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\"This book overviews chiral drugs and their impact in the pharmaceutical industry. The chapters detail basics and trends in chiral drug discovery and development, as well as techniques / skills and trends of asymmetric synthesis. The first section introduces the general concept of chirality and its impact on the drug discovery and development. This part includes history of chiral drug development, key technologies for preparation of chiral drugs, and industrial applications of chiral technologies\"--Provided by publisher.

Sets forth the history, state of the science, and future directions of drug discovery Edited by Jie Jack Li and Nobel laureate E. J. Corey, two leading pioneers in drug discovery and medicinal chemistry, this book synthesizes great moments in history, the current state of the science, and future directions of drug discovery into one expertly written and organized work. Exploring all major therapeutic areas, the book introduces readers to all facets and phases of drug discovery, including target selection, biological testing, drug metabolism, and computer-assisted drug design. Drug Discovery features chapters written by an international team of pharmaceutical and medicinal chemists. Contributions are based on a thorough review of the current literature as well as the authors' firsthand laboratory experience in drug discovery. The book begins with the history of drug discovery, describing groundbreaking moments in the field. Next, it covers such topics as: * Target identification and validation * Drug metabolism and pharmacokinetics * Central nervous system drugs * In vitro and in vivo assays * Cardiovascular drugs * Cancer drugs Each chapter features a case study, helping readers understand how science is put into practice throughout all phases of drug discovery. References at the end of each chapter serve as a gateway to groundbreaking original research studies and reviews in the field. Drug Discovery is ideal for newcomers to medicinal chemistry and drug discovery, providing a comprehensive overview of the field. Veterans in the field will also benefit from the perspectives of leading international experts in all aspects of drug discovery.

Digital twins represent a key technology for precision health. Medical digital twins consist of computational models that represent the health state of individual patients over time, enabling optimal therapeutics and forecasting patient prognosis. Many health conditions involve the immune system, so it is crucial to include its key features when designing medical digital twins. The immune response is complex and varies across diseases and patients, and its modelling requires the collective expertise of the clinical, immunology, and computational modelling communities. This review outlines the initial progress on immune digital twins and the various initiatives to facilitate communication between interdisciplinary communities. We also outline the crucial aspects of an immune digital twin design and the prerequisites for its implementation in the clinic. We propose some initial use cases that could serve as “proof of concept” regarding the utility of immune digital technology, focusing on diseases with a very different immune response across spatial and temporal scales (minutes, days, months, years). Lastly, we discuss the use of digital twins in drug discovery and point out emerging challenges that the scientific community needs to collectively overcome to make immune digital twins a reality.

BackgroundTrypanosoma brucei is the causative agent of Human African Trypanosomiasis (also known as sleeping sickness), a disease causing serious neurological disorders and fatal if left untreated. Due to its lethal pathogenicity, a variety of treatments have been developed over the years, but which have some important limitations such as acute toxicity and parasite resistance. Metabolomics is an innovative tool used to better understand the parasite’s cellular metabolism, and identify new potential targets, modes of action and resistance mechanisms. The metabolomic approach is mainly associated with robust analytical techniques, such as NMR and Mass Spectrometry. Applying these tools to the trypanosome parasite is, thus, useful for providing new insights into the sleeping sickness pathology and guidance towards innovative treatments.Aim of reviewThe present review aims to comprehensively describe the T. brucei biology and identify targets for new or commercialized antitrypanosomal drugs. Recent metabolomic applications to provide a deeper knowledge about the mechanisms of action of drugs or potential drugs against T. brucei are highlighted. Additionally, the advantages of metabolomics, alone or combined with other methods, are discussed.Key scientific concepts of reviewCompared to other parasites, only few studies employing metabolomics have to date been reported on Trypanosoma brucei. Published metabolic studies, treatments and modes of action are discussed. The main interest is to evaluate the metabolomics contribution to the understanding of T. brucei’s metabolism.

Leading experts illustrate how sophisticated computational data mining techniques can impact contemporary drug discovery and development In the era of post-genomic drug development, extracting and applying knowledge from chemical, biological, and clinical data is one of the greatest challenges facing the pharmaceutical industry. Pharmaceutical Data Mining brings together contributions from leading academic and industrial scientists, who address both the implementation of new data mining technologies and application issues in the industry. This accessible, comprehensive collection discusses important theoretical and practical aspects of pharmaceutical data mining, focusing on diverse approaches for drug discovery-including chemogenomics, toxicogenomics, and individual drug response prediction. The five main sections of this volume cover: A general overview of the discipline, from its foundations to contemporary industrial applications Chemoinformatics-based applications Bioinformatics-based applications Data mining methods in clinical development Data mining algorithms, technologies, and software tools, with emphasis on advanced algorithms and software that are currently used in the industry or represent promising approaches In one concentrated reference, Pharmaceutical Data Mining reveals the role and possibilities of these sophisticated techniques in contemporary drug discovery and development. It is ideal for graduate-level courses covering pharmaceutical science, computational chemistry, and bioinformatics. In addition, it provides insight to pharmaceutical scientists, principal investigators, principal scientists, research directors, and all scientists working in the field of drug discovery and development and associated industries.

