Natural Products as Anticancer Agents introduces the different types of natural products that have been used for cancer treatment. Divided into four parts, covering anticancer agents derived from terrestrial plants, anticancer agents derived from the marine environment, and anticancer agents derived from microorganisms, as well as evaluation of new anticancer agents, each part includes discussion of the properties, synthesis/extraction, storage, mechanism of action, and usage of the molecules. Discussion of the future prospects in anticancer natural products—including several new trends and an indication of where research in this area is likely to go in the future—is also included. - Assists in the identification of risk factors associated with the drug development process - Includes process diagrams detailing the procedure for the isolation and purification of molecules - Covers cost-effective green approaches in the development of anticancer drugs

This book highlights the role of Biomedical Engineering (BME) used in diagnosis (e.g., body scanners) and treatment (radiation therapy and minimal access surgery in order to prevent various diseases). In recent years, an important progress has been made in the expansion of biomedical microdevices which has a major role in diagnosis and therapy of cancer. When fighting cancer, efficacy and speed are of the utmost importance. A recently developed microfluidic chip has enabled a breakthrough in testing the efficacy of specialized cancer drugs. Effective cancer-targeting therapies will require both passive and active targeting strategies and a thorough understanding of physiologic barriers to targeted drug delivery. Targeted cancer treatments in development and the new combinatorial approaches show promise for improving targeted anticancer drug delivery and improving treatment outcomes. This book discusses the advancements and innovations in the field of BME that improve the diagnosis and treatment of cancer. This book is focused on bioengineering approaches to improve targeted delivery for cancer therapeutics, which include particles, targeting moieties, and stimuli-responsive drug release mechanisms. This book is a useful resource for students, researchers, and professionals in BME and medicine.

Tyrosine kinases, which catalyze the transfer of a phosphate from ATP to a hydroxyl group of a tyrosine residue, play a plethora of roles in the regulation of diverse functions in normal cells including cell growth, motility, differentiation, and metabolism. Moreover, they are also associated with oncogenesis. Tyrosine kinases are mainly classified as receptor tyrosine kinases and non-receptor tyrosine kinases, including crucial members. Epidermal growth factor receptor (EGFR) belongs to the ERBB family of receptor tyrosine kinases along with three other closely related receptors, namely HER-2, HER-3 and HER-4. EGFR and HER-2, lead to autophosphorylation of the intracellular domain through tyrosine kinase activity and subsequent stimulation of downstream cascade that may result in proliferation, suppression of apoptosis, metastasis and angiogenesis. On the other hand, c-Abl (Abl-1) is a non-receptor tyrosine kinase, which is also essential in the regulation of several antiapoptotic and proliferative signal transduction pathways. They have mainly been identified as important targets for several types of cancer such as EGFR for non-small-cell lung cancer, glioma and colorectal cancer, HER-2 for breast and colorectal cancers and Abl for chronic myeloid leukemia. One of the major platforms that they have participated in is neurodegenerative disorders such as Parkinson’s disease, Alzheimer’s disease and amyotrophic lateral sclerosis. Aberrant activity of tyrosine kinases, in particular EGFR and Abl, have been reported to induce apoptosis and cell cycle arrest in response to a wide range of stimuli resulting in neurodegeneration and neuroinflammation. The main goal of this Research Topic is to identify new and potent effective tyrosine kinase inhibitors to be effective in cancer and neurodegenerative disorders.

