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54 result(s) for "Alturki, Mansour S."
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Exploring Marine-Derived Compounds: In Silico Discovery of Selective Ketohexokinase (KHK) Inhibitors for Metabolic Disease Therapy
The increasing prevalence of metabolic diseases, including nonalcoholic fatty liver disease (NAFLD), obesity, and type 2 diabetes, poses significant global health challenges. Ketohexokinase (KHK), an enzyme crucial in fructose metabolism, is a potential therapeutic target due to its role in these conditions. This study focused on the discovery of selective KHK inhibitors using in silico methods. We employed structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches, beginning with molecular docking to identify promising compounds, followed by induced-fit docking (IFD), molecular mechanics generalized Born and surface area continuum solvation (MM-GBSA), and molecular dynamics (MD) simulations to validate binding affinities. Additionally, shape-based screening was conducted to assess structural similarities. The findings highlight several potential inhibitors with favorable ADMET profiles, offering promising candidates for further development in the treatment of fructose-related metabolic disorders.
Frankincense oil-loaded nanoemulsion formulation of paclitaxel and erucin: A synergistic combination for ameliorating drug resistance in breast cancer: In vitro and in vivo study
Nanoformulation-based combinational drug delivery systems are well known to overcome drug resistance in cancer management. Among them, nanoemulsions are well-known and thermodynamically stable drug delivery systems suitable for carrying hydrophobic drugs and phytoconstituents to tackle drug-resistant cancers. In the present study, we have investigated the effect of paclitaxel in combination with erucin (natural isothiocyanate isolated from the seeds of Eruca sativa ) loaded in the frankincense oil-based nanoemulsion formulation. The choice of frankincense oil for the current study was based on reported research investigations stating its magnificient therapeutic potential against breast cancer. Optimized nanoemulsion of paclitaxel (PTX) and erucin (ER) combination (EPNE) provided sustained release and exhibited enhanced cytotoxicity towards human epithelial breast cancer cells (T-47D) as compared to individual ER and PTX. EPNE was further assessed for its antitumor activity in the 7,12-dimethylbenz(a)anthracene (DMBA)-induced breast cancer mice model. EPNE significantly decreased the levels of hepatic and renal parameters along with oxidative stress in breast cancer mice. Furthermore, EPNE also showed decreased levels of inflammatory cytokines TNF-α, IL-6. Histopathological examinations revealed restoration of the tumorous breast to normal tissues in EPNE-treated breast cancer mice. Therefore, EPNE can act as a viable lead and therapeutic option for drug-resistant breast cancer.
Discovery of Non-Peptide GLP-1 Positive Allosteric Modulators from Natural Products: Virtual Screening, Molecular Dynamics, ADMET Profiling, Repurposing, and Chemical Scaffolds Identification
Background/Objectives: Glucagon-like peptide-1 (GLP-1) receptor is currently one of the most explored targets exploited for the management of diabetes and obesity, with many aspects of its mechanisms behind cardiovascular protection yet to be fully elucidated. Research dedicated towards the development of oral GLP-1 therapy and non-peptide ligands with broader clinical applications is crucial towards unveiling the full therapeutic capacity of this potent class of medicines. Methods: This study describes the virtual screening of a natural product database consisting of 695,133 compounds for positive GLP-1 allosteric modulation. The database, obtained from the Coconut website, was filtered according to a set of physicochemical descriptors, then was shape screened against the crystal ligand conformation. This filtered database consisting of 26,325 compounds was used for virtual screening against the GLP-1 allosteric site. Results: The results identified ten best hits with the XP score ranging from −9.6 to −7.6 and MM-GBSA scores ranging from −50.8 to −32.4 and another 58 hits from docked pose filter and a second round of XP docking and MM-GBSA calculation followed by molecular dynamics. The analysis of results identified hits from various natural products (NPs) classes, to whom attributed antidiabetic and anti-obesity effects have been previously reported. The results also pointed to β-lactam antibiotics that may be evaluated in drug repurposing studies for off-target effects. The calculated ADMET properties for those hits revealed suitable profiles for further development in terms of bioavailability and toxicity. Conclusions: The current study identified several NPs as potential GLP-1 positive allosteric modulators and revealed common structural scaffolds including peptidomimetics, lactams, coumarins, and sulfonamides with peptidomimetics being the most prominent especially in indole and coumarin cores.
