Outcomes of Hospital-Acquired Hypernatremia

BACKGROUND

Hospital-acquired hypernatremia is highly prevalent, overlooked, and associated with unfavorable consequences. There are limited studies examining the outcomes and discharge dispositions of various levels of hospital-acquired hypernatremia in patients with or without CKD.

METHODS

We conducted an observational retrospective cohort study, and we analyzed the data of 1,728,141 patients extracted from the Cerner Health Facts database (January 1, 2000, to June 30, 2018). In this report, we investigated the association between hospital-acquired hypernatremia (serum sodium [Na] levels >145 mEq/L) and in-hospital mortality or discharge dispositions with kidney function status at admission using adjusted multinomial regression models.

RESULTS

Of all hospitalized patients, 6% developed hypernatremia after hospital admission. The incidence of in-hospital mortality was 12% and 1% in patients with hypernatremia and normonatremia, respectively. The risk of all outcomes was significantly greater for serum Na >145 mEq/L compared with the reference interval (serum Na, 135-145 mEq/L). In patients with hypernatremia, odds ratios (95% confidence interval) for in-hospital mortality, discharge to hospice, and discharge to nursing facilities were 14.04 (13.71 to 14.38), 4.35 (4.14 to 4.57), and 3.88 (3.82 to 3.94), respectively ( P < 0.001, for all). Patients with eGFR (Chronic Kidney Disease Epidemiology Collaboration) 60-89 ml/min per 1.73 m 2 and normonatremia had the lowest odds ratio for in-hospital mortality (1.60 [1.52 to 1.70]).

CONCLUSIONS

Hospital-acquired hypernatremia is associated with in-hospital mortality and discharge to hospice or to nursing facilities in all stages of CKD.

Toxicology knowledge graph for structural birth defects

BACKGROUND

Birth defects are functional and structural abnormalities that impact about 1 in 33 births in the United States. They have been attributed to genetic and other factors such as drugs, cosmetics, food, and environmental pollutants during pregnancy, but for most birth defects there are no known causes.

METHODS

To further characterize associations between small molecule compounds and their potential to induce specific birth abnormalities, we gathered knowledge from multiple sources to construct a reproductive toxicity Knowledge Graph (ReproTox-KG) with a focus on associations between birth defects, drugs, and genes. Specifically, we gathered data from drug/birth-defect associations from co-mentions in published abstracts, gene/birth-defect associations from genetic studies, drug- and preclinical-compound-induced gene expression changes in cell lines, known drug targets, genetic burden scores for human genes, and placental crossing scores for small molecules.

RESULTS

Using ReproTox-KG and semi-supervised learning (SSL), we scored >30,000 preclinical small molecules for their potential to cross the placenta and induce birth defects, and identified >500 birth-defect/gene/drug cliques that can be used to explain molecular mechanisms for drug-induced birth defects. The ReproTox-KG can be accessed via a web-based user interface available at https://maayanlab.cloud/reprotox-kg . This site enables users to explore the associations between birth defects, approved and preclinical drugs, and all human genes.

CONCLUSIONS

ReproTox-KG provides a resource for exploring knowledge about the molecular mechanisms of birth defects with the potential of predicting the likelihood of genes and preclinical small molecules to induce birth defects.

Hypernatremia in Hospitalized Patients: A Large Population-Based Study

Background

Hypernatremia is a frequently encountered electrolyte disorder in hospitalized patients. Controversies still exist over the relationship between hypernatremia and its outcomes in hospitalized patients. This study examines the relationship of hypernatremia to outcomes among hospitalized patients and the extent to which this relationship varies by kidney function and age.

Methods

We conducted an observational study to investigate the association between hypernatremia, eGFR, and age at hospital admission and in-hospital mortality, and discharge dispositions. We analyzed the data of 1.9 million patients extracted from the Cerner Health Facts databases (2000-2018). Adjusted multinomial regression models were used to estimate the relationship of hypernatremia to outcomes of hospitalized patients.

Results

Of all hospitalized patients, 3% had serum sodium (Na) >145 mEq/L at hospital admission. Incidence of in-hospital mortality was 12% and 2% in hyper- and normonatremic patients, respectively. The risk of all outcomes increased significantly for Na >155 mEq/L compared with the reference interval of Na=135-145 mEq/L. Odds ratios (95% confidence intervals) for in-hospital mortality and discharge to a hospice or nursing facility were 34.41 (30.59-38.71), 21.14 (17.53-25.5), and 12.21 (10.95-13.61), respectively (all P<0.001). In adjusted models, we found that the association between Na and disposition was modified by eGFR (P<0.001) and by age (P<0.001). Sensitivity analyses were performed using the eGFR equation without race as a covariate, and the inferences did not substantially change. In all hypernatremic groups, patients aged 76-89 and ≥90 had higher odds of in-hospital mortality compared with younger patients (all P<0.001).

Conclusions

Hypernatremia was significantly associated with in-hospital mortality and discharge to a hospice or nursing facility. The risk of in-hospital mortality and other outcomes was highest among those with Na >155 mEq/L. This work demonstrates that hypernatremia is an important factor related to discharge disposition and supports the need to study whether protocolized treatment of hypernatremia improves outcomes.

A Comprehensive COVID-19 Daily News and Medical Literature Briefing to Inform Health Care and Policy in New Mexico: Implementation Study

BACKGROUND

On March 11, 2020, the New Mexico Governor declared a public health emergency in response to the COVID-19 pandemic. The New Mexico medical advisory team contacted University of New Mexico (UNM) faculty to form a team to consolidate growing information on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its disease to facilitate New Mexico's pandemic management. Thus, faculty, physicians, staff, graduate students, and medical students created the "UNM Global Health COVID-19 Intelligence Briefing."

OBJECTIVE

In this paper, we sought to (1) share how to create an informative briefing to guide public policy and medical practice and manage information overload with rapidly evolving scientific evidence; (2) determine the qualitative usefulness of the briefing to its readers; and (3) determine the qualitative effect this project has had on virtual medical education.

