An Analytical Solution for Saturable Absorption in Pharmacokinetics Models.

OBJECTIVE: The first-order absorption is a common model used in Pharmacokinetics. The absorption of some drugs follows carrier mediated transport. It has been proposed that the amount of drug available may saturate the transport mechanism resulting in an absorption slower than the one predicted by the first-order model. Saturable absorption has been modeled at the differential equation level by substituting the constant rate absorption by a Hill kinetics absorption. However, its exact solution is so far unknown. The goal of this is to know the exact solution of different Hill kinetic absorption models. METHODS: We start defining different absorption models and increasing then their complexity. The simplest case is the first-order absorption model and the most complex will be a generalized Hill kinetic absorption model. The differential equation of each model is integrated. RESULTS: The complexity of the models their solutions may be not expressed in a close-form, or in term of elementary functions. We obtain and discuss the exact solutions of the different Hill kinetics absorption models. To do that, the solutions are studied according to the possible values of the free parameters of the models. We show the differences between models through simulations. CONCLUSIONS: The knowledge of closed-form solutions allows to illustrate the differences between the different absorption models and minimizes the errors of numerical integration.

A Signal Processing Approach to Pharmacokinetic Data Analysis.

The connection between pharmacokinetic models and system theory has been established for a long time. In this approach, the drug concentration is seen as the output of a system whose input is the drug administered at different times. In this article we further explore this connection. We show that system theory can be used to easily accommodate any therapeutic regime, no matter its complexity, allowing the identification of the pharmacokinetic parameters by means of a non-linear regression analysis. We illustrate how to exploit the properties of linear systems to identify non-linearities in the pharmacokinetic data. We also explore the use of bootstrapping as a way to compare populations of pharmacokinetic parameters and how to handle the common situation of using multiple hypothesis tests as a way to distinguish two different populations. Finally, we demonstrate how the bootstrap values can be used to estimate the distribution of derived parameters, as can be the allometric scale factors.