Predicting drug metabolism by CYP1A1, CYP1A2, and CYP1B1: insights from MetaSite, molecular docking and quantum chemical calculations

Prediction of Sites of Metabolism of CYP3A4 Substrates Utilizing Docking-Derived Geometric Features

Cytochrome P450 3A4 (CYP3A4) is one of the major drug-metabolizing enzymes in the human body and is responsible for the metabolism of ∼50% of clinically used drugs. Therefore, the identification of the compound's sites of metabolism (SOMs) mediated by CYP3A4 is of utmost importance in the early stage of drug discovery and development. Herein, docking-based approaches incorporating geometric features were used for SOMs prediction of CYP3A4 substrates. The cross-docking poses of a relatively large data set containing 474 substrates were analyzed in depth, and a widely observed geometric pattern called the close proximity of SOMs was derived from the poses. On the basis of the close proximity, several structure-based models have been constructed, which demonstrated better performance than those structure-based models using the criterion of Fe-SOM distance. For further improving the prediction performance, the structure-based models were also combined with the well-known ligand-based model SMARTCyp. One combined model exhibited good performance on the SOMs prediction of an external substrate set containing kinase inhibitors, PROTACs, approved drugs, and some lead compounds.

Aryl Hydrocarbon Receptor and Cysteine Redox Dynamics Underlie (Mal)adaptive Mechanisms to Chronic Intermittent Hypoxia in Kidney Cortex

We hypothesized that an interplay between aryl hydrocarbon receptor (AhR) and cysteine-related thiolome at the kidney cortex underlies the mechanisms of (mal)adaptation to chronic intermittent hypoxia (CIH), promoting arterial hypertension (HTN). Using a rat model of CIH-HTN, we investigated the impact of short-term (1 and 7 days), mid-term (14 and 21 days, pre-HTN), and long-term intermittent hypoxia (IH) (up to 60 days, established HTN) on CYP1A1 protein level (a sensitive hallmark of AhR activation) and cysteine-related thiol pools. We found that acute and chronic IH had opposite effects on CYP1A1 and the thiolome. While short-term IH decreased CYP1A1 and increased protein-S-thiolation, long-term IH increased CYP1A1 and free oxidized cysteine. In addition, an in vitro administration of cystine, but not cysteine, to human endothelial cells increased Cyp1a1 expression, supporting cystine as a putative AhR activator. This study supports CYP1A1 as a biomarker of obstructive sleep apnea (OSA) severity and oxidized pools of cysteine as risk indicator of OSA-HTN. This work contributes to a better understanding of the mechanisms underlying the phenotype of OSA-HTN, mimicked by this model, which is in line with precision medicine challenges in OSA.

Effects of Xiaochaihu decoction on the expression of cytochrome P450s in rats

Xiaochaihu decoction is one of the most important traditional Chinese medicines that is widely used with other drugs in clinical practice, and may cause drug-drug interactions. However, there is not sufficient experimental evidence for the effects of Xiaochaihu decoction on cytochrome P450s (CYPs). The aim of the present study was to investigate the effects of Xiaochaihu decoction on the mRNA and protein levels of hepatic CYPs. Eighty normal male Sprague-Dawley (SD) rats were randomly divided into two groups based on body weight and duration of drug administration (3 and 6 days). Each group was further divided into subgroups: Control group (2 ml 5‰ CMC-Na); hepatic enzyme inducer group (50 mg/kg/day rifampicin); and experimental groups (Xiaochaihu decoction: Low dose, 1.7 g/kg/day; medium dose, 3.4 g/kg/day; high dose, 6.8 g/kg/day). The effects of Xiaochaihu decoction on Cyp1a2, Cyp3a1, Cyp2d6, and Cyp1b1 mRNA and protein expression in rats were evaluated using reverse transcription quantitative reverse transcription polymerase chain reaction and western blot analysis. After 3 days, medium dose of Xiaochaihu decoction inhibited the mRNA and protein expression of Cyp1a2, Cyp3a1 and Cyp1b1. In addition, after 6 days, Xiaochaihu decoction induced Cyp3a1 mRNA expression at low and medium doses; Cyp2d6 mRNA expression at low and high doses; and Cyp2d6 protein expression at high doses. Nonetheless, the gene and protein expression of Cyp1b1 was not affected at any dose. The findings of the present study may provide insights into potential drug-drug interactions associated with Xiaochaihu decoction.