METHODS

Microsoft Teams was used for manual and automated capture of COVID-19 articles and composition of briefings. Multilevel triaging saved impactful articles to be reviewed, and priority was placed on randomized controlled studies, meta-analyses, systematic reviews, practice guidelines, and information on health care and policy response to COVID-19. The finalized briefing was disseminated by email, a listserv, and posted on the UNM digital repository. A survey was sent to readers to determine briefing usefulness and whether it led to policy or medical practice changes. Medical students, unable to partake in direct patient care, proposed to the School of Medicine that involvement in the briefing should count as course credit, which was approved. The maintenance of medical student involvement in the briefings as well as this publication was led by medical students.

RESULTS

An average of 456 articles were assessed daily. The briefings reached approximately 1000 people by email and listserv directly, with an unknown amount of forwarding. Digital repository tracking showed 5047 downloads across 116 countries as of July 5, 2020. The survey found 108 (95%) of 114 participants gained relevant knowledge, 90 (79%) believed it decreased misinformation, 27 (24%) used the briefing as their primary source of information, and 90 (79%) forwarded it to colleagues. Specific and impactful public policy decisions were informed based on the briefing. Medical students reported that the project allowed them to improve on their scientific literature assessment, stay current on the pandemic, and serve their community.

CONCLUSIONS

The COVID-19 briefings succeeded in informing and guiding New Mexico policy and clinical practice. The project received positive feedback from the community and was shown to decrease information burden and misinformation. The virtual platforms allowed for the continuation of medical education. Variability in subject matter expertise was addressed with training, standardized article selection criteria, and collaborative editing led by faculty.

Getting Started with the IDG KMC Datasets and Tools

The Illuminating the Druggable Genome (IDG) consortium is a National Institutes of Health (NIH) Common Fund program designed to enhance our knowledge of under-studied proteins, more specifically, proteins unannotated within the three most commonly drug-targeted protein families: G-protein coupled receptors, ion channels, and protein kinases. Since 2014, the IDG Knowledge Management Center (IDG-KMC) has generated several open-access datasets and resources that jointly serve as a highly translational machine-learning-ready knowledgebase focused on human protein-coding genes and their products. The goal of the IDG-KMC is to develop comprehensive integrated knowledge for the druggable genome to illuminate the uncharacterized or poorly annotated portion of the druggable genome. The tools derived from the IDG-KMC provide either user-friendly visualizations or ways to impute the knowledge about potential targets using machine learning strategies. In the following protocols, we describe how to use each web-based tool to accelerate illumination in under-studied proteins. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Interacting with the Pharos user interface Basic Protocol 2: Accessing the data in Harmonizome Basic Protocol 3: The ARCHS4 resource Basic Protocol 4: Making predictions about gene function with PrismExp Basic Protocol 5: Using Geneshot to illuminate knowledge about under-studied targets Basic Protocol 6: Exploring under-studied targets with TIN-X Basic Protocol 7: Interacting with the DrugCentral user interface Basic Protocol 8: Estimating Anti-SARS-CoV-2 activities with DrugCentral REDIAL-2020 Basic Protocol 9: Drug Set Enrichment Analysis using Drugmonizome Basic Protocol 10: The Drugmonizome-ML Appyter Basic Protocol 11: The Harmonizome-ML Appyter Basic Protocol 12: GWAS target illumination with TIGA Basic Protocol 13: Prioritizing kinases for lists of proteins and phosphoproteins with KEA3 Basic Protocol 14: Converting PubMed searches to drug sets with the DrugShot Appyter.

TIGA: target illumination GWAS analytics

MOTIVATION

Genome-wide association studies can reveal important genotype-phenotype associations; however, data quality and interpretability issues must be addressed. For drug discovery scientists seeking to prioritize targets based on the available evidence, these issues go beyond the single study.

RESULTS

Here, we describe rational ranking, filtering and interpretation of inferred gene-trait associations and data aggregation across studies by leveraging existing curation and harmonization efforts. Each gene-trait association is evaluated for confidence, with scores derived solely from aggregated statistics, linking a protein-coding gene and phenotype. We propose a method for assessing confidence in gene-trait associations from evidence aggregated across studies, including a bibliometric assessment of scientific consensus based on the iCite relative citation ratio, and meanRank scores, to aggregate multivariate evidence.This method, intended for drug target hypothesis generation, scoring and ranking, has been implemented as an analytical pipeline, available as open source, with public datasets of results, and a web application designed for usability by drug discovery scientists.

AVAILABILITY AND IMPLEMENTATION

Web application, datasets and source code via https://unmtid-shinyapps.net/tiga/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

High performance implementation of the hierarchical likelihood for generalized linear mixed models: an application to estimate the potassium reference range in massive electronic health records datasets

Background

Converting electronic health record (EHR) entries to useful clinical inferences requires one to address the poor scalability of existing implementations of Generalized Linear Mixed Models (GLMM) for repeated measures. The major computational bottleneck concerns the numerical evaluation of multivariable integrals, which even for the simplest EHR analyses may involve millions of dimensions (one for each patient). The hierarchical likelihood (h-lik) approach to GLMMs is a methodologically rigorous framework for the estimation of GLMMs that is based on the Laplace Approximation (LA), which replaces integration with numerical optimization, and thus scales very well with dimensionality.

Methods

We present a high-performance, direct implementation of the h-lik for GLMMs in the R package TMB. Using this approach, we examined the relation of repeated serum potassium measurements and survival in the Cerner Real World Data (CRWD) EHR database. Analyzing this data requires the evaluation of an integral in over 3 million dimensions, putting this problem beyond the reach of conventional approaches. We also assessed the scalability and accuracy of LA in smaller samples of 1 and 10% size of the full dataset that were analyzed via the a) original, interconnected Generalized Linear Models (iGLM), approach to h-lik, b) Adaptive Gaussian Hermite (AGH) and c) the gold standard for multivariate integration Markov Chain Monte Carlo (MCMC).

Results

Random effects estimates generated by the LA were within 10% of the values obtained by the iGLMs, AGH and MCMC techniques. The H-lik approach was 4–30 times faster than AGH and nearly 800 times faster than MCMC. The major clinical inferences in this problem are the establishment of the non-linear relationship between the potassium level and the risk of mortality, as well as estimates of the individual and health care facility sources of variations for mortality risk in CRWD.

