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Optimization of metabolic parameters using physiologically based pharmacokinetic (PBPK) modeling and chloroform (CHCl3) vapor-uptake inhalation data in F344 rats
Citation:
Evans, M. Optimization of metabolic parameters using physiologically based pharmacokinetic (PBPK) modeling and chloroform (CHCl3) vapor-uptake inhalation data in F344 rats. Society ot Toxicology Annual Meeting, Baltimore, Maryland, March 10 - 14, 2019.
Impact/Purpose:
Physiologically-based pharmacokinetic models are used increasingly for extrapolation across species and exposure route, duration and concentration. Depending on the chemical being examined, metabolic rate constants, such as Vmax and Km, are frequently among the input parameters with strong influence on model estimates of internal dosimetry. The work described in this abstract provides a logical flowchart for more accurate estimation of Vmax and Km.
Description:
PBPK models are well established frameworks used to describe administration, distribution, metabolism, and excretion (ADME) of xenobiotics. To quantify metabolism, a PBPK model for a volatile compound can be calibrated with closed chamber (i.e., vapor uptake) inhalation data. Here, we graphically highlight a component of the optimization process to illustrate a strategy for metabolic parameter estimation when using vapor uptake data. Male F344 rats were exposed in vapor uptake chambers to initial concentrations of 100, 500, 1000, and 3000 ppm CHCl3. Inhalation time course data from these experiments, in combination with optimization using a chemical specific PBPK model, were used to estimate metabolism parameters Vmax,c and Km. Matlab® simulation software was used to integrate the mass balance equations and to perform the optimizations. The cost function used the logarithmic transformation of the chamber time-course data and least squares to minimize the differences between data and simulation values. The final values for Vmax,c and Km were 4.9 mg/h/kg0.75 and 0.26 mg/L, respectively. Additionally, cost function and contour plots were used to analyze the dose-dependent capacity to estimate Vmax,c and Km within the experimental range. Based on the combined analysis, the best concentrations for determination of Vmax,c were 500 and 1000 ppm, with 3000 ppm having a small contribution. Least square plots confirmed 500 ppm to be the best concentration to determine Vmax,c, showing the smallest differences between experimental and simulated data. The contour plots showed narrow valleys where the optimization found the best values for Vmax,c at both 500 and 1000 ppm. The estimation of Km was more involved, needing data across the full range of experimental concentrations (100-3000 ppm). In summary, this work should help toxicologists interested in optimization techniques to understand the overall strategy employed when calibrating metabolic parameters in a PBPK model with inhalation data. Subjecting preliminary data to the analyses described here would guide selection of optimal exposure concentrations for more accurate estimation of Vmax,c and Km. This strategy could be explored for in vitro metabolic parameter optimization. (This abstract does not reflect EPA policy).