Systems pharmacology of the nerve growth factor pathway: use of a systems biology model for the identification of key drug targets using sensitivity analysis and the integration of physiology and pharmacology
Author(s) -
Neil Benson,
Tomomi Matsuura,
С. В. Смирнов,
Oleg Demin,
Hannah M. Jones,
Vivek Dua,
Piet H. van der Graaf
Publication year - 2013
Publication title -
interface focus
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 49
eISSN - 2042-8901
pISSN - 2042-8898
DOI - 10.1098/rsfs.2012.0071
Subject(s) - druggability , tropomyosin receptor kinase a , computational biology , context (archaeology) , drug discovery , identification (biology) , drug , computer science , systems biology , pharmacology , bioinformatics , intuition , neuroscience , biology , nerve growth factor , receptor , psychology , cognitive science , genetics , gene , paleontology , botany
The nerve growth factor (NGF) pathway is of great interest as a potential source of drug targets, for example in the management of certain types of pain. However, selecting targets from this pathway either by intuition or by non-contextual measures is likely to be challenging. An alternative approach is to construct a mathematical model of the system and via sensitivity analysis rank order the targets in the known pathway, with respect to an endpoint such as the diphosphorylated extracellular signal-regulated kinase concentration in the nucleus. Using the published literature, a model was created and, via sensitivity analysis, it was concluded that, after NGF itself, tropomyosin receptor kinase A (TrkA) was one of the most sensitive druggable targets. This initial model was subsequently used to develop a further model incorporating physiological and pharmacological parameters. This allowed the exploration of the characteristics required for a successful hypothetical TrkA inhibitor. Using these systems models, we were able to identify candidates for the optimal drug targets in the known pathway. These conclusions were consistent with clinical and human genetic data. We also found that incorporating appropriate physiological context was essential to drawing accurate conclusions about important parameters such as the drug dose required to give pathway inhibition. Furthermore, the importance of the concentration of key reactants such as TrkA kinase means that appropriate contextual data are required before clear conclusions can be drawn. Such models could be of great utility in selecting optimal targets and in the clinical evaluation of novel drugs.
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