Browsing by Author "Rashid, Summya"

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A Decade's Worth of Impact Dox Loaded Liposomes in Anticancer Activity
(Daffodil International University, 22-11-01) Ghosh, Puja; Tiwari, Himja; Lakkakula, Jaya; Roy, Arpita; Emran, Talha Bin; Rashid, Summya; Alghamdi, Saad; Rajab, Bodour S.; Almehmadi, Mazen; Allahyani, Mamdouh; Aljuaid, Abdulelah; Alsaiari, Ahad Amer; Sharma, Rohit; Babalghith, Ahmad O.
Clinically approved therapeutics associated with cancer are limited to mostly chemotherapy, surgery and radiotherapy in spite of the advancements in the biomedical field. Due to the cardiotoxicity and uncountable side effects brought by the prevailing treatment strategies, demands are growing for targeted drug delivery using nanomaterials. For this the most commonly used drug, doxorubicin (DOX) is encapsulated within several type of nanovesicles to observe their anticancer activity. Among them, DOX encapsulated liposomes gained popularity because of their clinical success and lower toxicity. To enhance their efficiency and site specific delivery, attempts are made to modify the liposomes by combining them with peptides, aptamers, antibodies etc to develop pH, thermal, UV-sensitive and electro-magnetic liposomal nanocarriers for controlled drug release. The novel strategies for the treatment of Breast, Lung, Liver, Pancreatic, Prostate, Ovarian, Cervical, Blood, Brain and Colon cancer using modified liposomes encapsulating DOX are illustrated in this review.
A Drug Design Strategy Based on Molecular Docking and Molecular Dynamics Simulations Applied to Development of Inhibitor Against Triple-Negative Breast Cancer by Scutellarein Derivatives
(PLOS, 2023-10-12) Akash, Shopnil; Aovi, Farjana Islam; Azad, Md. A. K.; Kumer, Ajoy; Chakma, Unesco; Islam, Md. Rezaul; Mukerjee, Nobendu; Rahman, Md. Mominur; Bayıl, Imren; Rashid, Summya; Sharma, Rohit
Triple-negative breast cancer (TNBC), accounting for 10–15% of all breast malignancies, is more prevalent in women under 40, particularly in those of African descent or carrying the BRCA1 mutation. TNBC is characterized by the absence of estrogen and progesterone receptors (ER, PR) and low or elevated HER2 expression. It represents a particularly aggressive form of breast cancer with limited therapeutic options and a poorer prognosis. In our study, we utilized the protein of TNBC collected from the Protein Data Bank (PDB) with the most stable configuration. We selected Scutellarein, a bioactive molecule renowned for its anti-cancer properties, and used its derivatives to design potential anti-cancer drugs employing computational tools. We applied and modified structural activity relationship methods to these derivatives and evaluated the probability of active (Pa) and inactive (Pi) outcomes using pass prediction scores. Furthermore, we employed in-silico approaches such as the assessment of absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters, and quantum calculations through density functional theory (DFT). Within the DFT calculations, we analyzed Frontier Molecular Orbitals, specifically the Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO). We then conducted molecular docking and dynamics against TNBC to ascertain binding affinity and stability. Our findings indicated that Scutellarein derivatives, specifically DM03 with a binding energy of -10.7 kcal/mol and DM04 with -11.0 kcal/mol, exhibited the maximum binding tendency against Human CK2 alpha kinase (PDB ID 7L1X). Molecular dynamic simulations were performed for 100 ns, and stability was assessed using root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) parameters, suggesting significant stability for our chosen compounds. Furthermore, these molecules met the pharmacokinetics requirements for potential therapeutic candidates, displaying non-carcinogenicity, minimal aquatic and non-aquatic toxicity, and greater aqueous solubility. Collectively, our computational data suggest that Scutellarein derivatives may serve as potential therapeutic agents for TNBC. However, further experimental investigations are needed to validate these findings.
Multifunctional role of nanoparticles for the diagnosis and therapeutics of cardiovascular diseases
(2024-02-01) Arshad, Ihtesham; Kanwal, Ayesha; Zafar, Imran; Unar, Ahsanullah; Mouada, Hanane; Tur Razia, Iashia; Arif, Safina; Ahsan, Muhammad; Kamal, Mohammad Amjad; Rashid, Summya; Khan, Khalid Ali; Sharma, Rohit
The increasing burden of cardiovascular disease (CVD) remains responsible for morbidity and mortality worldwide; their effective diagnostic or treatment methods are of great interest to researchers. The use of NPs and nanocarriers in cardiology has drawn much interest. The present comprehensive review provides deep insights into the use of current and innovative approaches in CVD diagnostics to offer practical ways to utilize nanotechnological interventions and the critical elements in the CVD diagnosis, associated risk factors, and management strategies of patients with chronic CVDs. We proposed a decision tree-based solution by discussing the emerging applications of NPs for the higher number of rules to increase efficiency in treating CVDs. This review-based study explores the screening methods, tests, and toxicity to provide a unique way of creating a multi-parametric feature that includes cutting-edge techniques for identifying cardiovascular problems and their treatments. We discussed the benefits and drawbacks of various NPs in the context of cost, space, time and complexity that have been previously suggested in the literature for the diagnosis of CVDs risk factors. Also, we highlighted the advances in using NPs for targeted and improved drug delivery and discussed the evolution toward the nano-cardiovascular potential for medical science. Finally, we also examined the mixed-based diagnostic approaches crucial for treating cardiovascular disorders, broad applications and the potential future applications of nanotechnology in medical sciences.
