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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
Sensitivity analysis on a physiologically-based pharmacokinetic and pharmacodynamic model for
Kaizhen Chen1, Shiyi Teo, Kok-Yong Seng
1Bioengineering Laboratory, Defence Medical & Environmental Research Institute, DSO National Laboratories, Singapore, Republic of Singapore.
Abstract:
A physiologically-based pharmacokinetic and pharmacodynamic (PBPK/PD) model was recently developed to study the effect of diisopropylfluorophosphate (DFP) on acetylcholinesterase (AChE) activity in mouse and rat. That model takes into account relatively complex interactions involving many parameters, some of which may be uncertain and/or highly variable, especially those characterizing AChE activity after DFP intoxication. The primary objective of this study was to identify parameters that contribute most to the variability of AChE dynamics for model optimization against data. For this purpose, the influence of the variability of the rate constants for synthesis (K(syn)) and degradation (K(deg)) of AChE, and regeneration (K(reg)) and aging (K(age)) of inhibited AChE on the variability of AChE activity in mice and rat venous blood and brain was first calculated by a global sensitivity analysis. Next, the mouse PBPK/PD model was calibrated by optimizing the values of K(syn), K(deg), K(reg) and K(age). Thereafter, scale-up of the DFP-induced AChE activity was performed from mouse to rat. Validation of the rat model was performed by comparing the time course of venous blood and brain AChE activities from a Monte Carlo analysis to those obtained in vivo. Sensitivity analysis on the verified models showed that K(reg) and K(syn) were the most influential factors of AChE activity at shorter and longer durations, respectively, after DFP challenge. Scale-up of the AChE dynamics from mouse to rat was also successful, as evidenced by significant overlapping between the predicted 95(th) percentile confidence intervals and the experimental data.
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