4.7 Article

Towards Decoding Hepatotoxicity of Approved Drugs through Navigation of Multiverse and Consensus Chemical Spaces

Journal

BIOMOLECULES
Volume 13, Issue 1, Pages -

Publisher

MDPI
DOI: 10.3390/biom13010176

Keywords

clustering; chemoinformatics; consensus chemical space; data fusion; drug design; drug-induced liver injury; multi-objective optimization; unsupervised learning

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Drug-induced liver injury (DILI) is a major obstacle in drug development and approval, with animal models, liver function tests, and chemical properties being crucial for understanding and preventing DILI events. This study implements data fusion to analyze DILI events by considering different criteria simultaneously, highlighting the importance of in vitro and chemical data analysis for improving our understanding of DILI.
Drug-induced liver injury (DILI) is the principal reason for failure in developing drug candidates. It is the most common reason to withdraw from the market after a drug has been approved for clinical use. In this context, data from animal models, liver function tests, and chemical properties could complement each other to understand DILI events better and prevent them. Since the chemical space concept improves decision-making drug design related to the prediction of structure-property relationships, side effects, and polypharmacology drug activity (uniquely mentioning the most recent advances), it is an attractive approach to combining different phenomena influencing DILI events (e.g., individual chemical spaces) and exploring all events simultaneously in an integrated analysis of the DILI-relevant chemical space. However, currently, no systematic methods allow the fusion of a collection of different chemical spaces to collect different types of data on a unique chemical space representation, namely consensus chemical space. This study is the first report that implements data fusion to consider different criteria simultaneously to facilitate the analysis of DILI-related events. In particular, the study highlights the importance of analyzing together in vitro and chemical data (e.g., topology, bond order, atom types, presence of rings, ring sizes, and aromaticity of compounds encoded on RDKit fingerprints). These properties could be aimed at improving the understanding of DILI events.

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