Parameter Trajectory Analysis to Identify Treatment Effects of Pharmacological Interventions

Abstract

The field of medical systems biology aims to advance understanding of molecular mechanisms that drive disease progression and to translate this knowledge into therapies to effectively treat diseases. A challenging task is the investigation of long-term effects of a (pharmacological) treatment, to establish its applicability and to identify potential side effects. We present a new modeling approach, called Analysis of Dynamic Adaptations in Parameter Trajectories (ADAPT), to analyze the long-term effects of a pharmacological intervention. A concept of time-dependent evolution of model parameters is introduced to study the dynamics of molecular adaptations. The progression of these adaptations is predicted by identifying necessary dynamic changes in the model parameters to describe the transition between experimental data obtained during different stages of the treatment. The trajectories provide insight in the affected underlying biological systems and identify the molecular events that should be studied in more detail to unravel the mechanistic basis of treatment outcome. Modulating effects caused by interactions with the proteome and transcriptome levels, which are often less well understood, can be captured by the time-dependent descriptions of the parameters. ADAPT was employed to identify metabolic adaptations induced upon pharmacological activation of the liver X receptor (LXR), a potential drug target to treat or prevent atherosclerosis. The trajectories were investigated to study the cascade of adaptations. This provided a counter-intuitive insight concerning the function of scavenger receptor class B1 (SR-B1), a receptor that facilitates the hepatic uptake of cholesterol. Although activation of LXR promotes cholesterol efflux and -excretion, our computational analysis showed that the hepatic capacity to clear cholesterol was reduced upon prolonged treatment. This prediction was confirmed experimentally by immunoblotting measurements of SR-B1 in hepatic membranes. Next to the identification of potential unwanted side effects, we demonstrate how ADAPT can be used to design new target interventions to prevent these.

Evaluating computational models of cholesterol metabolism

“…In this way, parameter trajectories with parameters changing gradually over time are favored over parameter trajectories with highly fluctuating values, thus selecting for solutions that are biologically plausible. Moreover, by repeating the procedure for sets of continuous functions obtained by fitting to points sampled from within the distribution of the experimental data, the method takes experimental error into account [79].…”

A systems biology approach reveals the physiological origin of hepatic steatosis induced by liver X receptor activation

“…A modeling approach referred to as ADAPT (16,17) was applied to identify which metabolic adaptations are induced in mice by treatment with the LXR agonist T0901317. The ADAPT methodology uses a mathematical model of a biological system to analyze or predict long-term adaptations caused by diseases or treatment interventions.…”

“…An extensive set of biochemical, kinetic, and gene expression data obtained in C57BL/6J mice treated with T0901317 for 0, 1, 2, 4, 7, 14, and 21 d was generated and used as input for ADAPT. For the present study, the previous version of ADAPT (17) was further optimized. First, hepatic fatty acid catabolism was constrained to the rate of myristate oxidation in primary hepatocytes, i.e., 70 nmol/h per mg protein (Out et al, unpublished data, November 27, 2014).…”

“…1 and see Materials and Methods). The previous version of ADAPT adequately described the different components of systemic cholesterol metabolism (17). For this study, additional constraints and gene expression data were added to the modeling approach (see Materials and Methods).…”

“…This allowed us to evaluate the consequences of acute and prolonged pharmacologic LXR activation by T0901317 on systemic TG metabolism in mice. To unravel the physiologic forces driving the observed time-dependent effects, we used a computational modeling approach referred to as analysis of dynamic adaptations in parameter trajectories (ADAPT) (16,17), which was applied to integrate metabolic and gene expression data to allow for full analysis of the changes induced by LXR activation.…”