Analysis of Mechanistic Pathway Models in Drug Discovery: p38 Pathway
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- 作者：Bart S. Hendriks ; Fei Hua ; Jeffrey R. Chabot
- 刊名：Biotechnology Progress
- 出版年：2008
- 出版时间：February 2008
- 年：2008
- 卷：24
- 期：1
- 页码：96 - 109
- 全文大小：531K
- 年卷期：v.24,no.1(February 2008)
- ISSN：1520-6033
文摘
Mechanistic models of signal transduction have emerged as valuable tools for untangling complexsignaling networks and gaining detailed insight into pathway dynamics. The natural extensionof these tools is for the design of therapeutic strategies. We have generated a novel computationalmodel of lipopolysaccharide-induced p38 signaling in the context of TNF-
production ininflammatory disease. Using experimental measurement of protein levels and phospho-proteintime courses, populations of model parameters were estimated. With a collection of parametersets, reflecting virtual diversity, we step through analysis of the p38 signaling pathway modelto answer specific drug discovery questions regarding target prioritization, inhibitor simulation,model robustness and co-drugging. We demonstrate that target selection cannot be assessedindependently from inhibitor mechanism of action and is also linked with robustness to cellularvariability. Finally, we assert that in the face of parameter uncertainty one can still uncoverconsistent findings that can guide drug discovery efforts.
production ininflammatory disease. Using experimental measurement of protein levels and phospho-proteintime courses, populations of model parameters were estimated. With a collection of parametersets, reflecting virtual diversity, we step through analysis of the p38 signaling pathway modelto answer specific drug discovery questions regarding target prioritization, inhibitor simulation,model robustness and co-drugging. We demonstrate that target selection cannot be assessedindependently from inhibitor mechanism of action and is also linked with robustness to cellularvariability. Finally, we assert that in the face of parameter uncertainty one can still uncoverconsistent findings that can guide drug discovery efforts.