DrugCentral 2021 supports drug discovery and repositioning.

DrugCentral is a public resource (http://drugcentral.org) that serves the scientific community by providing up-to-date drug information, as described in previous papers. The current release includes 109 newly approved (October 2018 through March 2020) active pharmaceutical ingredients in the US, Europe, Japan and other countries; and two molecular entities (e.g. mefuparib) of interest for COVID19. New additions include a set of pharmacokinetic properties for ∼1000 drugs, and a sex-based separation of side effects, processed from FAERS (FDA Adverse Event Reporting System); as well as a drug repositioning prioritization scheme based on the market availability and intellectual property rights forFDA approved drugs. In the context of the COVID19 pandemic, we also incorporated REDIAL-2020, a machine learning platform that estimates anti-SARS-CoV-2 activities, as well as the 'drugs in news' feature offers a brief enumeration of the most interesting drugs at the present moment. The full database dump and data files are available for download from the DrugCentral web portal.

Publisher Correction: A mass spectrometry-based proteome map of drug action in lung cancer cell lines.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A mass spectrometry-based proteome map of drug action in lung cancer cell lines.

Mass spectrometry-based discovery proteomics is an essential tool for the proximal readout of cellular drug action. Here, we apply a robust proteomic workflow to rapidly profile the proteomes of five lung cancer cell lines in response to more than 50 drugs. Integration of millions of quantitative protein-drug associations substantially improved the mechanism of action (MoA) deconvolution of single compounds. For example, MoA specificity increased after removal of proteins that frequently responded to drugs and the aggregation of proteome changes across cell lines resolved compound effects on proteostasis. We leveraged these findings to demonstrate efficient target identification of chemical protein degraders. Aggregating drug response across cell lines also revealed that one-quarter of compounds modulated the abundance of one of their known protein targets. Finally, the proteomic data led us to discover that inhibition of mitochondrial function is an off-target mechanism of the MAP2K1/2 inhibitor PD184352 and that the ALK inhibitor ceritinib modulates autophagy.

PepSeA: Peptide Sequence Alignment and Visualization Tools to Enable Lead Optimization.

Therapeutic peptides offer potential advantages over small molecules in terms of selectivity, affinity, and their ability to target "undruggable" proteins that are associated with a wide range of pathologies. Despite their importance, current molecular design capabilities that inform medicinal chemistry decisions on peptide programs are limited. More specifically, there are unmet needs for structure-activity relationship (SAR) analysis and visualization of linear, cyclic, and cross-linked peptides containing non-natural motifs, which are widely used in drug discovery. To bridge this gap, we developed PepSeA (Peptide Sequence Alignment and Visualization), an open-source, freely available package of sequence-based tools (https://github.com/Merck/PepSeA). PepSeA enables multiple sequence alignment of non-natural amino acids and enhanced visualization with the hierarchical editing language for macromolecules (HELM). Via stepwise SAR analysis of a ChEMBL peptide data set, we demonstrate the utility of PepSeA to accelerate decision making in lead optimization campaigns in pharmaceutical setting. PepSeA represents an initial attempt to expand cheminformatics capabilities for therapeutic peptides and to enable rapid and more efficient design-make-test cycles.

DrugCentral 2018: an update.

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.

DrugCentral: online drug compendium.

DrugCentral (http://drugcentral.org) is an open-access online drug compendium. DrugCentral integrates structure, bioactivity, regulatory, pharmacologic actions and indications for active pharmaceutical ingredients approved by FDA and other regulatory agencies. Monitoring of regulatory agencies for new drugs approvals ensures the resource is up-to-date. DrugCentral integrates content for active ingredients with pharmaceutical formulations, indexing drugs and drug label annotations, complementing similar resources available online. Its complementarity with other online resources is facilitated by cross referencing to external resources. At the molecular level, DrugCentral bridges drug-target interactions with pharmacological action and indications. The integration with FDA drug labels enables text mining applications for drug adverse events and clinical trial information. Chemical structure overlap between DrugCentral and five online drug resources, and the overlap between DrugCentral FDA-approved drugs and their presence in four different chemical collections, are discussed. DrugCentral can be accessed via the web application or downloaded in relational database format.

