High-throughput virtual screening approach of natural compounds as target inhibitors of plasmepsin-II

En-nahli, Fatima; Baammi, Soukayna; Hajji, Halima; Alaqarbeh, Marwa; Lakhlifi, Tahar; Bouachrine, Mohammed

doi:10.1080/07391102.2022.2152871

Cited by 15 publications

(6 citation statements)

References 42 publications

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“…Hence, early prediction of these properties would result in substantial cost savings within the field of drug research ( Mandlik et al, 2016 ). Some of the parameters evaluated for the ADMET includes adsorption, distribution, metabolism, excretion, and toxicity ( En-nahli et al, 2022 ). To accurately predict drug efficacy, penetration through the blood-brain barrier, absorption in the human intestine, CNS permeability, inhibition of cytochrome P450 2D6 and 3A4, hepatotoxicity, and AMES toxicity are also considered ( Lagorce et al, 2017 ).…”

Section: Methodsmentioning

confidence: 99%

Unleashing Nature’s potential: a computational approach to discovering novel VEGFR-2 inhibitors from African natural compound using virtual screening, ADMET analysis, molecular dynamics, and MMPBSA calculations

Baammi,

El Allali,

Daoud

2023

Front. Mol. Biosci.

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One of the characteristic features of cancer is angiogenesis, the process by which new, aberrant blood vessels are formed from pre-existing blood vessels. The process of angiogenesis begins when VEGF binds to its receptor, the VEGF receptor (VEGFR). The formation of new blood vessels provides nutrients that can promote the growth of cancer cells. When it comes to new blood vessel formation, VEGFR2 is a critical player. Therefore, inhibiting VEGFR2 is an effective way to target angiogenesis in cancer treatment. The aim of our research was to find new VEGFR-2 inhibitors by performing a virtual screening of 13313 from African natural compounds using different in silico techniques. Using molecular docking calculations and ADMET properties, we identified four compounds that exhibited a binding affinity ranging from −11.0 kcal/mol to −11.5 Kcal/mol when bound to VEGFR-2. These four compounds were further analyzed with 100 ns simulations to determine their stability and binding energy using the MM-PBSA method. After comparing the compounds with Regorafenib, a drug approved for anti-angiogenesis treatment, it was found that all the candidates (EANPDB 252, NANPDB 4577, and NANPDB 4580), with the exception of EANPDB 76, could target VEGFR-2 similarly effectively to Regorafenib. Therefore, we recommend three of these agents for anti-angiogenesis treatment because they are likely to deactivate VEGFR-2 and thus inhibit angiogenesis. However, it should be noted that the safety and suitability of these agents for clinical use needs further investigation, as the computer-assisted study did not include in vitro or in vivo experiments.

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Section: Methodsmentioning

confidence: 99%

Unleashing Nature’s potential: a computational approach to discovering novel VEGFR-2 inhibitors from African natural compound using virtual screening, ADMET analysis, molecular dynamics, and MMPBSA calculations

Baammi,

El Allali,

Daoud

2023

Front. Mol. Biosci.

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“…Our findings demonstrate that pkCSM performs comparably or even superior to other freely available methods for various pharmacokinetic properties. The second server, SWISS-ADME ( En-nahli et al, 2022 ; Azzam, 2023 ), served the purpose of computing physicochemical descriptors and predicting ADME parameters, pharmacokinetic properties, drug-like characteristics, and suitability for medicinal chemistry of one or multiple small molecules, providing valuable support for drug discovery.…”

Section: Admetmentioning

confidence: 99%

Revealing innovative JAK1 and JAK3 inhibitors: a comprehensive study utilizing QSAR, 3D-Pharmacophore screening, molecular docking, molecular dynamics, and MM/GBSA analyses

Faris,

Cacciatore,

Alnajjar

et al. 2024

Front. Mol. Biosci.

