AUTHOR=Ren Rui , Gao Liyu , Li Guoqi , Wang Shuqiang , Zhao Yangzhong , Wang Haitong , Liu Jianwei
TITLE=2D, 3D-QSAR study and docking of vascular endothelial growth factor receptor 3 (VEGFR3) inhibitors for potential treatment of retinoblastoma
JOURNAL=Frontiers in Pharmacology
VOLUME=14
YEAR=2023
URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2023.1177282
DOI=10.3389/fphar.2023.1177282
ISSN=1663-9812
ABSTRACT=Background: Retinoblastoma is currently the most common malignant tumor seen in newborns and children’s eyes worldwide, posing a life-threatening hazard. Chemotherapy is an integral part of retinoblastoma treatment. However, the chemotherapeutic agents used in clinics often lead to drug resistance. Thus there is a need to investigate new chemotherapy-targeted agents. VEGFR3 inhibitors are anti-tumour-growth and could be used to develop novel retinoblastoma-targeted agents.
Objective: To predict drug activity, discover influencing factors and design new drugs by building 2D, 3D-QSAR models.
Method: First, linear and non-linear QSAR models were built using heuristic methods and gene expression programming (GEP). 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 drug activity factors in both models, and molecular docking experiments were performed.
Result: The best linear model created using HM had an R2, S2, and R2cv of 0.82, 0.02, and 0.77, respectively. For the training and test sets, the best non-linear model created using GEP had correlation coefficients of 0.83 and 0.72 with mean errors of 0.02 and 0.04. The 3D model designed using SYBYL passed external validation due to its high Q2 (0.503), R2 (0.805), and F-value (76.52), as well as its low standard error of SEE value (0.172). This demonstrates the model’s reliability and excellent predictive ability. 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. We performed activity prediction and molecular docking experiments on them, in which compound 14.d performed best regarding combined drug activity and docking ability.
Conclusion: The non-linear model created using GEP was more stable and had a more substantial predictive power than the linear model built using the heuristic technique (HM). 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.