QUANTITATIVE STRUCTURE–ACTIVITY RELATIONSHIP STUDY OF NEWLY SYNTHESIZED BENZIMIDAZOLE DERIVATIVES-TARGETING ALDOSE REDUCTASE

Aldose Reductase (ALR2) plays a crucial role in the pathogenesis of diabetic complications, especially diabetic neuropathy. Targeted inhibition of ALR2 is a promising therapeutic strategy. This study focused on the design, synthesis, and computational analysis of ten novel benzimidazole-based thiosemicarbazone derivatives (CPD-7, CPD-9, CPD-11, CPD-12, CPD-22, CPD-27, CPD-30, CPD-31, CPD-33, and CPD-35) to evaluate their potential as ALR2 inhibitors¹. We employed a QSAR (Quantitative Structure–Activity Relationship) approach to correlate molecular descriptors with ALR2 inhibitory activity (IC₅₀ values). The chemical structures were drawn using ChemDraw⁶, and SMILES notations were used for computational analysis. Descriptor calculation was performed using RDKit in Python⁸, while model building and validation were conducted via multiple linear regression using scikit-learn⁴. The model performance was visualized through actual vs. predicted plots, residual analysis, and descriptor correlation heatmaps⁴. The QSAR model revealed a strong correlation between hydrophobicity (LogP) and ALR2 inhibition, with higher lipophilicity favoring lower IC₅₀ values. Conversely, increased polarity (TPSA, HBD) negatively influenced potency. Among the tested compounds, CPD-33 emerged as the most potent inhibitor with an IC₅₀ of 1.47 μM, owing to its dual trifluoromethyl substitutions and favorable physicochemical profile. In contrast, CPD-11 displayed the least potency (IC₅₀ = 34.7 μM), likely due to suboptimal substituent placement and higher polarity¹. In conclusion, the developed QSAR model effectively predicted the biological activity of the test compounds and offered valuable insights into the structural features responsible for ALR2 inhibition. These findings pave the way for the rational design of next-generation ALR2 inhibitors with enhanced potency and drug-like properties for managing diabetic neuropathy¹. Keywords: QSAR modeling, IC50 value, ALR2 inhibitors, Topological polar surface area, Diabetic neuropathy