Conclusions

We found that the direct implementation of the h-lik offers a computationally efficient, numerically accurate approach for the analysis of extremely large, real world repeated measures data via the h-lik approach to GLMMs. The clinical inference from our analysis may guide choices of treatment thresholds for treating potassium disorders in the clinic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01318-6.

How to Illuminate the Druggable Genome Using Pharos

Pharos is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied "dark" targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. Two basic protocols illustrate the levels of detail available for targets and several methods of finding targets of interest. An Alternate Protocol illustrates the difference in available knowledge between less and more studied targets. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Search for a target and view details Alternate Protocol: Search for dark target and view details Basic Protocol 2: Filter a target list to get refined results.

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.

TCRD and Pharos 2021: mining the human proteome for disease biology

In 2014, the National Institutes of Health (NIH) initiated the Illuminating the Druggable Genome (IDG) program to identify and improve our understanding of poorly characterized proteins that can potentially be modulated using small molecules or biologics. Two resources produced from these efforts are: The Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/) and Pharos (https://pharos.nih.gov/), a web interface to browse the TCRD. The ultimate goal of these resources is to highlight and facilitate research into currently understudied proteins, by aggregating a multitude of data sources, and ranking targets based on the amount of data available, and presenting data in machine learning ready format. Since the 2017 release, both TCRD and Pharos have produced two major releases, which have incorporated or expanded an additional 25 data sources. Recently incorporated data types include human and viral-human protein-protein interactions, protein-disease and protein-phenotype associations, and drug-induced gene signatures, among others. These aggregated data have enabled us to generate new visualizations and content sections in Pharos, in order to empower users to find new areas of study in the druggable genome.

Virtual and In Vitro Antiviral Screening Revive Therapeutic Drugs for COVID-19

The urgent need for a cure for early phase COVID-19 infected patients critically underlines drug repositioning strategies able to efficiently identify new and reliable treatments by merging computational, experimental, and pharmacokinetic expertise. Here we report new potential therapeutics for COVID-19 identified with a combined virtual and experimental screening strategy and selected among already approved drugs. We used hydroxychloroquine (HCQ), one of the most studied drugs in current clinical trials, as a reference template to screen for structural similarity against a library of almost 4000 approved drugs. The top-ranked drugs, based on structural similarity to HCQ, were selected for in vitro antiviral assessment. Among the selected drugs, both zuclopenthixol and nebivolol efficiently block SARS-CoV-2 infection with EC50 values in the low micromolar range, as confirmed by independent experiments. The anti-SARS-CoV-2 potential of ambroxol, amodiaquine, and its active metabolite (N-monodesethyl amodiaquine) is also discussed. In trying to understand the "hydroxychloroquine" mechanism of action, both pK a and the HCQ aromatic core may play a role. Further, we show that the amodiaquine metabolite and, to a lesser extent, zuclopenthixol and nebivolol are active in a SARS-CoV-2 titer reduction assay. Given the need for improved efficacy and safety, we propose zuclopenthixol, nebivolol, and amodiaquine as potential candidates for clinical trials against the early phase of the SARS-CoV-2 infection and discuss their potential use as adjuvant to the current (i.e., remdesivir and favipiravir) COVID-19 therapeutics.

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.

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.

Activation of Rho Family GTPases by Small Molecules

Ras and Ras-related small GTPases are key regulators of diverse cellular functions that impact cell growth, survival, motility, morphogenesis, and differentiation. They are important targets for studies of disease mechanisms as well as drug discovery. Here, we report the characterization of small molecule agonists of one or more of six Rho, Rab, and Ras family GTPases that were first identified through flow cytometry-based, multiplexed high-throughput screening of 200000 compounds. The activators were categorized into three distinct chemical families that are represented by three lead compounds having the highest activity. Virtual screening predicted additional compounds with potential GTPase activating properties. Secondary dose–response assays performed on compounds identified through these screens confirmed agonist activity of 43 compounds. While the lead and second most active small molecules acted as pan activators of multiple GTPase subfamilies, others showed partial selectivity for Ras and Rab proteins. The compounds did not stimulate nucleotide exchange by guanine nucleotide exchange factors and did not protect against GAP-stimulated GTP hydrolysis. The activating properties were caused by a reversible stabilization of the GTP-bound state and prolonged effector protein interactions. Notably, these compounds were active both in vitro and in cell-based assays, and small molecule-mediated changes in Rho GTPase activities were directly coupled to measurable changes in cytoskeletal rearrangements that dictate cell morphology.

Unexplored therapeutic opportunities in the human genome

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 one out of three 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.

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.

Unexplored therapeutic opportunities in the human genome

This corrects the article DOI: 10.1038/nrd.2018.14.

Formalizing drug indications on the road to therapeutic intent

Therapeutic intent, the reason behind the choice of a therapy and the context in which a given approach should be used, is an important aspect of medical practice. There are unmet needs with respect to current electronic mapping of drug indications. For example, the active ingredient sildenafil has 2 distinct indications, which differ solely on dosage strength. In progressing toward a practice of precision medicine, there is a need to capture and structure therapeutic intent for computational reuse, thus enabling more sophisticated decision-support tools and a possible mechanism for computer-aided drug repurposing. The indications for drugs, such as those expressed in the Structured Product Labels approved by the US Food and Drug Administration, appears to be a tractable area for developing an application ontology of therapeutic intent.

High-Throughput Flow Cytometry Screening of Multidrug Efflux Systems

The resistance nodulation cell division (RND) family of proteins are inner membrane transporters that associate with periplasmic adaptor proteins and outer membrane porins to affect substrate transport from the cytosol and periplasm in Gram-negative bacteria. Various structurally diverse compounds are substrates of RND transporters. Along with their notable role in antibiotic resistance, these transporters are essential for niche colonization, quorum sensing, and virulence as well as for the removal of fatty acids and bile salts. As such, RNDs are an attractive target for antimicrobial development. However, while enhancing the utility of antibiotics with an RND inhibitor is an appealing concept, only a small core of chemotypes has been identified as efflux pump inhibitors (EPIs). Thus, our key objective is the development and validation of an efflux profiling and discovery strategy for RND model systems. Here we describe a flow cytometric dye accumulation assay that uses fluorescein diacetate (FDA) to interrogate the model Gram-negative pathogens Escherichia coli, Franscisella tularensis, and Burkholderia pseudomallei. Fluorochrome retention is increased in the presence of known efflux inhibitors and in RND deletion strains. The assay can be used in a high-throughput format to evaluate efflux of dye-substrate candidates and to screen chemical libraries for novel EPIs. Triaged compounds that inhibit efflux in pathogenic strains are tested for growth inhibition and antibiotic potentiation using microdilution culture plates in a select agent Biosafety Level-3 (BSL3) environment. This combined approach demonstrates the utility of flow cytometric analysis for efflux activity and provides a useful platform in which to characterize efflux in pathogenic Gram-negative bacteria. Screening small molecule libraries for novel EPI candidates offers the potential for the discovery of new classes of antibacterial compounds.

