One major task in clinical pharmacology is to determine the pharmacokinetic-pharmacodynamic (PK-PD) parameters of a drug in a patient population. NONMEM is a program commonly used to build population PK-PD models, that is, models that characterize the relationship between a patient's PK-PD parameters and other patient specific covariates such as the patient's (patho) physiological condition, concomitant drug therapy, etc. This paper extends a previously described approach to efficiently find the relationships between the PK-PD parameters and covariates. In a first step, individual estimates of the PK-PD parameters are obtained as empirical Bayes estimates, based on a prior NONMEN fit using no covariates. In a second step, the individual PK-PD parameter estimates are regressed on the covariates using a generalized additive model. In a third and final step, NONMEM is used to optimize and finalize the population model. Four real-data examples are used to demonstrate the effectiveness of the approach. The examples show that the generalized additive model for the individual parameter estimates is a good initial guess for the NONMEM population model. In all four examples, the approach successfully selects the most important covariates and their functional representation. The great advantage of this approach is speed. The time required to derive a population model is markedly reduced because the number of necessary NONMEM runs is reduced. Furthermore, the approach provides a nice graphical representation of the relationships between the PK-PD parameters and covariates.

A major goal in clinical pharmacology is the quantitative prediction of drug effects. The field of pharmacokinetic-pharmacodynamic (PK/PD) modelling has made many advances from the basic concept of the dose-response relationship to extended mechanism-based models. The purpose of this article is to review, from a historical perspective, the progression of the modelling of the concentration-response relationship from the first classic models developed in the mid-1960s to some of the more sophisticated current approaches. The emphasis is on general models describing key PD relationships, such as: simple models relating drug dose or concentration in plasma to effect, biophase distribution models and in particular effect compartment models, models for indirect mechanism of action that involve primarily the modulation of endogenous factors, models for cell trafficking and transduction systems. We show the evolution of tolerance and time-variant models, non- and semi-parametric models, and briefly discuss population PK/PD modelling, together with some example of more recent and complex pharmacodynamic models for control system and nonlinear HIV-1 dynamics. We also discuss some future possible directions for PK/PD modelling, report equations for general classes of novel semi-parametric models, as well as describing two new classes, additive or set-point, of regulatory, additive feedback models in their direct and indirect action variants.

The pharmacodynamics of a racemic mixture of ketamine R,S(+/-)-ketamine and of each enantiomer, S(+)-ketamine and R(-)-ketamine, were studied in five volunteers. The median frequency of the electroencephalogram (EEG) power spectrum, a continuous noninvasive measure of the degree of central nervous system (CNS) depression (pharmacodynamics), was related to measured serum concentrations of drug (pharmacokinetics). The concentration-effect relationship was described by an inhibitory sigmoid Emax pharmacodynamic model, yielding estimates of both maximal effect (Emax) and sensitivity (IC50) to the racemic and enantiomeric forms of ketamine. R(-)-ketamine was not as effective as R,S(+/-)-ketamine or S(+)-ketamine in causing EEG slowing. The maximal decrease (mean +/- SD) of the median frequency (Emax) for R(-)-ketamine was 4.4 +/- 0.5 Hz and was significantly different from R,S(+/-)-ketamine (7.6 +/- 1.7 Hz) and S(+)-ketamine (8.3 +/- 1.9 Hz). The ketamine serum concentration that caused one-half of the maximal median frequency decrease (IC50) was 1.8 +/- 0.5 micrograms/mL for R(-)-ketamine; 2.0 +/- 0.5 micrograms/mL for R,S(+/-)-ketamine; and 0.8 +/- 0.4 microgram/mL for S(+)-ketamine. Because the maximal effect (Emax) of the R(-)-ketamine was different from that of S(+)-ketamine and R,S(+/-)-ketamine, it was not possible to directly compare the potency (i.e., IC50) of these compounds. Accordingly, a classical agonist/partial-agonist interaction model was examined, using the separate enantiomer results to predict racemate results. Although the model did not predict racemate results well, its failure was not so great as to provide clear evidence of synergism (or excess antagonism) of the enantiomers.

Total 25(OH)D, health condition, race, and DBP haplotype affected free 25(OH)D, but only health conditions and BMI affected relationships between total and free 25(OH)D. Clinical importance of free 25(OH)D needs to be established in studies assessing outcomes.