Mechanisms of Action: Model-based Drug Design
Posted July 12, 2007
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Overview
Biological processes, from development to disease, are governed by complex networks of interacting genes, proteins, and other molecules. New experimental and computational tools are revealing these networks in ever greater detail. For society, however, the goal of this fascinating research is not just understanding, but the development and improvement of drugs and treatments to improve the quality of life. Improving drug efficacy while minimizing side effects is in some ways a more limited goal than finding the "real" biological networks, but it also provides concrete measures of success.
On January 11, 2007, the Systems Biology Discussion Group explored these themes in a symposium at the Academy entitled Model-Based Drug Design: Lessons Learned. The three speakers came from different types of company: a large pharmaceutical firm and a small one, and a company that helps others to mathematically analyze their data. However, the speakers largely agreed on the potential and the challenges of using systems biology to help drug development, illustrating the process with examples from their respective companies.
Use the tabs above to view the meeting report.
Journal Articles
Systems Biology at Pfizer: From Physiology to Pharmacology for p38 Inhibitors
Kumar N, Hendriks BS, Janes KA, et al. 2006. Applying computational modeling to drug discovery and development. Drug Discov. Today 11: 806-811.
Systems Analysis of Cellular Responses to Therapeutic Interventions
Fitzgerald JB, Schoeberl B, Nielsen UB. 2006. Systems biology and combination therapy in the quest for clinical efficacy. Nat. Chem. Biol. 2: 458-466.
Hornberg JJ, Binder B, Bruggeman FJ, et al. 2005. Control of MAPK signalling: from complexity to what really matters. Oncogene 24: 5533-5542.
Nielsen UB, Schoeberl B. 2005. Using computational modeling to drive the development of targeted therapeutics. IDrugs 8: 822-826.
Schoeberl B, Eichler-Jonsson C, Gilles ED, Muller G. 2002. Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat. Biotechnol. 20: 370-375.
Schoeberl B, Nielsen UB, Paxson R. 2006. Model-based Design Approaches in Drug Discovery: A Parallel to Traditional Engineering Approaches. IBM Journal of Research and Development 50(6): 645. FULL TEXT
Reverse Engineering and Forward Simulations of Regulatory Networks in Preclinical and Clinical Applications
Aksenov SV, Church B, Dhiman A, et al. 2005. An integrated approach for inference and mechanistic modeling for advancing drug development. FEBS Lett. 579: 1878-1883. FULL TEXT
Haberichter T, Madge B, Christopher RA, et al. 2007. A systems biology dynamical model of mammalian G1 cell cycle progression. Mol. Syst. Biol. 3: 84. FULL TEXT
Khalil IG, Hill C. 2005. Systems biology for cancer. Current Opinion in Oncology 17: 44-48.
Speakers
David de Graaf, PhD
Pfizer RTC Cambridge
e-mail | web site | publications
David de Graaf is the first director of systems biology at Pfizer. He did his undergraduate work at the University of Utrecht, the Netherlands, where he obtained a Master's degree in Evolutionary Genetics. His doctoral work, under supervision of Professor Igor Roninson at the University of Illinois at Chicago, focused on the multi-drug resistance pump p-glycoprotein and its transport mechanisms. At this time, De Graaf got involved in some of the earliest functional genomics approaches using gene fragment libraries in retroviral vectors.
His postdoctoral work focused on the pharmacogenomics of olfaction. His work with Doron Lancet at the Weizmann Institute of Science in Israel, used computational and wet biology approaches to try to deconvolute odorant binding to olfactory receptors.
De Graaf then relocated to the Center for Genome Research at the Whitehead/MIT, where he set up a group working on target validation, and worked in close collaboration with colleagues at Millennium, Bristol-Myers Squibb, and Affymetrix.
De Graaf joined AstraZeneca, where he built the first systems biology team, focusing on modeling and simulation in both the UK and the US. De Graaf has a global strategic role in determining how systems biology can transform drug research for Pfizer, and a local role, heading a team at the Pfizer Research and Technology Center, which works on applying systems biology approaches to issues across the Pfizer pipeline.
Birgit Schoeberl, PhD
Merrimack Pharmaceuticals
e-mail | web site | publications
Birgit Schoeberl is the director of Network Biology at Merrimack Pharmaceuticals. Together with Ulrik Nielsen, she helped formulate Merrimack's research and development platform, Network Biology. Schoeberl received an MS degree in chemical engineering from the University of Karlsruhe, Germany, and a PhD degree from the Max-Planck-Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
Iya Khalil, PhD
Gene Network Sciences
e-mail | web site | publications
Iya Khalil is the executive vice president and cofounder of Gene Network Sciences. Khalil oversees the application of the company's compound simulation technology in alliances with pharmaceutical and biotech companies, with a special focus in cancer clinical trials. She is an expert at applying analytical and computational methods to various stages of the drug development process. A frequent speaker at industry events and conferences, she has extensive experience in reverse engineering and forward simulations of large-scale genetic and biochemical networks. Khalil is an inventor on a number of pending patents and has published multiple articles on in silico technologies applied to drug discovery and development. Prior to joining GNS, she worked at Cornell University, University of Washington and Abbott Labs. Khalil holds a PhD in physics from Cornell University.
Don Monroe
Don Monroe is a science writer based in Murray Hill, New Jersey. After getting a PhD in physics from MIT, he spent more than fifteen years doing research in physics and electronics technology at Bell Labs. He writes on physics, technology, and biology.