Design of novel anti-cancer agents targeting COX-2 inhibitors based on computational studies

Er-rajy, Mohammed; El fadili, Mohamed; Mujwar, Somdutt; Imtara, Hamada; Al kamaly, Omkulthom; Zuhair Alshawwa, Samar; Nasr, Fahd A.; Zarougui, Sara; Elhallaoui, Menana

doi:10.1016/j.arabjc.2023.105193

Cited by 9 publications

(2 citation statements)

References 67 publications

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“…The contour maps in the QSAR models represent the relationships between the structures and activities of the series of chemicals. In this study, we analyzed the contour maps of the developed models to determine the factors influencing their anticancer activities ( Er-rajy et al, 2023b ). By conducting a three-dimensional analysis of these visualizations, we identified the regions in the template molecule that require modifications for designing new molecules with enhanced biological activities compared to the investigated derivatives.…”

Section: Resultsmentioning

confidence: 99%

3D computer modeling of inhibitors targeting the MCF-7 breast cancer cell line

Zarougui,

Er-Rajy,

Faris

et al. 2024

Front. Chem.

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This study focused on developing new inhibitors for the MCF-7 cell line to contribute to our understanding of breast cancer biology and various experimental techniques. 3D QSAR modeling was used to design new tetrahydrobenzo[4, 5]thieno[2, 3-d]pyrimidine derivatives with good characteristics. Two robust 3D-QSAR models were developed, and their predictive capacities were confirmed through high correlations [CoMFA (Q2 = 0.62, R2 = 0.90) and CoMSIA (Q2 = 0.71, R2 = 0.88)] via external validations (R2ext = 0.90 and R2ext = 0.91, respectively). These successful evaluations confirm the potential of the models to provide reliable predictions. Six candidate inhibitors were discovered, and two new inhibitors were developed in silico using computational methods. The ADME-Tox properties and pharmacokinetic characteristics of the new derivatives were evaluated carefully. The interactions between the new tetrahydrobenzo[4, 5]thieno[2, 3-d]pyrimidine derivatives and the protein ERα (PDB code: 4XO6) were highlighted by molecular docking. Additionally, MM/GBSA calculations and molecular dynamics simulations provided interesting information on the binding stabilities between the complexes. The pharmaceutical characteristics, interactions with protein, and stabilities of the inhibitors were examined using various methods, including molecular docking and molecular dynamics simulations over 100 ns, binding free energy calculations, and ADME-Tox predictions, and compared with the FDA-approved drug capivasertib. The findings indicate that the inhibitors exhibit significant binding affinities, robust stabilities, and desirable pharmaceutical characteristics. These newly developed compounds, which act as inhibitors to mitigate breast cancer, therefore possess considerable potential as prospective drug candidates.

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Section: Resultsmentioning

confidence: 99%

3D computer modeling of inhibitors targeting the MCF-7 breast cancer cell line

Zarougui,

Er-Rajy,

Faris

et al. 2024

Front. Chem.

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“…This method allows researchers to predict the activity of new compounds based on their chemical properties, thereby streamlining the identification of promising candidates for further experimental validation. QSAR models provide insights into the essential features influencing drug efficacy and safety, guiding the synthesis of compounds with optimized pharmacological profiles 8 , 9 .…”

Section: Introductionmentioning

confidence: 99%

QSAR, ADMET, molecular docking, and dynamics studies of 1,2,4-triazine-3(2H)-one derivatives as tubulin inhibitors for breast cancer therapy

Moussaoui,

Baammi,

Soufi

et al. 2024

Sci Rep

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Breast cancer remains a leading cause of cancer-related deaths among women globally, necessitating the development of more effective therapeutic agents with minimal side effects. This study explores novel 1,2,4-triazine-3(2H)-one derivatives as potential inhibitors of Tubulin, a pivotal protein in cancer cell division, highlighting a targeted approach in cancer therapy. Using an integrated computational approach, we combined quantitative structure–activity relationship (QSAR) modeling, ADMET profiling, molecular docking, and molecular dynamics simulations to evaluate and predict the efficacy and stability of these compounds. Our QSAR models, developed through rigorous statistical analysis, revealed that descriptors such as absolute electronegativity and water solubility significantly influence inhibitory activity, achieving a predictive accuracy (R2) of 0.849. Molecular docking studies identified compounds with high binding affinities, particularly Pred28, which exhibited the best docking score of − 9.6 kcal/mol. Molecular dynamics simulations conducted over 100 ns provided further insights into the stability of these interactions. Pred28 demonstrated notable stability, with the lowest root mean square deviation (RMSD) of 0.29 nm and root mean square fluctuation (RMSF) values indicative of a tightly bound conformation to Tubulin. The novelty of this work lies in its methodological rigor and the integration of multiple advanced computational techniques to pinpoint compounds with promising therapeutic potential. Our findings advance the current understanding of Tubulin inhibitors and open avenues for the synthesis and experimental validation of these compounds, aiming to offer new solutions for breast cancer treatment.

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