Browsing by Author "Theyab, Abdulrahman"

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    Investigating Polyphenol Nanoformulations for Therapeutic Targets against Diabetes Mellitus
    (Daffodil International University, 2022-06-21) Islam, Fahadul; Khadija, Jannatul Fardous; Islam, Md. Rezaul; Shohag, Sheikh; Mitra, Saikat; Alghamdi, Saad; Babalghith, Ahmad O.; Theyab, Abdulrahman; Rahman, Mohammad Tauhidur; Akter, Aklima; Mamun, Abdullah Al; Alhumaydhi, Fahad A.; Emran, Talha Bin
    Diabetes mellitus (DM) is a fatal metabolic disorder, and its prevalence has escalated in recent decades to a greater extent. Since the incidence and severity of the disease are constantly increasing, plenty of therapeutic approaches are being considered as a promising solution. Many dietary polyphenols have been reported to be effective against diabetes along with its accompanying vascular consequences by targeting multiple therapeutic targets. Additionally, the biocompatibility of these polyphenols raises questions about their use as pharmacological mediators. Nevertheless, the pharmacokinetic and biopharmaceutical properties of these polyphenols limit their clinical benefit as therapeutics. Pharmaceutical industries have attempted to improve compliance and therapeutic effects. However, nanotechnological approaches to overcome the pharmacokinetic and biopharmaceutical barriers associated with polyphenols as antidiabetic medications have been shown to be effective to improve clinical compliance and efficacy. Therefore, this review highlighted a comprehensive and up-to-date assessment of polyphenol nanoformulations in the treatment of diabetes and vascular consequences.
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    Recognizing Novel Drugs against Keap1 in Alzheimer's disease Using Machine Learning Grounded Computational Studies
    (Scopus, 22-12-31) Mukerjee, Nobendu; Al-Khafaji, Khattab; Maitra, Swastika; Wadi, Jaafar Suhail; Sachdeva, Punya; Ghosh, Arabinda; Buchade, Rahul Subhash; Chaudhari, Somdatta Yashwant; Jadhav, Shailaja B.; Das, Padmashree; Hasan, Mohammad Mehedi; Rahman, Md. Habibur; Albadrani, Ghadeer M.; Albadrani, M.; Altyar, Ahmed E.; Kamel, Mohamed; Algahtani, Mohammad; Shinan, Khlood; Theyab, Abdulrahman; Daim, Mohamed M. Abdel-; Ashraf, Md.; Rahman, Md. Mominur; Sharma, Rohit
    Alzheimer’s disease (AD) is the most common neurodegenerative disorder in the world, affecting an estimated 50 million individuals. The nerve cells become impaired and die due to the formation of amyloid-beta (Aβ) plaques and neurofibrillary tangles (NFTs). Dementia is one of the most common symptoms seen in people with AD. Genes, lifestyle, mitochondrial dysfunction, oxidative stress, obesity, infections, and head injuries are some of the factors that can contribute to the development and progression of AD. There are just a few FDA-approved treatments without side effects in the market, and their efficacy is restricted due to their narrow target in the etiology of AD. Therefore, our aim is to identify a safe and potent treatment for Alzheimer’s disease. We chose the ursolic acid (UA) and its similar compounds as a compounds’ library. And the ChEMBL database was adopted to obtain the active and inactive chemicals against Keap1. The best Quantitative structure-activity relationship (QSAR) model was created by evaluating standard machine learning techniques, and the best model has the lowest RMSE and greatest R2 (Random Forest Regressor). We chose pIC50 of 6.5 as threshold, where the top five potent medicines (DB06841, DB04310, DB11784, DB12730, and DB12677) with the highest predicted pIC50 (7.091184, 6.900866, 6.800155, 6.768965, and 6.756439) based on QSAR analysis. Furthermore, the top five medicines utilize as ligand molecules were docked in Keap1’s binding region. The structural stability of the nominated medications was then evaluated using molecular dynamics simulations, RMSD, RMSF, Rg, and hydrogen bonding. All models are stable at 20 ns during simulation, with no major fluctuations observed. Finally, the top five medications are shown as prospective inhibitors of Keap1 and are the most promising to battle AD.