Application of probabilistic neural networks to population pharmacokineties

Abstract

Pharmacokinetics (PK) studies the absorption and the elimination of drugs in the human body. Most PK models are parametric. Once it is identified, a PK model can estimate e.g. plasma concentrations over time for a given individual. Population PK aims at making sensible predictions even when measurements are so scarce to prevent model identification. In fact those predictions integrate past experimental evidence concerning some reference population. Again, integration typically relies on a (meta)-parametric model of the distribution of individual PK parameters in the population. In this paper we propose a fully data-driven, model-independent, non-parametric approach to population PK that makes use of Probabilistic Neural Networks (PNNs). The method incorporates regularized optimization of the kernels' width and is able to deal with missing data. It was validated on a problem that is refractory to traditional PK modeling techniques. We carried out a clinical study where a single oral dose of racemic ibuprofen was administered to 18 healthy volunteers. In each subject plasma concentrations of the R- and S-ibuprofen enantiomers were measured at predefined times after administration, together with some co-variates (e.g. age, body mass). Estimates on each single individual were obtained by taking the remaining ones as the reference population. Results show the ability to infer the whole concentration profile of either enantiomer from few concentrations of the other one. In conclusion, our approach does not require any explicit model to take advantage of any statistical dependencies among (perhaps heterogeneous) quantities