Premium
Mechanistic disease modeling as a useful tool for improving CNS drug research and development
Author(s) -
Geerts Hugo
Publication year - 2011
Publication title -
drug development research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.582
H-Index - 60
eISSN - 1098-2299
pISSN - 0272-4391
DOI - 10.1002/ddr.20403
Subject(s) - drug development , drug discovery , neuroscience , pharmaceutical sciences , disease , drug , computer science , medicine , computational biology , bioinformatics , pharmacology , psychology , biology , pathology
Abstract Despite tremendous advances in the basic understanding of CNS diseases, successful clinical drug development in psychiatry and neurology has been limited, putting huge pressure on remaining CNS R&D programs in pharmaceutical companies. In this report, it is proposed that re‐engineering parts of the pharmaceutical Research and Development process by integrating complex modeling and simulation approaches, similar to the aerospace and micro‐electronics industry, has the potential to increase the clinical predictability of animal models and to reduce the attrition rate in clinical drug development. This report will present top‐down Mechanistic Disease Modeling approaches in relation to bottom‐up Systems Biology with specific emphasis on CNS drug R&D. Both combine basic research data with human clinical outcome, but in contrast to System Biology that generically models intracellular pathways and protein‐protein networks, Mechanistic Disease Modeling models the emergent properties of neuronal cell firing activity in large interacting neuronal networks. Such an outcome is much closer to physiological and behavioral processes that drive actual clinical scales. Also illustrated here are some practical applications in the area of Alzheimer's disease and schizophrenia for CNS Research and Development, such as guiding multitarget drug discovery, evaluating both the harmful and beneficial off‐target human effects of candidate drugs, as well as exploring the effect of co‐medications and functional genotypes on the candidate drug efficacy and sensitivity analysis for responder identification. Drug Dev Res 72: 66–73, 2011. © 2010 Wiley‐Liss, Inc.