A combinatorial approach for the discovery of cytochrome P450 2D6 inhibitors from nature

Computer-Aided (In Silico) Modeling of Cytochrome P450-Mediated Food–Drug Interactions (FDI)

Modifications of the activity of Cytochrome 450 (CYP) enzymes by compounds in food might impair medical treatments. These CYP-mediated food–drug interactions (FDI) play a major role in drug clearance in the intestine and liver. Inter-individual variation in both CYP expression and structure is an important determinant of FDI. Traditional targeted approaches have highlighted a limited number of dietary inhibitors and single-nucleotide variations (SNVs), each determining personal CYP activity and inhibition. These approaches are costly in time, money and labor. Here, we review computational tools and databases that are already available and are relevant to predicting CYP-mediated FDIs. Computer-aided approaches such as protein–ligand interaction modeling and the virtual screening of big data narrow down hundreds of thousands of items in databanks to a few putative targets, to which the research resources could be further directed. Structure-based methods are used to explore the structural nature of the interaction between compounds and CYP enzymes. However, while collections of chemical, biochemical and genetic data are available today and call for the implementation of big-data approaches, ligand-based machine-learning approaches for virtual screening are still scarcely used for FDI studies. This review of CYP-mediated FDIs promises to attract scientists and the general public.

In Silico Prediction of Drug-Drug Interactions Mediated by Cytochrome P450 Isoforms

Drug–drug interactions (DDIs) can cause drug toxicities, reduced pharmacological effects, and adverse drug reactions. Studies aiming to determine the possible DDIs for an investigational drug are part of the drug discovery and development process and include an assessment of the DDIs potential mediated by inhibition or induction of the most important drug-metabolizing cytochrome P450 isoforms. Our study was dedicated to creating a computer model for prediction of the DDIs mediated by the seven most important P450 cytochromes: CYP1A2, CYP2B6, CYP2C19, CYP2C8, CYP2C9, CYP2D6, and CYP3A4. For the creation of structure–activity relationship (SAR) models that predict metabolism-mediated DDIs for pairs of molecules, we applied the Prediction of Activity Spectra for Substances (PASS) software and Pairs of Substances Multilevel Neighborhoods of Atoms (PoSMNA) descriptors calculated based on structural formulas. About 2500 records on DDIs mediated by these cytochromes were used as a training set. Prediction can be carried out both for known drugs and for new, not-yet-synthesized substances. The average accuracy of the prediction of DDIs mediated by various isoforms of cytochrome P450 estimated by leave-one-out cross-validation (LOO CV) procedures was about 0.92. The SAR models created are publicly available as a web resource and provide predictions of DDIs mediated by the most important cytochromes P450.

In Silico Pharmacogenetics CYP2D6 Study Focused on the Pharmacovigilance of Herbal Antidepressants

The annual increase in depression worldwide together with an upward trend in the use of alternative medicine as treatment asks for developing reliable safety profiles of herbal based medicine. A considerable risk on adverse reactions exists when herbal remedies are combined with prescription medication. Around 25% of the drugs, including many antidepressants, depend on the activity of CYP2D6 for their metabolism and corresponding efficacy. Therefore, probing CYP2D6 inhibition by the active substances in herbal based medicine within the wild-type enzyme and clinically relevant allelic variants is crucial to avoid toxicity issues. In this in silico study several compounds with herbal origin suggested to have antidepressant activity were analyzed on their CYP2D6 wild-type and CYP2D6*53 inhibition potential using molecular docking. In addition, several pharmacokinetic properties were evaluated to assess their probability to cross the blood brain barrier and subsequently reach sufficient brain bioavailability for the modulation of central nervous system targets as well as characteristics which may hint toward potential safety issues.

Prediction of Severity of Drug-Drug Interactions Caused by Enzyme Inhibition and Activation

Drug-drug interactions (DDIs) severity assessment is a crucial problem because polypharmacy is increasingly common in modern medical practice. Many DDIs are caused by alterations of the plasma concentrations of one drug due to another drug inhibiting and/or inducing the metabolism or transporter-mediated disposition of the victim drug. Accurate assessment of clinically relevant DDIs for novel drug candidates represents one of the significant tasks of contemporary drug research and development and is important for practicing physicians. This work is a development of our previous investigations and aimed to create a model for the severity of DDIs prediction. PASS program and PoSMNA descriptors were implemented for prediction of all five classes of DDIs severity according to OpeRational ClassificAtion (ORCA) system: contraindicated (class 1), provisionally contraindicated (class 2), conditional (class 3), minimal risk (class 4), no interaction (class 5). Prediction can be carried out both for known drugs and for new, not yet synthesized substances using only their structural formulas. Created model provides an assessment of DDIs severity by prediction of different ORCA classes from the first most dangerous class to the fifth class when DDIs do not take place in the human organism. The average accuracy of DDIs class prediction is about 0.75.

