different existing in silico (Q)SAR models into a single predictive value, while also expanding chemical space coverage. This study developed consensus models for nine toxicological endpoints relating to estrogen receptor and androgen receptor interactions (i.e., binding, agonism, antagonism) and genotoxicity (i.e., bacterial mutation, in vitro chromosomal aberration, in vivo micronucleus). Consensus models were created by combining different (Q)SAR models using various weighting schemes. As a multi-objective optimization problem, there is no single best consensus model, and therefore Pareto fronts were determined for each endpoint to identify the consensus models that optimize the multiple-criteria decisions simultaneously. Accordingly, this work presents sets of solutions for each endpoint that contain the optimal combination regardless of the tradeoff, with the results demonstrating that the consensus models improved both predictive power and chemical space coverage. These solutions were further analyzed to find trends between the best consensus models and their components. Here, we demonstrate the development of a flexible and adaptable approach for in silico consensus modelling, and it's application across nine toxicological endpoints related to ER-receptor activity, AR-receptor activity, and genotoxicity. These consensus models are developed to be integrated into a larger multi-tier NAM-based framework to prioritize chemicals for further investigation and supports the transition to a non-animal approach to risk assessment in Canada.