Drug Repurposing for Paracoccidioidomycosis Through a Computational Chemogenomics Framework

Area of Science:

• Mycology
• Computational Biology
• Drug Discovery

Background:

• Paracoccidioidomycosis (PCM) is a prevalent fungal infection in Latin America, primarily affecting rural workers.
• Current treatments for PCM involve long protocols, high toxicity, frequent relapses, and incomplete fungal elimination.
• Limitations of existing therapies necessitate novel therapeutic strategies for PCM.

Purpose of the Study:

• To identify approved drugs or clinical candidates with anti-PCM activity using a computational drug repurposing-chemogenomics approach.
• To predict drug-target interactions beyond the traditional one-drug-one-target model.
• To validate computationally predicted drug candidates through experimental testing.

Main Methods:

• Genome data compilation and preparation for *Paracoccidioides* species.
• Identification of orthologous and homologous proteins, and essential *Paracoccidioides* targets.
• Homology modeling, molecular docking, and experimental validation of drug candidates.

Main Results:

• A computational workflow identified 14 prioritized compounds.
• Two antineoplastic drug candidates (vistusertib, BGT-226) showed activity against PCM pathogens.
• Four antifungal azole drugs (bifonazole, luliconazole, butoconazole, sertaconazole) demonstrated potent activity at nanomolar concentrations.

Conclusions:

• The computational drug repurposing-chemogenomics strategy is effective for identifying new anti-PCM drugs.
• Identified antifungal azoles represent promising leads for further optimization and development.
• Further studies should focus on hit-to-lead optimization and pharmaceutical formulations to enhance efficacy and bioavailability.