Application of modelling techniques to the planning of in vitro arsenic kinetic studies

A kinetic model describing the hepatic methylation of arsenite ([As[III]) w as developed on the basis of limited data from in vitro mechanistic studies . The model structure is as Follows: sequential enzymic methylation of arse nite to its monomethylated (MMA) and dimethylated (DMA) products by first-o rder and Michaelis-Menten kinetics, respectively; uncompetitive inhibition of the formation of DMA by As(III); and first-order reversible binding of A s(III), MMA and DMA to cytosolic proteins. Numerical sensitivity analysis w as used to evaluate systematically the impact of changes in input parameter s on model responses. Sensitivity analysis was used to investigate the poss ibility of designing experiments for robust testing of the uncompetitive in hibition hypothesis, and for further refining the model. Based on the sensi tivity analysis, the MMA concentration is the most important response on wh ich to focus. The parameters V-max and k(i) can be reliably estimated by us ing the same concentration time-course data at intermediate initial arsenit e concentrations of 1-5 muM at 30 +/- 5 minutes. K-m must be estimated inde pendently of V-max, since the two parameters are highly correlated at all t imes, and the optimal experimental conditions would include lower initial c oncentrations of arsenite (0.1-0.5 muM) and earlier time-points (about 8-18 minutes). The use of initial arsenite concentrations much above 5 muM woul d not yield additional useful information, because the sensitivity coeffici ents for MMA, protein-hound MMA, DMA and protein-hound DMA tend to become e xtremely small or exhibit erratic trends. Overall trends in the sensitivity analysis indicated the desirability of performing measurements at times sh orter than 60 minutes. This work demonstrates that physiological modelling and sensitivity analysis can be efficient tools for experimental planning a nd hypothesis testing when applied in the earliest phases of kinetic model development, thus allowing more-efficient and more-directed experimentation , and minimising the use of laboratory animals.