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.

Study Objective To evaluate the pharmacokinetics of gentamicin in neonates with hypoxic ischemic encephalopathy (HIE) receiving hypothermia and to identify an empiric gentamicin dosing strategy in this population that optimizes achievement of target peak and trough concentrations. Design Population pharmacokinetic study using retrospective medical record data. Setting Tertiary neonatal intensive care unit. Patients A total of 29 term neonates diagnosed with HIE treated with hypothermia who received gentamicin and underwent therapeutic drug monitoring. Measurement and Main Results Patient demographics and gentamicin concentration data were retrospectively collected over a 2-year period. A population-based pharmacokinetic model was developed using nonlinear mixed-effects modeling (NONMEM). Using the developed model, Monte Carlo simulations were performed to evaluate the probability of achieving target peak (>6 mg/L) and trough (<2 mg/L) gentamicin concentrations for various potential dosing regimens. A one-compartment model best described the available gentamicin concentration data. Birthweight (BW) and serum creatinine (SCr) significantly influenced gentamicin clearance. For the typical study neonate (BW 3.3 kg; SCr 0.9 mg/dL), clearance was 0.034 L/H/kg and volume was 0.52 L/kg. At a 24-hour dosing interval, Monte Carlo simulations predicted target gentamicin peak and trough concentrations could not be reliably achieved at any dose. At a 36-hour dosing interval, a dose of 4-5 mg/kg is predicted to achieve target gentamicin peak and trough concentrations in >90% of neonates. Conclusions Gentamicin clearance is decreased in neonates with HIE treated with hypothermia compared with previous reports in nonasphyxiated normothermic term neonates. A prolonged 36-hour dosing interval will be needed to achieve target gentamicin trough concentrations in this population. Further prospective evaluation of this dosing recommendation is needed.

The OROS formulation of hydromorphone produced continued release of medication over 24 h, which should allow for once-daily oral dosing. The extended release of hydromorphone will produce less fluctuation of plasma concentrations compared with IR formulations, which should provide for more constant pain control. The in vivo release of hydromorphone from both IR and OROS formulations were adequately described using a linear spline deconvolution approach. The increased bioavailability from the OROS formulation may be related to decreased metabolism by a first-pass effect or enterohepatic recycling of hydromorphone.

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Quinolinic acid (QUIN) is a product of tryptophan metabolism that can act as an endogenous brain excitotoxin when released by activated macrophages. Previous studies have shown correlations between increased CSF QUIN levels and the presence of the AIDS dementia complex (ADC), a neurodegenerative condition complicating late-stage human immunodeficiency virus type 1 (HIV) infection in some patients. CSF QUIN is putatively one of the important molecular mediators of the brain injury in this clinical setting and, more generally, serves as a marker of local macrophage activation. This study was undertaken to examine the relationship of CSF QUIN concentrations to local HIV infection and to define the effects of antiretroviral drug treatment on CSF QUIN using two complementary approaches. The first was an exploratory cross-sectional analysis of a clinically heterogeneous sample of 62 HIV-infected subjects, examining correlations of CSF QUIN levels with CSF and plasma HIV RNA levels and other salient parameters of infection. The second involved longitudinal observations of a subset of 20 of these subjects who initiated new antiretroviral therapy regimens. In addition to descriptive analysis, we used kinetic modelling of QUIN decay in relation to that of HIV RNA to assess further the relationship between CSF QUIN and infection in the dynamic setting of treatment. The cross-sectional studies showed strong correlations of CSF QUIN with both CSF HIV RNA and blood QUIN levels, as well as with elevations in CSF white blood cells, CSF total protein and CSF:blood albumin ratio. In this group of subjects with a low incidence of active, untreated ADC, CSF QUIN did not correlate with ADC stage or measures of quantitative neurological performance. Antiviral treatment reduced the CSF QUIN levels in all the longitudinally followed, treated subjects. Kinetic modelling of CSF QUIN decay indicated that CSF QUIN levels were driven primarily by CSF HIV infection with a lesser contribution from blood QUIN levels. In three subjects with new-onset, untreated ADC, CSF QUIN decay paralleled both CSF HIV decrement and improvement in neurological performance. These studies show that CSF QUIN concentrations relate primarily to active CSF HIV infection and to a lesser extent to plasma QUIN. CSF QUIN serves as a marker of local infection with a wide dynamic range. The time course of therapy-induced changes links CSF QUIN to local infection and supports the action of antiviral therapy in ameliorating immunopathological brain injury and ADC.