Bioactivity Descriptors for in Vivo Toxicity Prediction: Now and the Future

Expert Opinion on Drug Metabolism & Toxicology
Helmke Palle
Palle Helmke, Barbara Füzi, Gerhard F Ecker
DOI: 10.1080/17425255.2024.2334308
PMID: 38530269
Keyword: Toxicity prediction; adverse events; biological fingerprints; chemical fingerprints; machine learning.


Development of a new drug currently takes 10–12 years with costs of around 2 billion EUR. The two main reasons for failures comprise lack of efficacy and unforeseen toxicity. For the latter, a standard process pursued to minimize the risk is the so-called toxicological read across. Briefly, toxicologists query the available literature and databases for compounds, which are structurally similar to their development candidate in order to retrieve information on potential hazards. In addition, computational models might be applied which are either trained for a single protein, such as hERG or P-glycoprotein, or for a respective in vivo endpoint (cholestasis, steatosis, drug-induced liver injury (DILI), ….). In both cases, a proper ‘description’ of the compound of interest is key for the predictive ability of the models. In the following editorial, we will highlight a few general approaches for compound description with a focus on bioactivity-based characterization of compounds.