Structure-based design and computational evaluation of tamoxifen derivatives as estrogen receptor antagonists against breast cancer
Mouad Lahyaoui, Rachid Haloui, Mohamed El Yaqoubi, Boutaina Moumni, Ahmed Mazzah, Amal Haoudi, Taoufiq Saffaj, Bouchaîb Ihssane, Riham Sghyar, Youssef Kandri Rodi · Scientific African · 2025
Breast cancer remains one of the leading causes of cancer-related mortality worldwide, with a particularly heavy burden in low- and middle-income countries, underscoring the need for affordable, effective anti-estrogenic therapies. Tamoxifen is the cornerstone of endocrine treatment, but resistance, adverse effects, and limited pharmacokinetics necessitate the development of safer and more potent alternatives. In this study, 29 tamoxifen-like derivatives were computationally analyzed using quantitative structure–activity relationship (QSAR) modeling. Among multiple regression approaches, Principal Component Regression (PCR) showed the highest predictive performance (R² = 0.755; R²_test > 0.6). Guided by these models, four novel derivatives (D1–D4) were rationally designed and evaluated through integrative in silico techniques including ADME profiling, molecular docking, and 100 ns molecular dynamics simulations. All designed compounds complied with drug-likeness filters (Lipinski, Veber, Egan) and showed favorable oral absorption (>91%) with moderate synthetic accessibility. Compared with tamoxifen (LogP ∼6.3), the derivatives exhibited more balanced physicochemical properties (LogP 3.3–5.2; TPSA < 60 Ų). Docking results demonstrated comparable or stronger affinities than tamoxifen (–7.2 kcal/mol), with derivative D3 achieving the best binding energy (–8.14 kcal/mol) through hydrogen bonding and π–π stacking. MD simulations further confirmed the stability of the D3–ERα complex, with RMSD fluctuations (0.8–1.4 Å) slightly lower than those of tamoxifen (1.2–1.6 Å). These results highlight D3 as a promising next-generation estrogen receptor antagonist with improved pharmacokinetic features and stable receptor binding. More broadly, the integrative computational workflow presented here demonstrates how multi-model QSAR combined with structure-based methods can accelerate cost-effective drug discovery. This approach not only advances the field beyond single-technique studies but also supports global and regional health strategies, offering a rational foundation for developing novel breast cancer therapies relevant to the African context and the UN Sustainable Development Goal 3 (Good health and well-being).