Pharos: Collating protein information to shed light on the druggable genome.

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.

TIN-X: target importance and novelty explorer.

MOTIVATION: The increasing amount of peer-reviewed manuscripts requires the development of specific mining tools to facilitate the visual exploration of evidence linking diseases and proteins. RESULTS: We developed TIN-X, the Target Importance and Novelty eXplorer, to visualize the association between proteins and diseases, based on text mining data processed from scientific literature. In the current implementation, TIN-X supports exploration of data for G-protein coupled receptors, kinases, ion channels, and nuclear receptors. TIN-X supports browsing and navigating across proteins and diseases based on ontology classes, and displays a scatter plot with two proposed new bibliometric statistics: Importance and Novelty. AVAILABILITY AND IMPLEMENTATION: http://www.newdrugtargets.org. CONTACT: cbologa@salud.unm.edu.

Protein biomarker druggability profiling.

Developing automated and interactive methods for building a model by incorporating mechanistic and potentially causal annotations of ranked biomarkers of a disease or clinical condition followed by a mapping into a contextual framework in disease-linked biochemical pathways can be used for potential drug-target evaluation and for proposing new drug targets. We demonstrate the potential of this approach using ranked protein biomarkers obtained in neonatal sepsis by enrolling 127 infants (39 infants with late onset neonatal sepsis and 88 control infants) and by performing a focused proteomic profile of the sera and by applying the interactive druggability profiling algorithm (DPA) developed by us.

CHEMICAL AND BIOLOGICAL DESCRIPTOR INTEGRATION IMPROVES COMPUTATIONAL MODELING OF IN VIVO RAT TOXICITY.

Computational toxicology is a new discipline in the area of computational molecular sciences, which is rapidly developing as a result of the public interest stirred by several European and US initiatives. Here, we report the use of primary high throughput screening (HTS) data as biological descriptors to complement the chemical descriptors for the modelling of the acute toxicity. The combination of biological and chemical descriptors was performed on the median lethal dose following oral administration in rats (rat LD50). The hybrid model developed based on chemical and biological descriptors is superior to models based on the chemical or biological description alone. Using this model, besides the accurately prediction of a compound's toxicity we also identified molecular fragments whose presence may contribute to increase or decrease of the toxicity.

Characterization of a Cdc42 protein inhibitor and its use as a molecular probe.

Cdc42 plays important roles in cytoskeleton organization, cell cycle progression, signal transduction, and vesicle trafficking. Overactive Cdc42 has been implicated in the pathology of cancers, immune diseases, and neuronal disorders. Therefore, Cdc42 inhibitors would be useful in probing molecular pathways and could have therapeutic potential. Previous inhibitors have lacked selectivity and trended toward toxicity. We report here the characterization of a Cdc42-selective guanine nucleotide binding lead inhibitor that was identified by high throughput screening. A second active analog was identified via structure-activity relationship studies. The compounds demonstrated excellent selectivity with no inhibition toward Rho and Rac in the same GTPase family. Biochemical characterization showed that the compounds act as noncompetitive allosteric inhibitors. When tested in cellular assays, the lead compound inhibited Cdc42-related filopodia formation and cell migration. The lead compound was also used to clarify the involvement of Cdc42 in the Sin Nombre virus internalization and the signaling pathway of integrin VLA-4. Together, these data present the characterization of a novel Cdc42-selective allosteric inhibitor and a related analog, the use of which will facilitate drug development targeting Cdc42-related diseases and molecular pathway studies that involve GTPases.

A high throughput flow cytometric assay platform targeting transporter inhibition.

This review highlights the concepts, recent applications and limitations of High Throughput Screening (HTS) flow cytometry-based efflux inhibitory assays. This platform has been employed in mammalian and yeast efflux systems leading to the identification of small molecules with transporter inhibitory capabilities. This technology offers the possibility of substrate multiplexing and may promote novel strategies targeting microbial efflux systems. This platform can generate a comprehensive dataset that may support efforts to map the interface between chemistry and transporter biology in a variety of pathogenic systems.

