CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes

Płonka, W; Stork, Conrad; Kirchmair, Johannes

doi:10.1016/j.bmc.2021.116388

Cited by 24 publications

(24 citation statements)

References 35 publications

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“…A benchmark of different datasets used and models’ performances as reported in the literature is given in S5 Table . In order to directly compare the performance of our models with other recent ones, we show here the performance of our final models and two state-of-the arts models available at the web servers CYPlebrity [ 44 ] and ADMETlab [ 45 ] on our external validation test set of inhibitors and non-inhibitors (see Table 3 ). Overall, our models performed better than the others (Tables 3 and S5 ), and showed remarkably better sensitivity, which is critical to detect inhibitors.…”

Section: Resultsmentioning

confidence: 99%

Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9

et al. 2022

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Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug–drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 μM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 μM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 μM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines.

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Section: Resultsmentioning

confidence: 99%

Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9

et al. 2022

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“…Accurate prediction of the CYP3A4 inhibitory activity is crucial in experiments. Previously reported models for CYP inhibitors prediction have primarily focused on binary classification, ,, distinguishing between compounds with and without inhibitory activity using a threshold such as IC 50 at 10 μM. In the actual drug discovery process, it is essential to predict the inhibitory intensity and confidence level to provide better guidance for drug design.…”

Section: Results and Discussionmentioning

confidence: 99%

An Uncertainty-Guided Deep Learning Method Facilitates Rapid Screening of CYP3A4 Inhibitors

Wang,

Liu,

Gong

et al. 2023

J. Chem. Inf. Model.

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“…The CYP2B6 inhibitor data sets were gained through the following three sources: (1) The ChEMBL bioactivity database (ChEMBL ID: 4729), 21 which contains information on enzyme inhibition, was mined. As reported in a previous study, 22 the compounds assigned by "standard type" "IC 50 " or "K i " and "standard unit" "nM" were retained and the rest were excluded. If "Standard Relation" was one of "<", "=", or "≤" and the "Standard Value" was less than 10,000, the entries were defined as active.…”

Section: Data Collection 211 Cyp2b6 Inhibitor Datamentioning

confidence: 99%

“…The CYP2B6 substrate and inhibitor data sets were compiled from three sources: the ChEMBL bioactivity database, the DrugBank database, and scientific literature. From the ChEMBL bioactivity database, according to the previously reported study, 22 we gathered the inhibitors/noninhibitors based on IC 50 and K i values, which allowed us to obtain 109 inhibitors and 148 noninhibitors. From the DrugBank database, we obtained the inhibitors or substrates depending on the "action" of the drugs, which led us to collect 50 inhibitors and 73 substrates.…”

Section: Data Collection and Analysismentioning

confidence: 99%

Machine Learning Models to Predict Cytochrome P450 2B6 Inhibitors and Substrates

Liu

et al. 2023

Chem. Res. Toxicol.

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Cytochrome P450 2B6 (CYP2B6) is responsible for the metabolism of ∼7% of marketed drugs. The in vitro drug interaction studies guidance for industry issued by the FDA stipulates that drug sponsors need to evaluate whether the investigated drugs interact with the major drug-metabolizing P450s including CYP2B6. Therefore, there has been greater attention to the development of predictive models for CYP2B6 inhibitors and substrates. In this study, conventional machine learning and deep learning models were developed to predict CYP2B6 inhibitors and substrates. Our results showed that the best CYP2B6 inhibitor model yielded the AUC values of 0.95 and 0.75 with the 10-fold cross-validation and the test set, respectively, and the best CYP2B6 substrate model produced the AUC values of 0.93 and 0.90 with the 10-fold cross-validation and the test set, respectively. The generalization ability of the CYP2B6 inhibitor and substrate models was assessed by using the external validation sets. Several significant substructural fragments relevant to CYP2B6 inhibitors and substrates were detected via frequency substructure analysis and information gain. In addition, the applicability domain of the models was defined by employing a nonparametric method based on the probability density distribution. We anticipate that our results would be useful for the prediction of potential CYP2B6 inhibitors and substrates in the early stage of drug discovery.

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CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes

Cited by 24 publications

References 35 publications

Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9

Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9

An Uncertainty-Guided Deep Learning Method Facilitates Rapid Screening of CYP3A4 Inhibitors

Machine Learning Models to Predict Cytochrome P450 2B6 Inhibitors and Substrates

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