Drug treatment efficiency depends on the initial state of activation in nonlinear pathways

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.This work has been supported by the Ministry of Science and Technology of Spain via a Ramón y Cajal Fellowship (Ref. RYC-2010-07450), a grant from Plan Nacional framework (Ref. BFU2011-30303 and & BFU2014-53299-P) and a FPU fellowship. We thank Raúl Guantes, Juan Díaz Colunga, Marta Ibañes, Rosa Martínez Corral, Saúl Ares and Katherine Gonzales for invaluable help and technical assistance

Circadian-Related Heteromerization of Adrenergic and Dopamine D4 Receptors Modulates Melatonin Synthesis and Release in the Pineal Gland

Dopamine and adrenergic receptor complexes form under a circadian-regulated cycle and directly modulate melatonin synthesis and release from the pineal gland

Circadian-Related Heteromerization of Adrenergic and Dopamine D4 Receptors Modulates Melatonin Synthesis and Release in the Pineal Gland

Dopamine and adrenergic receptor complexes form under a circadian-regulated cycle and directly modulate melatonin synthesis and release from the pineal gland

A Tunable Coarse-Grained Model for Ligand-Receptor Interaction

<div><p>Cell-surface receptors are the most common target for therapeutic drugs. The design and optimization of next generation synthetic drugs require a detailed understanding of the interaction with their corresponding receptors. Mathematical approximations to study ligand-receptor systems based on reaction kinetics strongly simplify the spatial constraints of the interaction, while full atomistic ligand-receptor models do not allow for a statistical many-particle analysis, due to their high computational requirements. Here we present a generic coarse-grained model for ligand-receptor systems that accounts for the essential spatial characteristics of the interaction, while allowing statistical analysis. The model captures the main features of ligand-receptor kinetics, such as diffusion dependence of affinity and dissociation rates. Our model is used to characterize chimeric compounds, designed to take advantage of the receptor over-expression phenotype of certain diseases to selectively target unhealthy cells. Molecular dynamics simulations of chimeric ligands are used to study how selectivity can be optimized based on receptor abundance, ligand-receptor affinity and length of the linker between both ligand subunits. Overall, this coarse-grained model is a useful approximation in the study of systems with complex ligand-receptor interactions or spatial constraints.</p></div

Dependence of the reaction rates on the system parameters.

<p>(<b>A</b> and <b>B</b>): Dependence of and with the tuning parameters for and . (<b>A</b>) was determined via association experiments with and . (<b>B</b>) was determined via dissociation experiments with . <b>C</b> and <b>D</b>): and as a function of the diffusion coefficient for , , , and . Dashed lines: linear fit for the dependence of the reaction rates with the diffusion coefficient , with slopes: , . In all four panels each outcome is the result of 4 independent simulations and has an error . In panels C and D, error bars are within the size of the symbols.</p

Affinity and dissociation simulations.

<p><b>A</b>) Schematic of the initial configuration of ligands and receptors to calculate the dissociation rate. <b>B</b>) Dynamics of ligand-receptor dissociation follows a exponential decay that allows to calculate the dissociation constant . <b>C</b>) Schematic of the initial configuration of ligands and receptors to calculate the affinity rate. <b>D</b>) Dynamics of ligand-receptor affinity follows a linear growth at short times following <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003274#pcbi.1003274.e077" target="_blank">Eq. (8)</a>.</p

Binding specificity.

<p><b>A</b>) and <b>B</b>): Percentage of occupied activity receptors as a function of time for different number of total target element receptors (color lines) for (<b>A</b>) high () and (<b>B</b>) low affinity (). <b>C</b>): corresponding proportional increment in the number of activity complexes at equilibrium versus the total number of s for the low (blue) and high (red) affinities of the previous panels. Simulations are performed for diffusions , ; polymer length ; number of receptors , and chimeras at concentration . The affinity rate is set to , and geometric factors to . Each trajectory is the result of averaging over 12 independent simulations. Lines connecting points are represented as a guide to the eye.</p