ExEmPLAR (Extracting, Exploring, and Embedding Pathways Leading to Actionable Research): a user-friendly interface for knowledge graph mining.

SUMMARY: Knowledge graphs are being increasingly used in biomedical research to link large amounts of heterogenous data and facilitate reasoning across diverse knowledge sources. Wider adoption and exploration of knowledge graphs in the biomedical research community is limited by requirements to understand the underlying graph structure in terms of entity types and relationships, represented as nodes and edges, respectively, and learn specialized query languages for graph mining and exploration. We have developed a user-friendly interface dubbed ExEmPLAR (Extracting, Exploring, and Embedding Pathways Leading to Actionable Research) to aid reasoning over biomedical knowledge graphs and assist with data-driven research and hypothesis generation. We explain the key functionalities of ExEmPLAR and demonstrate its use with a case study considering the relationship of Trypanosoma cruzi, the etiological agent of Chagas disease, to frequently associated cardiovascular conditions. AVAILABILITY AND IMPLEMENTATION: ExEmPLAR is freely accessible at https://www.exemplar.mml.unc.edu/. For code and instructions for the using the application, see: https://github.com/beasleyjonm/AOP-COP-Path-Extractor.

Small molecule antiviral compound collection (SMACC): A comprehensive, highly curated database to support the discovery of broad-spectrum antiviral drug molecules.

Diseases caused by new viruses cost thousands if not millions of human lives and trillions of dollars. We have identified, collected, curated, and integrated all chemogenomics data from ChEMBL for 13 emerging viruses that hold the greatest potential threat to global human health. By identifying and solving several challenges related to data annotation accuracy, we developed a highly curated and thoroughly annotated database of compounds tested in both phenotypic and target-based assays for these viruses that we dubbed SMACC (Small Molecule Antiviral Compound Collection). The pilot version of the SMACC database contains over 32,500 entries for 13 viruses. By analyzing data in SMACC, we have identified ∼50 compounds with polyviral inhibition profile, mostly covering flavi- and coronaviruses. The SMACC database may serve as a reference for virologists and medicinal chemists working on the development of novel BSA agents in preparation for future viral outbreaks. SMACC is publicly available at https://smacc.mml.unc.edu.

Integrated approach to elucidate metal-implant related adverse outcome pathways.

Exogenous metal particles and ions from implant devices are known to cause severe toxic events with symptoms ranging from adverse local tissue reactions to systemic toxicities, potentially leading to the development of cancers, heart conditions, and neurological disorders. Toxicity mechanisms, also known as Adverse Outcome Pathways (AOPs), that explain these metal-induced toxicities are severely understudied. Therefore, we deployed in silico structure- and knowledge-based approaches to identify proteome-level perturbations caused by metals and pathways that link these events to human diseases. We captured 177 structure-based, 347 knowledge-based, and 402 imputed metal-gene/protein relationships for chromium, cobalt, molybdenum, nickel, and titanium. We prioritized 72 proteins hypothesized to directly contact implant surfaces and contribute to adverse outcomes. Results of this exploratory analysis were formalized as structured AOPs. We considered three case studies reflecting the following possible situations: (i) the metal-protein-disease relationship was previously known; (ii) the metal-protein, protein-disease, and metal-disease relationships were individually known but were not linked (as a unified AOP); and (iii) one of three relationships was unknown and was imputed by our methods. These situations were illustrated by case studies on nickel-induced allergy/hypersensitivity, cobalt-induced heart failure, and titanium-induced periprosthetic osteolysis, respectively. All workflows, data, and results are freely available in https://github.com/DnlRKorn/Knowledge_Based_Metallomics/. An interactive view of select data is available at the ROBOKOP Neo4j Browser at http://robokopkg.renci.org/browser/.

PreS/MD: Predictor of Sensitization Hazard for Chemical Substances Released From Medical Devices.