Repurposing FDA-Approved Agents to Develop a Prototype Helicobacter pylori Shikimate Kinase (HPSK) Inhibitor: A Computational Approach Using Virtual Screening, MM-GBSA Calculations, MD Simulations, and DFT Analysis
Background/Objectives: Helicobacter pylori infects approximately half of the global population, causing chronic gastritis, peptic ulcers, and gastric cancer, a leading cause of cancer mortality. While current therapies face challenges from rising antibiotic resistance, particularly to clarithromycin, alongside treatment complexity and costs, the World Health Organization has prioritized the development of new antibiotics to combat this high-risk pathogen. In this study, we employed computer-aided drug design (CADD) methodologies, including molecular docking, Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) analysis, molecular dynamics (MD) simulations, and Density Functional Theory (DFT) calculations, to explore the potential repurposing of FDA-approved agents as inhibitors of Helicobacter pylori shikimate kinase (HpSK). Methods: Using the Glide module, the HTVS method was initially applied to screen 1615 FDA-approved agents followed by extra-precision (XP) docking for the obtained 111 hits. The obtained XP scores were used to confine the results to those hits with a score above the reference ligand, shikimate, score. This yielded 31 final hits with an XP score above −5.867. MM-GBSA calculations were performed on these top candidates and the reference ligand to refine the analysis and compounds’ prioritization. Results: The 31 compounds displayed binding free energy (ΔGbind) values ranging from 3.61 to −55.92 kcal/mol, with shikimate exhibiting a ΔGbind of −34.24 kcal/mol and 10 hits having a lower ΔGbind value. Out of these ten, three drugs—Dolutegravir, Cangrelor, and Isavuconazonium—were selected for further analysis based on their drug-like properties. Robust and stable binding profiles for both Isavuconazonium and Cangrelor were verified via molecular dynamics simulation. Additionally, Density Functional Theory (DFT) analysis was conducted, and the Highest Occupied Molecular Orbitals (HOMOs), Lowest Unoccupied Molecular Orbitals (LUMOs), and the energy gap (HLG) between them were calculated. All three drug candidates displayed lower HLG values than shikimate, suggesting higher reactivity and more efficient electronic transitions than the reference ligand. Conclusions: These findings suggest that the identified drugs, although not optimal for direct repurposing, would serve as promising leads against Helicobacter pylori shikimate kinase. These drugs could be valuable leads for experimental assessment and further optimization, particularly with no prototype yet identified. In terms of potential for clinical repurposing, the results point to diflunisal as a promising candidate for further testing.
AI-Driven Discovery of Prototype CLEC4M Inhibitors Targeting Marburg Virus Entry via Integrated Machine Learning and Molecular Modeling
Marburg virus (MARV), a highly pathogenic member of the Filoviridae family, causes severe hemorrhagic fever with a high case fatality rate and currently lacks effective therapeutics. The viral entry process, mediated by the interaction between the MARV glycoprotein (GP) and host receptor C-type lectin domain family 4 member M (CLEC4M) (L-SIGN), represents a critical target for early-stage intervention. The active compounds from BindingDB and the decoy from DUDE were used. The RDKit was used for feature engineering. Machine learning models were trained on an initial dataset consisting of 56 active chemicals and 1232 decoys. Among the tested algorithms, the Random Forest model demonstrated superior performance, achieving the highest discriminative ability (AUC = 0.93, MCC = 0.88) on the test set. Virtual screening of 11,032 phytochemicals resulted in 120 predicted actives, of which 42 compounds satisfied drug-likeness criteria. Subsequent molecular docking identified three lead compounds (PubChem IDs: 42608095, 5281601, and 11243993) with moderate-to-promising binding affinities (-6.3 to -6.5 kcal/mol) toward the CLEC4M binding site. ADMET analysis revealed favorable pharmacokinetic and toxicity profiles for the selected lead compounds. DFT calculations of the three compounds highlighted their electronic stability and reactive nature, indicating that PubChem IDs 42608095 and 5281601 possess particularly stable electronic properties conducive to favorable target interactions. Combining machine learning models with molecular docking and Molecular Dynamics (MD) simulations worked well in finding promising phytochemical inhibitors. The MM/GBSA binding free energy calculations further confirmed binding affinities, with values of -10.83 and -11.08 kcal/mol, respectively, suggesting favorable complex stability. These findings provide a pathway for developing new antiviral agents against MARV, pending further experimental validation and optimization.
