Computer-aided drug repurposing to tackle antibiotic resistance based on topological data analysis

• Published in: Computers in biology and medicine 2023-11, Vol.166, p.107496, Article 107496
• Main Authors: Tarín-Pelló, Antonio, Suay-García, Beatriz, Forés-Martos, Jaume, Falcó, Antonio, Pérez-Gracia, María-Teresa
• Format: Article
• Language: English
• Subjects: Anti-Bacterial Agents - pharmacology; Anti-Bacterial Agents - therapeutic use; Antibiotic resistance; Antibiotics; Anticancer properties; Antidepressants; Antihistamines; Antimicrobial; Antimicrobial agents; Antimicrobial resistance; Bacterial infections; Bacterial proteins; Carbapenems; Cephalosporins; Computer applications; Cost control; Data analysis; Drug Repositioning - methods; Drug repurposing; Drug resistance; Drug Resistance, Bacterial - drug effects; E coli; Escherichia coli - drug effects; FDA approval; GDP; Global health; Gross Domestic Product; Humans; Hypoglycemic agents; Infections; Lipids; Mathematical analysis; Mathematical models; Molecular docking; Nucleotides; Pharmaceutical industry; Pharmacokinetics; Prediction models; Proteins; Public health; R&D; Research & development; Therapeutic targets; Topological data analysis; Topology
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.107496

The progressive emergence of antimicrobial resistance has become a global health problem in need of rapid solution. Research into new antimicrobial drugs is imperative. Drug repositioning, together with computational mathematical prediction models, could be a fast and efficient method of searching for new antibiotics. The aim of this study was to identify compounds with potential antimicrobial capacity against Escherichia coli from US Food and Drug Administration-approved drugs, and the similarity between known drug targets and E. coli proteins using a topological structure-activity data analysis model. This model has been shown to identify molecules with known antibiotic capacity, such as carbapenems and cephalosporins, as well as new molecules that could act as antimicrobials. Topological similarities were also found between E. coli proteins and proteins from different bacterial species such as Mycobacterium tuberculosis, Pseudomonas aeruginosa and Salmonella Typhimurium, which could imply that the selected molecules have a broader spectrum than expected. These molecules include antitumor drugs, antihistamines, lipid-lowering agents, hypoglycemic agents, antidepressants, nucleotides, and nucleosides, among others. The results presented in this study prove the ability of computational mathematical prediction models to predict molecules with potential antimicrobial capacity and/or possible new pharmacological targets of interest in the design of new antibiotics and in the better understanding of antimicrobial resistance. •In silico trial using Structure-Activity Topological Data Analysis.•Repurposing of FDA-approved drugs that could present antimicrobial activity.•Relationship between antidepressant and altered microbiota and antibiotic resistance.•Potential lead compounds in the development of new and rapidly available antibiotics.•New approaches to antimicrobial resistances for the latest machine learning models.