Predicting the binding modes and sites of metabolism of xenobiotics

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.

Understanding the metabolism of the anticancer drug Triapine: electrochemical oxidation, microsomal incubation and in vivo analysis using LC-HRMS

α-N-Heterocyclic thiosemicarbazones are among the most promising ribonucleotide reductase inhibitors identified so far. Triapine, the most prominent representative of this class of substances, has been investigated in multiple phase I and II clinical trials. With regard to clinical practice, Triapine showed activity against hematological diseases, but ineffectiveness against a variety of solid tumors. However, the reasons are still vague and the amount of ADME (absorption, distribution, metabolism and excretion) data for Triapine available in the literature is very limited. Therefore, different analytical tools were used to investigate the metabolism of Triapine including electrochemical oxidations, liver microsomes and in vivo samples from mice. The main metabolic reactions, observed by all three methods, were dehydrogenation and hydroxylations, confirming that electrochemistry, as a purely instrumental approach, can be applied for the simulation of metabolic pathways. The dehydrogenated metabolite M1 was identified as a thiadiazole ring-closed oxidation product of Triapine. From a biological point of view, M1, as a key metabolite, is of interest since the crucial chemical property of α-N-heterocyclic thiosemicarbazones to bind metal ions is lost and cytotoxicity studies showed no anticancer activity of M1. The in vivo data of the urine samples revealed very high levels of the metabolites and Triapine itself already 15 min after treatment. This clearly indicates that Triapine is rapidly metabolised and excreted, which represents an important step forward to understand the possible reason for the inefficiency of Triapine against solid tumors.

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.

Interactions of omeprazole-based analogues with cytochrome P450 2C19: a computational study

Cytochrome P450 2C19 (CYP2C19) is one of 57 drug metabolizing enzymes in humans and is responsible for the metabolism of ∼7-10% of drugs in clinical use. Recently omeprazole-based analogues were reported to be the potent inhibitors of CYP2C19 and have the potential to be used as the tool compounds for studying the substrate selectivity of CYP2C19. However, the binding modes of these compounds with CYP2C19 remain to be elucidated. In this study, a combination of molecular docking, molecular dynamics (MD), and MM/GBSA calculations was employed to systematically investigate the interactions between these compounds and CYP2C19. The binding modes of these analogues were analyzed in detail. The results indicated that the inclusion of explicit active site water molecules could improve binding energy prediction when the water molecules formed a hydrogen bonding network between the ligand and protein. We also found that the effect of active site water molecules on binding free energy prediction was dependent on the ligand binding modes. Our results unravel the interactions of these omeprazole-based analogues with CYP2C19 and might be helpful for the future design of potent CYP2C19 inhibitors with improved metabolic properties.

Exploring β-Tubulin Inhibitors from Plant Origin using Computational Approach

INTRODUCTION

β-Tubulin is an important target for the binding of anti-cancer drugs, in particular, paclitaxel (taxol), vinblastine and epothilone. However, mutations in β-tubulin structure give resistance to chemotherapeutic agents. Notably, mutations at R306C, F270 V, L217R, L228F, A185T and A248V positions in β-tubulin give high resistance for paclitaxel binding.

OBJECTIVE

To discover novel inhibitors of β-tubulin from natural sources, particularly alkaloids, using a virtual screening approach.

METHODOLOGY

A virtual screening approach was employed to find potent lead molecules from the Naturally-occurring Plant-based Anti-cancer Compound-activity Target (NPACT) database. Alkaloids have great potential to be anti-cancer agents. Therefore, we have screened all alkaloids from a total of 1574 molecules from the NPACT database for our study. Initially, Molinspiration and DataWarrior programs were utilised to calculate pharmacokinetics and toxicity risks of the alkaloids, respectively. Subsequently, AutoDock algorithm was employed to understand the binding efficiency of alkaloids against β-tubulin. The binding affinity of the docked complex was confirmed by means of an intermolecular interaction study. Moreover, oral toxicity was predicted by using ProTox program. Further, metabolising capacity of drugs was studied by using SmartCYP software. Additionally, scaffold analysis was done with the help of scaffold trees and dendrograms, providing knowledge about the building blocks for parent-compound synthesis.

RESULTS

Overall, the results of our computational analysis indicate that isostrychnine, obtained from Strychnosnux-vomica, satisfies pharmacokinetic and bioavailability properties, binds efficiently with β-tubulin. Thus, it could be a promising lead for the treatment of paclitaxel resistant cancer types.

CONCLUSION

This is the first observation of inhibitory activity of isostrychnine against β-tubulin and warrants further experimental investigation. Copyright © 2016 John Wiley & Sons, Ltd.

Analysis of Species-Selectivity of Human, Mouse and Rat Cytochrome P450 1A and 2B Subfamily Enzymes using Molecular Modeling, Docking and Dynamics Simulations

Cytochrome P450 (CYP) 1A and 2B subfamily enzymes are important drug metabolizing enzymes, and are highly conserved across species in terms of sequence homology. However, there are major to minor structural and macromolecular differences which provide for species-selectivity and substrate-selectivity. Therefore, species-selectivity of CYP1A and CYP2B subfamily proteins across human, mouse and rat was analyzed using molecular modeling, docking and dynamics simulations when the chiral molecules quinine and quinidine were used as ligands. The three-dimensional structures of 17 proteins belonging to CYP1A and CYP2B subfamilies of mouse and rat were predicted by adopting homology modeling using the available structures of human CYP1A and CYP2B proteins as templates. Molecular docking and dynamics simulations of quinine and quinidine with CYP1A subfamily proteins revealed the existence of species-selectivity across the three species. On the other hand, in the case of CYP2B subfamily proteins, no role for chirality of quinine and quinidine in forming complexes with CYP2B subfamily proteins of the three species was indicated. Our findings reveal the roles of active site amino acid residues of CYP1A and CYP2B subfamily proteins and provide insights into species-selectivity of these enzymes across human, mouse, and rat.

Effects of protein flexibility and active site water molecules on the prediction of sites of metabolism for cytochrome P450 2C19 substrates

Structure-based prediction of sites of metabolism (SOMs) mediated by cytochrome P450s (CYPs) is of great interest in drug discovery and development.