Reviewing Methods of Deep Learning for Intelligent Healthcare Systems in Genomics and Biomedicine
(Elsevier, 2023-09-15) Zafar, Imran; Anwar, Shakila; kanwal, Faheem; Yousaf, Waqas; Nisa, Fakhar Un; Kausar, Tanzeela; Ain, Qurat Ul; Unar, Ahsanullah; Kamal, Mohammad Amjad; Rashid, Summya; Khan, Khalid Ali; Sharma, Rohit
The advancements in genomics and biomedical technologies have generated vast amounts of biological and physiological data, which present opportunities for understanding human health. Deep learning (DL) and machine learning (ML) are frontiers and interdisciplinary fields of computer science that consider comprehensive computational models and provide integral roles for disease diagnosis and therapy investigation. DL-based algorithms can discover the intrinsic hierarchies in the training data to show great promise for extracting features and learning patterns from complex datasets and performing various analytical tasks. This review comprehensively discusses the wide-ranging DL approaches for intelligent healthcare systems (IHS) in genomics and biomedicine. This paper explores advanced concepts in deep learning (DL) and discusses the workflow of utilizing role-based algorithms in genomics and biomedicine to integrate intelligent healthcare systems (IHS). The aim is to overcome biomedical obstacles like patient disease classification, core biomedical processes, and empowering patient-disease integration. The paper also highlights how DL approaches are well-suited for addressing critical challenges in these domains, offering promising solutions for improved healthcare outcomes. We also provided a concise concept of DL architectures and model optimization in genomics and bioinformatics at the molecular level to deal with biomedicine classification, genomic sequence analysis, protein structure classification, and prediction. Finally, we discussed DL's current challenges and future perspectives in genomics and biomedicine for future directions.
Synthesis and Characterization of Copper Oxide Nanoparticles: Its Influence on Corn (Z. mays) and Wheat (Triticum aestivum) Plants by Inoculation of Bacillus Subtilis
(Springer Nature, 2022-12-26) Haider, Hafiz Imran; Zafar, Imran; Ain, Qurat Ul; Noreen, Asifa; Nazir, Aamna; Javed, Rida; Sehgal, Sheikh Arslan; Khan, Azmat Ali; Rahman, Md. Mominur; Rashid, Summya; Garai, Somenath; Sharma, Rohit
Nanotechnology is now playing an emerging role in green synthesis in agriculture as nanoparticles (NPs) are used for various applications in plant growth and development. Copper is a plant micronutrient; the amount of copper oxide nanoparticles (CuONPs) in the soil determines whether it has positive or adverse effects. CuONPs can be used to grow corn and wheat plants by combining Bacillus subtilis. In this research, CuONPs were synthesized by precipitation method using different precursors such as sodium hydroxide (0.1 M) and copper nitrate (Cu(NO3)2) having 0.1 M concentration with a post-annealing method. The NPs were characterized through X-ray diffraction (XRD), scanning electron microscope (SEM), and ultraviolet (UV) visible spectroscopy. Bacillus subtilis is used as a potential growth promoter for microbial inoculation due to its prototrophic nature. The JAR experiment was conducted, and the growth parameter of corn (Z. mays) and wheat (Triticum aestivum) was recorded after 5 days. The lab assay evaluated the germination in JARs with and without microbial inoculation under CuONP stress at different concentrations (25 and 50 mg). The present study aimed to synthesize CuONPs and systematically investigate the particle size effects of copper (II) oxide (CuONPs) (< 50 nm) on Triticum aestivum and Z. mays. In our results, the XRD pattern of CuONPs at 500 °C calcination temperature with monoclinic phase is observed, with XRD peak intensity slightly increasing. The XRD patterns showed that the prepared CuONPs were extremely natural, crystal-like, and nano-shaped. We used Scherrer’s formula to calculate the average size of the particle, indicated as 23 nm. The X-ray diffraction spectrum of synthesized materials and SEM analysis show that the particles of CuONPs were spherical in nature. The results revealed that the synthesized CuONPs combined with Bacillus subtilis used in a field study provided an excellent result, where growth parameters of Z. Mays and Triticum aestivum such as root length, shoot length, and plant biomass was improved as compared to the control group.