Accelerating the Hit-To-Lead Optimization of a SARS-CoV-2 Mpro Inhibitor Series by Combining High-Throughput...

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• SARS-CoV-2 main protease (Mpro) is a key target for antiviral drug development.
• Previous work identified a diazepane hit compound with moderate inhibitory activity against Mpro.

Purpose of the Study:

• To optimize the identified SARS-CoV-2 Mpro diazepane hit into a potent lead compound.
• To explore the S1, S2, and S1' binding pockets of Mpro for enhanced binding affinity.
• To demonstrate the efficiency of combining computational methods with high-throughput medicinal chemistry (HTMC) for accelerated drug discovery.

Main Methods:

• Hit-to-lead optimization using a combination of computational simulations (machine learning, molecular docking, molecular dynamics) and HTMC.
• Structure-based drug design targeting Mpro's S1 and S2 binding pockets.
• Identification of a novel exit vector towards the S1' pocket to enhance binding affinity.

Main Results:

• Transformed a 14 μM hit compound into a 16 nM lead compound with significantly enhanced binding affinity.
• Rapid optimization achieved with a limited number of synthesized compounds.
• Key pharmacological properties of the lead compound were evaluated.

Conclusions:

• The integrated strategy of computational technologies and HTMC efficiently accelerates hit identification and lead generation for SARS-CoV-2 Mpro inhibitors.
• Targeting specific binding pockets and exit vectors can lead to potent antiviral compounds.
• This approach offers a rapid and effective pathway for developing new therapeutic agents against viral targets.