Pharmacodynamic Parameter Estimates Research Articles

Pharmacokinetic-Pharmacodynamic Model for the Testosterone-Suppressive Effect of Leuprolide in Normal and Prostate Cancer Rats.

This study developed the pharmacokinetic (PK)–pharmacodynamic (PD) model of the testosterone-suppressive effect of leuprolide for evaluation of the sustained release (SR) depot and leuprolide solution in rats with or without prostate cancer. Six groups of rats were divided by the routes of administration (intravenous and subcutaneous injection) and kinds of formulation (vehicle, leuprolide solution, and SR depot). The PK profile after subcutaneous injection of leuprolide solution could be well-described by the one-compartment model. The absorption rate constant, the total body clearance, and the volume of distribution were estimated at 16.67 h−1, 514.46 mL/h, and 487.40 mL. Using PK parameters in the solution-administered group, the PK model for the SR depot was developed. The PK–PD model was constructed by describing the testosterone-suppressive effect of leuprolide using the feedback turnover model. The response of testosterone after administration of each formulation was well described using this PK–PD model for the estimation of PD parameters (EC50, Emax, h) and systemic parameters (kin, kout, kf on, kf off). The developed PK–PD model containing an inhibitory feedback system could successfully describe the testosterone-suppressive effect of leuprolide in the type of formulation. The PK–PD model developed would be useful for evaluating the formulation of similar drugs whose effect is regulated through the feedback mechanism.

Dose–response-time modelling: Second-generation turnover model with integral feedback control

This study presents a dose–response-time (DRT) analysis based on a large preclinical biomarker dataset on the interaction between nicotinic acid (NiAc) and free fatty acids (FFA). Data were collected from studies that examined different rates, routes, and modes of NiAc provocations on the FFA time course. All information regarding the exposure to NiAc was excluded in order to demonstrate the utility of a DRT model. Special emphasis was placed on the selection process of the biophase model. An inhibitory Imax-model, driven by the biophase amount, acted on the turnover rate of FFA. A second generation NiAc/FFA model, which encompasses integral (slow buildup of tolerance — an extension of the previously used NiAc/FFA turnover models) and moderator (rapid and oscillatory) feedback control, was simultaneously fitted to all time courses in normal rats. The integral feedback control managed to capture an observed 90% adaptation (i.e., almost a full return to baseline) when 10 days constant-rate infusion protocols of NiAc were used. The half-life of the adaptation process had a 90% prediction interval between 3.5–12 in the present population. The pharmacodynamic parameter estimates were highly consistent when compared to an exposure-driven analysis, partly validating the DRT modelling approach and suggesting the potential of DRT analysis in areas where exposure data are not attainable. Finally, new numerical algorithms, which rely on sensitivity equations to robustly and efficiently compute the gradients in the parameter optimization, were successfully used for the mixed-effects approach in the parameter estimation.

Dose selection of siltuximab for multicentric Castleman's disease.

Siltuximab is a monoclonal antibody that binds to interleukin (IL)-6 with high affinity and specificity; C-reactive protein (CRP) is an acute-phase protein induced by IL-6. CRP suppression is an indirect measurement of IL-6 activity. Here, modeling and simulation of the pharmacokinetic (PK)/pharmacodynamic (PD) relationship between siltuximab and CRP were used to support dose selection for multicentric Castleman's disease (CD). PK/PD modeling was applied to explore the relationship between siltuximab PK and CRP suppression following intravenous siltuximab infusion in 47 patients with B cell non-Hodgkin's lymphoma (n=17), multiple myeloma (n=13), or CD (n=17). Siltuximab was administered as 2.8, 5.5, or 11mg/kg q2wks, 11mg/kg q3wks, or 5.5mg/kg weekly. Simulations of studied or hypothetical siltuximab dosage regimens (15mg/kg q4wks) were also performed to evaluate maintenance of CRP suppression below the cutoff value of 1mg/L. A two-compartment PK model and an inhibitory indirect response PD model adequately described the serum siltuximab and CRP concentration-time profiles simultaneously. PD parameter estimates were physiologically plausible. For all disease types, simulations showed that 11mg/kg q3wks or 15mg/kg q4wks would reduce serum CRP to below 1mg/L after the second dose and throughout the treatment period. PK/PD modeling was used to select doses for further development of siltuximab in multicentric CD. The dosing recommendation was also supported by the observed efficacy dose-response relationship. CRP suppression in the subsequent randomized multicentric CD study was in agreement with the modeling predictions.

