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

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.

Impact of E-Cigarette Liquid Flavoring Agents on Activity of Microsomal Recombinant CYP2A6, the Primary Nicotine-Metabolizing Enzyme.

Nicotine is the primary psychoactive chemical in both traditional and electronic cigarettes (e-cigarettes). Nicotine levels in both traditional cigarettes and e-cigarettes are an important concern for public health. Nicotine exposure due to e-cigarette use is of importance primarily due to the addictive potential of nicotine, but there is also concern for nicotine poisoning in e-cigarette users. Nicotine concentrations in e-liquids vary widely. Additionally, there is significant genetic variability in the rate of metabolism of nicotine due to polymorphisms of CYP2A6, the enzyme responsible for the metabolism of approximately 80% of nicotine. Recent studies have shown CYP2A6 activity is also reduced by aromatic aldehydes such as those added to e-liquids as flavoring agents, which may increase nicotine serum concentrations. However, the impacts of flavored e-liquids on CYP2A6 activity are unknown. In this study, we investigated the impact of three flavored e-liquids on microsomal recombinant CYP2A6. Microsomal recombinant CYP2A6 was challenged at e-liquid concentrations ranging up to 0.125% (v/v) and monitored for metabolic activity using a probe molecule approach. Two e-liquids exhibited dose-dependent inhibition of CYP2A6 activity. Mass spectrometry was conducted to identify flavoring agents in flavored e-liquids that inhibited CYP2A6. Microsomal recombinant CYP2A6 was subsequently exposed to flavoring agents at concentrations ranging from 0.03 µM to 500 µM. Cinnamaldehyde and benzaldehyde were found to be the most potent inhibitors of microsomal CYP2A6 of the flavoring agents tested, with identified IC50 values of 1.1 µM and 3.0 µM, respectively. These data indicate certain aromatic aldehyde flavoring agents are potent inhibitors of CYP2A6, which may reduce nicotine metabolism in vivo. These findings indicate an urgent need to evaluate the effects of flavoring agents in e-cigarette liquids on the pharmacokinetics of nicotine in vivo.

[Discovery of cytochrome P450 enzymes-inhibiting components in traditional Chinese medicine].

With the widespread use of traditional Chinese medicine(TCM) and the integration of TCM and western medicine, drug-drug interaction(DDI) is considered as a major cause of therapeutic failures and side effects. Cytochrome P450 enzymes(CYPs) are responsible for large number of drug metabolism. CYP3 A4 and CYP2 D6, two important CYP isoforms, are responsible for about 80% drug metabolism of CYPs super family. The inhibition of CYPs is likely to be the most common factor leading to adverse DDI. Therefore, it is of great significance to predict potential CYP3 A4 and CYP2 D6 inhibitors to prevent the DDI. A fast and low-cost me-thod for calculating and predicting CYP inhibiting components was established in this paper, namely support vector machine(SVM) and molecular docking technology which are used to predict and screen drugs. Firstly, 12 qualitative models of two targets were established by using SVM, and the optimal model was selected to predict the compounds in traditional Chinese medicine database(TCMD). Then, molecular docking technology was used to establish docking model. By analyzing the key amino acids involved in drug-target interactions and combining with SVM model, potential inhibitors of CYP3 A4 and CYP2 D6 were found. From the computational results, astin D and epiberberine exhibited inhibition effect on CYP3 A4 and CYP2 D6, respectively. Astin D was only found in astins family from Aster tataricus, while epiberberine was considered to be the active constituent of Coptidis Rhizoma. Therefore, for the risk of DDI, extra attention should be paid to the source of these potential inhibitors, Asteris Radix et Rhizoma and Coptidis Rhizoma. This computational method provides technical support for discovering potential natural inhibitors of CYPs from Chinese herbs by using SVM and molecular docking model, and it is also helpful to recognize the CYPs-mediated DDI existing in TCM, providing research ideas for further pharmacovigilance of integrated therapy.

GABAA receptor activity modulating piperine analogs: In vitro metabolic stability, metabolite identification, CYP450 reaction phenotyping, and protein binding.

In a screening of natural products for allosteric modulators of GABAA receptors (γ-aminobutyric acid type A receptor), piperine was identified as a compound targeting a benzodiazepine-independent binding site. Given that piperine is also an activator of TRPV1 (transient receptor potential vanilloid type 1) receptors involved in pain signaling and thermoregulation, a series of piperine analogs were prepared in several cycles of structural optimization, with the aim of separating GABAA and TRPV1 activating properties. We here investigated the metabolism of piperine and selected analogs in view of further cycles of lead optimization. Metabolic stability of the compounds was evaluated by incubation with pooled human liver microsomes, and metabolites were analyzed by UHPLC-Q-TOF-MS. CYP450 isoenzymes involved in metabolism of compounds were identified by reaction phenotyping with Silensomes™. Unbound fraction in whole blood was determined by rapid equilibrium dialysis. Piperine was the metabolically most stable compound. Aliphatic hydroxylation, and N- and O-dealkylation were the major routes of oxidative metabolism. Piperine was exclusively metabolized by CYP1A2, whereas CYP2C9 contributed significantly in the oxidative metabolism of all analogs. Extensive binding to blood constituents was observed for all compounds.

