3D computer modeling of inhibitors targeting the MCF-7 breast cancer cell line - Zarougui, Sara; Er-Rajy, Mohammed; Faris, Abdelmoujoud; Imtara, Hamada; El fadili, Mohamed; Qurtam, Ashraf Ahmed; Nasr, Fahd A.; Al-Zharani, Mohammed; Elhallaoui, Menana (Frontiers in Chemistry, 2024)
The work focused on developing new inhibitors of the MCF-7 cell line, which is contributors to our understanding of breast cancer biology and various experimental techniques. 3D QSAR-modeling were used in order to design new tetrahydrobenzo [4, 5] thieno [2, 3-d] pyrimidines derivatives with good characteristics. Two robust 3D-QSAR models were developed, and their predictive capacity was confirmed by the high correlation: COMFA (Q 2 = 0.62, R 2 = 0.90) and COMSIA (Q 2 = 0.71, R 2 = 0.88) with an external validation R 2 ext=0.90, R 2 ext= 0.91 respectively. Successful evaluation confirms the models' potential for reliable predictions. Six candidate inhibitors were discovered, and two new inhibitors were developed in silico using computational methods. The ADMET properties and pharmacokinetics characteristics of those new derivatives were carefully evaluated. The interactions between new tetrahydrobenzo [4, 5] thieno [2, 3-d] pyrimidines derivatives and the protein ERα (PDB code: 4XO6) have been highlighted by molecular docking. In addition, MM/GBSA calculations and molecular dynamics simulations provided interesting information on the binding stability between the complexes. The pharmaceutical characteristics, interaction with protein and stability of the inhibitors were examined using various methods including docking molecular, molecular dynamics simulations during100 ns, binding free energy calculations, and ADMET predictions, in comparison to the FDA-drug Capivasertib. The findings indicate that the inhibitors exhibit significant binding affinity, robust stability, and desirable pharmaceutical characteristics. These newly projected compounds, which act as inhibitors to mitigate breast cancer possess considerable potential as prospective drug candidates.