Pharmacokinetic parameter transformations for educational simulations

Abstract

SANTANDER-02-05 Simulations developed for training of healthcare professionals typically use computerized mathematical models of human physiology and pharmacology. Educational simulations may involve many drugs and patient groups. Pharmacokinetic (PK) data in the scientific literature are presented in a wide variety of PK parameter set formats. This complicates the incorporation of PK data in a simulation model with a given structure and associated parameter format. To solve this problem, we defined three sets of independent parameters, referring as much as possible to the pharmacokinetic literature [1], and localized and/or derived the analytical relationships between them. Methods:Figure 1 shows the names of the defined parameter sets and the transformations between them, and Tables 1 and 2 define the 'Clinical' and 'Base' sets for 2nd order PKs, respectively.Figure 1: Pharmacokinetic parameter sets and transformations.Table 1: Second order clinical set.Table 2: Second order base set.Strictly speaking, half-life is the time required for the plasma concentration to be reduced by 50%. The half-times used in the clinical set directly refer to the individual exponentials and disposition rate constants of the response to a unit bolus dose: which is the model underlying the base set. The relationships between the half-times and the disposition rate constants reflect this observation. Assuming that the plasma concentration data can be described by the parametric bolus response function we can calculate the areas under the concentration-time curve and under the first moment of the concentration time curve, respectively [1]. The parameter A1 is derived from the clinical set based on this observation. Results:Table 3 shows examples of derived transformations.Table 3: Parameter transformations from the second order clinical set to the second order base set.The complete analytical relationships between the parameter sets for first, second, and third order PKs facilitate the incorporation of diverse pharmacokinetic data in educational simulations. Acknowledgements: This work was funded in part by a grant from Medical Education Technologies, Inc., Sarasota, Florida, USA. The Fundação para a Ciência e a Tecnologia, of the Portuguese Ministry of Science and Technology provided funds for participation in this conference.