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

Bhanupriya Bhrigu; Shikha Sharma; Bhumika Yogi

doi:10.24092/CRPS.2025.150206

Bhanupriya Bhrigu Department of Pharmaceutical Science, Lords University, Alwar Rajsthan-301001
Shikha Sharma Department of Pharmaceutical Science, Lords University, Alwar Rajsthan-301001
Bhumika Yogi J.S. Singh Institute of Pharmacy, Sitapur, Uttar Pradesh, India

DOI https://doi.org/10.24092/CRPS.2025.150206

Abstract

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

References

1. Srivastava S, Ramana KV, Bhatnagar A. Role of aldose reductase in diabetes and oxidative damage. Toxicol Appl Pharmacol. 2005;207(3):282–290.
2. Li Z, Wan H, Shi Y, Ouyang P. Personal experience with Four Kinds of Chemical Structure Drawing Software: review on ChemDraw, ChemWindow, and ChemSketch. Journal of Chemical Information and Computer Sciences. 2004; 44(5):1886–1890.
3. Riniker S, Landrum GA. Better Informed Distance Geometry: Using What We Know to Improve Conformation Generation. J. Chem. Inf. Comput. Sci. 2015;55(12):2562–2574. https://doi.org/10.1021/acs.jcim.5b00654.
4. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 2011; 12:2825–2830.
5. Bhatnagar A. Aldose reductase pathway implications. Curr Med Chem. 2004;11(11):1375–1390.
6. Maccari R, Ottana R. Targeting aldose reductase for diabetic complications: A structural perspective. Journal of Medicinal Chemistry. 2015;58(5):2047–2067.
7. Sambasivarao K, Ramanathan S, Rajasekaran K, Thirumalai R, Gopalakrishnan C. QSAR Modelling of Triazino-Benzimidazole Derivatives. Arabian Journal of Chemistry. 2013;6(2):211–222.
8. Rogers D, Hahn M. Extended-Connectivity Fingerprints. Journal of Chemical Information and Modeling. 2010;50(5):742–754. https://doi.org/10.1021/ci100050t.
9. Hussain M, Zia-ur-Rehman M, Sherazi STH, Atif M, Channar PA, Zafar MN, Rauf A. QSAR modeling of benzimidazole derivatives as inhibitors of heat shock protein 90 (Hsp90). Journal of Molecular Graphics and Modelling. 2019;88:249–258. https://doi.org/10.1016/j.jmgm.2019.02.009.
10. Gedeck P. Single-mode compound retrieval for QSAR, QSPR data sets by capturing individual structures from SciFinder to Accord for Excel. Journal of Pharmaceutical Sciences. 2002;91(12):2882–2885. https://doi.org/10.1002/jps.10260.
11. Hunter JD. Matplotlib: A 2D Graphics Environment. Comput Sci Eng. 2007; 9(3):90–95.
12. Neese F. The ORCA program system. Wiley Interdiscip Rev Comput Mol Sci. 2012;2(1):73–78.
13. Ertl P, Rohde B, Selzer P. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J Med Chem. 2000;43(20):3714–3717.
14. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings. Advanced Drug Delivery Reviews. 2001;46(1–3):3–26.
15. Waskom ML. Seaborn: Statistical Data Visualization. J Open Source Softw. 2021;6(60):3021.
16. Todeschini R, Ballabio D, Consonni V, Mauri A, Pavan M. Binary Classification of Drugs: A Case Study for Anticancer Compounds. Molecular Informatics. 2009;28(8–9):556–569. https://doi.org/10.1002/minf.200900051.
17. Brownlee J. Statistics for Machine Learning: Techniques for Exploring Supervised, Unsupervised and Reinforcement Learning Models. Machine Learning Mastery; 2018. ISBN: 9781929954984.
18. Gramatica P, Moretti R, Consonni V. The effect of dataset splitting on predictive ability in QSAR/QSPR modeling. Structural Chemistry. 2011; 22(3):457–472. doi:10.1007/s11224-011-9737-4.
19. Kohavi R. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. IJCAI. 1995;14(2):1137–1143.
20. Seber GAF, Lee AJ. Linear Regression Analysis. 2nd ed., Wiley-Interscience, Hoboken, NJ, USA, 2012. ISBN: 978-0471415404.
21. Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin TE, Todeschini R, Consonni V, Kuzmin VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard AM, Tropsha Alexander. QSAR modeling: Where have you been? Where are you going to? Journal of Medicinal Chemistry. 2014;57(12):4977–5010. doi:10.1021/jm4004285.
22. Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological). 1996;58(1): 267–288. doi:10.1111/j.2517-6161. 1996.tb02080. x.
23. Roy K, Kar S, Das RN, Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment. 1st ed., Academic Press,
Elsevier, London, UK, 2015. ISBN: 978-0128015056. https://doi.org/10.1016/C2014-0-04062-1
24. Tropsha A. Best practices for QSAR model development, validation, and exploitation. Molecular Informatics. 2010;29(6–7):476–488. https://doi.org/10.1002/minf.201000061.
25. Consonni V, Todeschini R, Ballabio D. Comments on the definition of the Q² parameter for QSAR validation. J Chem Inf Model. 2009;49(7):1669–1678.
26. Kluyver T, Ragan KB, Pérez F, Granger B, Bussonnier M, Frederic J, Kelley K, Hamrick J, Grout J, Corlay S, Ivanov P. Jupyter Notebooks – a publishing format for reproducible computational workflows. In: Positioning and Power in Academic Publishing: Players, Agents and Agendas; IOS Press. 2016;87–90.
27. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry. 2002;45(12):2615–2623. https://doi.org/10.1021/jm020017n.
28. Ghose AK, Viswanadhan VN, Wendoloski JJ. A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. Journal of Combinatorial Chemistry. 1999;1(1):55–68. doi:10.1021/cc9800071.
29. Meanwell NA. Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound disposition and safety. Chem Res Toxicol. 2011;24(9):1420–1456.