Drug target ontology to classify and integrate drug discovery data

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.

Electronic supplementary material

The online version of this article (10.1186/s13326-017-0161-x) contains supplementary material, which is available to authorized users.

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.

A comprehensive map of molecular drug targets

The success of mechanism-based drug discovery depends on the definition of the drug target. This definition becomes even more important as we try to link drug response to genetic variation, understand stratified clinical efficacy and safety, rationalize the differences between drugs in the same therapeutic class and predict drug utility in patient subgroups. However, drug targets are often poorly defined in the literature, both for launched drugs and for potential therapeutic agents in discovery and development. Here, we present an updated comprehensive map of molecular targets of approved drugs. We curate a total of 893 human and pathogen-derived biomolecules through which 1,578 US FDA-approved drugs act. These biomolecules include 667 human-genome-derived proteins targeted by drugs for human disease. Analysis of these drug targets indicates the continued dominance of privileged target families across disease areas, but also the growth of novel first-in-class mechanisms, particularly in oncology. We explore the relationships between bioactivity class and clinical success, as well as the presence of orthologues between human and animal models and between pathogen and human genomes. Through the collaboration of three independent teams, we highlight some of the ongoing challenges in accurately defining the targets of molecular therapeutics and present conventions for deconvoluting the complexities of molecular pharmacology and drug efficacy.

Post-High-Throughput Screening Analysis: An Empirical Compound Prioritization Scheme

An empirical scheme to evaluate and prioritize screening hits from high-throughput screening (HTS) is proposed. Negative scores are given when chemotypes found in the HTS hits are present in annotated databases such as MDDR and WOMBAT or for testing positive in toxicity-related experiments reported in TOXNET. Positive scores were given for higher measured biological activities, for testing negative in toxicity-related literature, and for good overlap when profiled against drug-related properties. Particular emphasis is placed on estimating aqueous solubility to prioritize in vivo experiments. This empirical scheme is given as an illustration to assist the decision-making process in selecting chemotypes and individual compounds for further experimentation, when confronted with multiple hits from high-throughput experiments. The decision-making process is discussed for a set of G-protein coupled receptor antagonists and validated on a literature example for dihydrofolate reductase inhibition.

Badapple: promiscuity patterns from noisy evidence

Background

Bioassay data analysis continues to be an essential, routine, yet challenging task in modern drug discovery and chemical biology research. The challenge is to infer reliable knowledge from big and noisy data. Some aspects of this problem are general with solutions informed by existing and emerging data science best practices. Some aspects are domain specific, and rely on expertise in bioassay methodology and chemical biology. Testing compounds for biological activity requires complex and innovative methodology, producing results varying widely in accuracy, precision, and information content. Hit selection criteria involve optimizing such that the overall probability of success in a project is maximized, and resource-wasteful “false trails” are avoided. This “fail-early” approach is embraced both in pharmaceutical and academic drug discovery, since follow-up capacity is resource-limited. Thus, early identification of likely promiscuous compounds has practical value.

Results

Here we describe an algorithm for identifying likely promiscuous compounds via associated scaffolds which combines general and domain-specific features to assist and accelerate drug discovery informatics, called Badapple: bioassay-data associative promiscuity pattern learning engine. Results are described from an analysis using data from MLP assays via the BioAssay Research Database (BARD) http://bard.nih.gov. Specific examples are analyzed in the context of medicinal chemistry, to illustrate associations with mechanisms of promiscuity. Badapple has been developed at UNM, released and deployed for public use two ways: (1) BARD plugin, integrated into the public BARD REST API and BARD web client; and (2) public web app hosted at UNM.

Conclusions

Badapple is a method for rapidly identifying likely promiscuous compounds via associated scaffolds. Badapple generates a score associated with a pragmatic, empirical definition of promiscuity, with the overall goal to identify “false trails” and streamline workflows. Unlike methods reliant on expert curation of chemical substructure patterns, Badapple is fully evidence-driven, automated, self-improving via integration of additional data, and focused on scaffolds. Badapple is robust with respect to noise and errors, and skeptical of scanty evidence.

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-016-0137-3) contains supplementary material, which is available to authorized users.

Impact of similarity threshold on the topology of molecular similarity networks and clustering outcomes

BACKGROUND

Complex network theory based methods and the emergence of "Big Data" have reshaped the terrain of investigating structure-activity relationships of molecules. This change gave rise to new methods which need to face an important challenge, namely: how to restructure a large molecular dataset into a network that best serves the purpose of the subsequent analyses. With special focus on network clustering, our study addresses this open question by proposing a data transformation method and a clustering framework.

RESULTS

Using the WOMBAT and PubChem MLSMR datasets we investigated the relation between varying the similarity threshold applied on the similarity matrix and the average clustering coefficient of the emerging similarity-based networks. These similarity networks were then clustered with the InfoMap algorithm. We devised a systematic method to generate so-called "pseudo-reference" clustering datasets which compensate for the lack of large-scale reference datasets. With help from the clustering framework we were able to observe the effects of varying the similarity threshold and its consequence on the average clustering coefficient and the clustering performance.

CONCLUSIONS

We observed that the average clustering coefficient versus similarity threshold function can be characterized by the presence of a peak that covers a range of similarity threshold values. This peak is preceded by a steep decline in the number of edges of the similarity network. The maximum of this peak is well aligned with the best clustering outcome. Thus, if no reference set is available, choosing the similarity threshold associated with this peak would be a near-ideal setting for the subsequent network cluster analysis. The proposed method can be used as a general approach to determine the appropriate similarity threshold to generate the similarity network of large-scale molecular datasets.

