Current Drug Metabolism - Volume 15, Issue 5, 2014

Co-administration of drugs is a primary cause of Adverse Drug Reactions (ADRs) and a drain on the health care industry costing billions of dollars and reducing quality of life. Drug-Drug Interactions (DDIs) account for as much as 30% of all ADRs. Unfortunately, DDIs are not systematically explored pre-clinically and are difficult to detect in post-marketing drug surveillance. For this reason, the detection and prediction of DDIs is an important problem in both drug development and pharmacovigilance. The comparison of the 3D drug structures provides a powerful tool for DDI prediction. In this article, we present the first large scale model for predicting DDIs using the drug’s 3D molecular structure. In addition to identifying putative drug interactions we can also isolate the pharmacological or clinical effect associated with the predicted interactions. The model has good performance in two different hold-out validations and in external test sets. We found that the top scored drug pairs were significantly enriched for known clinically relevant interactions and that 3D structure data is providing significantly independent information from other approaches, including 2D structure (p=0.003). We demonstrated the usefulness of the proposed methodology to systematically identify pharmacokinetic and pharmacodynamic interactions, provided an exploratory tool that can be used for patient safety and pre-clinical toxicity screening, and reviewed the state of the art methods used to detect DDIs.

As a kind of monooxygenase with the function of catalyzing many reactions involved in drug metabolism and synthesis of cholesterol, steroids and other lipids, CYP2J2 is an important member of the cytochrome P450 superfamily. Located at the endoplasmic reticulum, CYP2J2 is responsible for epoxidation of endogenous arachidonic acid in cardiac tissue to produce cis-epoxyeicosatrienoic acids (EETs), which have anti-inflammatory and antifibrinolytic properties, and can protect endothelial cells from ischemic or hypoxic injuries. Some polymorphisms, e.g., CYP2J2 with mutation T143A, R158C, I192N or N404Y, could significantly reduce the metabolism of the arachidonic acid, causing or deteriorating the coronary artery disease. However, so far the detailed mechanism for the mutationinduced dysfunction of arachidonic metabolism is still unknown. To reveal its mechanism, a 3D (three-dimensional) structure for human CYP2J2 was developed, followed by docking the arachidonic acid ligand into the active site of the receptor. It was observed based on the binding mode thus found that Gly486 and Leu378 in the active site of the receptor played a key role in recognizing and positioning the carboxyl group of the ligand via hydrogen bonding interactions, and that any of the aforementioned five mutations might have, either directly or indirectly, impact to their role and hence causing the mutation-induced dysfunction of CYP2J2-mediated arachidonic acid metabolism. It is anticipated that the findings as reported in this review article may stimulate new strategy for finding novel therapeutic approaches to treat coronary artery disease.

In the drug discovery cascade, metabolism studies should be performed as early as possible to allow an early evaluation of the metabolism profiles of drug candidates. To help design new drug candidates with improved pharmacokinetics, the knowledge of the site of metabolism is necessary. Computational or in silico metabolism approaches can be broadly classified into (i) ligand-based methods, and (ii) structure-based methods. This review highlight tools used to predict P450-mediated metabolism including ligand-based and structure-based approaches. Some examples of successful application of an integrated in silico approach for the prediction of Phase I metabolism for some flavonoids and lead compounds are presented. Moreover, an integrated in silico approach for the prediction of P450- mediated metabolism is described.

The M2 proton channel is translated by the M gene segment of influenza viruses, and has been adopted as an attractive target for influenza A viruses, on which a series of adamantane-based drugs act. However, recently epidemic influenza viruses have had strong resistant effects against the adamantane-based drugs. In this paper, we combined evolutionary analyses, linkage disequilibrium as well as molecular dynamics simulations to explore the drug resistance of the M2 proton channel, with an aim of providing an in-depth understanding of the resistant mechanism for adamantane-based drugs. We collected 2746 coding sequences for swine, avian, and human M2 proteins. After evolutionary and linkage disequilibrium analyses, we found that the some residues in the C-terminal were associated with the famed resistant mutation S31N. Subsequently, we constructed the 3D structures of the swine, avian as well as human M2 channel, and performed MD simulations on these channels with a typical adamantane-based drug rimantadine. From the simulation trajectories, we found that the resistance against the adamantane-based drugs for the M2 channel from 2009 A(H1N1) viruses was derived from the structural allostery in the transmembrane and C-terminal regions. The helices in the transmembrane region were irregular in formation and employed larger distances between the adjacent 2 helices, which can weaken the interactions between the adjacent 2 helices and destabilize the helix-helix assembly, resulting in a comparatively loosely structure. The helices in the C-terminal region show a disordered configuration, giving chances for solvent molecules to enter into the channel pore.