Impact of aminophylline on the pharmacodynamics of propofol in beagle dogs.

This study aimed to characterize pharmacodynamic interaction between propofol and aminophylline. Nine beagle dogs were randomly allocated at the propofol rates of 0.75 (group A), 1.00 (group B), and 1.25 (group C) mg/kg/min. During period 1, propofol only was infused, while during period 2, aminophylline only, at the rate of 0.69 (group A), 1.37 (group B), and 2.62 (group C) mg/kg/h. During periods 3-5, the two drugs were co-administered. The aminophylline infusion rate was 0.69 (period 3), 1.37 (period 4), and 2.62 (period 5) mg/kg/h. The aminophylline was infused from 0 to 30 h, and the propofol was infused at 24 h for 20 min. Blood samples and electroencephalograms were obtained at preset intervals. In the linear regression between log-transformed doses of aminophylline and AUC inf, the slope was 0.6976 (95% CI 0.5242-0.8710). Pharmacokinetics of aminophylline was best described by a one-compartment, with enzyme auto-induction, model. Pharmacokinetics and pharmacodynamics of propofol were best described by a three-compartment model and a sigmoid Emax model, respectively. Pharmacodynamic parameter estimates of propofol were: k(e0) = 0.805/min, E0 = 0.76, Emax = 0.398, Ce(50 na) = 2.38 μg/mL (without aminophylline-exposure), C(e50 wa) = 4.49 μg/mL (with aminophylline-exposure), and γ = 2.21. Propofol becomes less potent when exposed to aminophylline. Pharmacodynamic antagonistic interaction of aminophylline with propofol sedation, may occur, not in a dose-dependent manner, but in an all-or-none response.

Population Pharmacokinetic and Pharmacodynamic Modeling of an Anti–Interleukin-6 Chimeric Monoclonal Antibody, Siltuximab (CNTO 328), in Patients with B-Cell Non-Hodgkin’s Lymphoma, Multiple Myeloma, or Castleman’s Disease

AbstractAbstract 1365 IntroductionSiltuximab (CNTO 328) is a chimeric, murine-human, monoclonal antibody that specifically binds human interleukin (IL)-6 with high affinity. C-reactive protein (CRP) can be a pharmacodynamic (PD) marker of IL-6 bioactivity, i.e., reductions in CRP suggest inhibition of systemic IL-6. A population mechanistic pharmacokinetic (PK)/PD model was developed to describe the relationship between siltuximab serum concentrations and CRP suppression in patients with B-cell non-Hodgkin's lymphoma (NHL), multiple myeloma (MM), or Castleman's disease (CD). Simulation was used to support the dose selection in the CD registration study and future clinical studies. MethodsPK/PD data were obtained from a phase 1 clinical study examining multiple dosing regimens of siltuximab administered intravenously in patients with NHL, MM, or CD. Dosing regimens included siltuximab 2.8, 5.5, or 11 mg/kg every 2 weeks; 11 mg/kg every 3 weeks; or 5.5 mg/kg every week. Serial samples to determine serum concentration of siltuximab and serial CRP samples were collected following the first dose. NONMEM 7 was used to simultaneously fit a two-compartment PK model and an inhibitory indirect-response PD model to the observed data. Simulation of 1000 replications was then used to identify siltuximab dosage regimens that would maintain CRP suppression below the lower limit of quantification (LLOQ) of 1 mg/L. ResultsThe mechanistic PK/PD model was able to describe the serum siltuximab and CRP concentration-time profiles. Volume of distribution and systemic clearance rate constant of siltuximab were estimated at 68.42 mL/kg and 0.0584/day, respectively. The PD parameter estimates (Kin and Kout of CRP) were 5.03 mg/L/day and 0.457/day, respectively, and were similar between the three disease types in this study. IC50was estimated at 9.73 μg/mL and was also similar between disease types. For all disease types, simulations showed that siltuximab 11 mg/kg every 3 weeks or 15 mg/kg every 4 weeks after the second dose would reduce serum CRP to below the LLOQ throughout the entire treatment period. However, lower dose intensive schedules, including a dose of 5.5 mg/kg every 2 weeks, would not reduce CRP to below the LLOQ at any time point during the treatment period. ConclusionThe population PK/PD modeling and simulation support using a siltuximab dose of 11 mg/kg every 3 weeks or 15 mg/kg every 4 weeks in future clinical development studies. This dosing recommendation is supported by the observed efficacy dose-response relationship in patients with CD (J Clin Oncol 2010;28:3701–8). Disclosures:Xie:Johnson & Johnson: Employment, Equity Ownership. Li:Johnson & Johnson: Employment, Equity Ownership. Kurzrock:Johnson & Johnson: Honoraria, Research Funding. van Rhee:Johnson & Johnson: Research Funding. Qin:Johnson & Johnson: Employment, Equity Ownership. Reddy:Johnson & Johnson: Employment, Equity Ownership. Qi:Johnson & Johnson: Employment, Equity Ownership. Davis:Johnson & Johnson: Employment, Equity Ownership. Zhou:Johnson & Johnson: Employment, Equity Ownership. Puchalski:Johnson & Johnson: Employment, Equity Ownership.

