Laureano E. Carpio, Rita Ortega-Vallbona, David Talavera-Cortés, Addel Goya-Jorge, Eva Serrano-Candelas, Rafael Gozalbes.
MolDrug AI Systems SL, Valencia, Spain.
ProtoQSAR SL, CEEI Parque Tecnológico de Valencia, Spain.
Type 2 diabetes mellitus (T2DM) is a major public health issue, characterized by hyperglycemia due to insulin resistance and/or insufficient insulin production. Uncontrolled T2DM can lead to severe complications, such as nephropathy, retinopathy, and cardiovascular disease (1,2). Among potential therapeutic agents, peroxisome proliferator-activated receptor alpha (PPARα) agonists emerge promising in T2DM management due to their ability to improve lipid profiles and enhance glycemic control. Particularly, PPARα agonists exhibit both lipid-lowering and glucose-modulating effects, making them valuable, especially for patients with coexisting dyslipidemia (1). Emerging evidence suggests that combining PPARα agonists with established T2DM treatments like biguanides or gliptins may provide synergistic benefits (3, 4). In this study, we have developed new ligand-based (Q)SAR models and employed existing ones in our platform ProtoPRED, combined with structure-based docking with our platform DockTox, in order to search for potential PPARα agonists with favorable pharmacokinetic and toxicological properties. To achieve this,natural compound databases, including NPASS andNuBBE, were screened , yielding candidate compounds with optimal binding interaction profiles and toxicology predictions. Our findings demonstrate the efficacy of an integrative computational approach for identifying novel PPARα agonists, offering promising groundwork for future research in T2DM adjunctive therapies. Keywords: Molecular modeling; (Q)SAR; Type II Diabetes Mellitus; Drug Discovery; PPARα.