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The heterocycle compounds, with their diverse functionalities, are particularly effective in inhibiting Janus kinases (JAKs). Therefore, it is crucial to identify the correlation between their complex structures and biological activities for the development of new drugs for the treatment of rheumatoid arthritis (RA) and cancer. In this study, a diverse set of 28 heterocyclic compounds selective for JAK1 and JAK3 was employed to construct quantitative structure-activity relationship (QSAR) models using multiple linear regression (MLR). Artificial neural network (ANN) models were employed in the development of QSAR models. The robustness and stability of the models were assessed through internal and external methodologies, including the domain of applicability (DoA). The molecular descriptors incorporated into the model exhibited a satisfactory correlation with the receptor-ligand complex structures of JAKs observed in X-ray crystallography, making the model interpretable and predictive. Furthermore, pharmacophore models ADRRR and ADHRR were designed for each JAK1 and JAK3, proving effective in discriminating between active compounds and decoys. Both models demonstrated good performance in identifying new compounds, with an ROC of 0.83 for the ADRRR model and an ROC of 0.75 for the ADHRR model. Using a pharmacophore model, the most promising compounds were selected based on their strong affinity compared to the most active compounds in the studied series each JAK1 and JAK3. Notably, the pharmacokinetic, physicochemical properties, and biological activities of the selected compounds (As compounds ZINC79189223 and ZINC66252348) were found to be consistent with their therapeutic effects in RA, owing to their non-toxic, cholinergic nature, absence of P-glycoprotein, high gastrointestinal absorption, and ability to penetrate the blood-brain barrier. Furthermore, ADMET properties were assessed, and molecular dynamics and MM/GBSA analysis revealed stability in these molecules.

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“…By combining the ADMET study with a CovDock study, molecules can be filtered to select only those with a high affinity for the therapeutic target [45]. Indeed, drugs need to interact specifically and strongly with their biological target if they are to be effective.…”

Section: Covalent Docking (Covdock)mentioning

confidence: 99%

Computer-Aided Drug Design of Novel Derivatives of 2-Amino-7,9-dihydro-8H-purin-8-one as Potent Pan-Janus JAK3 Inhibitors

et al. 2023

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Rheumatoid arthritis (RA) remains one of the most prevalent autoimmune diseases worldwide. Janus kinase 3 (JAK3) is an essential enzyme for treating autoimmune diseases, including RA. Molecular modeling techniques play a crucial role in the search for new drugs by reducing time delays. In this study, the 3D-QSAR approach is employed to predict new JAK3 inhibitors. Two robust models, both field-based with R2 = 0.93, R = 0.96, and Q2 = 87, and atom-based with R2 = 0.94, R = 0.97, and Q2 = 86, yielded good results by identifying groups that may readily direct their interaction. A reliable pharmacophore model, DHRRR1, was provided in this work to enable the clear characterization of chemical features, leading to the design of 13 inhibitors with their pIC50 values. The DHRRR1 model yielded a validation result with a ROC value of 0.87. Five promising inhibitors were selected for further study based on an ADMET analysis of their pharmacokinetic properties and covalent docking (CovDock). Compared to the FDA-approved drug tofacitinib, the pharmaceutical features, binding affinity and stability of the inhibitors were analyzed through CovDock, 300 ns molecular dynamics simulations, free energy binding calculations and ADMET predictions. The results show that the inhibitors have strong binding affinity, stability and favorable pharmaceutical properties. The newly predicted molecules, as JAK3 inhibitors for the treatment of RA, are promising candidates for use as drugs.

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High-throughput virtual screening approach of natural compounds as target inhibitors of plasmepsin-II

Cited by 15 publications

References 42 publications

Unleashing Nature’s potential: a computational approach to discovering novel VEGFR-2 inhibitors from African natural compound using virtual screening, ADMET analysis, molecular dynamics, and MMPBSA calculations

Unleashing Nature’s potential: a computational approach to discovering novel VEGFR-2 inhibitors from African natural compound using virtual screening, ADMET analysis, molecular dynamics, and MMPBSA calculations

Revealing innovative JAK1 and JAK3 inhibitors: a comprehensive study utilizing QSAR, 3D-Pharmacophore screening, molecular docking, molecular dynamics, and MM/GBSA analyses

Computer-Aided Drug Design of Novel Derivatives of 2-Amino-7,9-dihydro-8H-purin-8-one as Potent Pan-Janus JAK3 Inhibitors

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