Targeting the Crosstalk Between Metabolism and Chronic Inflammation: In Silico Multitargeting Drug Design Approach for Cardiometabolic Syndrome

Βackround/Objectives: The rising global burden of cardiometabolic disorders and chronic low-grade inflammation underscores the need for therapies capable of modulating multiple interconnected pathways. Methods: In this work, a ligand-based virtual screening campaign centered on a previously reported scaffold (compound 1a) was combined with molecular docking, 200 ns molecular dynamics simulations and ADMET prediction to identify and prioritize small-molecule multitarget candidates against PCSK9, GLP1R, FGFR1, GIPR, NF-κB and NLRP3. Results: Among the screened analogs, D4Z emerged as the most balanced lead, displaying consistently favorable binding profiles, stable interactions within functionally relevant pockets and a drug-like physicochemical and pharmacokinetic profile with high predicted oral absorption. Conclusions: Although these findings remain purely computational, they support D4Z as a prioritized multitarget lead for synthesis and experimental validation and illustrate the potential of rational multitarget design for addressing the cardiometabolic–inflammatory axis.