Network Analysis Guided Designing of Multi-Targeted Anti-Fungal Agents: Synthesis and Biological Evaluation

Area of Science:

• Medicinal Chemistry
• Mycology
• Computational Chemistry

Background:

• Invasive fungal disease (IFD) is a growing concern, especially in immunocompromised individuals during the pandemic.
• Candida albicans presents a significant health threat, often complicating COVID-19 treatments and immunosuppressive therapies.
• Benzothiazole scaffolds, particularly mercapto-substituted ones, are recognized for their antifungal potential, with modifications at the 2nd position showing promise.

Purpose of the Study:

• To optimize an existing benzothiazole derivative for improved anti-Candida albicans activity using a lead optimization strategy.
• To identify potential molecular targets for the designed compounds through network analysis.
• To synthesize and evaluate novel benzothiazole derivatives for their in vitro antifungal efficacy.

Main Methods:

• Lead optimization employing bioisosteric replacement on a known benzothiazole derivative.
• Network analysis to predict anti-Candida targets.
• Molecular docking and molecular dynamics (MD) simulations to assess compound-target interactions.
• In vitro antifungal assays against Candida species.

Main Results:

• Seven novel benzothiazole derivatives were synthesized.
• Compounds 1.2c and 1.2f exhibited enhanced and comparable in vitro antifungal activity against Candida, respectively.
• Network analysis and molecular simulations provided insights into potential drug targets and binding interactions.

Conclusions:

• The study successfully optimized a benzothiazole derivative, yielding compounds with significant in vitro antifungal activity against Candida.
• The findings suggest that these novel derivatives are potential candidates for further development as antifungal agents.
• The integration of computational methods aided in the rational design and target identification process.