[Screen potential CYP450 2E1 inhibitors from Chinese herbal medicine based on support vector regression and molecular docking method].

Inhibition of cytochrome P450 (CYP450) enzymes is the most common reasons for drug interactions, so the study on early prediction of CYPs inhibitors can help to decrease the incidence of adverse reactions caused by drug interactions.CYP450 2E1(CYP2E1), as a key role in drug metabolism process, has broad spectrum of drug metabolism substrate. In this study, 32 CYP2E1 inhibitors were collected for the construction of support vector regression (SVR) model. The test set data were used to verify CYP2E1 quantitative models and obtain the optimal prediction model of CYP2E1 inhibitor. Meanwhile, one molecular docking program, CDOCKER, was utilized to analyze the interaction pattern between positive compounds and active pocket to establish the optimal screening model of CYP2E1 inhibitors.SVR model and molecular docking prediction model were combined to screen traditional Chinese medicine database (TCMD), which could improve the calculation efficiency and prediction accuracy. 6 376 traditional Chinese medicine (TCM) compounds predicted by SVR model were obtained, and in further verification by using molecular docking model, 247 TCM compounds with potential inhibitory activities against CYP2E1 were finally retained. Some of them have been verified by experiments. The results demonstrated that this study could provide guidance for the virtual screening of CYP450 inhibitors and the prediction of CYPs-mediated DDIs, and also provide references for clinical rational drug use.

High-Throughput Cytochrome P450 Cocktail Inhibition Assay for Assessing Drug-Drug and Drug-Botanical Interactions.

Detection of drug-drug interactions is essential during the early stages of drug discovery and development, and the understanding of drug-botanical interactions is important for the safe use of botanical dietary supplements. Among the different forms of drug interactions that are known, inhibition of cytochrome P450 (P450) enzymes is the most common cause of drug-drug or drug-botanical interactions. Therefore, a rapid and comprehensive mass spectrometry-based in vitro high-throughput P450 cocktail inhibition assay was developed that uses 10 substrates simultaneously against nine CYP isoforms. Including probe substrates for CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and two probes targeting different binding sites of CYP3A4/5, this cocktail simultaneously assesses at least as many P450 enzymes as previous assays while remaining among the fastest due to short incubation times and rapid analysis using ultrahigh pressure liquid chromatography-tandem mass spectrometry. The method was validated using known inhibitors of each P450 enzyme and then shown to be useful not only for single-compound testing but also for the evaluation of potential drug-botanical interactions using the botanical dietary supplement licorice (Glycyrrhiza glabra) as an example.

Development and validation of a liquid-chromatography high-resolution tandem mass spectrometry approach for quantification of nine cytochrome P450 (CYP) model substrate metabolites in an in vitro CYP inhibition cocktail.

Knowledge about the cytochrome P450 (CYP) inhibition potential of new drug candidates is important for drug development because of its risk of interactions. For novel psychoactive substances (NPS), corresponding data are not available. For developing a general drug inhibition cocktail assay, a liquid-chromatography high-resolution tandem mass spectrometry multi-analyte approach was developed and validated for quantifying low concentrations of O-diethyl phenacetin for CYP 1A2, 7-hydroxy coumarin for CYP 2A6, 4-hydroxy bupropion for CYP 2B6, N-diethyl amodiaquine for CYP 2C8, 4-hydroxy diclofenac for CYP 2C9, 5-hydroxy omeprazole for CYP 2C19, O-dimethyl dextromethorphan for CYP 2D6, 6-hydroxy chlorzoxazone for CYP 2E1, and 6-beta-hydroxy testosterone for CYP 3A in the incubation mixture in the presence of substrates and inhibitors. The tested matrix effects ranged from 63 to 141 % and the recoveries from 95 to 110 %. Time-saving one-point calibration allowed sufficient quantification, although some of the validation results for 7-hydroxy coumarin, 4-hydroxy bupropion, 4-hydroxy diclofenac, and 6-beta-hydroxy testosterone were outside the acceptance criteria (AC) but without influence of the IC50 calculation. Validation showed also that the approach was sensitive and selective using mass spectral multiplexing. In conclusion, the presented assay was suitable for the quantification of the model substrate metabolites and could be used for the development of a CYP inhibition assay for testing most CYPs and a wide range of drugs of abuse.