Discovery of Novel MsbA Inhibitors with a Bivalent Molecular Generative Model

Area of Science:

• Microbiology
• Drug Discovery
• Computational Chemistry

Background:

• MsbA is a crucial bacterial ABC transporter for lipopolysaccharide (LPS) transport, making it a key target for combating multidrug-resistant infections.
• Developing effective MsbA inhibitors is challenging due to factors like high plasma protein binding observed with compounds like cerastecin C.

Purpose of the Study:

• To optimize the structure of cerastecin C using a bivalent molecular generative model, BL-INVENT, with a focus on linker design.
• To identify novel MsbA inhibitors with enhanced potency and improved pharmacokinetic properties, such as reduced plasma protein binding.

Main Methods:

• Development of a bivalent molecular generative model, BL-INVENT, for designing novel MsbA inhibitors.
• Synthesis and antibacterial activity evaluation of compounds generated by the BL-INVENT model.
• Structure-activity relationship studies and optimization leading to compound 12.

Main Results:

• Several synthesized compounds designed by the generative model demonstrated antibacterial activity.
• Compound 12 exhibited enhanced potency against multidrug-resistant bacteria compared to cerastecin C.
• Compound 12 showed significantly lower plasma protein binding than cerastecin C.

Conclusions:

• The AI-driven BL-INVENT model is effective for designing next-generation MsbA inhibitors.
• Optimized bivalent inhibitors, like compound 12, offer a promising strategy for treating multidrug-resistant Gram-negative bacterial infections.
• Reducing plasma protein binding is a viable approach to improve the efficacy of MsbA inhibitors.