Mohamed MOUSSAOUI

Cancer remains one of the leading causes of death globally, presenting significant challenges to healthcare systems due to its complexity and the limitations of current therapeutic strategies. Despite advancements in anticancer drug development, monotherapies often fail to provide long-term efficacy due to the emergence of drug resistance. This resistance is primarily due to the activation of compensatory pathways in cancer cells, which allows them to bypass the effects of single-target therapies. To overcome this, targeting multiple key proteins simultaneously has emerged as a promising strategy to enhance therapeutic outcomes and address resistance mechanisms. In this study, 2-Phenylindole derivatives were explored as MCF7 breast cancer cell line inhibitors using 3D-QSAR modeling to design more effective compounds. The CoMSIA/ SEHDA model demonstrated high reliability (R² = 0.967) and a strong Leave-One-Out cross-validation coefficient (Q² = 0.814), further validated by external testing (R²Pred = 0.722). Six new compounds with potent inhibitory activity were designed, and their favorable ADMET profiles were confirmed. Molecular docking studies revealed that the newly designed compounds exhibited better binding affinities (−7.2 to −9.8 kcal/mol) to key cancer-related targets (CDK2, EGFR, and Tubulin) compared to the reference drug and the most active molecule (molecule 39) in the dataset. Additionally, 100 ns molecular dynamics simulations confirmed the stability of the best-docked complexes, highlighting their potential as promising candidates for anticancer drug development.

The epidermal growth factor receptor (EGFR) is part of a protein family that controls cell growth and development. Due to its importance, EGFR has been identified as a suitable target for creating novel drugs. For this research, we conducted a 2D-QSAR analysis on a set of 31 molecules derived from quinazoline, which exhibited inhibitory activity against human lung cancer. This investigation incorporated principal component analysis (PCA) and multiple linear regression (MLR), leading to the development of QSAR models with strong predictive capabilities (R2 = 0.745, R2_adj = 0.723, MSE = 0.061, R2_test = 0.941, and Q2_cv = 0.669). The reliability of these models was confirmed through internal, external, Y-randomization, and applicability domain validations. Leveraging the predictions from the QSAR model, we designed 18 new molecules based on the modifications at the N-3 and C-6 positions of the quinazoline ring, with electronegative substituents at these positions fostering optimal polar interactions and hydrophobic contacts within the ATP-binding site of EGFR, significantly enhancing the inhibitory activity against the lung cancer cell line. Subsequently, ADMET predictions were conducted for these 18 compounds, revealing outstanding ADMET profiles. Molecular docking analyses were performed to investigate the interactions between the newly designed molecules─Pred15, Pred17, Pred20, Pred21─and the EGFR protein, indicating high affinity of these proposed compounds to EGFR. Furthermore, molecular dynamics (MD) simulations were utilized to assess the stability and binding modes of compounds Pred17, Pred20, and Pred21, reinforcing their potential as novel inhibitors against human lung cancer. Overall, our findings suggest that these investigated compounds can serve as effective inhibitors, showcasing the utility of our analytical and design approach in the identification of promising therapeutic agents.

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 nanoseconds 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.

The present study aims to investigate about the quantitative structure-activity relationship (QSAR) of a series of Thiazole derivatives reported as anticancer agents (hepatocellular carcinoma), using principally the electronic descriptors calculated by the DFT method and by applying the multiple linear regression method. The developed model showed good statistical parameters (R2= 0.725, R2adj = 0.653, MSE = 0.060, R2test = 0.827, Q2cv = 0.536). The energy EHOMO orbital, electronic energy (TE), shape coefficient (I), number of rotatable bonds (NROT), and index of refraction (n) were revealed to be the main descriptors influencing the anti-cancer activity. Further, new Thiazole derivatives have been designed and their activities and pharmacokinetic properties have been predicted using the validated QSAR model. The designed molecules were then assessed to molecular docking (MD), and molecular dynamic (MDs) simulation accompanied by the calculation of the binding affinity using MMPBSA script according to 100 ns a simulation trajectory, to study both their affinity and their stability towards CDK2 as a target protein for the cancer disease treatment. This research concluded with the identification of four new CDK2 inhibitors which are A1, A3, A5, and A6 showing good pharmacokinetic properties. The MDs results revealed that the newly designed compound A5 remained stable in the active center of the discovered CDK2 protein, indicating its potential as a novel inhibitor for the treatment of hepatocellular carcinoma. The current findings may eventually contribute to the development of robust CDK2 inhibitors in the future.

