Structure-Activity Relationship Studies Based on 3D-QSAR CoMFA/CoMSIA for Thieno-Pyrimidine Derivatives as Triple Negative Breast Cancer Inhibitors

Triple-negative breast cancer (TNBC) is defined as a kind of breast cancer that lacks estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptors (HER2). This cancer accounts for 10-15% of all breast cancers and has the features of high invasiveness and metastatic potential. The treatment regimens are still lacking and need to develop novel inhibitors for therapeutic strategies. Three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses, based on a series of forty-seven thieno-pyrimidine derivatives, were performed to identify the key structural features for the inhibitory biological activities. The established comparative molecular field analysis (CoMFA) presented a leave-one-out cross-validated correlation coefficient q(2) of 0.818 and a determination coefficient r(2) of 0.917. In comparative molecular similarity indices analysis (CoMSIA), a q(2) of 0.801 and an r(2) of 0.897 were exhibited. The predictive capability of these models was confirmed by using external validation and was further validated by the progressive scrambling stability test. From these results of validation, the models were determined to be statistically reliable and robust. This study could provide valuable information for further optimization and design of novel inhibitors against metastatic breast cancer.

Automated summary

In this study, a series of thieno-pyrimidine derivatives were analyzed using three-dimensional quantitative structure-activity relationship (3D-QSAR) to identify key structural features for inhibitory biological activities. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were established, showing reliable and robust performance. The predictive capabilities of these models were confirmed through external validation and progressive scrambling stability test. This research provides valuable information for the further optimization and design of novel inhibitors against metastatic breast cancer.