In Silico Development of Combinatorial Therapeutic Approaches Targeting Key Signaling Pathways in Metabolic Syndrome

Area of Science:

• Metabolic Syndrome Research
• Pharmacology
• Immunology

Background:

• Metabolic syndrome involves complex dysregulations leading to cardiovascular events.
• Hyperglycemia, dyslipidemia, and chronic inflammation are key drivers of metabolic syndrome complications like atherosclerosis and diabetes.

Purpose of the Study:

• To model the outcomes of a novel combinatorial treatment for metabolic syndrome.
• To investigate the efficacy of targeting Interleukin-17A (IL-17a) and Dipeptidyl peptidase-4 (DPP4) pathways using FDA-approved drugs.

Main Methods:

• Utilized the BIOiSIM™ drug development platform to identify drug classes.
• Selected targets based on core metabolic syndrome risks: dyslipidemia, insulin resistance, impaired glucose control, and inflammation.
• Modeled pharmacokinetic and pharmacodynamic profiles of potential therapeutic interventions.

Main Results:

• Identified DPP-4 inhibitors and IL-17A inhibitors as promising drug classes.
• Evogliptin (DPP-4 inhibitor) was selected for glucose control, with benefits for weight and lipids.
• Secukinumab (IL-17A inhibitor) was chosen to address inflammatory aspects of metabolic syndrome.

Conclusions:

• A combinatorial approach suppressing DPP4 and IL-17a shows high potential for ameliorating metabolic syndrome risks.
• This strategy may significantly reduce clustered clinical risks associated with metabolic syndrome.