Reviewing the mechanisms of natural product-drug interactions involving efflux transporters and metabolic enzymes

The World Health Organization (WHO) and other worldwide health agencies have recently taken initiatives to encourage the use of traditional medicine and/or complementary/alternative medicine in order to promote well-being and public health. In this way, one of the WHO's concerns is the safe use of these therapies. Phytotherapy is a strategy consisting of the use of medicinal plants (MP) and/or herbal medicinal products (HMP) for medicinal purposes. The use of phytotherapy concomitantly with drugs may cause interactions compromising the expected pharmacological action or generating toxic effects. These interactions are complex processes that may occur with multiple medications targeting different metabolic pathways, and involving different compounds present in MP and HMP. Thus, the aim of this review was to summarize the main MP- and HMP-drug interactions that involve specific transporters (P-glycoprotein and BCRP) and CYP450 enzymes (CYP3A4 and CYP2D6), which play relevant roles in the mechanisms of interactions. Firstly, multiple databases were used to search studies describing in vitro or in vivo MP and HMP-drug interactions and, after that, a systematic note-taking and appraisal of the literature was conducted. It was observed that several MP and HMP, metabolic pathways and transcription factors are involved in the transporters and enzymes expression or in the modulation of their activity having the potential to provide such interactions. Thus, the knowledge of MP- and HMP-drug interaction mechanisms could contribute to prevent harmful interactions and can ensure the safe use of these products to help the establishment of the therapeutic planning in order to certify the best treatment strategy to be used.

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

Exploring the Chemical Space of Cytochrome P450 Inhibitors Using Integrated Physicochemical Parameters, Drug Efficiency Metrics and Decision Tree Models

The cytochrome P450s (CYPs) play a central role in the metabolism of various endogenous and exogenous compounds including drugs. CYPs are vulnerable to inhibition and induction which can lead to adverse drug reactions. Therefore, insights into the underlying mechanism of CYP450 inhibition and the estimation of overall CYP inhibitor properties might serve as valuable tools during the early phases of drug discovery. Herein, we present a large data set of inhibitors against five major metabolic CYPs (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) for the evaluation of important physicochemical properties and ligand efficiency metrics to define property trends across various activity levels (active, efficient and inactive). Decision tree models for CYP inhibition were developed with an accuracy >90% for both the training set and 10-folds cross validation. Overall, molecular weight (MW), hydrogen bond acceptors/donors (HBA/HBD) and lipophilicity (clogP/logPo/w) represent important physicochemical descriptors for CYP450 inhibitors. However, highly efficient CYP inhibitors show mean MW, HBA, HBD and logP values between 294.18–482.40,5.0–8.2,1–7.29 and 1.68–2.57, respectively. Our results might help in optimization of toxicological profiles associated with new chemical entities (NCEs), through a better understanding of inhibitor properties leading to CYP-mediated interactions.

Biotransformation, Using Recombinant CYP450-Expressing Baker's Yeast Cells, Identifies a Novel CYP2D6.10A122V Variant Which Is a Superior Metabolizer of Codeine to Morphine Than the Wild-Type Enzyme

CYP2D6, a cytochrome P450 (CYP) enzyme, metabolizes codeine to morphine. Within the human body, 0-15% of codeine undergoes O-demethylation by CYP2D6 to form morphine, a far stronger analgesic than codeine. Genetic polymorphisms in wild-type CYP2D6 (CYP2D6-wt) are known to cause poor-to-extensive metabolism of codeine and other CYP2D6 substrates. We have established a platform technology that allows stable expression of human CYP genes from chromosomal loci of baker's yeast cells. Four CYP2D6 alleles, (i) chemically synthesized CYP2D6.1, (ii) chemically synthesized CYP2D6-wt, (iii) chemically synthesized CYP2D6.10, and (iv) a novel CYP2D6.10 variant CYP2D6-C (i.e., CYP2D6.10^A122V) isolated from a liver cDNA library, were cloned for chromosomal integration in yeast cells. When expressed in yeast, CYP2D6.10 enzyme shows weak activity compared with CYP2D6-wt and CYP2D6.1 which have moderate activity, as reported earlier. Surprisingly, however, the CYP2D6-C enzyme is far more active than CYP2D6.10. More surprisingly, although CYP2D6.10 is a known low metabolizer of codeine, yeast cells expressing CYP2D6-C transform >70% of codeine to morphine, which is more than twice that of cells expressing the extensive metabolizers, CYP2D6.1, and CYP2D6-wt. The latter two enzymes predominantly catalyze formation of codeine's N-demethylation product, norcodeine, with >55% yield. Molecular modeling studies explain the specificity of CYP2D6-C for O-demethylation, validating observed experimental results. The yeast-based CYP2D6 expression systems, described here, could find generic use in CYP2D6-mediated drug metabolism and also in high-yield chemical reactions that allow the formation of regio-specific dealkylation products.

Microsecond MD simulations of human CYP2D6 wild-type and five allelic variants reveal mechanistic insights on the function

Characterization of cytochrome P450 2D6 (CYP2D6) and the impact of the major identified allelic variants on the activity of one of the most dominating drug-metabolising enzymes is essential to increase drug safety and avoid adverse reactions. Microsecond molecular dynamics simulations have been performed to capture the dynamic signatures of this complex enzyme and five allelic variants with diverse enzymatic activity. In addition to the apo simulations, three substrates (bufuralol, veliparib and tamoxifen) and two inhibitors (prinomastat and quinidine) were included to explore their influence on the structure and dynamical features of the enzyme. Our results indicate that the altered enzyme activity can be attributed to changes in the hydrogen bonding network within the active site, and local structural differences in flexibility, position and shape of the binding pocket. In particular, the increased (CYP2D6*53) or the decreased (CYP2D6*17) activity seems to be related to a change in dynamics of mainly the BC loop due to a modified hydrogen bonding network around this region. In addition, the smallest active site volume was found for CYP2D6*4 (no activity). CYP2D6*2 (normal activity) showed no major differences in dynamic behaviour compared to the wild-type.