Global optimization of the Michaelis-Menten parameters using physiologically-based pharmacokinetic (PBPK) modeling and chloroform vapor uptake data in F344 rats.

Objective: To quantify metabolism, a physiologically based pharmacokinetic (PBPK) model for a volatile compound can be calibrated with the closed chamber (i.e. vapor uptake) inhalation data. Here, we introduce global optimization as a novel component of the predictive process and use it to illustrate a procedure for metabolic parameter estimation.Materials and methods: Male F344 rats were exposed in vapor uptake chambers to initial concentrations of 100, 500, 1000, and 3000 ppm chloroform. Chamber time-course data from these experiments, in combination with optimization using a chemical-specific PBPK model, were used to estimate Michaelis-Menten metabolic constants. Matlab® simulation software was used to integrate the mass balance equations and to perform the global optimizations using MEIGO (MEtaheuristics for systems biology and bIoinformatics Global Optimization - Version 64 bit, R2016A), a toolbox written for Matlab®. The cost function used the chamber time-course data and least squares to minimize the difference between data and simulation values.Results and discussion: The final values estimated for Vmax (maximum metabolic rate) and Km (affinity constant) were 1.2 mg/h and a range between 0.0005 and 0.6 mg/L, respectively. Also, cost function plots were used to analyze the dose-dependent capacity to estimate Vmax and Km within the experimental range used. Sensitivity analysis was used to assess identifiability for both parameters and show these kinetic data may not be sufficient to identify Km.Conclusion: In summary, this work should help toxicologists interested in optimization techniques understand the overall process employed when calibrating metabolic parameters in a PBPK model with inhalation data.

A General Physiological and Toxicokinetic (GPAT) Model for Simulating Complex Toluene Exposure Scenarios in Humans.

Realistic simulation of environmental exposure scenarios requires dynamic methods in which exposures and human activities vary continuously as a function of time. Simulation of such complex scenarios is, with conventional physiologically based methods, a complex and programming-intensive task. The goal of the present effort was to simplify this task by combining a commercially available general whole-body human physiological model (QCP2004) with a slightly extended physiologically based toxicokinetic (PBTK) model from the literature. The QCP2004 model is a differential equation-based model similar to PBTK models except that normal organ function is simulated and the body organs are appropriately interlinked. Here QCP2004 provided estimates of physiological parameters required by the PBTK model. These were updated as the model was iteratively executed appropriate to the varying activity of the human subject. The combined general physiological model and the PBTK model was called a general physiological and toxicokinetic (GPAT) model. The GPAT model was tested and (within the constraints of available toluene exposure experiments in the literature) found to predict toluene blood concentrations, even in dynamic situations. A model of the structure used in the present work is capable of expansion as new knowledge is developed and greater detail is desired. Similarly, multiple toxicant PBTK models can be developed and incorporated for applications to mixtures risk assessment. Additionally, toxicant effects on organ systems can be achieved by altering organ function during a simulation as a function of the internal dose of toxicants. By cumulatively adding detail to the model as new physiological and chemical-specific information becomes available, the model can become a repository of knowledge for increasingly sophisticated risk-assessment applications.