2D, 3D-QSAR study and docking of vascular endothelial growth factor receptor 3 (VEGFR3) inhibitors for potential treatment of retinoblastoma - Ren, Rui; Gao, Liyu; Li, Guoqi; Wang, Shuqiang; Zhao, Yangzhong; Wang, Haitong; Liu, Jianwei (Frontiers in Pharmacology, 2023)
Background: Retinoblastoma is currently the most common malignant tumor occurring in the eyes of infants and children worldwide, which often threatens their lives. Chemotherapy is an integral part of retinoblastoma treatment. However, the chemotherapeutic agents currently used in clinics often lead to drug resistance, thus there is a need to investigate new chemotherapy-targeted agents. VEGFR3 inhibitors are anti-tumor-growth and could be used for the development of novel retinoblastoma-targeted agents. Objective: To predict drug activity, discover influencing factors and design new drugs by building 2D, 3D-QSAR models. Method: First of all, linear and nonlinear QSAR models were built using heuristic methods and gene expression programming (GEP) respectively. The comparative molecular similarity indices analysis (COMISA) was then used to construct 3D-QSAR models through the SYBYL software. New drugs were designed by changing the factors affecting drug activity in both models and molecular docking experiments were performed. Result: The best linear model built through HM had a R2 of 0.82, a S2 of 0.02 and a R2cv of 0.77. The best nonlinear model built through GEP had correlation coefficients of 0.83 and 0.72 for the training and test set respectively, with mean errors of 0.02 and 0.04. The 3D model built through SYBYL had a high Q2 (0.503), R2 (0.805 ) and F-value (76.52) as well as a low standard error of SEE value (0.172), which passed the external validation. This proves that the model is reliable and possesses excellent predictive power. Based on the molecular descriptors of the 2D model and the contour plots of the 3D model, we designed 100 new compounds using the best active compound 14 as a template and performed activity prediction as well as molecular docking experiments on them, in which compound 14.d performed best in terms of combined drug activity and docking ability. Conclusion: Compared with the linear model built through heuristic method (HM), the nonlinear model built through GEP is more stable and has a better predictive ability. The compound 14.d designed in this experiment has the potential for anti-retinoblastoma treatment, which provides new design ideas and directions for retinoblastoma-targeted drugs.