In the United States, a pre-market regulatory submission for any medical device that comes into contact with either a patient or the clinical practitioner must include an adequate toxicity evaluation of chemical substances that can be released from the device during its intended use. These substances, also referred to as extractables and leachables, must be evaluated for their potential to induce sensitization/allergenicity, which traditionally has been done in animal assays such as the guinea pig maximization test (GPMT). However, advances in basic and applied science are continuously presenting opportunities to employ new approach methodologies, including computational methods which, when qualified, could replace animal testing methods to support regulatory submissions. Herein, we developed a new computational tool for rapid and accurate prediction of the GPMT outcome that we have named PreS/MD (predictor of sensitization for medical devices). To enable model development, we (1) collected, curated, and integrated the largest publicly available dataset for GPMT results; (2) succeeded in developing externally predictive (balanced accuracy of 70%-74% as evaluated by both 5-fold external cross-validation and testing of novel compounds) quantitative structure-activity relationships (QSAR) models for GPMT using machine learning algorithms, including deep learning; and (3) developed a publicly accessible web portal integrating PreS/MD models that can predict GPMT outcomes for any molecule of interest. We expect that PreS/MD will be used by both industry and regulatory scientists in medical device safety assessments and help replace, reduce, or refine the use of animals in toxicity testing. PreS/MD is freely available at https://presmd.mml.unc.edu/.

Small Molecule Antiviral Compound Collection (SMACC): a database to support the discovery of broad-spectrum antiviral drug molecules.

Diseases caused by new viruses costs thousands if not millions of human lives and trillions of dollars in damage to the global economy. Despite the rapid development of vaccines for SARS-CoV-2, the lack of small molecule antiviral drugs that work against multiple viral families (broad-spectrum antivirals; BSAs) has left the entire world’s human population vulnerable to the infection between the beginning of the outbreak and the widespread availability of vaccines. Developing BSAs is an attractive, yet challenging, approach that could prevent the next, inevitable, viral outbreak from becoming a global catastrophe. To explore whether historical medicinal chemistry efforts suggest the possibility of discovering novel BSAs, we (i) identified, collected, curated, and integrated all chemical bioactivity data available in ChEMBL for molecules tested in respective assays for 13 emerging viruses that, based on published literature, hold the greatest potential threat to global human health; (ii) identified and solved the challenges related to data annotation accuracy including assay description ambiguity, missing cell or target information, and incorrect BioAssay Ontology (BAO) annotations; (iii) developed a highly curated and thoroughly annotated database of compounds tested in both phenotypic (21,392 entries) and target-based (11,123 entries) assays for these viruses; and (iv) identified a subset of compounds showing BSA activity. For the latter task, we eliminated inconclusive and annotated duplicative entries by checking the concordance between multiple assay results and identified eight compounds active against 3-4 viruses from the phenotypic data, 16 compounds active against two viruses from the target-based data, and 35 compounds active in at least one phenotypic and one target-based assay. The pilot version of our SMACC (Small Molecule Antiviral Compound Collection) database contains over 32,500 entries for 13 viruses. Our analysis indicates that previous research yielded very small number of BSA compounds. We posit that focused and coordinated efforts strategically targeting the discovery of such agents must be established and maintained going forward. The SMACC database publicly available at https://smacc.mml.unc.edu may serve as a reference for virologists and medicinal chemists working on the development of novel BSA agents in preparation for future viral outbreaks.

Defining clinical outcome pathways.

Here, we propose a broad concept of 'Clinical Outcome Pathways' (COPs), which are defined as a series of key molecular and cellular events that underlie therapeutic effects of drug molecules. We formalize COPs as a chain of the following events: molecular initiating event (MIE) â†’ intermediate event(s) â†’ clinical outcome. We illustrate the concept with COP examples both for primary and alternative (i.e., drug repurposing) therapeutic applications. We also describe the elucidation of COPs for several drugs of interest using the publicly accessible Reasoning Over Biomedical Objects linked in Knowledge-Oriented Pathways (ROBOKOP) biomedical knowledge graph-mining tool. We propose that broader use of COP uncovered with the help of biomedical knowledge graph mining will likely accelerate drug discovery and repurposing efforts.

STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity.

