Numerical stability of pharmacokinetic deconvolution algorithms.
The sensitivity of pharmacokinetic deconvolution algorithms with respect to simulated experimental error has been studied. Approximations of cumulative absorption profiles reconstructed from simulated data sets with normally distributed random error were compared with corresponding known input functions. The coincidence of both functions was assessed in 600 numerical experiments by the nonparametric Kolmogorov-Smirnov test. A constrained iterative nonlinear regression procedure based on the analytical convolution of multiphasic zero-order input functions with linear disposition models was found to perform well under conditions where the results of direct numerical deconvolution and model-based mass balance methods were unsatisfactory.
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