Chapter 15 - Absorption, distribution, metabolism, excretion, and toxicity assessment of drugs using computational tools

Abstract

Predictive tools for accurately accessing pharmacokinetic and toxicological properties as well as pharmacodynamic properties in early development stages are highly beneficial for increased productivity in drug discovery processes. From a commercial perspective, it is desirable that poorly behaved compounds are removed early in the discovery phase rather than during the more costly drug development phases. To accomplish the same, from a decade, in vitro experimental tools were used to characterize the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of compounds have been in existence and public repositories of measured ADMET properties have been growing rapidly in recent years, which navigates the drug discovery and development process more impactfully and accelerates the design of new ADMET prediction, models. However, data growth and availability alone do not guarantee improved modeling outcomes. There is also a need to ensure rigorous data processing and modeling tools ADMET profiling tools and techniques are very important as it allows prioritization of leads or drug candidates by their biopharmaceutical properties and it is a highly anticipated technique to expedite the overall safety and efficacy parameters of the drug. In recent days, the applicability and development of machine learning tools were much proven in their potential at diversifying the drug and protein structures with their complex mechanism and one that must be opted for further discovery to predict ADMET and regulatory properties. In succinct, this chapter is articulated to elicit various in silico tools for absorption, distribution, metabolism, and excretion/elimination (ADME) predictions and showcases various insights regarding the perspectives of the next generation of computational ADMET predictors and the pitfalls associated with discovering machine learning models for the estimation of ADME.