BACKGROUND: Modern chemical toxicology is facing a growing need to Reduce, Refine, and Replace animal tests (Russell 1959) for hazard identification. The most common type of animal assays for acute toxicity assessment of chemicals used as pesticides, pharmaceuticals, or in cosmetic products is known as a "6-pack" battery of tests, including three topical (skin sensitization, skin irritation and corrosion, and eye irritation and corrosion) and three systemic (acute oral toxicity, acute inhalation toxicity, and acute dermal toxicity) end points. METHODS: We compiled, curated, and integrated, to the best of our knowledge, the largest publicly available data sets and developed an ensemble of quantitative structure-activity relationship (QSAR) models for all six end points. All models were validated according to the Organisation for Economic Co-operation and Development (OECD) QSAR principles, using data on compounds not included in the training sets. RESULTS: In addition to high internal accuracy assessed by cross-validation, all models demonstrated an external correct classification rate ranging from 70% to 77%. We established a publicly accessible Systemic and Topical chemical Toxicity (STopTox) web portal (https://stoptox.mml.unc.edu/) integrating all developed models for 6-pack assays. CONCLUSIONS: We developed STopTox, a comprehensive collection of computational models that can be used as an alternative to in vivo 6-pack tests for predicting the toxicity hazard of small organic molecules. Models were established following the best practices for the development and validation of QSAR models. Scientists and regulators can use the STopTox portal to identify putative toxicants or nontoxicants in chemical libraries of interest. https://doi.org/10.1289/EHP9341.

Knowledge-based approaches to drug discovery for rare diseases.

The conventional drug discovery pipeline has proven to be unsustainable for rare diseases. Herein, we discuss recent advances in biomedical knowledge mining applied to discovering therapeutics for rare diseases. We summarize current chemogenomics data of relevance to rare diseases and provide a perspective on the effectiveness of machine learning (ML) and biomedical knowledge graph mining in rare disease drug discovery. We illustrate the power of these methodologies using a chordoma case study. We expect that a broader application of knowledge graph mining and artificial intelligence (AI) approaches will expedite the discovery of viable drug candidates against both rare and common diseases.

COVID-19 Knowledge Extractor (COKE): A Curated Repository of Drug-Target Associations Extracted from the CORD-19 Corpus of Scientific Publications on COVID-19.

The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19. SciBiteAI ontological tagging of the COVID Open Research Data set (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of the target protein and drug terms, and confidence scores were calculated for each entity pair. COKE processing of the current CORD-19 database identified about 3000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. The COKE repository and web application can serve as a useful resource for drug repurposing against SARS-CoV-2. COKE is freely available at https://coke.mml.unc.edu/, and the code is available at https://github.com/DnlRKorn/CoKE.

ZINC Express: A Virtual Assistant for Purchasing Compounds Annotated in the ZINC Database.

Many laboratories working in the field of drug discovery use the ZINC database to identify and then acquire commercially available chemicals. However, finding the best deal for a given compound is often time-intensive and laborious, as the process involves searching for all vendors selling the desired compound, comparing prices, and interacting with the preferred vendor. To streamline this process, we have developed ZINC Express, a web application that simplifies the online purchase of chemicals annotated in the ZINC database. For any compound with a known ZINC ID, ZINC Express finds a list of vendors offering that compound and for each such vendor returns the available package quantities, the price of each package, and the price per milligram along with a link to that vendor. We expect that ZINC Express will be of use to both computational and experimental researchers. ZINC Express is freely accessible online at https://zincexpress.mml.unc.edu/.

QSAR Modeling of SARS-CoV Mpro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS-CoV-2.

The main protease (Mpro) of the SARS-CoV-2 has been proposed as one of the major drug targets for COVID-19. We have identified the experimental data on the inhibitory activity of compounds tested against the closely related (96 % sequence identity, 100 % active site conservation) Mpro of SARS-CoV. We developed QSAR models of these inhibitors and employed these models for virtual screening of all drugs in the DrugBank database. Similarity searching and molecular docking were explored in parallel, but docking failed to correctly discriminate between experimentally active and inactive compounds, so it was not relied upon for prospective virtual screening. Forty-two compounds were identified by our models as consensus computational hits. Subsequent to our computational studies, NCATS reported the results of experimental screening of their drug collection in SARS-CoV-2 cytopathic effect assay (https://opendata.ncats.nih.gov/covid19/). Coincidentally, NCATS tested 11 of our 42 hits, and three of them, cenicriviroc (AC50 of 8.9 µM), proglumetacin (tested twice independently, with AC50 of 8.9 µM and 12.5 µM), and sufugolix (AC50 12.6 µM), were shown to be active. These observations support the value of our modeling approaches and models for guiding the experimental investigations of putative anti-COVID-19 drug candidates. All data and models used in this study are publicly available via Supplementary Materials, GitHub (https://github.com/alvesvm/sars-cov-mpro), and Chembench web portal (https://chembench.mml.unc.edu/).

