Fuzzy Evaluation of Pharmacokinetic Models.

Carlos Sepúlveda,Oscar Montiel,Roberto Sepúlveda,José M Cornejo Bravo

doi:10.1155/2018/1983897

Abstract

Population pharmacokinetic (PopPK) models allow researchers to predict and analyze drug behavior in a population of individuals and to quantify the different sources of variability among these individuals. In the development of PopPK models, the most frequently used method is the nonlinear mixed effect model (NLME). However, once the PopPK model has been developed, it is necessary to determine if the selected model is the best one of the developed models during the population pharmacokinetic study, and this sometimes becomes a multiple criteria decision making (MCDM) problem, and frequently, researchers use statistical evaluation criteria to choose the final PopPK model. The used evaluation criteria mentioned above entail big problems since the selection of the best model becomes susceptible to the human error mainly by misinterpretation of the results. To solve the previous problems, we introduce the development of a software robot that can automate the task of selecting the best PopPK model considering the knowledge of human expertise. The software robot is a fuzzy expert system that provides a method to systematically perform evaluations on a set of candidate PopPK models of commonly used statistical criteria. The presented results strengthen our hypothesis that the software robot can be successfully used to evaluate PopPK models ensuring the selection of the best PopPK model.

Highlights

Pharmacokinetics (PK) is a subdivision of pharmacology in which mathematical models are developed to study the processes of absorption, distribution, and elimination of a drug once a given dose has been administered to a given individual [1]. ese mathematical models are derived from representing the body as a system of compartments [2] (Figure 1), where the transfer rates of absorption, distribution, redistribution, and elimination between these compartments can be used to determine the parameters of the PK model such as clearance (Cl) and volume of distribution (V) that allow us to predict the drug concentration in plasma (Cp) in a given time [3]
E estimation of the parameters for all the generated PopPK models was made by applying restricted maximum estimation likelihood (REML). e optimization process was conducted with the quasi-Newton algorithm using the initial set values for fixed effects of 0.01 for Cl and 0.01 for V and with a maximum of 100 iterations. e same residual error model was applied to all the experiments. e only variations in the developed PopPK models are the covariate type and the number of parameters included in each model
Our results bolster the hypothesis that the software robot can be successfully implemented to evaluate PopPK models ensuring the selection of the best PopPK model when the choice of this becomes an MCDC problem

Summary

Introduction

Pharmacokinetics (PK) is a subdivision of pharmacology in which mathematical models are developed to study the processes of absorption, distribution, and elimination of a drug once a given dose has been administered to a given individual [1]. ese mathematical models are derived from representing the body as a system of compartments [2] (Figure 1), where the transfer rates of absorption, distribution, redistribution, and elimination between these compartments can be used to determine the parameters of the PK model such as clearance (Cl) and volume of distribution (V) that allow us to predict the drug concentration in plasma (Cp) in a given time [3]. Where V is the distribution volume of the drug within the body and Cl measures the ability of the liver and the kidney, mainly to extract a drug from the body In this context, Cl and V are fixed parameters that describe the drug concentration of a given dose D over time t. We contribute with a new software robot to facilitate the selection of the best pharmacokinetic model by an automated process when the comparison of the implemented evaluation criteria values among the models cannot be categorically determined, addressing the problem as a decision problem. We present results using a software robot to select the best PopPK model of tobramycin by an automated process.

Nonlinear Mixed-Effects Model Framework

Implementation of the Software Robot

Evaluation

Evaluation Evaluation

Conclusions