A Workflow for Global Sensitivity Analysis of PBPK Models

Abstract

Physiologically based pharmacokinetic (PBPK) models have a potentially significant role in the development of a reliable predictive toxicity testing strategy. The structure of PBPK models are ideal frameworks into which disparate in vitro and in vivo data can be integrated and utilized to translate information generated, using alternative to animal measures of toxicity and human biological monitoring data, into plausible corresponding exposures. However, these models invariably include the description of well known non-linear biological processes such as, enzyme saturation and interactions between parameters such as, organ mass and body mass. Therefore, an appropriate sensitivity analysis (SA) technique is required which can quantify the influences associated with individual parameters, interactions between parameters and any non-linear processes. In this report we have defined the elements of a workflow for SA of PBPK models that is computationally feasible, accounts for interactions between parameters, and can be displayed in the form of a bar chart and cumulative sum line (Lowry plot), which we believe is intuitive and appropriate for toxicologists, risk assessors, and regulators.

Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine

“…Thus, interspecies allometric scaling and single species methods, such as linear scaling or direct proportionality, represent another variability in model input [192–194]. Conducting a sensitivity analysis to establish the influence of certain parameters on the predicted drug exposure can aid in understanding the strength of the model [31,195,196,33]. To date, no perfect PBPK model exists.…”

Prediction of in vivo developmental toxicity of all-trans-retinoic acid based on in vitro toxicity data and in silico physiologically based kinetic modeling

“…For the model evaluation, we performed a global sensitivity analysis using a Morris test, which is preferred above a local sensitivity analysis, which may lead to misleading results when there are substantial interactions among multiple parameters (like with PBK models) (McNally et al 2011). The Morris test produces two sensitivity parameters for each PBK model parameter, which are µ and σ.…”

“…The Morris test produces two sensitivity parameters for each PBK model parameter, which are µ and σ. A high µ indicates a parameter with an overall influence on model output, whereas a high σ indicates a parameter interacting with other parameters, or a parameter with nonlinear effects (McNally et al 2011). A Morris test was performed for the blood concentration at 1.5 h (around T max ) and area under the blood concentration-time (AUC) curve between 0 and 24 h following an oral dose of 0.1 mg kg bw −1 in rats and 2 mg kg bw −1 in human to identify an approximate rank order of most sensitive model parameters.…”

Overview of the Current State-of-the-Art for Bioaccumulation Models in Marine Mammals

“…During a local sensitivity analysis (LSA), the model predictions are judged against pre-defined changes of for example 1%, 2% or 5% in each separate parameter value and expressed in, for instance, area under the curve (AUC) of the model values [52]. In a global sensitivity analysis (GSA), however, all model parameters vary in pre-defined ranges and their relative influence on the model output is assessed [53]. LSA generally works fine for simple and linear models in which there are no interactions between the parameters.…”

“…LSA generally works fine for simple and linear models in which there are no interactions between the parameters. However, interactions between parameters are usually unavoidable in more complex models which highlights the need for methods that are more global or randomized compared to LSA [53] (Figure 2). …”