COVID-KOP: integrating emerging COVID-19 data with the ROBOKOP database.

SUMMARY: In response to the COVID-19 pandemic, we established COVID-KOP, a new knowledgebase integrating the existing Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP) biomedical knowledge graph with information from recent biomedical literature on COVID-19 annotated in the CORD-19 collection. COVID-KOP can be used effectively to generate new hypotheses concerning repurposing of known drugs and clinical drug candidates against COVID-19 by establishing respective confirmatory pathways of drug action. AVAILABILITY AND IMPLEMENTATION: COVID-KOP is freely accessible at https://covidkop.renci.org/. For code and instructions for the original ROBOKOP, see: https://github.com/NCATS-Gamma/robokop.

COVID-19 Knowledge Extractor (COKE): A Tool and a Web Portal to Extract Drug - Target Protein Associations from the CORD-19 Corpus of Scientific Publications on COVID-19.

Objective: The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19 to assist drug repurposing efforts. Materials and Methods: SciBiteAI ontological tagging of the COVID Open Research Dataset (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of protein and drug terms, and confidence scores were calculated for each entity pair. Results: COKE processing of the current CORD-19 database identified about 3,000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. Discussion: The rapidly evolving situation concerning the COVID-19 pandemic has resulted in a dramatic growth of publications on this subject in a short period. These circumstances call for methods that can condense the literature into the key concepts and relationships necessary for insights into SARS-CoV-2 drug repurposing. Conclusion: The COKE repository and web application deliver key drug - target protein relationships to researchers studying SARS-CoV-2. COKE portal may provide comprehensive and critical information on studies concerning drug repurposing against COVID-19. COKE is freely available at https://coke.mml.unc.edu/ and the code is available at https://github.com/DnlRKorn/CoKE.

Learning from history: do not flatten the curve of antiviral research!

Here, we explore the dynamics of the response of the scientific community to several epidemics, including Coronavirus Disease 2019 (COVID-19), as assessed by the numbers of clinical trials, publications, and level of research funding over time. All six prior epidemics studied [bird flu, severe acute respiratory syndrome (SARS), swine flu, Middle East Respiratory Syndrome (MERS), Ebola, and Zika] were characterized by an initial spike of research response that flattened shortly thereafter. Unfortunately, no antiviral medications have been discovered to date as treatments for any of these diseases. By contrast, the HIV/AIDS pandemic has garnered consistent research investment since it began and resulted in drugs being developed within 7 years of its start date, with many more to follow. We argue that, to develop effective treatments for COVID-19 and be prepared for future epidemics, long-term, consistent investment in antiviral research is needed.

SCAM Detective: Accurate Predictor of Small, Colloidally Aggregating Molecules.

Small, colloidally aggregating molecules (SCAMs) are the most common source of false positives in high-throughput screening (HTS) campaigns. Although SCAMs can be experimentally detected and suppressed by the addition of detergent in the assay buffer, detergent sensitivity is not routinely monitored in HTS. Computational methods are thus needed to flag potential SCAMs during HTS triage. In this study, we have developed and rigorously validated quantitative structure-interference relationship (QSIR) models of detergent-sensitive aggregation in several HTS campaigns under various assay conditions and screening concentrations. In particular, we have modeled detergent-sensitive aggregation in an AmpC ß-lactamase assay, the preferred HTS counter-screen for aggregation, as well as in another assay that measures cruzain inhibition. Our models increase the accuracy of aggregation prediction by ∼53% in the ß-lactamase assay and by ∼46% in the cruzain assay compared to previously published methods. We also discuss the importance of both assay conditions and screening concentrations in the development of QSIR models for various interference mechanisms besides aggregation. The models developed in this study are publicly available for fast prediction within the SCAM detective web application (https://scamdetective.mml.unc.edu/).

COVID-KOP: Integrating Emerging COVID-19 Data with the ROBOKOP Database.

