DrugCentral is a drug information resource (http://drugcentral.org) open to the public since 2016 and previously described in the 2017 Nucleic Acids Research Database issue. Since the 2016 release, 103 new approved drugs were updated. The following new data sources have been included: Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), FDA Orange Book information, L1000 gene perturbation profile distance/similarity matrices and estimated protonation constants. New and existing entries have been updated with the latest information from scientific literature, drug labels and external databases. The web interface has been updated to display and query new data. The full database dump and data files are available for download from the DrugCentral website.

Personalized drug screening (PDS) of approved drug libraries enables rapid development of specific small-molecule therapies for individual patients. With a multidisciplinary team including clinicians, researchers, ethicists, informaticians and regulatory professionals, patient treatment can be optimized with greater efficacy and fewer adverse effects by using PDS as an approach to find remedies. In addition, PDS has the potential to rapidly identify therapeutics for a patient suffering from a disease without an existing therapy. From cancer to bacterial infections, we review specific maladies addressed with PDS campaigns. We predict that PDS combined with personal genomic analyses will contribute to the development of future precision medicine endeavors.

A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately two out of five proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development.

BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. RESULTS: As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

The 'druggable genome' encompasses several protein families, but only a subset of targets within them have attracted significant research attention and thus have information about them publicly available. The Illuminating the Druggable Genome (IDG) program was initiated in 2014, has the goal of developing experimental techniques and a Knowledge Management Center (KMC) that would collect and organize information about protein targets from four families, representing the most common druggable targets with an emphasis on understudied proteins. Here, we describe two resources developed by the KMC: the Target Central Resource Database (TCRD) which collates many heterogeneous gene/protein datasets and Pharos (https://pharos.nih.gov), a multimodal web interface that presents the data from TCRD. We briefly describe the types and sources of data considered by the KMC and then highlight features of the Pharos interface designed to enable intuitive access to the IDG knowledgebase. The aim of Pharos is to encourage 'serendipitous browsing', whereby related, relevant information is made easily discoverable. We conclude by describing two use cases that highlight the utility of Pharos and TCRD.

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

Advancement of in silico tools would be enabled by availability of data for metabolic reaction rates and intrinsic clearance (CLint) of a diverse compound structure dataset by specific metabolic enzymes. Our goal is to measure CLint for a large set of compounds with each major human cytochrome P450 (CYP) isozyme. In order to achieve our goal, it is of utmost importance to develop an automated, robust, sensitive, high-throughput metabolic stability assay that can efficiently handle large volume of compound sets. The substrate depletion method (in vitro half-life (t1/2) method) was chosen to determine CLint The assay (384-well format) consisted of three parts: a robotic system for incubation and sample clean up; two different, integrated, ultra-performance liquid chromatography/mass spectrometry (UPLC/MS) platforms to determine the percent remaining of parent compound, and an automated data analysis system. The CYP3A4 assay was evaluated using two long-t1/2 compounds, carbamazepine and antipyrine (t1/2>30 min), one moderate-t1/2 compound, ketoconazole (10<t1/2<30 min), and two short-t1/2 compounds, loperamide and buspirone (t1/2<10 min). Inter-day and intra-day precision and accuracy of the assay was within acceptable range (~12%) for the linear range observed. Using this assay, CYP3A4 CLint and t1/2 values for more than 3000 compounds were measured. This high-throughput, automated, and robust assay allows for rapid metabolic stability screening of large compound sets and enables advanced computational modeling for individual human CYP isozymes.

BARD, the BioAssay Research Database (https://bard.nih.gov/) is a public database and suite of tools developed to provide access to bioassay data produced by the NIH Molecular Libraries Program (MLP). Data from 631 MLP projects were migrated to a new structured vocabulary designed to capture bioassay data in a formalized manner, with particular emphasis placed on the description of assay protocols. New data can be submitted to BARD with a user-friendly set of tools that assist in the creation of appropriately formatted datasets and assay definitions. Data published through the BARD application program interface (API) can be accessed by researchers using web-based query tools or a desktop client. Third-party developers wishing to create new tools can use the API to produce stand-alone tools or new plug-ins that can be integrated into BARD. The entire BARD suite of tools therefore supports three classes of researcher: those who wish to publish data, those who wish to mine data for testable hypotheses, and those in the developer community who wish to build tools that leverage this carefully curated chemical biology resource.

The microtubule-associated protein (MAP) tau has been implicated in the pathology of numerous neurodegenerative diseases. In the past decade, the hyperphosphorylated and aggregated states of tau protein have been important targets in the drug discovery field for the potential treatment of Alzheimer's disease. Although several compounds have been reported to reduce the hyperphosphorylated state of tau or impact the stabilization of tau, their therapeutic activities are remain to be validated. Recently, reduction of total cellular tau protein has emerged as an alternate intervention point for drug development and a potential treatment of tauopathies. We have developed and optimized homogenous assays, using the AlphaLISA and HTRF assay technologies, for the quantification of total cellular tau protein levels in the SH-SY5Y neuroblastoma cell line. The signal-to-basal ratios were 375 and 5.3, and the Z' factors were 0.67 and 0.60 for the AlphaLISA and HTRF tau assays, respectively. The clear advantages of these homogeneous tau assays over conventional total tau assays, such as ELISA and Western blot, are the elimination of plate wash steps and miniaturization of the assay into 1536-well plate format for the ultra-high-throughput screening of large compound libraries.

Cryptococcus neoformans causes an estimated 600,000 AIDS-related deaths annually that occur primarily in resource-limited countries. Fluconazole and amphotericin B are currently available for the treatment of cryptococcal-related infections. However, fluconazole has limited clinical efficacy and amphotericin B requires intravenous infusion and is associated with high renal toxicity. Therefore, there is an unmet need for a new orally administrable anti-cryptococcal drug. We have developed a high-throughput screening assay for the measurement of C. neoformans viability in 1,536-well plate format. The signal-to-basal ratio of the ATP content assay was 21.9 fold with a coefficient of variation and Z' factor of 7.1% and 0.76, respectively. A pilot screen of 1,280 known compounds against the wild-type C. neoformans (strain H99) led to the identification of four active compounds including niclosamide, malonoben, 6-bromoindirubin-3'-oxime, and 5-[(4-ethylphenyl)methylene]-2-thioxo-4-thiazolidinone. These compounds were further tested against nine clinical isolates of C. neoformans, and their fungicidal activities were confirmed. The results demonstrate that this miniaturized C. neoformans assay is advantageous for the high-throughput screening of large compound collections to identify lead compounds for new anti-cryptococcal drug development.