Fluorescent substrates for flow cytometric evaluation of efflux inhibition in ABCB1, ABCC1, and ABCG2 transporters.

ATP binding cassette (ABC) transmembrane efflux pumps such as P-glycoprotein (ABCB1), multidrug resistance protein 1 (ABCC1), and breast cancer resistance protein (ABCG2) play an important role in anticancer drug resistance. A large number of structurally and functionally diverse compounds act as substrates or modulators of these pumps. In vitro assessment of the affinity of drug candidates for multidrug resistance proteins is central to predict in vivo pharmacokinetics and drug-drug interactions. The objective of this study was to identify and characterize new substrates for these transporters. As part of a collaborative project with Life Technologies, 102 fluorescent probes were investigated in a flow cytometric screen of ABC transporters. The primary screen compared substrate efflux activity in parental cell lines with their corresponding highly expressing resistant counterparts. The fluorescent compound library included a range of excitation/emission profiles and required dual laser excitation as well as multiple fluorescence detection channels. A total of 31 substrates with active efflux in one or more pumps and practical fluorescence response ranges were identified and tested for interaction with eight known inhibitors. This screening approach provides an efficient tool for identification and characterization of new fluorescent substrates for ABCB1, ABCC1, and ABCG2.

Machine learning prediction and tau-based screening identifies potential Alzheimer's disease genes relevant to immunity.

With increased research funding for Alzheimer's disease (AD) and related disorders across the globe, large amounts of data are being generated. Several studies employed machine learning methods to understand the ever-growing omics data to enhance early diagnosis, map complex disease networks, or uncover potential drug targets. We describe results based on a Target Central Resource Database protein knowledge graph and evidence paths transformed into vectors by metapath matching. We extracted features between specific genes and diseases, then trained and optimized our model using XGBoost, termed MPxgb(AD). To determine our MPxgb(AD) prediction performance, we examined the top twenty predicted genes through an experimental screening pipeline. Our analysis identified potential AD risk genes: FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2. FRRS1 and FAM92B are considered dark genes, while CTRAM, SCGB3A1, and TMEFF2 are connected to TREM2-TYROBP, IL-1ß-TNFα, and MTOR-APP AD-risk nodes, suggesting relevance to the pathogenesis of AD.

iPHACE: integrative navigation in pharmacological space.

SUMMARY: The increasing availability of experimentally determined binding affinities for drugs on multiple protein targets requires the design of specific mining and visualization tools that graphically integrate chemical and biological data in an efficient environment. With this aim, we developed iPHACE, an integrative web-based tool to navigate in the pharmacological space defined by small molecule drugs contained in the IUPHAR-DB, with additional interactions present in PDSP. Extending beyond traditional querying and filtering tools, iPHACE offers a means to extract knowledge from the target profile of drugs as well as from the drug profile of protein targets. AVAILABILITY: iPHACE is available at http://cgl.imim.es/iphace/ (EU site) and http://agave.health.unm.edu/iphace/ (US mirror).

A crowdsourcing evaluation of the NIH chemical probes.

Between 2004 and 2008, the US National Institutes of Health Molecular Libraries and Imaging initiative pilot phase funded 10 high-throughput screening centers, resulting in the deposition of 691 assays into PubChem and the nomination of 64 chemical probes. We crowdsourced the Molecular Libraries and Imaging initiative output to 11 experts, who expressed medium or high levels of confidence in 48 of these 64 probes.

Drug Repurposing from an Academic Perspective.