Ligand-directed targeting of lymphatic vessels uncovers mechanistic insights in melanoma metastasis

Metastasis is the most lethal step of cancer progression in patients with invasive melanoma. In most human cancers, including melanoma, tumor dissemination through the lymphatic vasculature provides a major route for tumor metastasis. Unfortunately, molecular mechanisms that facilitate interactions between melanoma cells and lymphatic vessels are unknown. Here, we developed an unbiased approach based on molecular mimicry to identify specific receptors that mediate lymphatic endothelial-melanoma cell interactions and metastasis. By screening combinatorial peptide libraries directly on afferent lymphatic vessels resected from melanoma patients during sentinel lymphatic mapping and lymph node biopsies, we identified a significant cohort of melanoma and lymphatic surface binding peptide sequences. The screening approach was designed so that lymphatic endothelium binding peptides mimic cell surface proteins on tumor cells. Therefore, relevant metastasis and lymphatic markers were biochemically identified, and a comprehensive molecular profile of the lymphatic endothelium during melanoma metastasis was generated. Our results identified expression of the phosphatase 2 regulatory subunit A, α-isoform (PPP2R1A) on the cell surfaces of both melanoma cells and lymphatic endothelial cells. Validation experiments showed that PPP2R1A is expressed on the cell surfaces of both melanoma and lymphatic endothelial cells in vitro as well as independent melanoma patient samples. More importantly, PPP2R1A-PPP2R1A homodimers occur at the cellular level to mediate cell-cell interactions at the lymphatic-tumor interface. Our results revealed that PPP2R1A is a new biomarker for melanoma metastasis and show, for the first time to our knowledge, an active interaction between the lymphatic vasculature and melanoma cells during tumor progression.

Ligand-directed profiling of organelles with internalizing phage libraries

Phage display is a resourceful tool to, in an unbiased manner, discover and characterize functional protein-protein interactions, create vaccines, and engineer peptides, antibodies, and other proteins as targeted diagnostic and/or therapeutic agents. Recently, our group has developed a new class of internalizing phage (iPhage) for ligand-directed targeting of organelles and to identify molecular pathways within live cells. This unique technology is suitable for applications ranging from fundamental cell biology to drug development. This unit describes the methods for generating and screening the iPhage display system, and explains how to select and validate candidate internalizing homing peptide.

FRET detection of lymphocyte function-associated antigen-1 conformational extension

Lymphocyte function–associated antigen 1 (LFA-1) and its ligands are essential for immune cell interactions. LFA-1 is regulated through conformational changes. The relationship between molecular conformation and function is unclear. Förster resonance energy transfer is used to assess LFA-1 conformation under real-time signaling conditions.

Lymphocyte function–associated antigen 1 (LFA-1, CD11a/CD18, αLβ2-integrin) and its ligands are essential for adhesion between T-cells and antigen-presenting cells, formation of the immunological synapse, and other immune cell interactions. LFA-1 function is regulated through conformational changes that include the modulation of ligand binding affinity and molecular extension. However, the relationship between molecular conformation and function is unclear. Here fluorescence resonance energy transfer (FRET) with new LFA-1–specific fluorescent probes showed that triggering of the pathway used for T-cell activation induced rapid unquenching of the FRET signal consistent with extension of the molecule. Analysis of the FRET quenching at rest revealed an unexpected result that can be interpreted as a previously unknown LFA-1 conformation.

Compound collection preparation for virtual screening

Virtual screening is an established technique that has successfully been deployed in the identification of novel biologically active molecules. 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 large collections of compounds prior to virtual screening.

Emerging trends in the discovery of natural product antibacterials

This article highlights current trends and advances in exploiting natural sources for the deployment of novel and potent anti-infective countermeasures. The key challenge is to therapeutically target bacterial pathogens that exhibit a variety of puzzling and evolutionarily complex resistance mechanisms. Special emphasis is given to the strengths, weaknesses, and opportunities in the natural product antibacterial drug discovery arena, and to emerging applications driven by advances in bioinformatics, chemical biology, and synthetic biology in concert with exploiting bacterial phenotypes. These efforts have identified a critical mass of natural product antibacterial lead compounds and discovery technologies with high probability of successful implementation against emerging bacterial pathogens.

The CARLSBAD database: a confederated database of chemical bioactivities

Many bioactivity databases offer information regarding the biological activity of small molecules on protein targets. Information in these databases is often hard to resolve with certainty because of subsetting different data in a variety of formats; use of different bioactivity metrics; use of different identifiers for chemicals and proteins; and having to access different query interfaces, respectively. Given the multitude of data sources, interfaces and standards, it is challenging to gather relevant facts and make appropriate connections and decisions regarding chemical-protein associations. The CARLSBAD database has been developed as an integrated resource, focused on high-quality subsets from several bioactivity databases, which are aggregated and presented in a uniform manner, suitable for the study of the relationships between small molecules and targets. In contrast to data collection resources, CARLSBAD provides a single normalized activity value of a given type for each unique chemical-protein target pair. Two types of scaffold perception methods have been implemented and are available for datamining: HierS (hierarchical scaffolds) and MCES (maximum common edge subgraph). The 2012 release of CARLSBAD contains 439 985 unique chemical structures, mapped onto 1,420 889 unique bioactivities, and annotated with 277 140 HierS scaffolds and 54 135 MCES chemical patterns, respectively. Of the 890 323 unique structure-target pairs curated in CARLSBAD, 13.95% are aggregated from multiple structure-target values: 94 975 are aggregated from two bioactivities, 14 544 from three, 7 930 from four and 2214 have five bioactivities, respectively. CARLSBAD captures bioactivities and tags for 1435 unique chemical structures of active pharmaceutical ingredients (i.e. 'drugs'). CARLSBAD processing resulted in a net 17.3% data reduction for chemicals, 34.3% reduction for bioactivities, 23% reduction for HierS and 25% reduction for MCES, respectively. The CARLSBAD database supports a knowledge mining system that provides non-specialists with novel integrative ways of exploring chemical biology space to facilitate knowledge mining in drug discovery and repurposing. Database URL: http://carlsbad.health.unm.edu/carlsbad/.