In response to the COVID-19 pandemic, we established COVID-KOP, a new knowledgebase integrating the existing ROBOKOP biomedical knowledge graph with information from recent biomedical literature on COVID-19 annotated in the CORD-19 collection. COVID-KOP can be used effectively to test new hypotheses concerning repurposing of known drugs and clinical drug candidates against COVID-19. COVID-KOP is freely accessible at https://covidkop.renci.org/. For code and instructions for the original ROBOKOP, see: https://github.com/NCATS-Gamma/robokop.

Computational Models Identify Several FDA Approved or Experimental Drugs as Putative Agents Against SARS-CoV-2.

The outbreak of a novel human coronavirus (SARS-CoV-2) has evolved into global health emergency, infecting hundreds of thousands of people worldwide. We have identified experimental data on the inhibitory activity of compounds tested against closely related (96% sequence identity, 100% active site conservation) protease of SARS-CoV and employed this data to build QSAR models for this dataset. We employed these models for virtual screening of all drugs from DrugBank, including compounds in clinical trials. Molecular docking and similarity search approaches were explored in parallel with QSAR modeling, but molecular docking failed to correctly discriminate between experimentally active and inactive compounds. As a result of our studies, we recommended 41 approved, experimental, or investigational drugs as potential agents against SARS-CoV-2 acting as putative inhibitors of Mpro. Ten compounds with feasible prices were purchased and are awaiting the experimental validation.
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Life Expectancy of Insured People With and Without Hepatitis C Virus Infection, 2007-2017.

Among 25 291 and 4 921 830 people with and without hepatitis C, life expectancy at age 20 increased 1.8 years and 0.3 years from the interferon to interferon-free era, respectively. Increases were highest for racial and/or ethnic minority groups with hepatitis C.

Prevalence of Spontaneous Clearance of Hepatitis C Virus Infection Doubled From 1998 to 2017.

Strategic planning for hepatitis C virus (HCV) screening and treatment requires up-to-date information on the prevalence of HCV spontaneous clearance. Published estimates of HCV spontaneous clearance range from 15% to 60%.1-3 We conducted an observational study over 20 years to evaluate trends in the prevalence of HCV spontaneous clearance. Our goals were to estimate the proportion of HCV-antibody-positive patients who were viremic, and to identify factors associated with viremia, thus facilitating prediction of the number of patients needing treatment.

Hepatitis C treatment uptake and response among human immunodeficiency virus/hepatitis C virus-coinfected patients in a large integrated healthcare system.

U.S. guidelines recommend that patients coinfected with human immunodeficiency virus (HIV) and hepatitis C virus (HCV) be prioritized for HCV treatment with direct-acting antiviral agents (DAAs), but the high cost of DAAs may contribute to disparities in treatment uptake and outcomes. We evaluated DAA initiation and effectiveness in HIV/HCV-coinfected patients in a U.S.-based healthcare system during October 2014-December 2017. Of 462 HIV/HCV-coinfected patients, 276 initiated DAAs (70% cumulative proportion treated over three years). Lower likelihood of DAA initiation was observed among patients with Medicare (government-sponsored insurance) versus commercial insurance (adjusted rate ratio [aRR] = 0.62, 95% CI = 0.46-0.84), patients with drug abuse diagnoses (aRR = 0.72, 95% CI = 0.54-0.97), patients with CD4 cell count <200 cells/µl versus ≥500 (aRR = 0.45, 95% CI = 0.23-0.91), and patients without prior HCV treatment (aRR = 0.68, 95% CI = 0.48-0.97). There were no significant differences in DAA initiation by age, gender, race/ethnicity, socioeconomic status, HIV transmission risk, alcohol use, smoking, fibrosis level, HIV RNA levels, antiretroviral therapy use, hepatitis B infection, or number of outpatient visits. Ninety-five percent of patients achieved sustained virologic response (SVR). We found little evidence of sociodemographic disparities in DAA initiation among HIV/HCV-coinfected patients, and SVR rates were high. Efforts are needed to increase DAA uptake among coinfected Medicare enrollees, patients with drug abuse diagnoses, patients with low CD4 cell count, and patients receiving first-time HCV treatment.