Some comments and suggestions concerning population pharmacokinetic modeling, especially of digoxin, and its relation to clinical therapy.
Population pharmacokinetic and dynamic modeling is often employed to analyze data of steady-state trough serum digoxin concentrations in the course of what is frequently regarded as routine therapeutic drug monitoring (TDM). Such a monitoring protocol is extremely uninformative. It permits only the estimation of a single parameter of a 1-compartment model, such as clearance. The use of D-optimal design strategies permits much more information to be obtained, employing models having a really meaningful structure. Strategies and protocols for routine TDM policies greatly need to be improved, incorporating these principles of optimal design. Software for population pharmacokinetic modeling has been dominated by NONMEM. However, because NONMEM is a parametric method, it must assume a shape for the model parameter distributions. If the assumption is not correct, the model will be in error, and the most likely results given the raw data will not be obtained. In addition, the likelihood as computed by NONMEM is only approximate, not exact. This impairs statistical consistency and reduces statistical efficiency and the resulting precision of model parameter estimates. Other parametric methods are superior, as they provide exact likelihoods. However, they still suffer from the constraints of assuming the shape of the model parameter distributions. Nonparametric methods are more flexible. One need not make any assumptions about the shape of the parameter distributions. Nonparametric methods also provide exact likelihoods and are statistically consistent, efficient, and precise. They also permit maximally precise dosage regimens to be developed for patients using multiple model dosage design, something parametric modeling methods cannot do. Laboratory assay errors are better described by the reciprocal of the assay variance of each measurement rather than by coefficient of variation. This is easy to do and permits more precise models to be made. This also permits estimation of assay error separately from the other sources of uncertainty in the clinical environment. This is most useful scientifically. Digoxin has at least 2-compartment behavior. Its pharmacologic and clinical effects correlate not with serum digoxin concentrations but with those in the peripheral nonserum compartment. Some illustrative clinical examples are discussed. It seems that digitalis therapy, guided by TDM and our 2 compartment models based on that of Reuning et al, can convert at least some patients with atrial fibrillation and flutter to regular sinus rhythm. Investigators have often used steady-state trough concentrations only to make a 1-compartment model and have sought only to predict future steady-state trough concentrations. Much more than this can be done, and clinical care can be much improved. Further work along these lines is greatly to be desired.
Laboratory of Applied Pharmacokinetics, USC Keck School of Medicine, Los Angeles, California 90033, USA. jelliffe@usc.edu
Roger W
RW
Jelliffe
D004077:Digoxin / Q000097:blood / Q000493:pharmacokinetics* / Q000627:therapeutic use*; D016903:Drug Monitoring / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D012984:Software
D004077:Digoxin