E-Book, Englisch, 365 Seiten, Format (B × H): 216 mm x 276 mm
From Bench to Bedside
E-Book, Englisch, 365 Seiten, Format (B × H): 216 mm x 276 mm
ISBN: 978-0-12-800721-1
Verlag: Elsevier Reference Monographs
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
The text presents a thorough discussion of biomarkers, early human trials, and networking models, and includes institutional and industrial support systems and algorithms which have influenced all major areas of biomedical research over the past several years resulting in an increasing numbers of new chemical/biological entities (NCEs or NBEs) as shown in FDA statistics.
It is ideal for use as a guide for biomedical scientists to establish a systematic approach to translational medicine.
Provides an in-depth description of novel tools for the assessment of translatability of trials to balance risk and improve projects at any given stage of product development
New chapters deal with translational issues in the fastest growing population (the elderly), case studies, translatability assessment tools, and advances in nanotherapies
Details IPR issues of translation, especially for public-private-partnerships
Contains contributions from world leaders in translational medicine, including the former NIH director and authorities from various European regulatory institutions
Zielgruppe
Biomedical researchers working on preclinical and clinical studies
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
1. Introduction and Definitions 2. What Is Translational Medicine? 3. Tools 4. Biomarkers 5. Early Clinical Trial Design 6. Pharmaceutical Toxicology 7. Translational Science Biostatistics 8. Intellectual Property Rights in Translation 9. Translational research in the Elderly 10.The View of Big Pharma 11. Translational Science in Medicine: Putting the Pieces Together 12. Biomarkers, Early Human Trials, Networking, Translatability Assessment 13. Learning by Experience: Examples of Translational Processes in the Cardiovascular Field
Chapter 2.1.1 "Omics" Translation
A Challenge for Laboratory Medicine
Mario Plebani, Martina Zaninotto, and Giuseppe Lippi Introduction
The rapid advances in medical research that have occurred over the past few years have allowed us to dissect molecular signatures and functional pathways that underlie disease initiation and progression, as well as to identify molecular profiles related to disease subtypes in order to determine their natural course, prognosis, and responsiveness to therapies (Dammann and Weber, 2012). The "omics" revolution of the past 15 years has represented the most compelling stimulus in personalized medicine that, in turn, should be simply defined as "getting the right treatment to the right patient at the right dose and schedule at the right time" (Schilsky, 2009). As a matter of fact, among the 20 most-cited papers in molecular biology and genetics that have been published in the past decade, 13 entail omics methods or applications (Ioannidis, 2010). "Omics": What does it mean?
Omics is an English-language neologism that refers to a field of study in biology focusing on large-scale and holistic data, as derived from its root of Greek origin which refers to wholeness or to completion. Initially, the suffix omics had been used in the word genome, a popular word for the complete genetic makeup of an organism, and later, in the term proteome. Genomics and proteomics succinctly describe a new way of holistic analysis of complete genomes and proteomes, and the success of these terms led to more emphasis in the trend of using omics as a convenient term to describe holistic ways of looking at complex systems, particularly in biology. Fields with names like genomics (genetic complement), transcriptomics (gene expression), proteomics (protein synthesis and signaling), metabolomics (concentration and fluxes of cellular metabolites), metabonomics (systemic profiling through the analysis of biological fluids), and cytomics (the study of cell systems-cytomes-at a single cell level) have been introduced in medicine with increasing emphasis (Plebani, 2005). However, beyond these terms, multiple "omics" fields, with names like epigenomics, ribonomics, epigenomics, oncopeptidomics, lipidomics, glycomics, spliceomics, and interactomics, have been similarly explored regarding molecular biomarkers for the diagnosis and prognosis of human diseases. Each of these emerging disciplines grouped under the umbrella of the term omics shares the simultaneous characterization of dozens, hundreds, or thousands of genes (genomics), gene transcripts (transcriptomics), or proteins (proteomics) and other molecules, that in aggregate and in parallel should be coupled with sophisticated bioinformatics to reveal aspects of biological function that cannot be culled from traditional linear methods of discovery (Finn, 2007). While an increasing body of literature has been produced to prove that "omics" will irrevocably modify the practice of medicine, that change has yet to occur and its precise details are still unclear. The reasonable assumption that the application of "omics" research will be riddled with difficulties has led to a much better appreciation of concepts of knowledge translation, translational research, and translational medicine. Proteomics as a Paradigm of Problems in Translational Medicine
The paradigm of obstacles in translating new "omics" insights into clinical practice is a study reporting that a blood test, based on pattern-recognition proteomics analysis of serum, was nearly 100% sensitive and specific for detecting ovarian cancer and was possibly useful fo
