Approaching Complex Diseases

This volume – for pharmacologists, systems biologists, philosophers and historians of medicine – points to investigate new avenues in pharmacology research, by providing a full assessment of the premises underlying a radical shift in the pharmacology paradigm. The pharmaceutical industry is currently facing unparalleled challenges in developing innovative drugs. While drug-developing scientists in the 1990s mostly welcomed the transformation into a target-based approach, two decades of experience shows that this model is failing to boost both drug discovery and efficiency. Selected targets were often not druggable and with poor disease linkage, leading to either high toxicity or poor efficacy. Therefore, a profound rethinking of the current paradigm is needed. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggest that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development, efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets.

This volume - for pharmacologists, systems biologists, philosophers and historians of medicine - points to investigate new avenues in pharmacology research, by providing a full assessment of the premises underlying a radical shift in the pharmacology paradigm. The pharmaceutical industry is currently facing unparalleled challenges in developing innovative drugs. While drug-developing scientists in the 1990s mostly welcomed the transformation into a target-based approach, two decades of experience shows that this model is failing to boost both drug discovery and efficiency. Selected targets were often not druggable and with poor disease linkage, leading to either high toxicity or poor efficacy. Therefore, a profound rethinking of the current paradigm is needed. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggest that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development, efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets.

According to the National Institute of Health, a genome-wide association study is defined as any study of genetic variation across the entire human genome that is designed to identify genetic associations with observable traits (such as blood pressure or weight), or the presence or absence of a disease or condition. Whole genome information, when combined with clinical and other phenotype data, offers the potential for increased understanding of basic biological processes affecting human health, improvement in the prediction of disease and patient care, and ultimately the realization of the promise of personalized medicine. In addition, rapid advances in understanding the patterns of human genetic variation and maturing high-throughput, cost-effective methods for genotyping are providing powerful research tools for identifying genetic variants that contribute to health and disease. This burgeoning science merges the principles of statistics and genetics studies to make sense of the vast amounts of information available with the mapping of genomes. In order to make the most of the information available, statistical tools must be tailored and translated for the analytical issues which are original to large-scale association studies. Analysis of Complex Disease Association Studies will provide researchers with advanced biological knowledge who are entering the field of genome-wide association studies with the groundwork to apply statistical analysis tools appropriately and effectively. With the use of consistent examples throughout the work, chapters will provide readers with best practice for getting started (design), analyzing, and interpreting data according to their research interests. Frequently used tests will be highlighted and a critical analysis of the advantages and disadvantage complimented by case studies for each will provide readers with the information they need to make the right choice for their research. Additional tools including links to analysis tools, tutorials, and references will be available electronically to ensure the latest information is available. Easy access to key information including advantages and disadvantage of tests for particular applications, identification of databases, languages and their capabilities, data management risks, frequently used tests Extensive list of references including links to tutorial websites Case studies and Tips and Tricks

The book introduces the bioinformatics tools, databases and strategies for the translational research, focuses on the biomarker discovery based on integrative data analysis and systems biological network reconstruction. With the coming of personal genomics era, the biomedical data will be accumulated fast and then it will become reality for the personalized and accurate diagnosis, prognosis and treatment of complex diseases. The book covers both state of the art of bioinformatics methodologies and the examples for the identification of simple or network biomarkers. In addition, bioinformatics software tools and scripts are provided to the practical application in the study of complex diseases. The present state, the future challenges and perspectives were discussed. The book is written for biologists, biomedical informatics scientists and clinicians, etc. Dr. Bairong Shen is Professor and Director of Center for Systems Biology, Soochow University; he is also Director of Taicang Center for Translational Bioinformatics.

Overview of mapping common and genetically complex human disease traits (J. L. Haines, M. A. Pericak-Vance). Basic concepts in genetics (M. C. Speer). Defining disease phenotypes (A. S. Aylsworth). Basic concepts in linkage analysis ( M. A. Pericak-Vance). Determining the genetic component of a disease (L. A. Farrer, L. A. Cupples). Patient and family participation in genetic research studies (P. E. Cohen, C. Wolpert). Sample size and power (M. C. Speer). The collection of biological samples for DNA analysis (J. M. Vance). Methods of genotyping (J. M. Vance, K. B. Othmane). Database design for gene mapping studies (C. Haynes, C. Blach). Genomic screening (J. L. Haines). Lod score analysis (J. Xu, D. A. Meyers, M. A. Pericak-Vance). Sib pair analysis (D. E. Goldgar). Affected relative pair analysis (J. L. Haines). Linkage disequilibrium and allelic association (M. A. Pericak-Vance). Using public databases (J. L. Haines). Laboratory approaches toward gene identification (D. A. Marchuk). Examining complex genetic interactions (J. M. Schildkraut). Glossary. Appendix: useful websites for genetic analysis. Index.

Despite what you may have read in the popular press and in social media, Precision Medicine is not devoted to finding unique treatments for individuals, based on analyzing their DNA. To the contrary, the goal of Precision Medicine is to find general treatments that are highly effective for large numbers of individuals who fall into precisely diagnosed groups. We now know that every disease develops over time, through a sequence of defined biological steps, and that these steps may differ among individuals, based on genetic and environmental conditions. We are currently developing rational therapies and preventive measures, based on our precise understanding of the steps leading to the clinical expression of diseases. Precision Medicine and the Reinvention of Human Disease explains the scientific breakthroughs that have changed the way that we understand diseases, and reveals how medical scientists are using this new knowledge to launch a medical revolution. Clarifies the foundational concepts of Precision Medicine, distinguishing this field from its predecessors such as genomics, pharmacogenetics, and personalized medicine. Gathers the chief conceptual advances in the fields of genetics, pathology, and bioinformatics, and synthesizes a coherent narrative for the field of Precision Medicine. Delivers its message in plain language, and in a relaxed, conversational writing style, making it easy to understand the complex subject matter. Guides the reader through a coherent and logical narrative, gradually providing expertise and skills along the way. Covers the importance of data sharing in Precision Medicine, and the many data-related challenges that confront this fragile new field.