Explaining the acetaminophen–ibuprofen analgesic interaction using a response surface model

The value of acetaminophen-ibuprofen combination therapy over single therapy is uncertain in acute pediatric pain management. A model describing the interaction between these two drugs would be useful both for understanding current literature and for future study design. Published pooled time-effect profiles in adults given combination or single therapy after dental extraction were used to construct an interaction model. Pain was measured using pain intensity differences (PRID, 0-10) from zero to eight hours postoperatively. Pharmacodynamic parameter estimates were assumed the same in adults as children. Pediatric pharmacokinetic estimates were scaled using allometric theory. Curve fitting was performed using nonlinear mixed effects models. Pooled data were available in adults given eight single and multiple dose combinations as well as placebo. The ibuprofen dose range was 100-400 mg, and acetaminophen dose range was 500-1000 mg. Pharmacodynamic parameter estimates, expressed using the Hill equation, were maximum effect (E(MAX) ) 4.06 (95% CI: 3.24, 5.51), the concentration of acetaminophen associated with 50% of the maximal drug effect (EC(50,ACET) ) 11.9 (95% CI: 6.0, 49.5) mg·l(-1) , the ibuprofen EC(50) (EC(50,IBU) ) 5.07 (95% CI: 3.50, 8.26) mg·l(-1), and Hill coefficient 2 (95% CI: 1.3, 2.8). An interaction term was fixed at zero (additive interaction). Simulation showed that the addition of acetaminophen to ibuprofen when less than 5 mg·kg(-1) was effective; acetaminophen had minimal effect when given with ibuprofen at doses greater than 5 mg·kg(-1) in the immediate postoperative period. A more sustained analgesic effect was noted at 4-8 h after combination dosing. This drug interaction modeling example is useful to explain combination therapy nuances and impacts on study design. Differences in effect between single drug therapy and combination therapy should be sought at lower doses and beyond the immediate postoperative period. Combination therapy may prolong the duration of analgesia. The maximum effect (E(MAX) ) limits the early additional analgesic gain from combination therapy beyond commonly used doses.

An Evaluation of Using Population Pharmacokinetic Models to Estimate Pharmacodynamic Parameters for Propofol and Bispectral Index in Children

To study propofol pharmacodynamics in a clinical setting a pharmacokinetic model must be used to predict drug plasma concentrations. Some investigators use a population pharmacokinetic model from existing literature and minimize the pharmacodynamic objective function. The purpose of the study was to determine whether this method selects the best-performing pharmacokinetic model in a set and provides accurate estimates of pharmacodynamic parameters in models for bispectral index in children after propofol administration. Twenty-eight children classified as American Society of Anesthesiologists physical status 1 who were given general anesthesia for dental treatment were studied. Anesthesia was given using target-controlled infusion of propofol based on the Kataria model. Propofol target plasma concentration was 7 μg/ml for 15 min, followed by 1 μg/ml for 15 min or until signs of awakening, followed by 5 μg/ml for 15 min. Venous blood samples were taken 1, 2, 5, 10, and 15 min after each change in target. A classic pharmacokinetic-pharmacodynamic model was estimated, and the methodology of other studies was duplicated using pharmacokinetic models from the literature and (re-)estimating the pharmacodynamic models. There is no clear relationship between pharmacokinetic precision and the pharmacodynamic objective function. Low pharmacodynamic objective function values are not associated with accurate estimation of the pharmacodynamic parameters when the pharmacokinetic model is taken from other sources. Minimization of the pharmacodynamic objective function does not select the most accurate pharmacokinetic model. Using population pharmacokinetic models from the literature instead of the 'true' pharmacokinetic model can lead to better predictions of bispectral index while incorrectly estimating the pharmacodynamic parameters.

