Development of In Silico Models for Predicting Potential Time-Dependent Inhibitors of Cytochrome P450 3A4

Xu, Minjie; Lu, Zhou; Wu, Zengrui; Gui, Minyan; Liu, Guixia; Tang, Yun; Li, Weihua

doi:10.1021/acs.molpharmaceut.2c00571

Cited by 5 publications

(5 citation statements)

References 67 publications

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“…To clarify the mechanism of interaction, we first performed IC 50 shift experiments to determine whether the inhibition was time dependent (Xu et al, 2023). The results showed that the ratio of IC 50 (-NADPH) to IC 50 (+NADPH) of telmisartan and carvedilol in RLM was less than 1.5, indicating that the inhibition was not time dependent (Supplementary Figure S4).…”

Section: Telmisartan and Carvedilol Inhibit The Metabolism Of Aumoler...mentioning

confidence: 99%

The influence of drug-induced metabolic enzyme activity inhibition and CYP3A4 gene polymorphism on aumolertinib metabolism

Ye,

Ni,

et al. 2024

Front. Pharmacol.

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The purpose of this study is to clarify the drug interaction profile of aumolertinib, and the influence of CYP3A4 genetic polymorphism on aumolertinib metabolic characteristics. Through microsomal enzyme reactions, we screened 153 drugs and identified 15 that significantly inhibited the metabolism of aumolertinib. Among them, telmisartan and carvedilol exhibited potent inhibitory activities in rat liver microsomes (RLM) and human liver microsomes (HLM). In vivo, the pharmacokinetic parameters of aumolertinib, including AUC and Cmax, were significantly altered when co-administered with carvedilol, with a notable decrease in the clearance rate CLz/F. Interestingly, the pharmacokinetic parameters of the metabolite HAS-719 exhibited a similar trend as aumolertinib when co-administered. Mechanistically, both telmisartan and carvedilol exhibited a mixed-type inhibition on the metabolism of aumolertinib. Additionally, we used a baculovirus-insect cell expression system to prepare 24 recombinant CYP3A4 microsomes and obtained enzymatic kinetic parameters using aumolertinib as a substrate. Enzyme kinetic studies obtained the kinetic parameters of various CYP3A4 variant-mediated metabolism of aumolertinib. Based on the relative clearance rates, CYP3A4.4, 5, 7, 8, 9, 12, 13, 14, 17, 18, 19, 23, 24, 33, and 34 showed significantly lower clearance rates compared to the wild-type. Among the different CYP3A4 variants, the inhibitory potency of telmisartan and carvedilol on the metabolism of aumolertinib also varied. The IC50 values of telmisartan and carvedilol in CYP3A4.1 were 6.68 ± 1.76 μM and 0.60 ± 0.25 μM, respectively, whereas in CYP3A4.12, the IC50 exceeded 100 μM. Finally, we utilized adeno-associated virus to achieve liver-specific high expression of CYP3A4*1 and CYP3A4*12. In the group with high expression of the less active CYP3A4*12, the magnitude of the drug-drug interaction was significantly attenuated. In conclusion, CYP3A4 genetic polymorphism not only influences the pharmacokinetic characteristics of aumolertinib, but also the inhibitory potency of telmisartan and carvedilol on it.

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Section: Telmisartan and Carvedilol Inhibit The Metabolism Of Aumoler...mentioning

confidence: 99%

The influence of drug-induced metabolic enzyme activity inhibition and CYP3A4 gene polymorphism on aumolertinib metabolism

Ye,

Ni,

et al. 2024

Front. Pharmacol.