Molecular modeling approaches to address drug-metabolizing enzymes (DMEs) mediated chemoresistance: a review

Resistance against clinically approved anticancer drugs is the main roadblock in cancer treatment. Drug metabolizing enzymes (DMEs) that are capable of metabolizing a variety of xenobiotic get overexpressed in malignant cells, therefore, catalyzing drug inactivation. As evident from the literature reports, the levels of DMEs increase in cancer cells that ultimately lead to drug inactivation followed by drug resistance. To puzzle out this issue, several strategies inclusive of analog designing, prodrug designing, and inhibitor designing have been forged. On that front, the implementation of computational tools can be considered a fascinating approach to address the problem of chemoresistance. Various research groups have adopted different molecular modeling tools for the investigation of DMEs mediated toxicity problems. However, the utilization of these in-silico tools in maneuvering the DME mediated chemoresistance is least considered and yet to be explored. These tools can be employed in the designing of such chemotherapeutic agents that are devoid of the resistance problem. The current review canvasses various molecular modeling approaches that can be implemented to address this issue. Special focus was laid on the development of specific inhibitors of DMEs. Additionally, the strategies to bypass the DMEs mediated drug metabolism were also contemplated in this report that includes analogs and pro-drugs designing. Different strategies discussed in the review will be beneficial in designing novel chemotherapeutic agents that depreciate the resistance problem.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

Effects of Saikosaponin D on CYP1A2 and CYP2D6 in HepaRG Cells

Background

Bupleurum is one of the most important traditional Chinese medicines and an ingredient in many compound preparations. It is widely used together with other drugs in clinical practice, and thus there is great potential for drug–drug interactions. Saikosaponin D (SsD) is a major bioactive triterpenoid saponin extracted from Bupleurum with anti-inflammatory, anticancer, antioxidative, and antihepatic fibrosis effects. Effects of the main components of Bupleurum on cytochromes P450 (CYPs) need to be clarified in the clinical application of combination therapies of formulations containing SsD or Bupleurum.

Purpose

This study aimed to investigate the effects of SsD on the CYP1A2 and CYP2D6 mRNAs, protein expression, and relative enzyme activities in HepaRG cells.

Methods

HepaRG cells were cultured with SsD at concentrations of 0.5, 1, 5 and 10 μM for 72 hours. mRNA and protein expression of CYP1A2 and CYP2D6 were analyzed with real-time PCR and Western blot analysis. Relative enzyme activities were analyzed with HPLC based on consumption of the specific probe substrate.

Results

SsD significantly induced expression of mRNA and increased relative activity of CYP1A2 in HepaRG cells after the cells had been treated with SsD at concentrations of 1, 5 and 10 μM. SsD also induced protein expression of CYP1A2 at concentrations of 5 and 10 μM. SsD exhibited an inductive effect on CYP2D6 mRNA and protein expression, while increasing the relative activity of CYP2D6 at concentrations of 5 and 10 μM.

Conclusion

This study is the first to investigate the effect of SsD on CYP1A2 and CYP2D6 in HepaRG cells, and the results may provide some useful information on potential drug–drug interactions related to clinical preparations containing SsD or Bupleurum.

Breast Tumor Kinase (Brk/PTK6) Mediates Advanced Cancer Phenotypes via SH2-Domain Dependent Activation of RhoA and Aryl Hydrocarbon Receptor (AhR) Signaling

Protein tyrosine kinase 6 (PTK6; also called Brk) is overexpressed in 86% of patients with breast cancer; high PTK6 expression predicts poor outcome. We reported PTK6 induction by HIF/GR complexes in response to either cellular or host stress. However, PTK6-driven signaling events in the context of triple-negative breast cancer (TNBC) remain undefined. In a mouse model of TNBC, manipulation of PTK6 levels (i.e., via knock-out or add-back) had little effect on primary tumor volume, but altered lung metastasis. To delineate the mechanisms of PTK6 downstream signaling, we created kinase-dead (KM) and kinase-intact domain structure mutants of PTK6 via in-frame deletions of the N-terminal SH3 or SH2 domains. While the PTK6 kinase domain contributed to soft-agar colony formation, PTK6 kinase activity was entirely dispensable for cell migration. Specifically, TNBC models expressing a PTK6 variant lacking the SH2 domain (SH2-del PTK6) were unresponsive to growth factor-stimulated cell motility relative to SH3-del, KM, or wild-type PTK6 controls. Reverse-phase protein array revealed that while intact PTK6 mediates spheroid formation via p38 MAPK signaling, the SH2 domain of PTK6 limits this biology, and instead mediates TNBC cell motility via activation of the RhoA and/or AhR signaling pathways. Inhibition of RhoA and/or AhR blocked TNBC cell migration as well as the branching/invasive morphology of PTK6⁺/AhR⁺ primary breast tumor tissue organoids. Inhibition of RhoA also enhanced paclitaxel cytotoxicity in TNBC cells, including in a taxane-refractory TNBC model. IMPLICATIONS: The SH2-domain of PTK6 is a potent effector of advanced cancer phenotypes in TNBC via RhoA and AhR, identified herein as novel therapeutic targets in PTK6⁺ breast tumors.

