Quantitative global sensitivity analysis of a biologically based dose-response pregnancy model for the thyroid endocrine system

Annie Lumen,Nysia George,George D Loizou,Kevin Mcnally,Jeffrey W Fisher

doi:10.3389/fphar.2015.00107

Abstract

A deterministic biologically based dose-response model for the thyroidal system in a near-term pregnant woman and the fetus was recently developed to evaluate quantitatively thyroid hormone perturbations. The current work focuses on conducting a quantitative global sensitivity analysis on this complex model to identify and characterize the sources and contributions of uncertainties in the predicted model output. The workflow and methodologies suitable for computationally expensive models, such as the Morris screening method and Gaussian Emulation processes, were used for the implementation of the global sensitivity analysis. Sensitivity indices, such as main, total and interaction effects, were computed for a screened set of the total thyroidal system descriptive model input parameters. Furthermore, a narrower sub-set of the most influential parameters affecting the model output of maternal thyroid hormone levels were identified in addition to the characterization of their overall and pair-wise parameter interaction quotients. The characteristic trends of influence in model output for each of these individual model input parameters over their plausible ranges were elucidated using Gaussian Emulation processes. Through global sensitivity analysis we have gained a better understanding of the model behavior and performance beyond the domains of observation by the simultaneous variation in model inputs over their range of plausible uncertainties. The sensitivity analysis helped identify parameters that determine the driving mechanisms of the maternal and fetal iodide kinetics, thyroid function and their interactions, and contributed to an improved understanding of the system modeled. We have thus demonstrated the use and application of global sensitivity analysis for a biologically based dose-response model for sensitive life-stages such as pregnancy that provides richer information on the model and the thyroidal system modeled compared to local sensitivity analysis.

Highlights

Computational modeling approaches, such as physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) and biologically based dose-response (BBDR) models, are currently being well embraced for the study of the system-compound interactions and are increasingly used in regulatory decision making for both pharmaceuticals and environmental chemicals (Zhao et al, 2011; Huang et al, 2013; McLanahan et al, 2014)
The input parameters of the euthyroid BBDR-Hypothalamus-Pituitary-Thyroid Axis (HPT) axis pregnancy model to predict maternal thyroid hormone levels were ranked by each sensitivity measure and compared across stochastic runs as shown in Supplementary
The biologically based dose-response model for the hypothalamus pituitary thyroid axis constitutes a complex network of anion kinetic submodels and thyroid hormone specific submodels

Summary

Introduction

Computational modeling approaches, such as physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) and biologically based dose-response (BBDR) models, are currently being well embraced for the study of the system-compound interactions and are increasingly used in regulatory decision making for both pharmaceuticals and environmental chemicals (Zhao et al, 2011; Huang et al, 2013; McLanahan et al, 2014). BBDR models can encompass multiple compound-specific PBPK submodels in addition to the pharmacodynamic submodel components, linking external exposure to a quantifiable biological response for an array of doses (Conolly and Butterworth, 1995; Setzer et al, 2001; McLanahan et al, 2008; Fisher et al, 2013; Lumen et al, 2013) The application of these models range from supporting risk assessment and public health decisions to identifying data gaps and research needs to further basic science (Doerge et al, 2008; Kenyon et al, 2008; Tan et al, 2012). Such models offer a useful framework for integrating available data from diverse platforms, including in vitro and in vivo studies, and offer means to scale and extrapolate across species to humans and to sensitive life-stages, such as pregnancy

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Results

Conclusion