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. 2013 Apr 6;3(2):20120071.
doi: 10.1098/rsfs.2012.0071.

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

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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

Neil Benson et al. Interface Focus. .
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Abstract

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.

Keywords: nerve growth factor; systems biology; systems pharmacology; tropomyosin receptor kinase A.

Figures

Scheme 1.
Scheme 1.
Diagram of model 2. (1) refers to compartment 1, the extra cellular body water compartment of 15 l. (2) is the neuronal intracellular compartment (0.001 l). Detailed descriptions of parameters and values used are given in the electronic supplementary material. I refers to a Mg.ATP non-competitive inhibitor of TrkA kinase and is assumed to be in rapid equilibrium between the compartments. The grey box labelled model 1 indicates that the components of model 1 are included in compartment 2.
Figure 1.
Figure 1.
Model 1 simulated time course of dppERKnuc response to 30 pM NGF (solid line). The dashed line shows the response in the presence of a TrkA binding inhibitor given at t = 0, Ki = 0.1 nM (at 1000×Kd of the inhibitor) and the dashed-dotted line at 1×10 000×Ki. (Online version in colour.)
Figure 2.
Figure 2.
Sensitivity analysis of model 1 using the initial concentrations of species or reactants. (a) Examples of the time-dependent sensitivities for NGFext (dashed line), pTrkA (solid line) and Grb2_SOS_pShC_pTrkA (dashed-dotted line). See table 1 for relative influence. (b) Time integral matrix subplot showing the influence of the concentration of each species in the model over the first 100 min of the response. (Online version in colour.)
Figure 3.
Figure 3.
Sensitivity analysis of model 1 using the parameter values. Time integral matrix subplot showing the influence of each parameter in the model over the first 100 min of the response on a logarithmic scale.
Figure 4.
Figure 4.
Model 2 simulated time course of dppERKnuc response to NGF (solid line). The dashed line shows the response in the presence of a TrkA binding inhibitor Ki = 0.1 nM (at 100 × Ki of the inhibitor) and the dashed-dotted line at 1000 × Ki, both given at t = 0. (Online version in colour.)
Figure 5.
Figure 5.
Model 3 simulations with and without hypothetical inhibitor of TrkA kinase, given at t = 0, Ki = 0.1 nM. Graph shows the accumulation of phosphorylated-NGF–TrkA complex concentration in the neuronal compartment over time with and without TrkA inhibitor. No inhibitor (solid line) 10×Ki (dashed line) 100×Ki (dashed-dotted line). (Online version in colour.)
Figure 6.
Figure 6.
(a) Model 1 predicted change in TrkA inhibitor concentration in the neuron at 1000×Ki (Ki = 0.1 nM) at t = 0. (b) Same plot and conditions but simulated using model 2. (Online version in colour.)

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