Last modifications: 20/08/2019 by FDM
Pharmacokinetics and Modelling
The large variability of a drug may be partially offset by the consideration of observed concentration and covariate values in a patient using a population pharmacokinetic model (PKPOP). The application of the Bayes theorem estimates (i) the individual pharmacokinetic parameters of patients, (ii) their exposure to the drug (Area under the curve of blood concentrations over time; AUC) and therefore suggests an individualized dosage to achieve the recommended AUC for therapeutic efficacy. This is the goal of the ISBA website (Immunosuppressants Bayesian Adaptation) set up by UMR Inserm U850 (now U1248) in 2005 as a secure platform accessible via the web (https://pharmaco.chu-limoges.fr/).
This application of POPPK modeling to the routine activity is presented in Figure 1. Our research activity is to develop new PKPOP models and implement them in the ISBA expert system, as well as to develop original pharmacokinetic models for particular situations, in the goal of routine or research applications (eg, joint PK modeling of blood and intracellular drug concentrations, pharmacogenetic modeling – pharmacokinetics, PK / PD relationships).
General process to develop a population pharmacokinetic model and use it for dose adjustment in new patients.
Heterogeneous patients with different characteristics are extensively sampled (10–12 samples per patient) with accurately timed dosing and sampling times. A population pharmacokinetic model is composed describing mean/SD value for pharmacokinetic parameters (parametric approaches) or a vector of PK parameters (non-parametric approaches), and including their variations and covariations in the population.
Some covariates are investigated to explain part of the inter-individual variability.
A limited number of samples (e.g. 2–4), are drawn from a renal transplant patient with a given dose and covariates (e.g. CYP3A5, haematocrit, corticosteroid dosing). A Bayesian estimation is performed, in different available tools, from the model previously developed (i.e. prior information), the dose, concentration and covariates measured in the patient (i.e. current information) to estimate the new patient’s PK parameters (i.e. posterior information) and from this estimate the patient’s AUC.Then a dose recommendation is proposed to the clinician to individualize the dose to fit a specific AUC target.