Integration of Omics Data and Systems Biology Modeling: Effect of Cyclosporine A on the Nrf2 Pathway in Human Renal Kidneys Cells

TL;DR

This study combines pharmacokinetics and systems biology modeling to quantitatively analyze how cyclosporine A activates the Nrf2 pathway in human renal cells, providing mechanistic insights and predictive capabilities.

Contribution

It introduces a coupled PK-systems biology model with MCMC parameter estimation to quantitatively simulate cyclosporine A effects on the Nrf2 pathway.

Findings

Model accurately predicts in vitro EC50 for cyclosporine A

Quantitative insights into oxidative-stress mechanisms

Framework for predicting stress under various exposure conditions

Abstract

In a recent paper, Wilmes et al. demonstrated a qualitative integration of omics data streams to gain a mechanistic understanding of cyclosporine A toxicity. One of their major conclusions was that cyclosporine A strongly activates the nuclear factor (erythroid-derived 2)-like 2 pathway (Nrf2) in renal proximal tubular epithelial cells exposed in vitro. We pursue here the analysis of those data with a quantitative integration of omics data with a differential equation model of the Nrf2 pathway. That was done in two steps: (i) Modeling the in vitro pharmacokinetics of cyclosporine A (exchange between cells, culture medium and vial walls) with a minimal distribution model. (ii) Modeling the time course of omics markers in response to cyclosporine A exposure at the cell level with a coupled PK-systems biology model. Posterior statistical distributions of the parameter values were obtained by Markov chain Monte Carlo sampling. Data were well simulated, and the known in vitro toxic effect EC50 was well matched by model predictions. The integration of in vitro pharmacokinetics and systems biology modeling gives us a quantitative insight into mechanisms of cyclosporine A oxidative-stress induction, and a way to predict such a stress for a variety of exposure conditions.