Computational prediction of cytochrome P450 inhibition and induction

Kato, Hidefumi

doi:10.1016/j.dmpk.2019.11.006

Cited by 48 publications

(35 citation statements)

References 160 publications

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“…Molecular docking study showed that SSd displayed a high binding energy of −11.3 kcal/mol. It has been reported that the amino acid residues of Phe57, Phe108, Ile120, Leu211, Phe241, Ile301, Phe304, Ile369, Leu373, Arg105, Leu364, Arg365, Leu366, Phe367, Pro368, Ala370, Met371, Arg372 and Glu374 in CYP3A4 played a key role in ligand binding 38‐44 . Another study suggested that Phe57, Arg105, Arg106, Phe108, Phe215, Met371, Arg372, Leu373, Glu374 and Arg375 were involved in interaction between sauchinone and CYP3A4 protein 45 .…”

Section: Discussionmentioning

confidence: 98%

Effects of saikosaponin‐d on CYP3A4 in HepaRG cell and protein‐ligand docking study

Tang

Wei

et al. 2021

Basic Clin Pharma Tox

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Saikosaponin‐d (SSd) is a major bioactive triterpenoid saponin extracted from Bupleurum, which has anti‐inflammatory, anticancer, antioxidative and anti‐hepatic fibrosis effects. Due to the effects of Bupleurum‐related formulations on cytochrome P450 (CYPs) expression still remain unclear, the combination therapies involved formulations containing Bupleurum may sometimes lead to unexpected drug‐drug interactions in clinical practice. These interactions can limit the clinical applications of related formulations. In this study, we tried to explore the effects of SSd on CYP3A4 mRNA, protein expression and the enzyme activity in HepaRG cells by real‐time quantitative reverse transcription polymerase chain reaction (RT‐qPCR), Western blot (WB) and HPLC method, respectively. The interaction between SSd and CYP3A4 was analysed by molecular docking. HepaRG cells were cultured with different concentrations of SSd (0.5, 1, 5 and 10 μmol/L) for 72 hours. It is revealed that SSd can inhibit CYP3A4 mRNA and its protein expression, and also the enzyme activity. Molecular docking study demonstrated that SSd can bind to several key active sites of amino acid residues of CYP3A4 protein with hydrogen bonds and hydrophobic interactions. Thus, drug‐drug interactions resulted by SSd inhibiting CYP3A4 need attention when formulations containing SSd or Bupleurum are co‐administrated with drugs metabolized by CYP3A4.

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Section: Discussionmentioning

confidence: 98%

Effects of saikosaponin‐d on CYP3A4 in HepaRG cell and protein‐ligand docking study

Tang

Wei

et al. 2021

Basic Clin Pharma Tox

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“…The xenobiotic-sensing receptors are ligand-activated transcription factors belonging structurally either to the nuclear receptors or the basic-helix–loop–helix Per-Arnt-Sim (bHLH-PAS) proteins. Today, activation of these receptors and subsequent CYP induction can be studied with a number of in silico, in vitro, and cell-based methods enabling relatively good prediction of in vivo induction (Bernasconi et al 2019 ; Kato 2020 ; Pelkonen et al 2008 ). However, not all the compounds found to be activators in cell or other in vitro assays are actual in vivo activators because of pharmacokinetic or other factors.…”

Section: Mechanisms Of Cyp Inductionmentioning

confidence: 99%

“…Prediction on the basis of in vitro studies is now an integral part of early drug development (Lu and Di 2020 ) as well as of the medicines agency guidelines (EMA, FDA, and MHLW/PMDA). Computational models such as physiologically based pharmacokinetic models are now being used for quantitative prediction of in vivo interactions from in vitro experiments (Kato 2020 ; Min and Bae 2017 ), and these models are used extensively by drug developers before and during clinical trials. After preclinical studies, there is an ultimate need of human in vivo studies and observations on inhibition and induction.…”

Section: Introductionmentioning

confidence: 99%

Inhibition and induction of CYP enzymes in humans: an update

et al. 2020

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The cytochrome P450 (CYP) enzyme family is the most important enzyme system catalyzing the phase 1 metabolism of pharmaceuticals and other xenobiotics such as herbal remedies and toxic compounds in the environment. The inhibition and induction of CYPs are major mechanisms causing pharmacokinetic drug–drug interactions. This review presents a comprehensive update on the inhibitors and inducers of the specific CYP enzymes in humans. The focus is on the more recent human in vitro and in vivo findings since the publication of our previous review on this topic in 2008. In addition to the general presentation of inhibitory drugs and inducers of human CYP enzymes by drugs, herbal remedies, and toxic compounds, an in-depth view on tyrosine-kinase inhibitors and antiretroviral HIV medications as victims and perpetrators of drug–drug interactions is provided as examples of the current trends in the field. Also, a concise overview of the mechanisms of CYP induction is presented to aid the understanding of the induction phenomena.

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“…the foreign substances [3]. More than 95% of FDA-approved drugs are metabolized by six isoforms of these subfamilies [4,5]. The CYP 2C subfamily is one of the most important CYP families, consisting of two main isoforms, 2C9 and 2C19.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, combined models are also represented in the literature, where either the applied datasets [13] or the algorithms [17] are combined. A great selection of the previous models (not just for the 2C9 isoform) can be found in the recent review by Kato [4].…”

Section: Introductionmentioning

confidence: 99%

Large-scale evaluation of cytochrome P450 2C9 mediated drug interaction potential with machine learning-based consensus modeling

Rácz

Keserü

2020

J Comput Aided Mol Des

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Cytochrome P450 (CYP) enzymes play an important role in the metabolism of xenobiotics. Since they are connected to drug interactions, screening for potential inhibitors is of utmost importance in drug discovery settings. Our study provides an extensive classification model for P450-drug interactions with one of the most prominent members, the 2C9 isoenzyme. Our model involved the largest set of 45,000 molecules ever used for developing prediction models. The models are based on three different types of descriptors, (a) typical one, two and three dimensional molecular descriptors, (b) chemical and pharmacophore fingerprints and (c) interaction fingerprints with docking scores. Two machine learning algorithms, the boosted tree and the multilayer feedforward of resilient backpropagation network were used and compared based on their performances. The models were validated both internally and using external validation sets. The results showed that the consensus voting technique with custom probability thresholds could provide promising results even in large-scale cases without any restrictions on the applicability domain. Our best model was capable to predict the 2C9 inhibitory activity with the area under the receiver operating characteristic curve (AUC) of 0.85 and 0.84 for the internal and the external test sets, respectively. The chemical space covered with the largest available dataset has reached its limit encompassing publicly available bioactivity data for the 2C9 isoenzyme.Keywords ADME-tox · Cytochrome P450 · CYP 2C9 · Machine learning · Classification AbbreviationsAUC Area under the ROC curve Ave Average CYP Cytochrome P450 DS Docking score ECFP Extended connectivity fingerprint GBT Gradient boosted tree IFP Interaction fingerprint Max Maximum MD Molecular descriptors Min Minimum P Probability PFP Pharmacophore fingerprint ROC Receiver operating characteristic curve RPropMLP/Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Computational prediction of cytochrome P450 inhibition and induction

Cited by 48 publications

References 160 publications

Effects of saikosaponin‐d on CYP3A4 in HepaRG cell and protein‐ligand docking study

Effects of saikosaponin‐d on CYP3A4 in HepaRG cell and protein‐ligand docking study

Inhibition and induction of CYP enzymes in humans: an update

Large-scale evaluation of cytochrome P450 2C9 mediated drug interaction potential with machine learning-based consensus modeling

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