Multi-agent therapies are an important treatment modality in many diseases based on the assumption that combining agents may result in increased therapeutic benefit by overcoming the mechanism of resistance and providing superior efficiency. Extensively validated 3D pharmacophore models for free fatty acid receptor-1 (FFAR-1), free fatty acid receptor-4 (FFAR-4), and peroxisome proliferator-activated receptor-G (PPAR-G) was developed. The pharmacophore model for FFAR-1 (r² = 0.98, q² = 0.90) and PPAR-G (r² = 0.89, q² = 0.88) suggested that one hydrogen bond acceptor, one hydrogen bond donor, three aromatic rings, and two hydrophobic groups arranged in 3D space are essential for the binding affinity of FFAR-1 and PPAR-G inhibitors. Similarly, the pharmacophore model for FFAR-4 (r² = 0.92, q² = 0.87) suggested that the presence of a hydrogen bond acceptor, one negative atom, two aromatic rings, and three hydrophobic groups plays a vital role in the binding of an inhibitor of FFAR-4. These pharmacophore models allowed searches for novel FFAR-1, PPAR-G, and FFAR-4 triple inhibitors from multi-conformer 3D databases (Asinex). Finally, the twenty-five best hits were selected for molecular docking, to study the interaction of their complexes with all the proteins and final binding orientations of these molecules. After molecular docking, ten hits have been predicted to possess good binding affinity as per the Molecular Mechanics Generalized Born Surface Area (MM-GBSA) calculation for FFAR-1, FFAR-4, and PPAR-G which can be further investigated for its experimental in-vitro/in-vivo anti-diabetic activities. Communicated by Ramaswamy H. Sarma

Diabetes mellitus is rising globally touching more than 180 million people worldwide. This is prevailing mostly in type 2 diabetes and according to WHO report the incidence is likely to be more than doubled by 2030. α-Glucosidase inhibitors work by reducing the amount of glucose that the intestines absorb from food. In this work, forty-five benzimidazole analogues were studied using 3D QSAR, HQSAR, pharmacophore mapping and based on their results 60 compounds were designed. The results show that the best Comparative Molecular Field Analysis (CoMFA) model has q² = 0.742 and r² = 0.973, and the best Comparative Molecular Similarity Indices Analysis (CoMSIA) model has q² = 0.679 and r² = 0.918. For HQSAR the best model has q² = 0.773 and r² = 0.964. The r² value for boostrap for CoMFA and CoMSIA are 0.98 and 0.97 respectively. Pharmacophore mapping revealed varied bioactive regions of ligand. Thus, these compounds could be used as lead for designing the synthesis of potent alpha-glucosidase inhibitors. Keywords: Acarbose, Alpha-glucosidase inhibition, Benzimidazoles, Molecular modelling, Post-prandial hyperglycemia

Diabetes mellitus is rising globally touching more than 180 million people worldwide. This is prevailing mostly in type 2 diabetes and according to WHO report the incidence is likely to be more than doubled by 2030. α-glucosidase inhibitors work by reducing the amount of glucose that the intestines absorb from food. In our previous work, forty-five benzimidazoles analogues were studied using 3D QSAR, HQSAR, and Pharmacophore mapping and based on their results 60 compounds were designed. Docking studies of those designed compounds showed that most of the compounds are bonding with important amino acids LEU 520, ARG 335 and ASP 69 through hydrogen bonds and steric interaction. In this work, synthesis of eleven compounds was done on the basis of molecular docking studies. Compounds containing hydroxyl and alkyl groups (compound no. 3, 9 and 10) were found to be five to eight folds more active with IC₉₀ values in the range of 6.02 ± 1.10 to 33.25 ± 1.20 µg/ml, in comparison with the standard drug, Acarbose (IC₉₀= 290.55 ± 0.081 µg/ml). Thus, these compounds after the toxicity studies could be of therapeutic use in treating diabetes. Keywords: Acarbose, Alpha-glucosidase inhibition, Benzimidazoles, Docking, Molecular modelling, Post-prandial hyperglycemia