Computational prediction and QSAR-based design of novel curcumin derivatives: Enhancing insulin receptor binding and pharmacokinetic properties for improving therapeutic efficacy
Kadry, A., Soliman, A., Abbas, M. (2025). Computational prediction and QSAR-based design of novel curcumin derivatives: Enhancing insulin receptor binding and pharmacokinetic properties for improving therapeutic efficacy. EKB Journal Management System, 3(2), 73-78. doi: 10.21608/bbj.2025.361161.1089
Asmaa M. Kadry; Ahmed G. Soliman; Mounir M. Abbas. "Computational prediction and QSAR-based design of novel curcumin derivatives: Enhancing insulin receptor binding and pharmacokinetic properties for improving therapeutic efficacy". EKB Journal Management System, 3, 2, 2025, 73-78. doi: 10.21608/bbj.2025.361161.1089
Kadry, A., Soliman, A., Abbas, M. (2025). 'Computational prediction and QSAR-based design of novel curcumin derivatives: Enhancing insulin receptor binding and pharmacokinetic properties for improving therapeutic efficacy', EKB Journal Management System, 3(2), pp. 73-78. doi: 10.21608/bbj.2025.361161.1089
Kadry, A., Soliman, A., Abbas, M. Computational prediction and QSAR-based design of novel curcumin derivatives: Enhancing insulin receptor binding and pharmacokinetic properties for improving therapeutic efficacy. EKB Journal Management System, 2025; 3(2): 73-78. doi: 10.21608/bbj.2025.361161.1089

Computational prediction and QSAR-based design of novel curcumin derivatives: Enhancing insulin receptor binding and pharmacokinetic properties for improving therapeutic efficacy

Biological and Biomedical Journal
Volume 3, Issue 2, July 2025, Page 73-78  XML PDF (1.89 MB)
Document Type: Research Articles
DOI: 10.21608/bbj.2025.361161.1089
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Authors
Asmaa M. Kadry email orcid 1; Ahmed G. Solimanorcid 2; Mounir M. Abbas1
1Chemistry Department, Faculty of Science, Sohag University, Sohag, Egypt
2Biotechnology Program, Faculty of Agriculture, Ain Shams University, Egypt
Abstract
Curcumin (CUR) demonstrates therapeutic potential for insulin resistance (IR) and type 2 diabetes through anti-inflammatory and insulin-sensitizing properties. However, clinical applications remains limited due to poor bioavailability and weak insulin receptor binding affinity. Despite extensive anti-diabetic studies, a critical gap exists in developing structurally optimized CUR-derivatives with enhanced insulin receptor binding and validated pharmacokinetic profiles. This study aims to design novel CUR- derivatives with improved insulin receptor interactions using integrated computational approaches. A comprehensive framework combining molecular docking, QSAR modelling, and ADMET profiling evaluated ten CUR-derivatives. Molecular docking simulations against insulin (PDB ID: 6JK8) and insulin receptor (PDB ID: 4ZXB) used AutoDock Vina, with genetic algorithm-based optimization guiding rational design. CUR-Derivatives demonstrated substantially improved binding affinities versus native CUR:-7.16 to -9.98 kcal/mol (insulin) and -7.86 to -10.49 kcal/mol (insulin receptor) compared to -6.81 and -5.03 kcal/mol, respectively. CUR-3 emerged as the lead candidate with superior binding affinity (-9.98/-10.49 kcal/mol), balanced ADMET properties (LogP = 3.876, LogS = -3.729), and favorable safety profile. QSAR analysis (R² ≈ 1, Q² = 0.75) identified moderate lipophilicity and balanced hydrogen bonding as critical activity determinants.CUR-3 represents a promising scaffold for anti-diabetic therapeutics requiring experimental validation to confirm therapeutic potential for IR management.
Keywords
Anti Diabetes; Curcumin; insulin resistance; insulin receptor
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