Challenges and Opportunities for Integrating In Silico Models and Adverse Outcomes Pathways to Set and Relate New Biomarkers

Carlos E Matos Dos Santos,Raul Ghiraldelli Miranda,Danielle Palma De Oliveira,Daniel Junqueira Dorta

doi:10.3390/w12123549

Abstract

The Adverse Outcome Pathway (AOP) framework has been considered the most innovative tool to collect, organize, and evaluate relevant information on the toxicological effects of chemicals, facilitating the establishment of links between molecular events and adverse outcomes at the critical level of biological organization. Considering the combination of the high volume of toxicological and ecotoxicological data produced and the application of artificial intelligence algorithms from the last few years, not only can higher mechanistic interpretability be reached with new in silico models, but also a potential increase in predictivity in hazard assessments and the identification of new potential biomarkers can be achieved. The current paper aims to discuss some potential challenges and ways of integrating in silico models and AOPs to predict toxicological effects and to set and relate new biomarkers for defined purposes. With the use of the AOP framework to organize the ecotoxicological, toxicological, and structural data generated from in chemico, in vitro, ex vivo, in vivo, and population studies, it is expected that the generated biological and chemical construct will improve its application, establishing a knowledge platform to set and relate new biomarkers by key event relationships (KERs).

Highlights

In Silico Toxicology (IST) integrates different mathematical models to predict the toxicity of chemicals based on patterns of structural and physicochemical properties related to the toxicological activity.With increasing relevance in different applications and as a cost-effective tool with a potentially higher mechanistic interpretability in evidence-driven assessments, in silico models are considered useful for predicting the toxicity of chemicals with unknown biological activity and are recommended by regulators and/or proposed by investigators for use in various contexts
Regarding the combination of the volume toxicological and ecotoxicological dataintelligence produced produced in the last few years and thehigh advances inof new powerful techniques and artificial inalgorithms, the last fewnot years and the advances in new powerful techniques and artificial intelligence algorithms, only can mechanistic interpretability be reached with in silico models, can mechanistic interpretability be reached in silico models, and the but so can a potential increase in predictivity in hazard assessments and the identification of new identification of new potential biomarkers
Using the Adverse Outcome Pathway (AOP) framework, Khan et al (2020) showed that early events on molecular levels can be connected to important effects on bivalves organisms, which can be used as relevant biomarkers for the assessment of environmental risk related to the presence of contaminants of emerging concern [16]

Summary

Introduction

In Silico Toxicology (IST) integrates different mathematical models to predict the toxicity of chemicals based on patterns of structural and physicochemical properties related to the toxicological activity. Using the AOP framework, Khan et al (2020) showed that early events on molecular levels can be connected to important effects on bivalves organisms, which can be used as relevant biomarkers for the assessment of environmental risk related to the presence of contaminants of emerging concern [16]. In this way, the AOP approach can be used as biomarkers of effects for different levels of organization, both for human health and environmental protection

In Silico Models

Conclusions