ARTICLE | doi:10.20944/preprints202003.0413.v1
Subject: Medicine & Pharmacology, Pharmacology & Toxicology Keywords: coronavirus; drug; COVID-19; SARS; MERS; ontology; ChEBI; NDF-RT; DrON; bioinformatics
Online: 29 March 2020 (01:58:40 CET)
Coronavirus-infected diseases have posed great threats to human health. In past years, highly infectious coronavirus-induced diseases, including COVID-19, SARS, and MERS, have resulted in world-wide severe infections. Our literature annotations identified 110 chemical drugs and 26 antibodies effective against at least one human coronavirus infection in vitro or in vivo. Many of these drugs inhibit viral entry to cells and viral replication inside cells or modulate host immune responses. Many antimicrobial drugs, including antimalarial (e.g., chloroquine and mefloquine) and antifungal (e.g., terconazole and rapamycin) drugs as well as antibiotics (e.g., teicoplanin and azithromycin) were associated with anti-coronavirus activity. A few drugs, including remdesivir, chloroquine, favipiravir, and tocilizumab, have already been reported to be effective against SARS-CoV-2 infection in vitro or in vivo. After mapping our identified drugs to three ontologies ChEBI, NDF-RT, and DrON, many features such as roles and mechanisms of action (MoAs) of these drugs were identified and categorized. For example, out of 57 drugs with MoA annotations in NDF-RT, 47 have MoAs of different types of inhibitors and antagonists. A total of 29 anticoronaviral drugs are anticancer drugs with the antineoplastic role. Two clustering analyses, one based on ChEBI-based semantic similarity, the other based on drug chemical similarity, were performed to cluster 110 drugs to new categories. Moreover, differences in physicochemical properties among the drugs were found between those inhibiting viral entry and viral replication. A total of 163 host genes were identified as the known targets of 68 anti-coronavirus drugs, resulting in a network of 428 interactions among these drugs and targets. Chlorpromazine, dasatinib, and anisomycin are the hubs of the drug-target network with the highest number of connected target proteins. Many enriched pathways such as calcium signaling and neuroactive ligand-receptor interaction pathways were identified. These findings may be used to facilitate drug repurposing against COVID-19.
ARTICLE | doi:10.20944/preprints202208.0305.v1
Subject: Medicine & Pharmacology, Allergology Keywords: drug repurposing; combination therapeutics; PubMed; ChEBI; disease ontology; gene ontology; drug interaction; MeSH terms; COVID-19
Online: 17 August 2022 (05:51:53 CEST)
This paper presents a computational approach designed to construct and query a literature-based knowledge graph for predicting novel drug therapeutics. The main objective is to offer a platform that discovers drug combinations from FDA-approved drugs and accelerates their investigations by domain scientists. Specifically, the paper introduced the following algorithms: (1) an algorithm for constructing the knowledge graph from drug, gene, and disease mentions in the biomedical literature; (2) an algorithm for vetting the knowledge graph from drug combinations that may pose a risk of drug interaction; (3) and two querying algorithms for searching the knowledge graph by a single drug or a combination of drugs. The resulting knowledge graph had 844 drugs, 306 gene/protein features, and 19 disease mentions. The original number of drug combinations generated was 2,001. We queried the knowledge graph to eliminate noise generated from chemicals that are not drugs. This step resulted in 614 drug combinations. When vetting the knowledge graph to eliminate the potentially risky drug combinations, it resulted in predicting 200 combinations. Our domain expert manually eliminated extra 54 combinations which left only 146 combination candidates. Our three-layered knowledge graph, empowered by our algorithms, offered a tool that predicted drug combination therapeutics for scientists who can further investigate from the viewpoint of drug targets and side effects.