4.6 Article

Going MAD: Development of a Matrix Academic Division to Facilitate Translating Research to Personalized Medicine

期刊

ACADEMIC MEDICINE
卷 86, 期 11, 页码 1353-1359

出版社

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/ACM.0b013e3182303d7a

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资金

  1. NIH [R01 DK061451]
  2. Frieda G. and Saul F. Shapira BRCA Cancer Research Program
  3. Wayne Fusaro Pancreatic Cancer Research Fund
  4. National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH) [UL1 RR024153]
  5. NIH Roadmap for Medical Research

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Personalized medicine integrates an individual's genetic and other information for the prevention or treatment of complex disorders, and translational research seeks to identify those data most important to disease processes based on observations at the bench and the bedside. To understand complex disorders such as chronic pancreatitis, inflammatory bowel disease, liver cirrhosis, and other idiopathic chronic inflammatory diseases, physician-scientists must systematically collect data on relevant risks, clinical status, biomarkers, and outcomes. The author describes a matrix academic division (MAD), a highly effective academic program created at the University of Pittsburgh School of Medicine and the University of Pittsburgh Medical Center using translational research to rapidly develop personalized medicine for digestive diseases. MAD is designed to capture patient-specific data and biologic samples for analysis of steps in a complex process (reverse engineering), reconstructing the system conceptually and mathematically (disease modeling), and deciphering disease mechanism in individual patients to predict the effects of interventions (personalized medicine). MAD draws on the expertise of the medical school's and medical center's physician-scientists to translate essential disease information between the bed and the bench and to communicate with researchers from multiple domains, including epidemiology, genetics, cell biology, immunology, regenerative medicine, neuroscience, and oncology. The author illustrates this approach by describing its successful application to the reverse engineering of chronic pancreatitis.

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