Plants and their natural components sophisticated with the cornerstone of traditional conventional medicinal system throughout the globe for many years and extend to furnish mankind with latest remedies. Natural Products act as lead molecules for the synthesis of various potent drugs. In the current research a study is conducted on herbal small molecule and their potential binding chemical affinity to the effect or molecules of major diseases such as pancreatic cancer. Clinical studies demonstrate correlation between Cyclin- Dependent Kinase 4 (CDK4) and malignant progression of Pancreatic Cancer. Using Bioruby Gem’s we were able to analyze better characteristics of the target protein. VegaZZ and NAMD were used to minimize the energy of the target protein. Therefore identification of effective, well- tolerated targets was analyzed. Further the target protein was subjected to docking with the anti cancer inhibitors which represents a rational chemo preventive strategy using AutoDock Vina. Later using the dock score top ranked phytochemicals were analyzed for Toxicity Analysis. Using the BioRuby gem we were able to measure the distance between the amino acid. Various R scripting libraries were used to hunt the best leads, as in this case the phytochemicals. Phytochemicals such as Wedelolactones and Catechin were analyzed computationally. This study has presented the various effects of naturally occurring anti pancreatic cancer compounds Catechin, Wedelolactones that inhibits Cyclin Dependent Kinase 4. The study results reveal that compounds use less binding energy to CDK4 and inhibit its activity. Future investigation of other various wet lab studies such as cell line studies will confirm results of these two herbal chemical formulations potential ones for treating Pancreatic Cancer.

Metabolic pools of biological matrices can be extensively analyzed by NMR. Measured data consist of hundreds of NMR signals with different chemical shifts and intensities representing different metabolites’ types and levels, respectively. Relevant predictive NMR signals need to be extracted from the pool using variable selection methods. This paper presents both a review and research on this metabolomics field. After reviews on discriminant potentials and statistical analyses of NMR data in biological fields, the paper presents an original approach to extract a small number of NMR signals in a biological matrix A (BM-A) in order to predict metabolic levels in another biological matrix B (BM-B). Initially, NMR dataset of BM-A was decomposed into several row-column homogeneous blocks using hierarchical cluster analysis (HCA). Then, each block was subjected to a complete set of Jackknifed correspondence analysis (CA) by removing separately each individual (row). Each CA condensed the numerous NMR signals into some principal components (PCs). The different PCs representing the (n – 1) active individuals were used as latent variables in a stepwise multi-linear regression to predict metabolic levels in BM-B. From the built regression model, metabolite level in the outside individual was predicted (for next model validation). From all the PCs-based regression models resulting from all the jackknifed CA applied on all the individuals, the most contributive NMR signals were identified by their highest absolute contributions to PCs. Finally, these selected NMR signals (measured in BMA) were used to build final population and sub-population regression models predicting metabolite levels in BM-B.

Topological Indices (TIs) are numerical parameters useful to carry out Quantitative Structure-Property Relationships (QSPR) analysis and predict the effect of perturbations in many types of Complex Networks. This work, focuses on a very powerful class of TIs called Galvez charge transfer indices. First, we review the classic concept and some applications of these indices. Next, we review the Galvez-Markov TIs of order k (GMk), a recent generalization to these TIs introduced by us. We also reviewed some previous examples of calculation of GMk values for different classes of networks, including metabolic networks. Here, we also demonstrated that Galvez- Markov TIs are useful to predict perturbations and the transferability of biochemical patterns forms metabolic networks of species to others. We report a linear QSPR-Perturbation theory model that predicts more than 300,000 perturbations in metabolic networks with 85 – 99% of good classification in training and validation series.