Abdelkader Ali Metwally

Abdelkader Ali Metwally

Ain Shams University

H-index: 21

Africa-Egypt

Abdelkader Ali Metwally Information

University

Ain Shams University

Position

1. Faculty of Pharmacy Kuwait University 2. Faculty of Pharmacy

Citations(all)

1082

Citations(since 2020)

907

Cited By

453

hIndex(all)

21

hIndex(since 2020)

18

i10Index(all)

27

i10Index(since 2020)

22

Email

University Profile Page

Ain Shams University

Abdelkader Ali Metwally Skills & Research Interests

Computational pharmaceutics

Gene therapy

Nanomedicines

Machine learning

Drug delivery

Top articles of Abdelkader Ali Metwally

Structural Optimization of Platinum Drugs to Improve the Drug-Loading and Antitumor Efficacy of PLGA Nanoparticles

Currently, molecular dynamics simulation is being widely applied to predict drug–polymer interaction, and to optimize drug delivery systems. Our study describes a combination of in silico and in vitro approaches aimed at improvement in polymer-based nanoparticle design for cancer treatment. We applied the PASS service to predict the biological activity of novel carboplatin derivatives. Subsequent molecular dynamics simulations revealed the dependence between the drug–polymer binding energy along with encapsulation efficacy, drug release profile, and the derivatives’ chemical structure. We applied ICP-MS analysis, the MTT test, and hemolytic activity assay to evaluate drug loading, antitumor activity, and hemocompatibility of the formulated nanoparticles. The drug encapsulation efficacy varied from 0.2% to 1% and correlated with in silico modelling results. The PLGA nanoparticles revealed higher antitumor activity against A549 human non-small-cell lung carcinoma cells compared to non-encapsulated carboplatin derivatives with IC50 values of 1.40–23.20 µM and 7.32–79.30 µM, respectively; the similar cytotoxicity profiles were observed against H69 and MCF-7 cells. The nanoparticles efficiently induced apoptosis in A549 cells. Thus, nanoparticles loaded with novel carboplatin derivatives demonstrated high application potential for anticancer therapy due to their efficacy and high hemocompatibility. Our results demonstrated the combination of in silico and in vitro methods applicability for the optimization of encapsulation and antitumor efficacy in novel drug delivery systems design.

Authors

Maria B Sokol,Margarita V Chirkina,Nikita G Yabbarov,Mariia R Mollaeva,Tatyana A Podrugina,Anna S Pavlova,Viktor V Temnov,Rania M Hathout,Abdelkader A Metwally,Elena D Nikolskaya

Journal

Pharmaceutics

Published Date

2022/11

Modeling Drugs-PLGA Nanoparticles Interactions Using Gaussian Processes: Pharmaceutics Informatics Approach

The objective of this study was to correlate the binding of drugs on a very popular nanoparticulate polymeric matrix; PLGA nanoparticles with their main constitutional, electronic and physico-chemical descriptors. Gaussian Processes (GPs) was the artificial intelligence machine learning method that was utilized to fulfil this task. The method could successfully model the results where optimum values of the investigated descriptors of the loaded drugs were deduced. A percentage bias of 12.68% ± 2.1 was obtained in predicting the binding energies of a group of test drugs. As a conclusion, GPs could successfully model the drugs-PLGA interactions associated with a good predicting power. The GPs-predicted binding energies (ΔG) can easily be projected to the drugs loading as was previously proven. Adopting the “Pharmaceutics Informatics” approach can save the pharmaceutical industry and the drug …

Authors

Rania M Hathout,Orchid A Mahmoud,Dalia S Ali,Marina Mamdouh,Abdelkader A Metwally

Journal

Journal of Cluster Science

Published Date

2022/9

Pharmaceutics Informatics: Bio/Chemoinformatics in Drug Delivery

Whilst bioinformatics deals with the biological receptors and proteins (biological macromolecules) and their manipulation, the chemoinformatics is usually concerned with the computational handling of small molecules and chemical compounds. The integration of the two approaches has been performed in several studies in order to study the interaction of drugs (therapeutically active compounds) with their macromolecular carriers or matrices on one hand and the biological targeted receptors and proteins on the other hand. Accordingly, the term “pharmaceutics informatics” can be introduced to compass the application of both the bioinformatics and chemoinformatics tools in drug delivery. This new approach would save the researchers and formulators the wet experiments for the preparation and characterization of drug-loaded delivery systems that consume a lot of resources, time and effort. The currently used …

Authors

Rania M Hathout,Abdelkader A Metwally

Published Date

2022

In Silico Prediction of siRNA Ionizable-Lipid Nanoparticles in vivo Efficacy: Machine Learning Modeling Based on Formulation and Molecular Descriptors

