Browsing by Author "Khan, Md. Shajib"

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Emerging Promise of Computational Techniques in Anti-Cancer Research
(Daffodil International University, 2022-07-25) Rahman, Md. Mominur; Islam, Md. Rezaul; Rahman, Firoza; Rahaman, Md. Saidur; Khan, Md. Shajib; Abrar, Sayedul; Ray, Tanmay Kumar; Uddin, Mohammad Borhan; Kali, Most. Sumaiya Khatun; Dua, Kamal; Kamal, Mohammad Amjad; Chellappan, Dinesh Kumar
Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.
Multifunctional Therapeutic Approach of Nanomedicines against Inflammation in Cancer and Aging
(Daffodil International University, 2022-03-21) Rahman, Md. Mominur; Islam, Fahadul; Mim, Sadia Afsana; Khan, Md. Shajib; Islam, Md. Rezaul; Haque, Md. Anamul; Mitra, Saikat; Emran, Talha Bin; Rauf, Abdur
Cancer is a fatal disorder that affects people across the globe, yet existing therapeutics are ineffective. The development of submicrometer transport for optimizing the biodistribution of systemically provided medications is the focus of nanomedicine. Nanoparticle- (NP-) based treatments may enable the development of novel therapeutic approaches to combat this deadly disorder. In multifunctional, multimodal imaging, and drug delivery carriers, NPs generally play a major role. They have emerged as potential strategies for the invention of innovative therapeutic procedures in the last decade. The exponential growth of nanotechnologies in recent years has increased public awareness of the application of these innovative therapeutic approaches. Many tumor-targeted nanomedicines have been studied in cancer therapy, and there is clear evidence for a significant improvement in the therapeutic index of antineoplastic drugs. Age-related factors such as metabolic and physiological alterations in old age and inadequate animal models are currently understudied in nanomedicine and pharmacology. This review highlighted the most important targeting approaches, as well as public awareness, therapeutic advancements, and future prospects in age-related metabolic variations, and tumor-targeted nanomedicine studies.