Cheminformatics aspects of high throughput screening: from robots to models: symposium summary

The "Cheminformatics aspects of high throughput screening (HTS): from robots to models" symposium was part of the computers in chemistry technical program at the American Chemical Society National Meeting in Denver, Colorado during the fall of 2011. This symposium brought together researchers from high throughput screening centers and molecular modelers from academia and industry to discuss the integration of currently available high throughput screening data and assays with computational analysis. The topics discussed at this symposium covered the data-infrastructure at various academic, hospital, and National Institutes of Health-funded high throughput screening centers, the cheminformatics and molecular modeling methods used in real world examples to guide screening and hit-finding, and how academic and non-profit organizations can benefit from current high throughput screening cheminformatics resources. Specifically, this article also covers the remarks and discussions in the open panel discussion of the symposium and summarizes the following talks on "Accurate Kinase virtual screening: biochemical, cellular and selectivity", "Selective, privileged and promiscuous chemical patterns in high-throughput screening" and "Visualizing and exploring relationships among HTS hits using network graphs".

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.

A selective ATP-binding cassette subfamily G member 2 efflux inhibitor revealed via high-throughput flow cytometry

Chemotherapeutics tumor resistance is a principal reason for treatment failure, and clinical and experimental data indicate that multidrug transporters such as ATP-binding cassette (ABC) B1 and ABCG2 play a leading role by preventing cytotoxic intracellular drug concentrations. Functional efflux inhibition of existing chemotherapeutics by these pumps continues to present a promising approach for treatment. A contributing factor to the failure of existing inhibitors in clinical applications is limited understanding of specific substrate/inhibitor/pump interactions. We have identified selective efflux inhibitors by profiling multiple ABC transporters against a library of small molecules to find molecular probes to further explore such interactions. In our primary screening protocol using JC-1 as a dual-pump fluorescent reporter substrate, we identified a piperazine-substituted pyrazolo[1,5-a]pyrimidine substructure with promise for selective efflux inhibition. As a result of a focused structure-activity relationship (SAR)-driven chemistry effort, we describe compound 1 (CID44640177), an efflux inhibitor with selectivity toward ABCG2 over ABCB1. Compound 1 is also shown to potentiate the activity of mitoxantrone in vitro as well as preliminarily in vivo in an ABCG2-overexpressing tumor model. At least two analogues significantly reduce tumor size in combination with the chemotherapeutic topotecan. To our knowledge, low nanomolar chemoreversal activity coupled with direct evidence of efflux inhibition for ABCG2 is unprecedented.

A novel flow cytometric HTS assay reveals functional modulators of ATP binding cassette transporter ABCB6

ABCB6 is a member of the adenosine triphosphate (ATP)-binding cassette family of transporter proteins that is increasingly recognized as a relevant physiological and therapeutic target. Evaluation of modulators of ABCB6 activity would pave the way toward a more complete understanding of the significance of this transport process in tumor cell growth, proliferation and therapy-related drug resistance. In addition, this effort would improve our understanding of the function of ABCB6 in normal physiology with respect to heme biosynthesis, and cellular adaptation to metabolic demand and stress responses. To search for modulators of ABCB6, we developed a novel cell-based approach that, in combination with flow cytometric high-throughput screening (HTS), can be used to identify functional modulators of ABCB6. Accumulation of protoporphyrin, a fluorescent molecule, in wild-type ABCB6 expressing K562 cells, forms the basis of the HTS assay. Screening the Prestwick Chemical Library employing the HTS assay identified four compounds, benzethonium chloride, verteporfin, tomatine hydrochloride and piperlongumine, that reduced ABCB6 mediated cellular porphyrin levels. Validation of the identified compounds employing the hemin-agarose affinity chromatography and mitochondrial transport assays demonstrated that three out of the four compounds were capable of inhibiting ABCB6 mediated hemin transport into isolated mitochondria. However, only verteporfin and tomatine hydrochloride inhibited ABCB6's ability to compete with hemin as an ABCB6 substrate. This assay is therefore sensitive, robust, and suitable for automation in a high-throughput environment as demonstrated by our identification of selective functional modulators of ABCB6. Application of this assay to other libraries of synthetic compounds and natural products is expected to identify novel modulators of ABCB6 activity.

Identification of a small molecule yeast TORC1 inhibitor with a multiplex screen based on flow cytometry

TOR (target of rapamycin) is a serine/threonine kinase, evolutionarily conserved from yeast to human, which functions as a fundamental controller of cell growth. The moderate clinical benefit of rapamycin in mTOR-based therapy of many cancers favors the development of new TOR inhibitors. Here we report a high-throughput flow cytometry multiplexed screen using five GFP-tagged yeast clones that represent the readouts of four branches of the TORC1 signaling pathway in budding yeast. Each GFP-tagged clone was differentially color-coded, and the GFP signal of each clone was measured simultaneously by flow cytometry, which allows rapid prioritization of compounds that likely act through direct modulation of TORC1 or proximal signaling components. A total of 255 compounds were confirmed in dose-response analysis to alter GFP expression in one or more clones. To validate the concept of the high-throughput screen, we have characterized CID 3528206, a small molecule most likely to act on TORC1 as it alters GFP expression in all five GFP clones in a manner analogous to that of rapamycin. We have shown that CID 3528206 inhibited yeast cell growth and that CID 3528206 inhibited TORC1 activity both in vitro and in vivo with EC(50)'s of 150 nM and 3.9 μM, respectively. The results of microarray analysis and yeast GFP collection screen further support the notion that CID 3528206 and rapamycin modulate similar cellular pathways. Together, these results indicate that the HTS has identified a potentially useful small molecule for further development of TOR inhibitors.