Human Immunodeficiency Virus type 1 protease (HIV-1 PR) is one of the most challenging targets of antiretroviral therapy used in the treatment of AIDS-infected people. The performance of protease inhibitors (PIs) is limited by the development of protease mutations that can promote resistance to the treatment. The current study was carried out using statistics and bioinformatics tools. A series of thirty-three compounds with known enzymatic inhibitory activities against HIV-1 protease was used in this paper to build a mathematical model relating the structure to the biological activity. These compounds were designed by software; their descriptors were computed using various tools, such as Gaussian, Chem3D, ChemSketch and MarvinSketch. Computational methods generated the best model based on its statistical parameters. The model’s applicability domain (AD) was elaborated. Furthermore, one compound has been proposed as efficient against HIV-1 protease with comparable biological activity to the existing ones; this drug candidate was evaluated using ADMET properties and Lipinski’s rule. Molecular Docking performed on Wild Type, and Mutant Type HIV-1 proteases allowed the investigation of the interaction types displayed between the proteases and the ligands, Darunavir (DRV) and the new drug (ND). Molecular dynamics simulation was also used in order to investigate the complexes’ stability allowing a comparative study on the performance of both ligands (DRV & ND). Our study suggested that the new molecule showed comparable results to that of darunavir and maybe used for further experimental studies. Our study may also be used as pipeline to search and design new potential inhibitors of HIV-1 proteases.

In searching for a new and efficient therapeutic agent against Alzheimer’s disease, a Quantitative structure-activity relationship (QSAR) was derived for 45 Flavonoid derivatives recently synthesized and evaluated as cholinesterase inhibitors. The multiple linear regression method (MLR) was adopted to develop an adequate mathematical model that describes the relationship between a variety of molecular descriptors of the studied compounds and their biological activities (cholinesterase inhibitors). Golbraikh and Tropsha criteria were applied to verify the validity of the built model. The built MLR model was statistically reliable, robust, and predictive (R2=0.801, Q2cv =0.876, R2test =0.824). Dreiding energy and Molar Refractivity were the major factors that govern the Anti-cholinesterase activity. These results were further exploited to design a new series of Flavonoid derivatives with higher Anti-cholinesterase activities than the existing ones. Thereafter, molecular docking and molecular dynamic studies were performed to predict the binding types of the designed compounds and to investigate their stability at the active site of the Butyrylcholinesterase BuChE protein. The negative and low binding affinity calculated for all designed compounds shows that designed compound 1 has a favorable affinity for the 4TPK. Moreover, molecular dynamics simulation studies confirmed the stability of designed compound 1 in the active pocket of 4TPK over 100 ns. Finally, the ADMET analysis was incorporated to analyze the pharmacokinetics and toxicity parameters. The designed compounds were found to meet the ADMET descriptor criteria at an acceptable level having respectable intestinal permeability and water solubility and can reach the intended destinations.

This study explores how changes to the arene ligands in the half-sandwich chromium complex (η6 -C6H5X)Cr (CO)3 influence its performance. A theoretical study was conducted on a series of modified chromium halfsandwich complexes to evaluate their potential as sensitizers in Dye-Sensitized Solar Cells (DSSCs), utilizing density functional theory (DFT) and time-dependent (TD-DFT). The research examined seven arene derivatives, evaluating the photoelectrical properties of the corresponding complexes. Calculations were performed at the B3LYP/6-31G/SDD level to determine the ground and excited state properties of the isolated dyes. Essential parameters, such as light harvesting efficiency (LHE), electron injection driving force (ΔGinj), regeneration driving force (ΔGreg), open-circuit voltage (VOC), and maximum absorption wavelength (λmax), were carefully analyzed. The findings suggest that the DA, DB, DD, DF, and DG complexes have strong potential as sensitizers in DSSCs. This study highlights the valuable role of theoretical methods in predicting and guiding the design of new dyes for solar cell applications.

Bulk Heterojunction (BHJ) solar cells offer cost-effective photovoltaic solutions, flexible manufacturing, and improved efficiency with reduced environmental impact. Organometallic half-sandwich complexes, specifically a series of monometallic ruthenium complexes (Ru(1)–Ru(6)), have emerged as promising candidates for BHJ solar cells due to their outstanding charge transport properties. This study utilised Density Functional Theory (DFT) calculations with the B3LYP functional and SDD basis set augmented with 6-31 + G(d,p) to assess these Ru complexes with various N,O-chelating ligands, featuring distinct π-conjugated systems and positions of nitro groups, for BHJ solar cell applications. Our investigation focused on elucidating their electronic structures, identifying key electrophilic and nucleophilic sites, and exploring their optical properties. Our findings indicate that these complexes have maximum absorption wavelengths (λmax) ranging from 702 to 1083 nm, highlighting their strong photon absorption capabilities. Specifically, Ru(6), which includes the NO2 group, exhibits the narrowest energy gap but shows relatively lower values for oscillator strengths (f) and light harvesting efficiencies (LHE), indicating efficient light absorption but reduced energy conversion efficiency. In contrast, Ru(3), lacking the NO2 group but featuring enhanced π conjugation through cyclisation, demonstrates the widest energy gap yet boasts the highest values for f and LHE, suggesting superior efficiency in converting absorbed energy into electronic energy. These results emphasise the crucial role of ligand π-conjugation in enhancing energy conversion efficiency, while the strategic positioning of the NO2 group aids effective light absorption. Our study offers valuable insights into the design principles for optimizing organometallic ruthenium complexes in BHJ solar cell applications.