http://dspace.univ-chlef.dz/handle/123456789/1052 Université Hassiba Benbouali de Chlef / Faculté de Génie Civil et d'Architecture 2026-04-05T22:03:18Z http://dspace.univ-chlef.dz/handle/123456789/2286 Targeting Central Nervous System (CNS) diseases with bioactive compounds using network pharmacology and screening tools MEKHANE, Selma Today we hear and see the increase in Parkinson disease, which have become among the most common diseases in the world. Therefore, we targeted some proteins that are related to the disease. Our goal in this study is to inhibit all of the A2AA-R, GABA-A, NrF2, and αsynuclein. After examining various plants from Chlef in Algeria, we selected salvia officinalis and curcuma longa. After that, we made the extraction of curcuma longa with ethanol, and we had obtained good yield. Our In Silico study demonstrated that there is an inhibitory combination of the bisdemethoxycurcumin compound with A2AA-R,with a binding capacity estimated at ∆G = -9.1 kcal/mol, and the ar-turmerone compound with the A2AA-R with a binding capacity estimated at ∆G = -8.6 kcal/mol. α -turmerone had make a cohesion with NrF2 with energy estimated at ∆G=-7.4kcal/mol. Consequently, makes the bisdemethoxycurcumin-A2AA-R interaction (ΔG = -9.1 kcal/mol) demonstrates the strongest binding affinity among the tested compounds. Our QSAR study approach was based on using two methods, multiple linear regression (MLR), support vector regression (SVR). SVR demonstrated superior predictive accuracy (Q² = 0.992, R² train = 0.989) compared to MLR (Q² = 0.559, R² train = 0.766). After the comparison between bisdemethoxycurcumin and l-dopa , our findings suggest bisdemethoxycurcumin (BDMC) offers dual benefits comparable binding affinity to key targets (∆G = -9.1 kcal/mol) while additionally addressing PD's core pathologies through antioxidant and anti-inflammatory mechanisms. This multifunctional activity positions BDMC as a potential disease-modifying adjunct to conventional therapy. THESIS PRESENTED FOR A MASTER DEGREE FIELD: Process Engineering SPECIALTY: Pharmaceutical Process Engineering 2025-01-01T00:00:00Z http://dspace.univ-chlef.dz/handle/123456789/2285 Synthesis of polymeric microparticles loaded with the antiinflammatory drug delivery TERMOUL, Chahrazed; GUERACHA, Manel This work is part of an innovative approach aimed at developing drug delivery systems based on natural polymers, with a view to sustainable biomedical solutions. The drug delivery system is designed in the form of polymeric microparticles composed of chitosan (Cs) and pectin (PEC). These natural biopolymers were selected for their complementary properties and their ability to form stable microparticles through ionotropic gelation. Two anti-inflammatory active ingredients were targeted for encapsulation: diclofenac sodium (DS), a widely used synthetic molecule, and ginger essential oil (GEO), a natural compound known for its antioxidant and anti-inflammatory properties. The resulting microparticles exhibited diameters ranging from 200 to 600 µm, high swelling capacity, and encapsulation efficiency exceeding 80%. In vitro release profiles revealed a prolonged release, reaching 50 to 60% after 24 hours at pH 7.4. These results confirm the potential of the Cs/PEC system as a promising platform for natural, targeted, and controlled release therapeutic applications. Synthesis of polymeric microparticles loaded with the antiinflammatory drug delivery 2025-01-01T00:00:00Z http://dspace.univ-chlef.dz/handle/123456789/2284 Study and modeling by artificial intelligence of occupational exposure limits to certain pharmaceutical products using chemoinformatics MORSLI, Yamina Artificial intelligence is increasingly being utilized to all field and specially to enhance occupational health within the pharmaceutical industry, where the rising potency of compounds contributes to elevated exposure risks. Due to limited toxicity data for many emerging drug candidates, traditional methods often fall short in accurately estimating safe handling thresholds. In this study, an AI-driven approach has been developed to predict Occupational Exposure Bands (OEBs) based on molecular structures. This method combines cheminformatics descriptors and molecular fingerprints with deep learning techniques to extract significant features, which are then classified using various machine learning algorithms. The resulting models exhibit strong predictive performance, although challenges remain in accurately identifying less-represented high-risk categories. Additionally, a practical software tool was created to facilitate real-time OEB predictions and molecular visualization, providing an accessible interface for researchers and safety professionals. Overall, this approach offers an innovative solution for early hazard assessment and highlights the potential of AI to improve workplace safety in pharmaceutical development. Final Year Project Towards a Master's Degree in Process Engineering Specialty: Pharmaceutical Engineering 2025-01-01T00:00:00Z http://dspace.univ-chlef.dz/handle/123456789/2283 QSAR, ADMET, Molecular docking and Dynamic studies of natural products as potential inhibitors for Breast Cancer KERROUZI, Ichraf This study was conducted to support the ongoing search for novel molecules effective enough to treat breast cancer derived from Trigonella foenum-graecum, Curcuma longa L., and Atriplex Halimus. The study combined computational techniques and computer simulations to evaluate their efficacy, as well as Fourier transform infrared (FTIR) and ultraviolet/visible (UV/VIS) spectroscopy of plant extract. Quantitative structure-activity relationship (QSAR) modeling was used to predict bioactivity; with 2D_3D-QSAR models developed using multiple linear regression (MLR) and support vector regression (SVR). Comparative analysis revealed that MLR outperformed SVR, achieving an R² value of 0.93, an RMSE value of 0.163, and a high Q² value of 0.87, demonstrating superior predictive accuracy. Model predictions were validated using cross-validation. Molecular docking simulations evaluated the binding interactions with key breast cancer targets (HER2, CDK4, AKT1, and MCF-7), revealing strong affinities ranging from -7 to -11.4 kcal/mol. Furthermore, in conjunction with the density functional theory (DFT) method (B3LYP/6- 311G(d,p)), molecular docking identified two promising candidates, fenugreekine and diosgenin, derived from fenugreek, which demonstrated potent inhibition of all proteins compared to the FDA-approved drugs Ibrance and Capivasertib, respectively. ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis of diosgenin, curcumin, and arbutin demonstrated their pharmacokinetic affinity and safety, making them promising candidates for further experimental validation THESIS Presented for graduation from MASTER 2 Field: Process Engineering Option: Pharmaceutical Process Engineering 2025-01-01T00:00:00Z