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1.
Figure 2

Figure 2. From: Network pharmacodynamic models for customized cancer therapy.

Pipeline to construct and utilize PK/ePD models to design multidrug regimens. The parameter estimation, sensitivity analyses and optimization-based controller steps are distinct computational steps and can be extensive. Modified from reference .

James M. Gallo, et al. Wiley Interdiscip Rev Syst Biol Med. ;7(4):243-251.
2.
Figure 1

Figure 1. From: Network pharmacodynamic models for customized cancer therapy.

Hypothetical cell signalling pathways that form the basis of an enhanced pharmacodynamic [ePD] model. Each shape is a protein with the rectangles representing receptor tyrosine kinases within 3 parallel and interconnected “units”. The central unit is the drug target pathway. Should the ePD model be confined to the central unit or also include the 2 other units? This idealized network illustrates a potential problem in defining the boundaries of the model.

James M. Gallo, et al. Wiley Interdiscip Rev Syst Biol Med. ;7(4):243-251.
3.
Figure 3

Figure 3. From: Network pharmacodynamic models for customized cancer therapy.

A) Optimization-based control multidrug regimen applied to the VEGFR biochemical pathway for a 28-day cycle. The five drugs available are shown in the legend. A relative dose of 0 is the lowest dose possible and a value of 1 the maximum defined from clinical data. B) The corresponding pERK and pAkt profiles expressed as the fractional response. The profiles show some adulations – particularly for pERK – but both proteins met the 80% inhibition criteria set for the controller. From reference .

James M. Gallo, et al. Wiley Interdiscip Rev Syst Biol Med. ;7(4):243-251.

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