Current Drug Metabolism (v.18, #6)
Meet Our Editorial Board Member by David A. Rodrigues (497-497).
Editorial: Theoretical Studies of the Metabolism in Drug Discovery by Marcus T. Scotti, Luciana Scotti (498-499).
CORAL Software: Analysis of Impacts of Pharmaceutical Agents Upon Metabolism via the Optimal Descriptors by Mariya A. Toropova, Ivan Raska Jr, Alla P. Toporova, Maria Raskova (500-510).
Backgrounds: The CORAL software has been developed as a tool to build up quantitative structure- activity relationships (QSAR) for various endpoints. Objective: The task of the present work was to estimate and to compare QSAR models for biochemical activity of various therapeutic agents, which are built up by the CORAL software. Method: The Monte Carlo technique gives possibility to build up predictive model of an endpoint by means of selection of so-called correlation weights of various molecular features extracted from simplified molecular input-line entry system (SMILES). Descriptors calculated with these weights are basis for building up correlations "structure - endpoint". Results: Optimal descriptors, which are aimed to predict values of endpoints with apparent influence upon metabolism are crytically compared in aspect of their robustness and heuristic potential. Arguments which are confirming the necessity of reformulation of basics of QSARs are listed: (i) each QSAR model is stochastic experiment. The result of this experiment is defined by distribution into the training set and validation set; (ii) predictive potential of a model should be checked up with a group of different splits; and (iii) only model stochastically stable for a group of splits can be estimated as a reliable tool for the prediction. Examples of the improvement of the models previously suggested are demonstrated. Conclusion: The current version of the CORAL software remains a convenient tool to build up predictive models. The Monte Carlo technique involved for the software confirms the principle “QSAR is a random event” is important paradigm for the QSPR/QSAR analyses.
Quantum Mechanical (QM) Calculations Applied to ADMET Drug Prediction: A Review by E. F. Silva-Júnior, T. M. Aquino, J. X. Araújo-Júnior (511-526).
The discovery of new drugs is generally considered a long and expensive process, which often leads to molecules with low efficacy and high toxicity, which in many cases can be related to metabolism. In an attempt to reduce these failures and the production costs of a new drug, in silico studies have been used to obtain important information about the behavior of these compounds in the metabolism phases: absorption, distribution, metabolism (or biotransformation) and elimination (or excretion). Quantum Mechanical (QM) calculations are based on Schrödinger's equation that can be used to develop models and theoretical parameters able to explain properties observed experimentally. In recent years, there has been an increase in the development of studies involving the application of QM methods to describe properties related to ADMET profile of new compounds. Amongst these, the most commonly used methods are ab initio (Hartree-Fock), Semiempirical (AM1 and PM3) and Density Functional Theory (DFT). The application of these methods allows the modeling of the predicted profile of absorption and elimination of chalcone-chloroquinoline hybrids; the ability of drugs to cross the blood-brain barrier (distribution); proposal of the route for oxidation of several compounds, via CYP450; and to predict the toxicity of pyrethroid analogs. Finally, QM methods can be considered as a valuable tool in the prediction of metabolism when applied to drug discovery.
Helix-Coil Transition Signatures B-Raf V600E Mutation and Virtual Screening for Inhibitors Directed Against Mutant B-Raf by Srinivas Bandaru, Tharaparambil Gangadharan Sumithnath, Saphy Sharda, Sanskruti Lakhotia, Anudeep Sharma, Amrita Jain, Tajamul Hussain, Anuraj Nayarisseri, Sanjeev Kumar Singh (527-534).