View full text Add to dashboard Cite

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“…To clarify the type of interaction, we first conducted IC 50 shift experiments to determine whether the inhibition was time-dependent (Xu et al, 2023). The results showed that the IC 50 (−NADPH) to IC 50 (+NADPH) ratio of nimodipine, amlodipine, and felodipine in the rat liver was less than 1.5, indicating that the inhibition was not time-dependent (Supplementary Figure 3).…”

Section: Downloaded Frommentioning

confidence: 99%

Gene Polymorphisms and Drug–Drug Interactions Determine the Metabolic Profile of Blonanserin

Ye,

Li,

et al. 2023

J Pharmacol Exp Ther

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This study aimed to evaluate the effects of cytochrome P450 3A4 (CYP3A4) gene polymorphism and drug interaction on the metabolism of blonanserin. Human recombinant CYP3A4 was prepared using the Bac-to-Bac baculovirus expression system. A microsomal enzyme reaction system was established, and drug-drug interactions were evaluated using Sprague-Dawley rats.Ultra-performance liquid chromatography-tandem mass spectrometry was used to detect the concentrations of blonanserin and its metabolite. Compared with wild-type CYP34A, the relative clearance of blonanserin by CYP3A4.29 significantly increased to 251.3%, while it decreased

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“…In their investigations, models were built with a highly imbalanced TDI data set with 7914 molecules and only 580 TDI positive data. 24 In this work, deep learning models were developed for the prediction of CYP3A4 TDI and compared to experimental variability with the aim of comparing their quality for decisionmaking in drug design. Realistic evaluation settings were applied, where the generalization ability of the models was estimated by temporal or prospective validations.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Tang et al reported a benchmark of ML methods and molecular features for categorical TDI CYP3A4 categorical predictions. In their investigations, models were built with a highly imbalanced TDI data set with 7914 molecules and only 580 TDI positive data …”

Section: Introductionmentioning

confidence: 99%

Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition

Fluetsch,

Trunzer,

Gerebtzoff

et al. 2024

Chem. Res. Toxicol.

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Most drugs are mainly metabolized by cytochrome P450 (CYP450), which can lead to drug−drug interactions (DDI). Specifically, time-dependent inhibition (TDI) of CYP3A4 isoenzyme has been associated with clinically relevant DDI. To overcome potential DDI issues, high-throughput in vitro assays were established to assess the TDI of CYP3A4 during the discovery and lead optimization phases. However, in silico machine learning models would enable an earlier and larger-scale assessment of TDI potential liabilities. For CYP inhibition, most modeling efforts have focused on highly imbalanced and small data sets. Moreover, assay variability is rarely considered, which is key to understand the model's quality and suitability for decision-making. In this work, machine learning models were built for the prediction of TDI of CYP3A4, evaluated prospectively, and compared to the variability of the experimental assay. Different modeling strategies were investigated to assess their influence on the model's performance. Through multitask learning, additional data sets were leveraged for model building, coming from public databases, in-house CYP-related assays, or other pharmaceutical companies (federated learning). Apart from the numerical prediction of inactivation rates of CYP3A4 TDI, three-class predictions were carried out, giving a negative (inactivation rate k obs < 0.01 min −1 ), weak positive (0.01 ≤ k obs ≤ 0.025 min −1 ), or positive (k obs > 0.025 min −1 ) output. The final multitask graph neural network model achieved misclassification rates of 8 and 7% for positive and negative TDI, respectively. Importantly, the presented deep learning-based predictions had a similar precision to the reproducibility of in vitro experiments and thus offered great opportunities for drug design, early derisk of DDI potential, and selection of experiments. To facilitate CYP inhibition modeling efforts in the public domain, the developed model was used to annotate ∼16 000 publicly available structures, and a surrogate data set is shared as Supporting Information.

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Development of In Silico Models for Predicting Potential Time-Dependent Inhibitors of Cytochrome P450 3A4

Cited by 5 publications

References 67 publications

The influence of drug-induced metabolic enzyme activity inhibition and CYP3A4 gene polymorphism on aumolertinib metabolism

The influence of drug-induced metabolic enzyme activity inhibition and CYP3A4 gene polymorphism on aumolertinib metabolism

Gene Polymorphisms and Drug–Drug Interactions Determine the Metabolic Profile of Blonanserin

Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition

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