In silico prediction of the in vivo efficacy of siRNA ionizable-lipid nanoparticles is desirable as it can save time and resources dedicated to wet-lab experimentation. This study aims to computationally predict siRNA nanoparticles in vivo efficacy. A data set containing 120 entries was prepared by combining molecular descriptors of the ionizable lipids together with two nanoparticles formulation characteristics. Input descriptor combinations were selected by an evolutionary algorithm. Artificial neural networks, support vector machines and partial least squares regression were used for QSAR modeling. Depending on how the data set is split, two training sets and two external validation sets were prepared. Training and validation sets contained 90 and 30 entries respectively. The results showed the successful predictions of validation set log(siRNA dose) with R2val = 0.86 – 0.89 and 0.75 – 80 for validation sets one and two respectively. Artificial neural networks resulted in the best R2val for both validation sets. For predictions that have high bias, improvement of R2val from 0.47 to 0.96 was achieved by selecting the training set lipids lying within the applicability domain. In conclusion, in vivo performance of siRNA nanoparticles was successfully predicted by combining cheminformatics with machine learning techniques.

Authors

Abdelkader A Metwally,Amira A Nayel,Rania M Hathout

Published Date

2021/8/11

Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers

This review describes different trials to model and predict drug payload in lipid and polymeric nanocarriers. It traces the evolution of the field from the earliest attempts when numerous solubility and Flory-Huggins models were applied, to the emergence of molecular dynamic simulations and docking studies, until the exciting practically successful era of artificial intelligence and machine learning. Going through matching and poorly matching studies with the wet lab-dry lab results, many key aspects were reviewed and addressed in the form of sequential examples that highlighted both cases.

Authors

Shaymaa A Abd-algaleel,Hend M Abdel-Bar,Abdelkader A Metwally,Rania M Hathout

Published Date

2021/7

Synchronizing In Silico, In Vitro, and In Vivo Studies for the Successful Nose to Brain Delivery of an Anticancer Molecule

Sesamol is a sesame seed constituent with reported activity against many types of cancer. In this work, two types of nanocarriers, solid lipid nanoparticles (SLNs) and polymeric nanoparticles (PNs), were exploited to improve sesamol efficiency against the glioma cancer cell line. The ability of the proposed systems for efficient brain targeting intranasally was also inspected. By the aid of two docking programs, the virtual loading pattern inside these nanocarriers was matched to the real experimental results. Interactions involved in sesamol–carrier binding were also assessed, followed by a discussion of how different scoring functions account for these interactions. The study is an extension of the computer-assisted drug formulation design series, which represents a promising initiative for an upcoming industrial innovation. The results proved the power of combined in silico tools in predicting members with the highest …

Authors

Shaymaa A Abd-Algaleel,Abdelkader A Metwally,Hend Mohamed Abdel-Bar,Dina H Kassem,Rania M Hathout

Journal

Molecular Pharmaceutics

Published Date

2021/8/30

Novel thymoquinone lipidic core nanocapsules with anisamide-polymethacrylate shell for colon cancer cells overexpressing sigma receptors

The biggest challenge in colorectal cancer therapy is to avoid intestinal drug absorption before reaching the colon, while focusing on tumor specific delivery with high local concentration and minimal toxicity. In our work, thymoquinone (TQ)-loaded polymeric nanocapsules were prepared using the nanoprecipitation technique using Eudragit S100 as polymeric shell. Conjugation of anisamide as a targeting ligand for sigma receptors overexpressed by colon cancer cells to Eudragit S100 was carried out via carbodiimide coupling reaction, and was confirmed by thin layer chromatography and 1H-NMR. TQ nanocapsules were characterized for particle size, surface morphology, zeta potential, entrapment efficiency % (EE%), in vitro drug release and physical stability. A cytotoxicity study on three colon cancer cell lines (HT-29, HCT-116, Caco-2) was performed. Results revealed that the polymeric nanocapsules were …

Authors

Lydia Ramzy,Abdelkader A Metwally,Maha Nasr,Gehanne AS Awad

Journal

Scientific Reports

Published Date

2020/7/3

Prediction of Drug Loading in the Gelatin Matrix Using Computational Methods

The delivery of drugs is a topic of intense research activity in both academia and industry with potential for positive economic, health, and societal impacts. The selection of the appropriate formulation (carrier and drug) with optimal delivery is a challenge investigated by researchers in academia and industry, in which millions of dollars are invested annually. Experiments involving different carriers and determination of their capacity for drug loading are very time-consuming and therefore expensive; consequently, approaches that employ computational/theoretical chemistry to speed have the potential to make hugely beneficial economic, environmental, and health impacts through savings in costs associated with chemicals (and their safe disposal) and time. Here, we report the use of computational tools (data mining of the available literature, principal component analysis, hierarchical clustering analysis, partial least …

Authors

Rania M Hathout,AbdelKader A Metwally,Timothy J Woodman,John G Hardy

Journal

ACS omega

Published Date

2020/1/13

Comparing cefotaxime and ceftriaxone in combating meningitis through nose-to-brain delivery using bio/chemoinformatics tools