Background: Mutation in the B RAF at V600E has been well implicated in the carcinogenesis that makes it as an attractive therapeutictarget. In the present study, we sought to identify the basis of V600E mutation at functional and structural grounds. The study also endeavors in identification of small molecule as a potential candidate with considerable pharmacological profile than available BRAF inhibitors through computational approaches. Methods: The functional effects of V600E mutation was predicted using SIFT and Polyphen servers. Protein structural alterations werepredicted using SDM server and RMSD calculations. Virtual screening was performed considering existing BRAF inhibitors viz., Vemurafenib, Sorafenib, Dabrfenib, Trametinibthat formed query compounds for shape similarity search by Tanimoto similarity indices with a threshold of 95%. Compound with high affinity as similar to query compound was retrieved and screened for its ADMET properties. Results: The SNP was shown to be highly vulnerable to malfunction and have damaging effects. Mutated protein showed that the secondary structure was irregular and side chain hydrogen bonds were unsaturated. The superimposition of wild onto mutated V600E BRAF revealed helix-coil transition occurring wherein residues Val 502, Leu 505, Arg506, Lys 507 assumed coiled conformation in the mutated BRAF. Virtual screening led to identification of SCHEMBL298689 akin to Vemurafenib as high affinity B-Raf inhibitors; with least toxicity and optimal bioactivity. Conclusion: In the present investigation, we put forth the structural and functional basis of B RAF V600E mutation showing helix coil transitions. In addition identified high affinity compound targeting V600E B RAF through virtual screening
In-silico ADME Studies for New Drug Discovery: From Chemical Compounds to Chinese Herbal Medicines by Guojun Yan, Xiaobing Wang, Zhou Chen, Xianhui Wu, Jinhuo Pan, Yushen Huang, Gang Wan, Zhaogang Yang (535-539).
Nowadays, in silico tools are widely used to provide the potential structure of the metabolites formed depending on the site of metabolism. These methods can also highlight the molecular moieties that help to direct the molecule into the cytochrome cavity so that the site of metabolism is in proximity to the catalytic center. In this minireview, we summarized three aspects of the in silico methods in the application of prediction of ADME (absorption, distribution, metabolism and excretion) properties of compounds: structure-based approaches for predicting molecular modeling of drug metabolizing enzymes; in silico metabolite prediction; and pharmacophore models for analysis substrate specificity. Moreover, we also extended the in silico studies in Chinese herbal medicines (CHM) research.
Towards Predicting the Cytochrome P450 Modulation: From QSAR to Proteochemometric Modeling by Watshara Shoombuatong, Philip Prathipati, Veda Prachayasittikul, Nalini Schaduangrat, Aijaz Ahmad Malik, Reny Pratiwi, Sompon Wanwimolruk, Jarl E. S. Wikberg, Matthew Paul Gleeson, Ola Spjuth, Chanin Nantasenamat (540-555).
Drug metabolism determines the fate of a drug when it enters the human body and is a critical factor in defining their absorption, distribution, metabolism, excretion and toxicity (ADMET) characteristics. Among the various drug metabolizing enzymes, cytochrome P450s (CYP450) constitute an important protein family that aside from functioning in xenobiotic metabolism, is also responsible for a diverse array of other roles encompassing steroid and cholesterol biosynthesis, fatty acid metabolism, calcium homeostasis, neuroendocrine functions and growth regulation. Although CYP450 typically converts xenobiotics into safe metabolites, there are some situations whereby the metabolite is more toxic than its parent molecule. Computational modeling has been instrumental in CYP450 research by rationalizing the nature of the binding event (i.e. inhibit or induce CYP450s) or metabolic stability of query compounds of interest. A plethora of computational approaches encompassing ligand, structure and systems based approaches have been utilized to model CYP450-ligand interactions. This review provides a brief background on the CYP450 family (i.e. its roles, advantages and disadvantages as well as its modulators) and then discusses the various computational approaches that have been used to model CYP450-ligand interaction. Particular focus was given to the use of quantitative structure-activity relationship (QSAR) and more recent proteochemometric modeling studies. Finally, a perspective on the current state of the art and future trends of the field is also provided.
Drug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools by Naiem T. Issa, Henri Wathieu, Abiola Ojo, Stephen W. Byers, Sivanesan Dakshanamurthy (556-565).
