Abstract
An accurate prediction of the outcome of a given drug treatment requires quantitative values for all parameters and concentrations involved as well as a detailed characterization of the network of interactions where the target molecule is embedded. Here, we present a high-throughput in silico screening of all potential networks of three interacting nodes to study the effect of the initial conditions of the network in the efficiency of drug inhibition. Our study shows that most network topologies can induce multiple dose-response curves, where the treatment has an enhanced, reduced or even no effect depending on the initial conditions. The type of dual response observed depends on how the potential bistable regimes interplay with the inhibition of one of the nodes inside a nonlinear pathway architecture. We propose that this dependence of the strength of the drug on the initial state of activation of the pathway may be affecting the outcome and the reproducibility of drug studies and clinical trials.
Highlights
Some of the main potential contributions of Systems Biology to the field of Pharmacology are to help design better drugs[1,2], to find better targets[3] or to optimize treatment strategies[4]
The most common scenario corresponds to a shift in the dose-response curve, i.e., the initial condition affects the efficiency of the inhibitor
The two dose-response curves are plotted in Fig. 2b, corresponding to each initial condition IClow and IChigh, in blue (DSlow) and red (DShigh), respectively. For this network configuration and these conditions, the EC50 of the inhibitor changes around 1.5 orders of magnitude. This type of dependence on the initial conditions is a result of a bistable regime, as shown in the phase plane in Fig. 2c
Summary
Some of the main potential contributions of Systems Biology to the field of Pharmacology are to help design better drugs[1,2], to find better targets[3] or to optimize treatment strategies[4]. To identify the source of these effects, large scale signaling networks are often dissected into minimal sets of recurring interaction patterns called network motifs[7] Many of these motifs are nonlinear, combining positive and negative feedback and feed-forward loops that introduce a rich variety of dynamic responses to a given stimulus. Characterization of inhibitors and its efficiency[11] and specificity towards all human kinases constitutes a highly active area of research[12,13,14] Since these inhibitors target interactions that are embedded in highly nonlinear biomolecular networks, the response to treatment is often influenced by the architecture of the network. Treatment, this dependence on initial conditions may result in differences in the effect of a given drug, depending on the initial state of the system
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