Sunitinib (SU) pharmacokinetic (PK)–pharmacodynamic (PD) modeling with respect to safety and efficacy endpoints in Asian patients (pts) with gastrointestinal stromal tumor (GIST) or renal cell carcinoma (RCC) in pursuit of a therapeutic window.

e15128 Background: SU is an oral multitargeted tyrosine kinase inhibitor, approved multinationally for the treatment of advanced RCC, imatinib-resistant or -intolerant GIST, and well-differentiated pancreatic neuroendocrine tumor. We explored SU PK–PD relationships with respect to safety and efficacy endpoints to determine a therapeutic window for SU in Asian pts with GIST and RCC. Methods: We analyzed pooled PK–PD data from two Japanese phase II studies in pts with GIST and RCC (N=81 combined) using NONMEM V7.0. We initially built a PK model (2-compartments with 1st order absorption and elimination) to describe SU PK data. Subsequently, different sequential semi-mechanistic or mechanism-based (i.e., transit compartments with feedback loop [TCF] or indirect response [IDR] with Emax or sigmoid Emax drug effect) PK–PD models were built and compared for selection of a PK–PD model to describe each PD endpoint; these models were internally validated by Visual Predictive Check using PsN and Xpose. Results: Key PK–PD model characteristics or parameter estimates are shown in the table below. There appeared to be great inter-pt variability in, and overlap between, the exposure-response curves for safety endpoints and that of the efficacy endpoint. Conclusions: We could not identify a therapeutic window for SU in Asian pts with GIST and RCC. SU dose modification based on individual pt safety/tolerability appears to be the best approach to ensure maximum SU plasma exposure and efficacy, consistent with the SU label recommendation. PD endpoint PK–PD model EC50 Mean (CV) ng/mL (%) γ Mean Safety Absolute neutrophil count TCF 6.91 (146) 1(F) Platelet count TCF 55.7 (27) 5.22 Lymphocyte count IDR 98.6 (NE) 3.09 LVEF IDR 1050 (180) 1(F) Diastolic blood pressure IDR 318 (44) 1(F) AST TCF 74.7 (NE) 4.66 Efficacy SLD IDR 80.1 (248) 1(F) Abbreviations: CV, coefficient of variation; EC50, SU concentration at which 50% of the maximum effect is achieved; F, fixed; γ (gamma), Hill coefficient; LVEF, left ventricular ejection fraction; NE, not estimable; SLD, sum of the largest diameters of target tumors.

Pharmacokinetic and Pharmacodynamic Modeling of an Anti–Interleukin-6 Chimeric Monoclonal Antibody (Siltuximab) in Patients with Metastatic Renal Cell Carcinoma

Interleukin-6 (IL-6) induces tumor growth, invasion, metastasis, and angiogenesis. Siltuximab (CNTO 328) is a chimeric, murine-human monoclonal antibody that specifically binds human IL-6 with high affinity. C-reactive protein (CRP) can be a pharmacodynamic (PD) marker of IL-6 bioactivity. Reductions in CRP may correlate with clinical activity and IL-6 bioactivity. Starting-dose selection for this study was based on a previous siltuximab study in multiple myeloma patients. Pharmacokinetic (PK)/PD modeling explored the relationship between siltuximab PK and CRP suppression following i.v. siltuximab infusion in a three-part phase I/II study in 68 metastatic renal cell carcinoma patients. Modeling results were then used to simulate and determine which siltuximab dosage regimens would maintain CRP suppression below the lower limit of quantification (4 mg/L). Siltuximab was given at 1, 3, 6, or 12 mg/kg at weeks 1 and 4 and then every 2 weeks for 2 cycles in part 1; at 3 or 6 mg/kg every 3 weeks for 4 cycles in part 2; and at 6 mg/kg every 2 weeks for 6 cycles in part 3. A two-compartment PK model adequately described the serum siltuximab concentration-time data. An inhibitory indirect response PD model examined the relationship between siltuximab concentrations and CRP suppression. PD parameter estimates seemed reliable and physiologically relevant. Simulations showed that 6 mg/kg siltuximab every 2 weeks or 9 mg/kg every 3 weeks would reduce serum CRP to below 4 mg/L. Using a stepwise design, PK/PD modeling was used to select the dose levels in this study. Furthermore, PK/PD modeling results were used to help select doses to be used in future siltuximab clinical development.

