DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs

DrugBank (http://www.drugbank.ca) is a richly annotated database of drug and drug target information. It contains extensive data on the nomenclature, ontology, chemistry, structure, function, action, pharmacology, pharmacokinetics, metabolism and pharmaceutical properties of both small molecule and large molecule (biotech) drugs. It also contains comprehensive information on the target diseases, proteins, genes and organisms on which these drugs act. First released in 2006, DrugBank has become widely used by pharmacists, medicinal chemists, pharmaceutical researchers, clinicians, educators and the general public. Since its last update in 2008, DrugBank has been greatly expanded through the addition of new drugs, new targets and the inclusion of more than 40 new data fields per drug entry (a 40% increase in data ‘depth’). These data field additions include illustrated drug-action pathways, drug transporter data, drug metabolite data, pharmacogenomic data, adverse drug response data, ADMET data, pharmacokinetic data, computed property data and chemical classification data. DrugBank 3.0 also offers expanded database links, improved search tools for drug–drug and food–drug interaction, new resources for querying and viewing drug pathways and hundreds of new drug entries with detailed patent, pricing and manufacturer data. These additions have been complemented by enhancements to the quality and quantity of existing data, particularly with regard to drug target, drug description and drug action data. DrugBank 3.0 represents the result of 2 years of manual annotation work aimed at making the database much more useful for a wide range of ‘omics’ (i.e. pharmacogenomic, pharmacoproteomic, pharmacometabolomic and even pharmacoeconomic) applications

Perspective: Dietary Biomarkers of Intake and Exposure - Exploration with Omics Approaches

While conventional nutrition research has yielded biomarkers such as doubly labeled water for energy metabolism and 24-h urinary nitrogen for protein intake, a critical need exists for additional, equally robust biomarkers that allow for objective assessment of specific food intake and dietary exposure. Recent advances in high-throughput MS combined with improved metabolomics techniques and bioinformatic tools provide new opportunities for dietary biomarker development. In September 2018, the NIH organized a 2-d workshop to engage nutrition and omics researchers and explore the potential of multiomics approaches in nutritional biomarker research. The current Perspective summarizes key gaps and challenges identified, as well as the recommendations from the workshop that could serve as a guide for scientists interested in dietary biomarkers research. Topics addressed included study designs for biomarker development, analytical and bioinformatic considerations, and integration of dietary biomarkers with other omics techniques. Several clear needs were identified, including larger controlled feeding studies, testing a variety of foods and dietary patterns across diverse populations, improved reporting standards to support study replication, more chemical standards covering a broader range of food constituents and human metabolites, standardized approaches for biomarker validation, comprehensive and accessible food composition databases, a common ontology for dietary biomarker literature, and methodologic work on statistical procedures for intake biomarker discovery. Multidisciplinary research teams with appropriate expertise are critical to moving forward the field of dietary biomarkers and producing robust, reproducible biomarkers that can be used in public health and clinical research

In silico prediction of metabolism as a tool to identify new metabolites of dietary terpenes

Dietary terpenes have been little studied, despite the fact that they are well absorbed and display a range of biological properties. Better knowledge about their metabolism will help understanding the health effects of plant foods, herbs and spices and may provide new biomarkers of food intake. As part of the FoodBAll project we are investigating the metabolism of terpenes, identifying metabolites and biotransformations involved in their metabolism.PhytoHub (database that compiles all known metabolites of dietary phytochemicals, including terpenes) and Nexus Meteor (in silico prediction of metabolism), were used to identify biotransformations involved in the metabolism of monoterpenoids. Selected biotransformations were used to predict the metabolism of camphene, camphor, carvacrol, carvone, caryophyllene, 1,4-cineole, 1,8-cineole, citral, citronellal, cuminaldehyde, p-cymene, fenchone, geraniol, limonene, linalool, menthol, myrcene, nootkatone, perillyl alcohol, pinene, pulegone, terpinen-4-ol and thymol. Wistar rats received a chemically defined diet with or without 0.05% of the referred compounds. Before and after 5 days of the exposure to the dietary monoterpenes, urine was collected and untargeted metabolomics analysis performed using high-resolution mass spectrometry (UPLC-QToF). We identified twenty-two enzymatic reactions involved in the metabolism of monoterpenoids leading to the synthesis of monoterpenoid metabolites described in the literature. In average, 10 metabolites per compound were identified in rat urine, including new and known ones. Identification of metabolites was based on monoisotopic mass and formula match, presence of adducts and specific mass losses indicative of glucuronidation and conjugation to amino acids. Validation of identification is being done using orbitrap MS/MS and hydrogen-deuterium exchange experiments.The combination of in silico prediction and in vivo experiment allowed the identification of known and new metabolites of different dietary terpenoids. Predicted metabolites of terpenes will be added in PhytoHub to complement the database of known metabolites

BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification

BackgroundA number of computational tools for metabolism prediction have been developed over the last 20years to predict the structures of small molecules undergoing biological transformation or environmental degradation. These tools were largely developed to facilitate absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, although there is now a growing interest in using such tools to facilitate metabolomics and exposomics studies. However, their use and widespread adoption is still hampered by several factors, including their limited scope, breath of coverage, availability, and performance.ResultsTo address these limitations, we have developed BioTransformer, a freely available software package for accurate, rapid, and comprehensive in silico metabolism prediction and compound identification. BioTransformer combines a machine learning approach with a knowledge-based approach to predict small molecule metabolism in human tissues (e.g. liver tissue), the human gut as well as the environment (soil and water microbiota), via its metabolism prediction tool. A comprehensive evaluation of BioTransformer showed that it was able to outperform two state-of-the-art commercially available tools (Meteor Nexus and ADMET Predictor), with precision and recall values up to 7 times better than those obtained for Meteor Nexus or ADMET Predictor on the same sets of pharmaceuticals, pesticides, phytochemicals or endobiotics under similar or identical constraints. Furthermore BioTransformer was able to reproduce 100% of the transformations and metabolites predicted by the EAWAG pathway prediction system. Using mass spectrometry data obtained from a rat experimental study with epicatechin supplementation, BioTransformer was also able to correctly identify 39 previously reported epicatechin metabolites via its metabolism identification tool, and suggest 28 potential metabolites, 17 of which matched nine monoisotopic masses for which no evidence of a previous report could be found.ConclusionBioTransformer can be used as an open access command-line tool, or a software library. It is freely available at https://bitbucket.org/djoumbou/biotransformerjar/. Moreover, it is also freely available as an open access RESTful application at www.biotransformer.ca, which allows users to manually or programmatically submit queries, and retrieve metabolism predictions or compound identification data