Critical Evaluation of Human Oral Bioavailability for Pharmaceutical Drugs by Using Various Cheminformatics Approaches

PURPOSE: Oral bioavailability (%F) is a key factor that determines the fate of a new drug in clinical trials. Traditionally, %F is measured using costly and time -consuming experimental tests. Developing computational models to evaluate the %F of new drugs before they are synthesized would be beneficial in the drug discovery process. METHODS: We employed Combinatorial Quantitative Structure-Activity Relationship approach to develop several computational %F models. We compiled a %F dataset of 995 drugs from public sources. After generating chemical descriptors for each compound, we used random forest, support vector machine, k nearest neighbor, and CASE Ultra to develop the relevant QSAR models. The resulting models were validated using five-fold cross-validation. RESULTS: The external predictivity of %F values was poor (R(2)=0.28, n=995, MAE=24), but was improved (R(2)=0.40, n=362, MAE=21) by filtering unreliable predictions that had a high probability of interacting with MDR1 and MRP2 transporters. Furthermore, classifying the compounds according to the %F values (%F<50% as “low”, %F≥50% as ‘high”) and developing category QSAR models resulted in an external accuracy of 76%. CONCLUSIONS: In this study, we developed predictive %F QSAR models that could be used to evaluate new drug compounds, and integrating drug-transporter interactions data greatly benefits the resulting models

Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project

Quantitative structure−activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020–2022) as a successor to the First Project (2014–2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models