Isoetin from Isoetaceae Exhibits Superior Pentatransferase Inhibition in Breast Cancer: Comparative Computational Profiling with FDA-Approved Tucatinib
Background: Breast cancer, the most prevalent cancer among women globally, develops primarily in the breast’s ducts or lobules. Drug resistance is a significant challenge in treating advanced cases, contributing to over 685,000 breast cancer-related deaths annually, and identifying novel compounds that inhibit key proteins is crucial for developing effective therapies. Methods: In this study, five transferase proteins with PDB IDs were selected due to their involvement in breast cancer: 1A52, 3PP0, 4EJN, 4I23, and 7R9V. Multitargeted docking studies were conducted using three different docking strategies and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) to calculate the binding affinities against the ZINC Natural compound library. Isoetin (ZINC000006523948), found mainly in Isoetaceae, was identified, and the results were compared with the Food and Drug Administration (FDA)-approved drug Tucatinib. In addition, molecular interaction fingerprints and pharmacokinetic profiling were evaluated. We also performed 5 ns WaterMap simulations to identify hydration sites and interactions, followed by 100 ns molecular dynamics (MD) simulations and MM/GBSA to assess the stability of the Isoetin–protein complexes. Results: The docking results indicated that Isoetin demonstrated superior binding and docking scores ranging from −9.901 to −13.903 kcal/mol compared to Tucatinib, which showed values between −4.875 and −10.948 kcal/mol, suggesting Isoetin’s potential efficacy as a therapeutic agent for breast cancer. Interaction fingerprints revealed significant interactions between Isoetin and key residues, including 28LEU, 12MET, 9PHE, 7ASP, 6ASN, and 6THR. The pharmacokinetics and DFT analysis of Isoetin supported its potential as a viable drug candidate. Furthermore, the 5 ns WaterMap simulations identified various hydration sites, and the 100 ns MD simulations showed that the Isoetin–protein complexes exhibited minimal deviations and fluctuations, indicating better stability than Tucatinib, and MM/GBSA confirmed Isoetin’s superior binding stability. Conclusions: Isoetin, a natural compound identified through in silico screening, demonstrates significant promise as a potential therapeutic agent for breast cancer as it outperforms the FDA-approved drug Tucatinib, the respective native and FDA-approved drug. However, experimental validation is necessary before considering Isoetin for clinical use.
Sustainable Anisaldehyde-Based Natural Deep Eutectic Solvent Dispersive Liquid–Liquid Microextraction for Monitoring Antibiotic Residues in Commercial Milk and Eggs: A Comprehensive Evaluation of Greenness, Practicality, Analytical Performance and Innovation
The widespread use of antibiotics in human medicine, veterinary care, and livestock production has resulted in their frequent detection in diverse environmental and food matrices, making continuous surveillance of antibiotic residues in food products essential for consumer protection. In this study, a sustainable analytical method based on dispersive liquid–liquid microextraction (DLLME) coupled with UHPLC–MS/MS was developed for the trace determination of sulfamethoxazole, sulfadimethoxine, and enrofloxacin in commercial cow milk and chicken eggs. A natural deep eutectic solvent (NADES) composed of anisaldehyde and octanoic acid (2:1, molar ratio) was employed as a biodegradable extraction solvent, and key extraction parameters were systematically optimized. Under optimized conditions, the method demonstrated excellent linearity (R2 ≥ 0.9982), recoveries of 89.5–98.7%, and RSDs ≤ 6.04%. Application to 44 commercial samples from the Saudi market revealed sulfamethoxazole as the most frequently detected antibiotic, occurring in 90% of egg samples (2.17–13.76 µg kg−1) and 70.8% of milk samples (0.26–26.67 µg L−1). A comprehensive evaluation using ten metrics confirmed the method’s greenness, practicality, analytical performance, and innovation. Overall, the proposed NADES–DLLME–UHPLC–MS/MS approach offers a rapid, cost-effective, and environmentally friendly alternative for routine monitoring of antibiotic residues in food matrices.
