Maud Hennion (1), Bruno Boulanger (1)
(1) Pharmalex Belgium, Mont-Saint-Guibert, Belgium
Introduction:
Population Pharmacokinetic/Pharmacodynamic (PK/PD) models describe the drug concentration-time course in body fluids resulting from administration of a certain drug dose. Compartment models, i.e. systems of differential equations, are typically used to conceptualize the mechanisms that take place in the interaction between an organism and a drug product. Software such as NONMEM is considered as the gold standard within the pharmacometrician community for population PK/PD modeling.
Objectives:
In recent years, the use of Bayesian methods has spread widely in many application sectors. While Bayesian applications in PK/PD modeling stay limited, statistical softwares such as SAS or STAN (R), are developing new functions for ODE system solving and more specifically for population pharmacokinetic modelling. The presentation will focus on the use of the MCMC procedure in SAS and the recent features proposed and tips to be used.
Methods:
The MCMC procedure is a general purpose Markov chain Monte Carlo (MCMC) simulation procedure that is designed to fit Bayesian models. In addition to the ODE solver already available in SAS, a new function (CMPTMODEL) is now proposed for Bayesian analyses to fit compartment models. The CMPTMODEL function is also available in the NLMIXED procedure for frequentist analyses. Whereas the ODE solver allows to fit a system of differential equations in any context, the CMPTMODEL function is dedicated to pharmacokinetic applications by computing predicted concentrations from a specified one-, two-, or three-compartment model.
The purpose of this talk is to present the different functionalities offered by SAS to fit differential equations system in a Bayesian context. The ODE solver will be compared to the CMPTMODEL statement in terms of programming, options, capabilities and computation time. Emphasis will be placed on the pharmacokinetic modelling and based on examples, the options (administration type, parametrisation type …) and capacities of the new CMPTMODEL statement will be presented.
As illustrative example, the results from a preclinical study with sparse designs (single and multiple administration, sparse sampling time) for supporting a PK/PD analysis will be shown.
Results:
In order to compare the ODE solver and CMPTMODEL statement, both programming codes will be presented based on this preclinical example. Options of the CMPTMODEL function will be detailed and the outputs of the MCMC procedure will be explained. As introduction, the basic principles of the Bayesian theory will be presented.
Conclusion:
The new tools proposed by SAS, CMPTMODEL function and ODE solver, allows to fit a large choice of differential equations systems. Currently, the focus is made on the pharmacokinetic model with development of new options for CMPTMODEL function. This function offer the possibility to fit quickly and easily classical PK model.
Reference: PAGE () Abstr 9295 [www.page-meeting.org/?abstract=9295]
Poster: Oral: Methodology - New Tools