Of possible cheminformatics futures

For over a decade, cheminformatics has contributed to a wide array of scientific tasks from analytical chemistry and biochemistry to pharmacology and drug discovery; and although its contributions to decision making are recognized, the challenge is how it would contribute to faster development of novel, better products. Here we address the future of cheminformatics with primary focus on innovation. Cheminformatics developers often need to choose between "mainstream" (i.e., accepted, expected) and novel, leading-edge tools, with an increasing trend for open science. Possible futures for cheminformatics include the worst case scenario (lack of funding, no creative usage), as well as the best case scenario (complete integration, from systems biology to virtual physiology). As "-omics" technologies advance, and computer hardware improves, compounds will no longer be profiled at the molecular level, but also in terms of genetic and clinical effects. Among potentially novel tools, we anticipate machine learning models based on free text processing, an increased performance in environmental cheminformatics, significant decision-making support, as well as the emergence of robot scientists conducting automated drug discovery research. Furthermore, cheminformatics is anticipated to expand the frontiers of knowledge and evolve in an open-ended, extensible manner, allowing us to explore multiple research scenarios in order to avoid epistemological "local information minimum trap".

Microbial efflux pump inhibition: tactics and strategies

Traditional antimicrobials are increasingly suffering from the emergence of multidrug resistance among pathogenic microorganisms. To overcome these deficiencies, a range of novel approaches to control microbial infections are under investigation as potential alternative treatments. Multidrug efflux is a key target of these efforts. Efflux mechanisms are broadly recognized as major components of resistance to many classes of chemotherapeutic agents as well as antimicrobials. Efflux occurs due to the activity of membrane transporter proteins widely known as Multidrug Efflux Systems (MES). They are implicated in a variety of physiological roles other than efflux and identifying natural substrates and inhibitors is an active and expanding research discipline. One plausible alternative is the combination of conventional antimicrobial agents/antibiotics with small molecules that block MES known as multidrug efflux pump inhibitors (EPIs). An array of approaches in academic and industrial research settings, varying from high-throughput screening (HTS) ventures to bioassay guided purification and determination, have yielded a number of promising EPIs in a series of pathogenic systems. This synergistic discovery platform has been exploited in translational directions beyond the potentiation of conventional antimicrobial treatments. This venture attempts to highlight different tactical elements of this platform, identifying the need for highly informative and comprehensive EPI-discovery strategies. Advances in assay development genomics, proteomics as well as the accumulation of bioactivity and structural information regarding MES facilitates the basis for a new discovery era. This platform is expanding drastically. A combination of chemogenomics and chemoinformatics approaches will integrate data mining with virtual and physical HTS ventures and populate the chemical-biological interface with a plethora of novel chemotypes. This comprehensive step will expedite the preclinical development of lead EPIs.

Identification of a GPER/GPR30 antagonist with improved estrogen receptor counterselectivity

GPER/GPR30 is a seven-transmembrane G protein-coupled estrogen receptor that regulates many aspects of mammalian biology and physiology. We have previously described both a GPER-selective agonist G-1 and antagonist G15 based on a tetrahydro-3H-cyclopenta[c]quinoline scaffold. The antagonist lacks an ethanone moiety that likely forms important hydrogen bonds involved in receptor activation. Computational docking studies suggested that the lack of the ethanone substituent in G15 could minimize key steric conflicts, present in G-1, that limit binding within the ERα ligand binding pocket. In this report, we identify low-affinity cross-reactivity of the GPER antagonist G15 to the classical estrogen receptor ERα. To generate an antagonist with enhanced selectivity, we therefore synthesized an isosteric G-1 derivative, G36, containing an isopropyl moiety in place of the ethanone moiety. We demonstrate that G36 shows decreased binding and activation of ERα, while maintaining its antagonist profile towards GPER. G36 selectively inhibits estrogen-mediated activation of PI3K by GPER but not ERα. It also inhibits estrogen- and G-1-mediated calcium mobilization as well as ERK1/2 activation, with no effect on EGF-mediated ERK1/2 activation. Similar to G15, G36 inhibits estrogen- and G-1-stimulated proliferation of uterine epithelial cells in vivo. The identification of G36 as a GPER antagonist with improved ER counterselectivity represents a significant step towards the development of new highly selective therapeutics for cancer and other diseases.

Structural determinants of the alpha2 adrenoceptor subtype selectivity

Alpha2-adrenergic receptor (α2-AR) subtypes, acting mainly on the central nervous and cardiovascular systems, represent important targets for drug design, confirmed by the high number of studies published so far. Presently, only a few α2-AR subtype selective compounds are known. Using homology modeling and ligand docking, the present study analyzes the similarities and differences between binding sites, and between extracellular loops of the three subtypes of α2-ARs. Several α2-AR subtype selective ligands were docked into the active sites of the three α2-AR subtypes, key interactions between ligands and receptors were mapped, and the predicted results were compared with the available experimental data. Binding site analysis reveals a strong identity between important amino acid residues in each receptor, the very few differences being the key toward modulating selectivity of α2-AR ligands. The observed differences between binding site residues provide an excellent starting point for virtual screening of chemical databases, in order to identify potentially selective ligands for α2-ARs.

High-throughput screen for the chemical inhibitors of antiapoptotic bcl-2 family proteins by multiplex flow cytometry

The human Bcl-2 family includes six antiapoptotic members (Bcl-2, Bcl-B, Bcl-W, Bcl-X(L), Bfl-1, and Mcl-1) and many proapoptotic members, wherein a balance between the two determines cell life or death in many physiological and disease contexts. Elevated expression of various antiapoptotic Bcl-2 members is commonly observed in cancers, and chemical inhibitors of these proteins have been shown to promote apoptosis of malignant cells in culture, in animal models, and in human clinical trials. All six antiapoptotic members bind a helix from the proapoptotic family member Bim, thus quenching Bim's apoptotic signal. Here, we describe the use of a multiplex, high-throughput flow cytometry assay for the discovery of small molecule modulators that disrupt the interaction between the antiapoptotic members of the Bcl-2 family and Bim. The six antiapoptotic Bcl-2 family members were expressed as glutathione-S-transferase fusion proteins and bound individually to six glutathione bead sets, with each set having a different intensity of red fluorescence. A fluorescein-conjugated Bcl-2 homology region 3 (BH3) peptide from Bim was employed as a universal ligand. Flow cytometry measured the amount of green peptide bound to each bead set in a given well, with inhibitory compounds resulting in a decrease of green fluorescence on one or more bead set(s). Hits and cheminformatically selected analogs were retested in a dose-response series, resulting in three "active" compounds for Bcl-B. These three compounds were validated by fluorescence polarization and isothermal titration calorimetry. We discuss some of the lessons learned about screening a chemical library provided by the National Institutes of Health Small Molecule Repository (∼195,000 compounds) using high-throughput flow cytometry.

