Digital alchemy: Exploring the pharmacokinetic and toxicity profiles of selected coumarin-heterocycle hybrids

Abstract

To take accelerated steps and achieve tangible success in the field of drug design and development, several machine learning-based models were employed to predict pharmaceutical traits. In this study, three online tools—SwissADME, PreADMET, and Pro-tox-3.0—were used to anticipate the pharmacokinetics and toxicity profiles of certain coumarin-heterocycle hybrids. These hybrids were distinguished from a large dataset based on their promising in vitro anticancer and radical scavenging activities. Based on the hybrids’ two-dimensional structure, the predictive tools provide comprehensive information about their physicochemical properties, water solubility, lipophilicity, and drug likeness attributes. These web platforms also provide predictions for the possibility of organ toxicity, molecular toxicology, and toxic targets. The authors presented several outcomes derived from the retrieved findings. All hybrids, except for the hybrid with diphenyl ester, were deemed appropriate for oral administration. Additionally, hybrids 6, 23, 30, and 31 exhibit a high likelihood of possessing lead-like properties, making them viable candidates for advancement in the therapeutic development process. Although the toxicity profiles of the analyzed hybrids exhibit a wide range of diversity and intricacy, hybrids 6, 18, 23, 66, 162, 171, and 216 were projected to possess a satisfactory toxicity profile. It could be concluded that, even though many hybrids have demonstrated outstanding IC50 values, it is evident that their pharmacokinetics and/or toxicity concerns hinder their progression in drug development. Modifying molecular structures at positions unrelated to their bioactivities could improve these compounds’ pharmacokinetics and toxicity characteristics.