The world of pharmaceutical research is moving at lightning speed, and the age-old approach to drug discovery faces many challenges. It's a fascinating time to be on the cutting edge of medical innovation, but it's certainly not without its obstacles. The process of developing new drugs is often time-consuming, expensive, and fraught with uncertainty. Researchers are constantly seeking ways to streamline this process, reduce costs, and increase the success rate of bringing new drugs to market. One promising solution lies in the convergence of pharmacy science and engineering, particularly in computational drug discovery. Converging Pharmacy Science and Engineering in Computational Drug Discovery presents a comprehensive solution to these challenges by exploring the transformative synergy between pharmacy science and engineering. This book demonstrates how researchers can expedite the identification and development of novel therapeutic compounds by harnessing the power of computational approaches, such as sophisticated algorithms and modeling techniques. Through interdisciplinary collaboration, pharmacy scientists and engineers can revolutionize drug discovery, paving the way for more efficient and effective treatments. This book is an invaluable resource for pharmaceutical scientists, researchers, and engineers seeking to enhance their understanding of computational drug discovery. This book inspires future innovations by showcasing cutting-edge methodologies and innovative research at the intersection of pharmacy science and engineering. It contributes to the ongoing evolution of pharmaceutical research. It offers practical insights and solutions that will shape the future of drug discovery, making it essential reading for anyone involved in the pharmaceutical industry.

The book is a comprehensive guide that explores the use of artificial intelligence and machine learning in drug discovery and development covering a range of topics, including the use of molecular modeling, docking, identifying targets, selecting compounds, and optimizing drugs. The intersection of Artificial Intelligence (AI) and Machine Learning (ML) within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine. AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the vast amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs that can be repurposed for alternative use in medicine. This book is a comprehensive exploration of this dynamic and rapidly evolving field. In an era where precision and efficiency are paramount in drug discovery, AI and ML have emerged as transformative tools, reshaping the way we identify, design, and develop pharmaceuticals. This book is a testament to the profound impact these technologies have had and will continue to have on the pharmaceutical industry, healthcare, and ultimately, patient well-being. The editors of this volume have assembled a distinguished group of experts, researchers, and thought leaders from both the AI, ML, and pharmaceutical domains. Their collective knowledge and insights illuminate the multifaceted landscape of AI and ML in drug design and development, offering a roadmap for navigating its complexities and harnessing its potential. In each section, readers will find a rich tapestry of knowledge, case studies, and expert opinions, providing a 360-degree view of AI and ML’s role in drug design and development. Whether you are a researcher, scientist, industry professional, policymaker, or simply curious about the future of medicine, this book offers 19 state-of-the-art chapters providing valuable insights and a compass to navigate the exciting journey ahead. Audience The book is a valuable resource for a wide range of professionals in the pharmaceutical and allied industries including researchers, scientists, engineers, and laboratory workers in the field of drug discovery and development, who want to learn about the latest techniques in machine learning and AI, as well as information technology professionals who are interested in the application of machine learning and artificial intelligence in drug development.

Massive experimental, computational, and clinical studies have been performed worldwide, and are still ongoing, to understand and characterize Covid-19 molecular basis and transmission mechanisms, to develop diagnostics and vaccines, and to search for small chemical drug candidates and therapeutic proteins and peptides. Impressive results have been obtained for transmission control and vaccines so far, but what is the status of the other therapeutic options? The crisis has exposed different types of weaknesses in biomedical research in many countries. What can we learn from this crisis in the field of drug discovery and development so as to emerge stronger? The Covid-19 crisis has revealed the strengths of modern drug discovery and vaccine development but also exposed different types of weaknesses that would need to be addressed to be better prepared for a possible next global health crisis. These challenges/weaknesses/obstacles are of very different nature and as such hard to tackle. For instance, in addition to the inherent challenging nature of the scientific discovery, some scientists have mentioned insufficient local/national coordination and observed a fragmented drug discovery research, others have underlined a lack of coordination between the academic system and the private sector, an inadequate international global coordination and cooperation, others highlighted insufficient infrastructures, inappropriate financial supports, limited discussions between the scientific community performing the research and the public health authorities, decision makers and the society, while some remarked insufficient education/training about drug discovery and development leading to confusion… The crisis also raised important scientific questions about the technologies that would need to be used during an emergency situation, combined, integrated, or developed so as to accelerate the identification of small molecule drug candidates and of therapeutic peptides/proteins.