CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on Modeling of Molecular Interactions

Abstract

Using computer models to accurately predict toxicity outcomes is considered to be a major challenge. However, state-of-the-art computational chemistry techniques can now be incorporated in predictive models, supported by advances in mechanistic toxicology and the exponential growth of computing resources witnessed over the past decade. The CADRE (Computer-Aided Discovery and REdesign) platform relies on quantum-mechanical modeling of molecular interactions that represent key biochemical triggers in toxicity pathways. Here, we present an external validation exercise for CADRE-SS, a variant developed to predict the skin sensitization potential of commercial chemicals. CADRE-SS is a hybrid model that evaluates skin permeability using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines reactivity with skin proteins via quantum-mechanical modeling. The results were promising with an overall very good concordance of 93% between experimental and predicted values. Comparison to performance metrics yielded by other tools available for this endpoint suggests that CADRE-SS offers distinct advantages for first-round screenings of chemicals and could be used as an in silico alternative to animal tests where permissible by legislative programs.

Computational approaches for skin sensitization prediction

“…Another approach combining a linear QSAR method with an expert-curated set of rules has been implemented in the CADRE-SS model for predicting the skin sensitization potency of compounds (Kostal and Voutchkova-Kostal 2016;ToxFix). The three-class categorical hybrid model consists of three modules describing different steps in the sensitization process.…”

QSAR models of human data can enrich or replace LLNA testing for human skin sensitization

“…Another study proposed a hierarchical model where skin permeability is evaluated using Monte Carlo simulations, chemical reactive centers are determined with expert rules, and protein reactivity is predicted by the means of quantum-mechanical modeling. 61 The authors reported conspicuously impressive results for predicting skin sensitization of an external set mostly composed by LLNA, Buehler’s test, and GMPT data: sensitivity as high as 87%, specificity as high as 100%, and balanced accuracy of 93%, which exceeded the reported 67 concordance of 89% between LLNA and GMPT.…”

In silico prediction of toxicity and its applications for chemicals at work

“…Using computer models to accurately predict toxicological outcomes is an important task, but current in silico toxicology is now based on advances in mechanotoxicity and predictive models that have grown remarkably in computing resources over the last decade. The integrated computer-aided discovery and redesign (CADRE) platform is used as a model for key molecular interactions in the toxic pathway [62]. CADRE offers distinct advantages over the primary screening of chemicals and suggests that it could be performed as an alternative in silico tool that is allowed in legislative programs [62].…”