Proving the Potential: External Validation of STopTox as in Silico Alternative to Animal Toxicity Testing

Over the past several decades, reducing, refining, and replacing animal testing (three R’s) has been a prominent goal in chemical toxicology.1 The STopTox (Systemic and Topical chemical Toxicity) platform was developed for this objective as an innovative in-silico alternative to conventional animal testing for acute systemic and topical toxicity testing.2 STopTox utilizes quantitative structure-activity relationship (QSAR) models to predict the toxicity of chemicals, providing a comprehensive, accessible, and user-friendly tool for hazard identification.2 STopTox models were rigorously validated during its initial development using extensive publicly available data sets, ensuring compliance with the Organisation for Economic Co-operation and Development (OECD) principles. These models boasted high internal accuracy and substantial external predictive power.2,3 Despite these promising results, continued validation with novel compounds is integral in establishing the robustness and reliability needed for STopTox to be used as a substitute for in vivo animal testing. In this research letter, we aim to evaluate the predictive performance of STopTox using independent data sets across the six major endpoints of acute toxicity: acute oral, dermal, and inhalation systemic toxicity, as well as skin sensitization, skin irritation/corrosion, and eye irritation/corrosion through external validation. The outcomes of this validation underscore the potential of STopTox to reliably predict toxicity, thereby supporting STopTox as a reliable regulatory decision-making tool that contributes to reducing animal testing in toxicological assessments.