Apply adaptive research to improve results in drug development The pharmaceutical industry today faces a deepening crisis: inefficiency in its core business, the development of new drugs. The Agile Approach to Adaptive Research offers a solution. It outlines how adaptive research, using already-available tools and techniques, can enable the industry to streamline clinical trials and reach decision points faster and more efficiently. With a wealth of real-world cases and examples, author Michael Rosenberg gives readers a practical overview of drug development, the problems inherent in current practices, and the advantages of adaptive research technology and methods. He explains the concepts, principles, and specific techniques of adaptive research, and demonstrates why it is an essential evolutionary step toward improving drug research and development. Chapters explore such subjects as: The adaptive concept Design and operational adaptations Sample-size reestimation Agile clinical development Safety and dose finding Statistics in adaptive research, including frequentist and Bayesian approaches Data management technologies The future of clinical development By combining centuries-old intellectual foundations, recent technological advances, and modern management techniques, adaptive research preserves the integrity and validity of clinical research but dramatically improves efficiency.

After more than fifty years of blockbuster drug development, skeptics are beginning to fear we are reaching the end of drug discovery to combat major diseases. In this engaging book, Brent R. Stockwell, a leading researcher in the exciting new science of chemical biology, describes this dilemma and the powerful techniques that may bring drug research into the twenty-first century. Filled with absorbing stories of breakthroughs, this book begins with the scientific achievements of the twentieth century that led to today's drug innovations. We learn how the invention of mustard gas in World War I led to early anti-cancer agents and how the efforts to decode the human genome might lead to new approaches in drug design. Stockwell then turns to the seemingly incurable diseases we face today, such as Alzheimer's, many cancers, and others with no truly effective medicines, and details the cellular and molecular barriers thwarting scientists equipped with only the tools of traditional pharmaceutical research. Scientists such as Stockwell are now developing methods to combat these complexities-technologies for constructing and testing millions of drug candidates, sophisticated computational modeling, and entirely new classes of drug molecules-all with an eye toward solving the most profound mysteries of living systems and finding cures for intractable diseases. If successful, these methods will unlock a vast terrain of untapped drug targets that could lead to a bounty of breakthrough medicines. Offering a rare, behind-the-scenes look at this cutting-edge research,The Quest for the Curetells a thrilling story of science, persistence, and the quest to develop a new generation of cures.

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments. Recent advances in computational approaches and challenges in their application to streamlining drug discovery are discussed.

Key Points This Analysis article describes the compilation and analysis of combined data on the attrition of drug candidates from AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer. The analysis reaffirms that control of physicochemical properties during compound optimization is beneficial in identifying compounds of candidate drug quality. Safety and toxicology are the largest sources of failure within the data set. The link between calculated physicochemical properties and frequent causes of attrition (preclinical toxicology, clinical safety and human pharmacokinetics) is assessed. Analysis of this data set shows that none of the physicochemical descriptors we examined correlates with preclinical toxicology outcomes. This work is the first to indicate a link between lipophilicity and clinical failure owing to safety issues. The utility of this finding in a prospective sense is discussed. Although control of physicochemical properties is clearly important, this analysis suggests that further stringency in this respect is unlikely to have a significant effect on attrition in development and that additional work is required to address safety-related failures. Attempts to reduce the number of efficacy- and safety-related failures that may be linked to the physicochemical properties of small-molecule drug candidates have been inconclusive owing to the limited size of data sets from individual companies. Waring and colleagues analyse the largest data set compiled so far on the causes of attrition for oral, small-molecule drug candidates, derived from a pioneering data-sharing effort by AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer. The pharmaceutical industry remains under huge pressure to address the high attrition rates in drug development. Attempts to reduce the number of efficacy- and safety-related failures by analysing possible links to the physicochemical properties of small-molecule drug candidates have been inconclusive because of the limited size of data sets from individual companies. Here, we describe the compilation and analysis of combined data on the attrition of drug candidates from AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer. The analysis reaffirms that control of physicochemical properties during compound optimization is beneficial in identifying compounds of candidate drug quality and indicates for the first time a link between the physicochemical properties of compounds and clinical failure due to safety issues. The results also suggest that further control of physicochemical properties is unlikely to have a significant effect on attrition rates and that additional work is required to address safety-related failures. Further cross-company collaborations will be crucial to future progress in this area.