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

Area of Science:

• Pharmacology
• Computational Biology
• Infectious Diseases

Background:

• Antimicrobial resistance is a growing global health threat requiring urgent development of new antibiotics.
• Drug repositioning offers a potentially rapid and cost-effective strategy for discovering novel antimicrobial agents.

Purpose of the Study:

• To identify potential antimicrobial compounds against Escherichia coli from FDA-approved drugs.
• To analyze structural similarities between known drug targets and E. coli proteins using a topological structure-activity relationship (SAR) model.

Main Methods:

• Utilized a topological SAR data analysis model to predict antimicrobial capacity.
• Screened US Food and Drug Administration (FDA)-approved drugs for activity against E. coli.
• Assessed topological similarities between E. coli proteins and proteins from other bacterial species.

Main Results:

• The model successfully identified known antibiotics (e.g., carbapenems, cephalosporins) and novel potential antimicrobial molecules.
• Topological similarities suggest broader antimicrobial spectrum for identified compounds across various bacterial species (e.g., Mycobacterium tuberculosis, Pseudomonas aeruginosa, Salmonella Typhimurium).
• Identified molecules span diverse therapeutic classes, including antitumor, antihistamine, lipid-lowering, hypoglycemic, and antidepressant agents, as well as nucleotides and nucleosides.

Conclusions:

• Computational mathematical prediction models are effective in identifying molecules with antimicrobial potential.
• This approach can uncover new pharmacological targets for antibiotic design.
• The study advances the understanding of antimicrobial resistance and aids in developing new therapeutic strategies.