Ecotoxicity databases for QSAR modeling

Abstract

Industrial chemicals, pharmaceuticals along with illicit drugs (IDs), as well as day-to-day personal care products (PCPs) are documented as contaminants of emerging concerns (CECs). They are environmental pollutants due to their substantial detrimental impacts on the environment through their frequent presence, persistence, and peril to the species living in aquatic, terrestrial, and soil compartments as well as to humans. Although the toxic effects and occurrence concentration of pharmaceuticals and chemicals have been studied and reported for the last three decades, PCPs and IDs are quite neglected substances along with the mixtures and transformation products (TPs) or metabolites of all CECs, in the context of their ecotoxicological risk evaluation. Among various compartments, the effects of CECs are largely documented for aquatic species where often very little information is available with regard to terrestrial and soil toxicity. This deficiency of knowledge has led to greater effort to create new methods and approaches which would measure their occurrence, metabolism, bioaccumulation, and biodegradability followed by the mechanism of action (MOA) behind toxicity to individual living species and ecosystem. This information is very important for risk assessment and risk management along with regulatory decision-making followed by in silico or computational modeling for future ecotoxicity. Thus, the obtained information needs to be documented under a system called “database” based on different categories including chemical class, toxicity testing, test species, environmental compartment-specific, toxicity MOA specific as well as country. Ecotoxicity has a number of open-access and commercial databases which are implemented for risk profiling, regulatory decision-making followed by ecotoxicity prediction of new and untested substances over the years. The present chapter deals with the most commonly used ecotoxicity databases followed by their detailed information so that one can use these databases efficiently in experimental as well as computational research.