Academia and small business research units are poised to play an increasing role in drug discovery, with drug repurposing as one of the major areas of activity. Here we summarize project status for a number of drugs or classes of drugs: raltegravir, cyclobenzaprine, benzbromarone, mometasone furoate, astemizole, R-naproxen, ketorolac, tolfenamic acid, phenothiazines, methylergonovine maleate and beta-adrenergic receptor drugs, respectively. Based on this multi-year, multi-project experience we discuss strengths and weaknesses of academic-based drug repurposing research. Translational, target and disease foci are strategic advantages fostered by close proximity and frequent interactions between basic and clinical scientists, which often result in discovering new modes of action for approved drugs. On the other hand, lack of integration with pharmaceutical sciences and toxicology, lack of appropriate intellectual coverage and issues related to dosing and safety may lead to significant drawbacks. The development of a more streamlined regulatory process world-wide, and the development of pre-competitive knowledge transfer systems such as a global healthcare database focused on regulatory and scientific information for drugs world-wide, are among the ideas proposed to improve the process of academic drug discovery and repurposing, and to overcome the "valley of death" by bridging basic to clinical sciences.

Identification of new GLUT2-selective inhibitors through in silico ligand screening and validation in eukaryotic expression systems.

Glucose is an essential energy source for cells. In humans, its passive diffusion through the cell membrane is facilitated by members of the glucose transporter family (GLUT, SLC2 gene family). GLUT2 transports both glucose and fructose with low affinity and plays a critical role in glucose sensing mechanisms. Alterations in the function or expression of GLUT2 are involved in the Fanconi-Bickel syndrome, diabetes, and cancer. Distinguishing GLUT2 transport in tissues where other GLUTs coexist is challenging due to the low affinity of GLUT2 for glucose and fructose and the scarcity of GLUT-specific modulators. By combining in silico ligand screening of an inward-facing conformation model of GLUT2 and glucose uptake assays in a hexose transporter-deficient yeast strain, in which the GLUT1-5 can be expressed individually, we identified eleven new GLUT2 inhibitors (IC50 ranging from 0.61 to 19.3 µM). Among them, nine were GLUT2-selective, one inhibited GLUT1-4 (pan-Class I GLUT inhibitor), and another inhibited GLUT5 only. All these inhibitors dock to the substrate cavity periphery, close to the large cytosolic loop connecting the two transporter halves, outside the substrate-binding site. The GLUT2 inhibitors described here have various applications; GLUT2-specific inhibitors can serve as tools to examine the pathophysiological role of GLUT2 relative to other GLUTs, the pan-Class I GLUT inhibitor can block glucose entry in cancer cells, and the GLUT2/GLUT5 inhibitor can reduce the intestinal absorption of fructose to combat the harmful effects of a high-fructose diet.

Model-free drug-likeness from fragments.

We developed a drug-likeness filter (DLF), starting from molecular fragments and molecular weight (MW), a key property relevant in drug design. The molecular fragments were selected from extended connectivity atom environments based on their occurrence ratio in our collection of drugs and "nondrugs". The DLF recalls 87.05% of compounds from DRUGS (N = 3823) and 40.25% of compounds from the Available Chemicals Directory, (ACD, N = 178 0 11), using molecular fragments only. By adding MW (under 600) as an additional filter, 78.81% of DRUGS and 40.17% of ACD are recalled. The DLF procedure was externally validated using the MDL Drug Data Report (MDDR) data set (N = 169 277): 78.45% of compounds were recalled using the molecular fragments only, while 65.64% pass the DLF-MW filter. Over 50% of a pesticides collection (N = 1482) passed the DLF, as these chemicals share molecular fragments with known drugs. Developed as a model-free filter, DLF is perhaps less useful in discriminating drugs from nondrugs but more likely to rapidly eliminate those chemicals rich in nondrug-like fragments. Since almost 40% of ACD, the standard reference set for nondrugs, contain drug-like molecules, by using a rule-based system such as DLF, one is less likely to mislabel nondrugs due to overfitting. Reliable benchmarks for nondrugs are not likely to exist since medicinal chemistry catalogs tend to be biased toward existing drugs.

How to Prepare a Compound Collection Prior to Virtual Screening.

Virtual screening is a well-established technique that has proven to be successful in the identification of novel biologically active molecules, including drug repurposing. Whether for ligand-based or for structure-based virtual screening, a chemical collection needs to be properly processed prior to in silico evaluation. Here we describe our step-by-step procedure for handling very large collections (up to billions) of compounds prior to virtual screening.