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, including CYP substrate and inhibitor predictors, site of metabolism predictors (i.e., metabolically labile sites within potential substrates) and metabolite structure predictors. We summarize the different approaches taken by these models, such as rule‐based methods, machine learning, data mining, quantum chemical methods, molecular interaction fields, and docking. We highlight the scope and limitations of each method and discuss future implications for the field of metabolism prediction in drug discovery.

Building a Quantitative Structure-Property Relationship (QSPR) Model

Knowing the physicochemical and general biochemical properties of a compound is critical to understanding how it behaves in different biological environments and to anticipating what is likely to happen in situations where that behavior cannot be measured directly. Quantitative structure-property relationship (QSPR) models provide a way to predict those properties even before a compound has been synthesized simply by knowing what its structure would be. This chapter describes a general workflow for compiling the data upon which a useful QSPR model is built, curating it, evaluating that model's performance, and then analyzing the predictive errors with an eye toward identifying systematic errors in the input data. The focus here is on models for the absorption, distribution, metabolism, and excretion (ADME) properties of drugs and toxins, but the considerations explored are general and applicable to any QSPR.

Structure-Based Drug Design for Cytochrome P450 Family 1 Inhibitors

Cytochromes P450 are a class of metalloproteins which are responsible for electron transfer in a wide spectrum of reactions including metabolic biotransformation of endogenous and exogenous substrates. The superfamily of cytochromes P450 consists of families and subfamilies which are characterized by a specific structure and substrate specificity. Cytochromes P450 family 1 (CYP1s) play a distinctive role in the metabolism of drugs and chemical procarcinogens. In recent decades, these hemoproteins have been intensively studied with the use of computational methods which have been recently developed remarkably to be used in the process of drug design by the virtual screening of compounds in order to find agents with desired properties. Moreover, the molecular modeling of proteins and ligand docking to their active sites provide an insight into the mechanism of enzyme action and enable us to predict the sites of drug metabolism. The review presents the current status of knowledge about the use of the computational approach in studies of ligand-enzyme interactions for CYP1s. Research on the metabolism of substrates and inhibitors of CYP1s and on the selectivity of their action is particularly valuable from the viewpoint of cancer chemoprevention, chemotherapy, and drug-drug interactions.

Identification of leads for antiproliferative activity on MDA-MB-435 human breast cancer cells through pharmacophore and CYP1A1-mediated metabolism

CYP1A1 is a potential target for anticancer drug development due to its overexpression in certain cancer cells and role in cancer progression. To identify new leads for CYP1A1 mediated anticancer action, we attempted ligand based pharmacophore mapping, virtual screening of databases, molecular docking, MetaSite based filtering, and molecular dynamics simulations. Initial computational and in vitro screening identified 11 compounds from which we identified two lead compounds, ZINC33468944 and ZINC32101539, showed potential antitumor activity on MDA-MB-435 cell lines (GI50 < 0.1 μM) and CYP1A1 inhibition of 0.13 and 0.3 μM, respectively. Furthermore, the lead compounds were evaluated for CYP1A1 mediated metabolism, showing N-hydroxylated metabolites, which have potential of DNA adduct formation and cause cancerous cell death. Analysis of molecular dynamics simulations provided important guidelines for the further modification of the lead compounds. Hence, we claim the lead molecules for further development in anticancer drug discovery.

Characterization of differences in substrate specificity among CYP1A1, CYP1A2 and CYP1B1: an integrated approach employing molecular docking and molecular dynamics simulations

Recent trends in new drug discovery of anticancer drugs have made oncologists more aware of the fact that the new drug discovery must target the developing mechanism of tumorigenesis to improve the therapeutic efficacy of antineoplastic drugs. The drugs designed are expected to have high affinity towards the novel targets selectively. Current research highlights overexpression of CYP450s, particularly cytochrome P450 1A1 (CYP1A1), in tumour cells, representing a novel target for anticancer therapy. However, the CYP1 family is identified as posing significant problems in selectivity of anticancer molecules towards CYP1A1. Three members have been identified in the human CYP1 family: CYP1A1, CYP1A2 and CYP1B1. Although sequences of the three isoform have high sequence identity, they have distinct substrate specificities. The understanding of macromolecular features that govern substrate specificity is required to understand the interplay between the protein function and dynamics, design novel antitumour compounds that could be specifically metabolized by only CYP1A1 to mediate their antitumour activity and elucidate the reasons for differences in substrate specificity profile among the three proteins. In the present study, we employed a combination of computational methodologies: molecular docking and molecular dynamics simulations. We utilized eight substrates for elucidating the difference in substrate specificity of the three isoforms. Lastly, we conclude that the substrate specificity of a particular substrate depends upon the type of the active site residues, the dynamic motions in the protein structure upon ligand binding and the physico-chemical characteristics of a particular ligand. Copyright © 2016 John Wiley & Sons, Ltd.