Background: While establishing efficacy in translational models and humans through clinically-relevant endpoints for disease is of great interest, assessing the potential toxicity of a putative therapeutic drug is critical. Toxicological assessments in the pre-clinical discovery phase help to avoid future failure in the clinical phases of drug development. Many in vitro assays exist to aid in modular toxicological assessment, such as hepatotoxicity and genotoxicity. While these methods have provided tremendous insight into human toxicity by investigational new drugs, they are expensive, require substantial resources, and do not account for pharmacogenomics as well as critical ADME properties. Computational tools can fill this niche in toxicology if in silico models are accurate in relating drug molecular properties to toxicological endpoints as well as reliable in predicting important drug-target interactions that mediate known adverse events or adverse outcome pathways (AOPs). Methods: We undertook an unstructured search of multiple bibliographic databases for peer-reviewed literature regarding computational methods in predictive toxicology for in silico drug discovery. As this review paper is meant to serve as a survey of available methods for the interested reader, no focused criteria were applied. Literature chosen was based on the writers' expertise and intent in communicating important aspects of in silico toxicology to the interested reader. Conclusion: This review provides a purview of computational methods of pre-clinical toxicologic assessments for novel small molecule drugs that may be of use for novice and experienced investigators as well as academic and commercial drug discovery entities.
In silico and In vivo Toxicological Evaluation of Cissampelos Sympodialis Secondary Metabolites in Rattus Norvegicus by Mateus F. Alves, Marcus T. Scotti, Mayara B. Felix, Hamilton M. Ishiki, Cinthia Rodrigues Melo, Frederico F. Ribeiro, Josean F. Tavares, José M.B. .Filho, Kardilândia M. de Oliveira, Andrea F. R. de Paula, Francisco J. B. Mendonça Jr., Luciana Scotti, Alexandre R. da Paz, Sócrates G. dos Santos, Margareth de F. F. M. Diniz (566-576).
Cissampelos sympodialis is a plant in northeastern Brazil used by the populace for treating respiratory diseases. Several studies have shown that ethanol leaf extracts have immunomodulatory and anti-inflammatory activities. Infusions are widely used, popular, and an ancient technique in traditional medicine, using hot water alone as the means of extraction. This study aimed to investigate acute toxicological potential of leaf infusions of Cissampelos sympodialis, when applied orally at a dose of 2000mg/kg to Rattus norvegicus, combined with an in silico study of 117 alkaloids present in the Cissampelos genus; five (5) of which were determined to have high toxicity (21, 8, 93, 32 and 88), and five (5) having both low toxicity (57, 77, 28, 25 and 67) and low liver metabolism. The in vivo toxicological evaluation showed that male water consumption decreased, and the feed intake decreased in both sexes. Yet, the figures as to change in weight gain of the animals were not statistically sufficient. As for the biochemical parameters, there was an increase in urea, and decreases in uric acid and AST in males. In females, there was a decrease in albumin and globulin which consequently leads to a total protein decrease. Despite biochemical changes suggestive of kidney damage, the histological sections revealed no kidney or liver changes. The results therefore indicate that despite presenting alkaloids which may be toxic, the genus Cissampelos, or leaf infusions of Cissampelos sympodialis, when applied orally at a dose of 2000mg/kg present low toxicity.
In-silico Design and ADMET Studies of Natural Compounds as Inhibitors of Xanthine Oxidase (XO) Enzyme by Neelam Malik, Priyanka Dhiman, Anurag Khatkar (577-593).
Background: Xanthine oxidase a ubiquitous enzyme has been found to be involved in various pathological disorders including gout, hyperuricemia, inflammation, oxidative stress and cardiovascular diseases. Inhibitors of xanthine oxidase thus find a crucial role in the therapeutic treatment of these deadly diseases. Objective: Considering the side effects of today's treatment regimen here we choose nature based compounds to act as xanthine oxidase inhibitors. In the present work, we performed in-silico docking of natural compounds to reveal the underlying mechanism of inhibition of xanthine oxidase. Further filtration of screened compounds with ADMET studies has been performed. Method: An in-house library of natural compounds screened through ADMET profile for the drug likeliness property was approached for docking studies using Schrödinger suite. Calculation of docking score was done by glide module and free binding energy calculations were performed through MM/GBSA software. Results: Natural leads having better pharmacokinetic profile and mechanism of inhibition were obtained. Docking score, binding energy and different forces involved in interaction were calculated for the top-ranked molecules and good comparison with the standard drugs was achieved Conclusion: Compounds having potential therapeutic activity with low systematic toxicity has been identified against xanthine oxidase which could serve as pharmacophore for the design and synthesis of new drug-like molecules