Statistical methods for in silico tools used for risk assessment and toxicology

Abstract

Abstract In silico toxicology is one type of toxicity assessment that uses computational methods to visualize, analyze, simulate, and predict the toxicity of chemicals. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. Animal studies for the type of toxicological information needed are both expensive and time-consuming, and to that, ethical consideration is added. Many different types of in silico methods have been developed to characterize the toxicity of chemical materials and predict their catastrophic consequences to humans and the environment. In light of European legislation such as Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) and the Cosmetics Regulation, in silico methods for predicting chemical toxicity have become increasingly important and used extensively worldwide e.g., in the USA, Canada, Japan, and Australia. A popular problem, concerning these methods, is the deficiency of the necessary data for assessing the hazards. REACH has called for increased use of in silico tools for non-testing data as structure-activity relationships, quantitative structure-activity relationships, and read-across. The main objective of the review is to refine the use of in silico tools in a risk assessment context of industrial chemicals.