Bio/chemoinformatics tools can be deployed to compare antimicrobial agents aiming to select an efficient nose-to-brain formulation targeting the meningitis disease by utilizing the differences in the main structural, topological and electronic descriptors of the drugs. Cefotaxime and ceftriaxone were compared at the formulation level (by comparing the loading in gelatin and tripalmitin matrices as bases for the formation of nanoparticulate systems), at the biopharmaceutical level (through the interaction with mucin and the P-gp efflux pumps) and at the therapeutic level (through studying the interaction with S. pneumoniae bacterial receptors). GROMACS v4.6.5 software package was used to carry-out all-atom molecular dynamics simulations. Higher affinity of ceftriaxone was observed compared to cefotaxime on the investigated biopharmaceutical and therapeutic macromolecules. Both drugs showed successful …

Authors

Rania M Hathout,Sherihan G Abdelhamid,Ghadir S El-Housseiny,Abdelkader A Metwally

Journal

Scientific Reports

Published Date

2020/12/4

Chloroquine and Hydroxychloroquine for combating COVID-19: Investigating efficacy and hypothesizing new formulations using Bio/chemoinformatics tools

Chloroquine (CQ) and hydroxychloroquine (HCQ) are undergoing several clinical trials for evaluating their efficacy and safety as antiviral drugs. Yet, there is still a great debate about their efficacy in combating COVID-19. This study aimed to evaluate the feasibility of intranasal and/or pulmonary administration of CQ/HCQ for COVID-19 using Bio/chemoinformatics tools. We, hereby, hypothesize the success of the intranasal and the pulmonary routes through a gelatin matrix to overcome several challenges related to CQ and HCQ pharmacodynamics and pharmacokinetics properties and to increase their local concentrations at the sites of initial viral entry while minimizing the potential side effects. Molecular docking on the gelatin-simulated matrix demonstrated high loading values and a sustained release profile. Moreover, the docking on mucin as well as various receptors including Angiotensin-converting enzyme 2 …

Authors

Rania M Hathout,Sherihan G Abdelhamid,Abdelkader A Metwally

Journal

Informatics in Medicine Unlocked

Published Date

2020/10/8

Chitosan-coated nanodiamonds: Mucoadhesive platform for intravesical delivery of doxorubicin

Nanodiamonds (NDs) are an emerging delivery system with a massive surface area qualifying them for efficient loading with various drugs. However, NDs easily scavenge ions upon mixing with physiological media leading to rapid aggregation. Herein, chitosan was employed to endue steric stabilization to NDs and confer adhesiveness to the particles improving their retention in the urinary bladder. The effect of chitosan molecular weight and pH on the particle size and surface charge of chitosan-coated doxorubicin-loaded NDs (Chi-NDX) was investigated. Selected formula exhibited high drug loading efficiency (>90 %), small particle size (<150 nm), good colloidal stability, acid-favored drug release but limited stability in cell culture media. After further stabilization with TPP or dextran sulfate, selected TPP-treated formula displayed more potent cytotoxic effect compared with free doxorubicin and uncoated …

Authors

Moustafa S Ali,Abdelkader A Metwally,Rania H Fahmy,Rihab Osman

Journal

Carbohydrate Polymers

Published Date

2020/10/1

Abdelkader Ali Metwally FAQs

What is Abdelkader Ali Metwally's h-index at Ain Shams University?

The h-index of Abdelkader Ali Metwally has been 18 since 2020 and 21 in total.

What are Abdelkader Ali Metwally's top articles?

The articles with the titles of

Structural Optimization of Platinum Drugs to Improve the Drug-Loading and Antitumor Efficacy of PLGA Nanoparticles

Modeling Drugs-PLGA Nanoparticles Interactions Using Gaussian Processes: Pharmaceutics Informatics Approach

Pharmaceutics Informatics: Bio/Chemoinformatics in Drug Delivery

In Silico Prediction of siRNA Ionizable-Lipid Nanoparticles in vivo Efficacy: Machine Learning Modeling Based on Formulation and Molecular Descriptors

Evolution of the Computational Pharmaceutics Approaches in the Modeling and Prediction of Drug Payload in Lipid and Polymeric Nanocarriers

Synchronizing In Silico, In Vitro, and In Vivo Studies for the Successful Nose to Brain Delivery of an Anticancer Molecule

Novel thymoquinone lipidic core nanocapsules with anisamide-polymethacrylate shell for colon cancer cells overexpressing sigma receptors

Prediction of Drug Loading in the Gelatin Matrix Using Computational Methods

...

are the top articles of Abdelkader Ali Metwally at Ain Shams University.

What are Abdelkader Ali Metwally's research interests?

The research interests of Abdelkader Ali Metwally are: Computational pharmaceutics, Gene therapy, Nanomedicines, Machine learning, Drug delivery

What is Abdelkader Ali Metwally's total number of citations?

Abdelkader Ali Metwally has 1,082 citations in total.

    academic-engine

    Useful Links