A Multifaceted Computational Approach to Identify PAD4 Inhibitors for the Treatment of Rheumatoid Arthritis (RA)
Background/Objectives: Neutrophil cells’ lysis forms the extracellular traps (NETs) to counter the foreign body during insults to the body. Peptidyl arginine deiminase (PAD) participates in this process and is then released into the extracellular fluid with the lysed cell components. In some diseases, patients with abnormal function of PADs, especially PAD 4, tend to form autoantibodies against the abnormal citrullinated proteins that are the result of PAD activity on arginine side chains. Those antibodies, which are highly distinct in RA, are distinctly anti-citrullinated protein antibodies (ACPA). This study used an in-silico drug repurposing approach of FDA-approved medications to identify potential alternative medications that can inhibit this process and address solutions to the current limitations of existing therapies. Methods: We utilized Maestro Schrödinger as a computational tool for preparing and docking simulations on the PAD 4 enzyme crystal structure that is retrieved from RCSB Protein Data Bank (PDB ID: 4X8G) while the docked FDA-approved medications are obtained from the Zinc 15 database. The protein was bound to GSK 199—an investigational compound—as a positive control for the docked molecules. Preparation of the protein was performed by Schrödinger Protein Preparation Wizard tool. Binding pocket determination was performed by Glide software (Schrödinger Release 2021–3:Schrödinger, LLC., New York, NY, USA, 2021). and validation of molecular docking was carried out through the redocking of GSK 199 and superimposition. After that, standard and induced fit docking were performed. Results/Conclusions: Among the four obtained hits Pemetrexed, Leucovorin, Chlordiazepoxide, and Ioversol, which showed the highest XP scores providing favorable binding interactions. The induced-fit docking (IFD) results displayed the strong binding affinities of Ioversol, Pemetrexed, Leucovorin, Chlordiazepoxide in the order IFD values −11.617, −10.599, −10.521, −9.988, respectively. This research investigates Pemetrexed, Leucovorin, Chlordiazepoxide, and Ioversol as potential repurposing agents in the treatment of rheumatoid arthritis (RA) as they are identified as PAD4 inhibitors.
Gallic Acid: A Potent Metabolite Targeting Shikimate Kinase in Acinetobacter baumannii
Background/Objectives: Acinetobacter baumannii is a highly multidrug-resistant pathogen resistant to almost all classes of antibiotics; new therapeutic strategies against this infectious agent are urgently needed. Shikimate kinase is an enzyme belonging to the shikimate pathway and has become a potential target for drug development. This work describes the search for Food and Drug Administration (FDA)-approved drugs and natural compounds, including gallic acid, that could be repurposed as selective shikimate kinase inhibitors by integrated computational and experimental approaches. Methods: Approaches to drug design using structure-based and ligand-based methodology, in-silico screening, molecular docking, and molecular dynamics for the study of both binding affinity and stability. Experimental Validation Determination of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) on Acinetobacter baumannii and Enterococcus faecalis. Results/Conclusions: Among them, gallic acid, obtained from plants, proved to be the most promising compound that showed sufficient binding with shikimate kinase through computational studies. Gallic acid showed very good activity against Acinetobacter baumannii and Enterococcus faecalis in the MIC and MBC assay, respectively. Gallic acid exhibited better activity against Acinetobacter baumannii due to the overexpression of shikimate kinase. Gallic acid has emerged as a potential therapeutic candidate drug against A. baumannii infection and, therefore, as a strategy against the appearance of multidrug-resistant microorganisms. This study not only identifies a novel repurposing opportunity for gallic acid but also provides a comprehensive computational and experimental framework for accelerating antimicrobial drug discovery against multidrug-resistant pathogens.
Drug Repurposing for Cancer Treatment: A Comprehensive Review
Cancer ranks among the primary contributors to global mortality. In 2022, the global incidence of new cancer cases reached about 20 million, while the number of cancer-related fatalities reached 9.7 million. In Saudi Arabia, there were 13,399 deaths caused by cancer and 28,113 newly diagnosed cases of cancer. Drug repurposing is a drug discovery strategy that has gained special attention and implementation to enhance the process of drug development due to its time- and money-saving effect. It involves repositioning existing medications to new clinical applications. Cancer treatment is a therapeutic area where drug repurposing has shown the most prominent impact. This review presents a compilation of medications that have been repurposed for the treatment of various types of cancers. It describes the initial therapeutic and pharmacological classes of the repurposed drugs and their new applications and mechanisms of action in cancer treatment. The review reports on drugs from various pharmacological classes that have been successfully repurposed for cancer treatment, including approved ones and those in clinical trials and preclinical development. It stratifies drugs based on their anticancer repurpose as multi-type, type-specific, and mechanism-directed, and according to their pharmacological classes. The review also reflects on the future potential that drug repurposing has in the clinical development of novel anticancer therapies.