Associating Drugs, Targets and Clinical Outcomes into an Integrated Network Affords a New Platform for Computer-Aided Drug Repurposing

Finding new uses for old drugs is a strategy embraced by the pharmaceutical industry, with increasing participation from the academic sector. Drug repurposing efforts focus on identifying novel modes of action, but not in a systematic manner. With intensive data mining and curation, we aim to apply bio- and cheminformatics tools using the DRUGS database, containing 3,837 unique small molecules annotated on 1,750 proteins. These are likely to serve as drug targets and antitargets (i.e., associated with side effects, SE). The academic community, the pharmaceutical sector and clinicians alike could benefit from an integrated, semantic-web compliant computer-aided drug repurposing (CADR) effort, one that would enable deep data mining of associations between approved drugs (D), targets (T), clinical outcomes (CO) and SE. We report preliminary results from text mining and multivariate statistics, based on 7,684 approved drug labels, ADL (Dailymed) via text mining. From the ADL corresponding to 988 unique drugs, the "adverse reactions" section was mapped onto 174 SE, then clustered via principal component analysis into a 5x5 self-organizing map that was integrated into a Cytoscape network of SE-D-T-CO. This type of data can be used to streamline drug repurposing and may result in novel insights that can lead to the identification of novel drug actions.

Structural simplification of bioactive natural products with multicomponent synthesis. 3. Fused uracil-containing heterocycles as novel topoisomerase-targeting agents

After the initial discovery of antiproliferative and apoptosis-inducing properties of a camptothecin-inspired pentacycle based on a 1H-indeno[2',1':5,6]dihydropyrido[2,3-d]pyrimidine scaffold, a library of its analogues as well as their oxidized planar counterparts were prepared utilizing a practical multicomponent synthetic protocol. The synthesized compounds exhibited submicromolar to low micromolar antiproliferative potencies toward a panel of human cancer cell lines. Biochemical experiments are consistent with the dihydropyridine library members undergoing intracellular oxidation to the corresponding planar pyridines, which then inhibit topoisomerase II activity, leading to inhibition of proliferation and cell death. Because of facile synthetic preparation and promising antitopoisomerase activity, both the dihydropyridine and planar pyridine-based compounds represent a convenient starting point for anticancer drug discovery.

Modulation of bitter taste perception by a small molecule hTAS2R antagonist

Human bitter taste is mediated by the hTAS2R family of G protein-coupled receptors. The discovery of the hTAS2Rs enables the potential to develop specific bitter receptor antagonists that could be beneficial as chemical probes to examine the role of bitter receptor function in gustatory and nongustatory tissues. In addition, they could have widespread utility in food and beverages fortified with vitamins, antioxidants, and other nutraceuticals, because many of these have unwanted bitter aftertastes. We employed a high-throughput screening approach to discover a novel bitter receptor antagonist (GIV3727) that inhibits activation of hTAS2R31 (formerly hTAS2R44) by saccharin and acesulfame K, two common artificial sweeteners. Pharmacological analyses revealed that GIV3727 likely acts as an orthosteric, insurmountable antagonist of hTAS2R31. Surprisingly, we also found that this compound could inhibit five additional hTAS2Rs, including the closely related receptor hTAS2R43. Molecular modeling and site-directed mutagenesis studies suggest that two residues in helix 7 are important for antagonist activity in hTAS2R31 and hTAS2R43. In human sensory trials, GIV3727 significantly reduced the bitterness associated with the two sulfonamide sweeteners, indicating that hTAS2R antagonists are active in vivo. Our results demonstrate that small molecule bitter receptor antagonists can effectively reduce the bitter taste qualities of foods, beverages, and pharmaceuticals.

Conformational mAb as a tool for integrin ligand discovery

alpha(4)beta(1)-Integrin (very late antigen-4 (VLA-4)) mediates cell adhesion to cell surface ligands (VCAM-1). Binding of VLA-4 to VCAM-1 initiates rolling and firm adhesion of leukocytes to vascular endothelium followed by the extravasation into the tissue. VLA-4-dependent adhesion plays a key role in controlling leukocyte adhesive events. Small molecules that bind to the integrin ligand-binding site and block its interaction with natural ligands represent promising candidates for treatment of several diseases. Following a flow cytometric screen for small molecule discovery, we took advantage of a conformationally sensitive anti-beta(1)-integrin antibody (HUTS-21) and a small LDV-containing ligand (LDV-FITC) with known affinity to study binding affinities of several known and recently discovered integrin ligands. We found that binding of the LDV-containing small molecule induced exposure of HUTS-21 epitope and that the EC(50) for antibody binding was equal to previously reported K(d) for fluorescent LDV (LDV-FITC). Thus, binding of HUTS-21 can be used to report ligand-binding site occupancy. We studied binding of two known integrin ligands (YLDV and TR14035), as well as of two novel compounds. EC(50) values for HUTS-21 binding showed good correlation with K(i)s determined in the competition assay with LDV-FITC for all ligands. A docking model suggests a common mode of binding for the small molecule VLA-4 ligands. This novel approach described here can be used to determine ligand-binding affinities for unlabeled integrin ligands, and can be adapted to a high-throughput screening format for identification of unknown integrin ligands.

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.

In vivo effects of a GPR30 antagonist

Estrogen is central to many physiological processes throughout the human body. We have previously shown that the G protein-coupled receptor GPR30/GPER, in addition to classical nuclear estrogen receptors (ERα/β), activates cellular signaling pathways in response to estrogen. In order to distinguish between the actions of classical estrogen receptors and GPR30, we have previously characterized a selective agonist of GPR30, G-1 (1). To complement the pharmacological properties of G-1, we sought to identify an antagonist of GPR30 that displays similar selectivity against the classical estrogen receptors. Here we describe the identification and characterization of a G-1 analog, G15 (2) that binds to GPR30 with high affinity and acts as an antagonist of estrogen signaling through GPR30. In vivo administration of G15 reveals that GPR30 contributes to both uterine and neurological responses initiated by estrogen. The identification of this antagonist will accelerate the evaluation of the roles of GPR30 in human physiology.