Predicting drug metabolism: experiment and/or computation?

Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.This is the accepted manuscript of a paper published in Nature Reviews Drug Discovery (Kirchmair J, Göller AH, Lang D, Kunze J, Testa B, Wilson ID, Glen RC, Schneider G, Nature Reviews Drug Discovery, 2015, 14, 387–404, doi:10.1038/nrd4581). The final version is available at http://dx.doi.org/10.1038/nrd458

Multivariate Modeling of Cytochrome P450 Enzymes for 4- Aminoquinoline Antimalarial Analogues using Genetic- Algorithms Multiple Linear Regression

Purpose: To develop QSAR modeling of the inhibition of cytochrome P450s (CYPs) by chloroquine and a new series of 4-aminoquinoline derivatives in order to obtain a set of predictive in-silico models using genetic algorithms-multiple linear regression (GA-MLR) methods.Methods: Austin model 1 (AM1) semi-empirical quantum chemical calculation method was used to find the optimum 3D geometry of the studied molecules. The relevant molecular descriptors were selected by genetic algorithm-based multiple linear regression (GA-MLR) approach. In silico predictive models were generated to predict the inhibition of CYP 2B6, 2C9, 2C19, 2D6, and 3A4 isoforms using a set of descriptors.Results: The results obtained demonstrate that our model is capable of predicting the potential of new drug candidates to inhibit multiple CYP isoforms. A cross-validated Q2 test and external validation showed that the models were robust. By inspection of R2pred, and RMSE test sets, it can be seen that the predictive ability of the different CYP models varies considerably.Conclusion: Apart from insights into important molecular properties for CYP inhibition, the findings may also guide further investigations of novel drug candidates that are unlikely to inhibit multiple CYP sub-types.Keywords: Antimalarial, Chloroquine, Cytochrome P450, Genetic algorithm-based multiple linear regression, QSAR

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.JK, MJW, JT, PJB, AB and RCG thank Unilever for funding

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.publishedVersio

Study on herb-drug interactions

Herbal medicines, such as St John's wort, garlic, gingko, and ginseng, are commonly used complementary therapies. The human CYP enzymes are a superfamily which consists of at least 57 functional CYP genes. Among them, CYP1A2, 2C9, 2C19, 2D6 and 3A4/5 are the most important enzymes responsible for the Phase I metabolism of therapeutic drugs. When different compounds (e.g., a drug and herbal compound) are co-administered, they may compete at the same active site of CYPs, resulting in potential inhibition. We hypothesize that the atom-atom interactions between the ligands and the residues at the active site of CYPs determine the substrate and inhibitor specificity of individual CYPs. To test our hypothesis, we conducted a series of experiments including in vitro assays to determine inhibitory actions of a variety of natural compounds on human CYPs, pharmacokinetic-based predication of in vivo situation using the in vitro data; and in silico studies to explore the ligand-CYP interactions using docking and pharmacophore modeling methods. We first determined the inhibitory effects (IC50) of 56 herbal compounds on activities of five human drug metabolising CYPs (CYP1A2, 2C9, 2C19, 2D6 and 3A4) in vitro using a high throughput approach. The tested herbal components included a variety of structurally distinct compounds such as triterpenoids of danshen (Salvia miltiorrhiza), flavonoids and their glycoside derivatives, saponine, other glucosides, lactones, alkaloids, and acids. A small number of them are found to significantly inhibit human CYP1A2, 2C9, 2C19, 2D6 and 3A4 with differential potency, including tanshinone I, tanshinone IIA, cryptotanshinone, baicalein, quercetin, silybin, osthole and ч-schisandrin. Based on the in vitro data obtained, we predicted metabolic herb-drug interactions of these compounds in vivo with the application of appropriate pharmacokinetic principles. Some predicting results were consistent with published clinical reports. For example, the prediction of S. miltiorrhiza increasing the AUC value of warfarin is consistent with the results from clinical case reports. However, a marked disparity has been observed when some predictions are compared with results from clinical studies. For example, the prediction of S. mariani (containing silybin) increasing the AUC of indinavir (a CYP3A4 substrate) is not in agreement with the result of a clinical report where the plasma concentration of indinavir was not altered by co-administered silymarin in healthy volunteers. Finally, we studied the interactions of a series of ligands including substrates and inhibitors with CYP1A2 using docking and pharmacophore modeling approaches. We have identified 6 residues at the active site of CYP1A2 which are essential for ligand recognition. Furthermore, the relative potency of potential inhibitors could be predicted through analysis of hydrophobic interactions between the ligand and the 6 essential residues at the active site of CYP1A2. Moreover, we developed a pharmacophore model on the basis of the common features of known CYP1A2 inhibitors. In combination with the docking results, the established pharmacophore model could be applied for screening novel CYP1A2 inhibitors