Progress And Challenges In Precision Medicine

Progress and Challenges in Precision Medicine presents an insightful overview to the myriad factors of personalized and precision medicine. The availability of the human genome, large amounts of data on individual genetic variations, environmental interactions, influence of lifestyle, and cutting-edge tools and technologies for big-data analysis have led to the age of personalized and precision medicine. Bringing together a global range of experts on precision medicine, this book collects previously scattered information into one concise volume which covers the most important developments so far in precision medicine and also suggests the most likely avenues for future development. The book includes clinical information, informatics, public policy implications, and information on case studies. It is a useful reference and background work for students, researchers, and clinicians working in the biomedical and medical fields, as well as policymakers in the health sciences. Provides an overview of the growing field of precision medicine Contains chapters from geographically diverse experts in their field Explores important aspects of precision medicine, including applications, ethics, and development

Motivated by the explosion of molecular data on humans-particularly data associated with individual patients-and the sense that there are large, as-yet-untapped opportunities to use this data to improve health outcomes, Toward Precision Medicine explores the feasibility and need for "a new taxonomy of human disease based on molecular biology" and develops a potential framework for creating one. The book says that a new data network that integrates emerging research on the molecular makeup of diseases with clinical data on individual patients could drive the development of a more accurate classification of diseases and ultimately enhance diagnosis and treatment. The "new taxonomy" that emerges would define diseases by their underlying molecular causes and other factors in addition to their traditional physical signs and symptoms. The book adds that the new data network could also improve biomedical research by enabling scientists to access patients' information during treatment while still protecting their rights. This would allow the marriage of molecular research and clinical data at the point of care, as opposed to research information continuing to reside primarily in academia. Toward Precision Medicine notes that moving toward individualized medicine requires that researchers and health care providers have access to very large sets of health- and disease-related data linked to individual patients. These data are also critical for developing the information commons, the knowledge network of disease, and ultimately the new taxonomy.

Precision medicine is focused on the individual and will require the rapid and accurate identification and prioritization of causative factors of disease. To move forward and accelerate the delivery of the anticipated benefits of precision medicine, developing predictable, reproducible, and reliable animal models will be essential. In order to explore the topic of animal-based research and its relevance to precision medicine, the National Academies of Sciences, Engineering, and Medicine convened a 2-day workshop on October 5 and 6, 2017. The workshop was designed to focus on the development, implementation, and interpretation of model organisms to advance and accelerate the field of precision medicine. Participants examined the extent to which next-generation animal models, designed using patient data and phenotyping platforms targeted to reveal and inform disease mechanisms, will be essential to the successful implementation of precision medicine. This publication summarizes the presentations and discussions from the workshop.

Genomic sequencing technologies have augmented the classification of cancer beyond tissue of origin and towards a molecular taxonomy of cancer. This has created opportunities to guide treatment decisions for individual patients with cancer based on their cancer’s unique molecular characteristics, also known as precision cancer medicine. The purpose of this text will be to describe the contribution and need for multiple disciplines working together to deliver precision cancer medicine. This entails a multi-disciplinary approach across fields including molecular pathology, computational biology, clinical oncology, cancer biology, drug development, genetics, immunology, and bioethics. Thus, we have outlined a current text on each of these fields as they work together to overcome various challenges and create opportunities to deliver precision cancer medicine. As trainees and junior faculty enter their respective fields, this text will provide a framework for understanding the role and responsibility for each specialist to contribute to this team science approach.

The high failure rate in the pharmaceutical industry has positioned biomarkers and personalized medicine in the frontline, as possible solutions. If executed right, biomarkers and companion diagnostics (CDx) can potentially help the drug industry enhance the probability of success, accelerate the time to market, and, more importantly, benefit patients by supporting accurate diagnosis and selection of the most effective and least toxic therapies. This book aims to examine the challenges and limitations in biomarkers and laboratory tests. It also offers advice on best practices to ensure proper application of biomarkers and bridges the gap between diagnostic business development claims and real-life deliverables. The book covers biomarkers for different purposes, provides examples from different technologies, which includes standard-of-care approved assays as well as for-investigational-use and for-research-use-only assays. It also includes new data for biomarkers in different therapeutic indications and offers case studies and practical examples. This book serves as a reference to drug developers, IVD providers, clinical labs, healthcare givers, academicians, and researchers for best practices to help increase the probability of success in drug development and improve patient management. Provides the unique insight of an expert with extensive experience in diagnostics and clinical laboratory on one side and drug discovery and development on the other side Addresses the challenges of drug development and precision medicine and suggests how to eliminate or mitigate these challenges through better utilization of biomarkers and diagnostics in drug development and patient management Features case studies and real-life examples from different classes of biomarkers on different platforms for different therapeutic areas and includes more than 200 illustrations

Precision Medicine and Artificial Intelligence: The Perfect Fit for Autoimmunity covers background on artificial intelligence (AI), its link to precision medicine (PM), and examples of AI in healthcare, especially autoimmunity. The book highlights future perspectives and potential directions as AI has gained significant attention during the past decade. Autoimmune diseases are complex and heterogeneous conditions, but exciting new developments and implementation tactics surrounding automated systems have enabled the generation of large datasets, making autoimmunity an ideal target for AI and precision medicine. More and more diagnostic products utilize AI, which is also starting to be supported by regulatory agencies such as the Food and Drug Administration (FDA). Knowledge generation by leveraging large datasets including demographic, environmental, clinical and biomarker data has the potential to not only impact the diagnosis of patients, but also disease prediction, prognosis and treatment options. Allows the readers to gain an overview on precision medicine for autoimmune diseases leveraging AI solutions Provides background, milestone and examples of precision medicine Outlines the paradigm shift towards precision medicine driven by value-based systems Discusses future applications of precision medicine research using AI Other aspects covered in the book include regulatory insights, data analytics and visualization, types of biomarkers as well as the role of the patient in precision medicine

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data