Pharmacokinetic/Pharmacodynamic Modeling of Glucose Clamp Effects of Inhaled and Subcutaneous Insulin in Healthy Volunteers and Diabetic Patients

The pharmacokinetics and pharmacodynamics (PK/PD) of inhaled insulin in humans have not been modeled previously. We rationalized a model for the effects of inhaled insulin on glucose infusion rate during a euglycemic clamp study based on the mechanism of insulin action and compared parameter estimates between subcutaneous and inhaled insulin in healthy and diabetic subjects. Published data from two studies in 11 healthy volunteers and 18 type 1 diabetes patients were digitized. The subjects received four different doses of inhaled insulin and one or three different doses subcutaneously at the start of a 10 h glucose clamp. All data were modeled simultaneously using NONMEM VI. Insulin pharmacokinetics were described by a one-compartment model with one (inhaled) or two (subcutaneous insulin) first-order absorption processes and first-order elimination. Insulin effects on glucose were described by an indirect response model. A biophase direct effect equation for the glucose infusion rate was implemented. Pharmacodynamic parameter estimates were 15.1 mg/min/kg for maximal glucose infusion rate (GIR(max)) and 88.0 mIU/L for SC(50) for diabetic patients and 62.9 mIU/L for healthy subjects. A PK/PD model based on fundamental principles of insulin action and glucose turnover suggests comparable potencies of inhaled and subcutaneous insulin.

Dosing Ketamine for Pediatric Procedural Sedation in the Emergency Department

To describe intravenous ketamine dosing regimens for children requiring brief procedural sedation. Time-concentration and sedation profiles were simulated in children (2, 6, and 12 years old) using published pediatric pharmacokinetic and pharmacodynamic parameter estimates. Single-dose, repeat-dosing, and infusion regimens to achieve sedation level of less than 2 (arouses slowly to consciousness, with sustained painful stimulus) for 15 minutes were investigated. A single bolus dose of 1.5 and 1.75, 2, and 2.125 mg/kg (for adult and 12-, 6-, and 2-year-olds, respectively) was required to achieve the desired sedation. Anticipated recovery would be slow, and a sedation level of 4 (drowsy, eyes open or closed but easily arouses to consciousness with verbal stimulus) was reached only after 70 minutes. The use of a smaller initial bolus with a subsequent half-dose "top-up" at 8 minutes achieves the same sedation level but with earlier recovery. A smaller initial dose of 0.25 and 0.275, 0.3, and 0.35 mg/kg followed by an infusion 2.5 and 2.75, 3, and 3.5 mg/kg per hour (for adult and 12-, 6-, and 2-year-olds, respectively) for 15 minutes gives a more even sedation level and rapid recovery (20 minutes to sedation level 4). Dosing increases with decreasing age. A large single dose is associated with deep sedation, possible adverse effects, and delayed recovery. Between-subjects variability is large, and dose should be tailored to clinical monitoring and requirement. Intermittent pain insult is better suited to a top-up technique, whereas continuous pain is better suited to an infusion technique.

Assessment of Dosing Impact on Intra-Individual Variability in Estimation of Parameters for Basic Indirect Response Models

The application of D-optimization and the assessment of bias and precision of parameter estimates for four basic pharmacodynamic (PD) indirect response (IDR) models for ascending doses was examined using simulated data. While D-optimization provided four sampling times, each IDR model was used to generate eight data points per dose level. The PD parameters were: input rate constant (k (in)), disposition rate constant (k (out)), capacity constant (I (max) or S (max)), and sensitivity constant (IC (50) or SC (50)). A monoexponential pharmacokinetic function was applied with single doses increased by a factor of 10 to generate responses that vary from weak to fully saturable. For each dose, 100 replications of response data were simulated using independent normally distributed errors of CV = 20%. The original IDR model was fitted and PD parameters estimated. Histograms and descriptive statistics were generated. All parameters exhibited asymmetric distributions with positive coefficients of skewness except for I (max). Higher doses resulted in unbiased estimates of all PD parameters. The precision of parameter estimates improved with increasing doses except for IC (50) and SC (50) indicating that a single dose experimental design cannot be corrected by increasing dose in order to improve precision of estimates of IC (50) or SC (50). Highest variability was for IC (50) and SC (50) parameters. This study provides new insights into optimum study designs and recovery of parameters for basic IDR models.

Pharmacodynamic parameter estimation: Population size versus number of samples

The purpose of this study was to evaluate the effects of population size, number of samples per individual, and level of interindividual variability (IIV) on the accuracy and precision of pharmacodynamic (PD) parameter estimates. Response data were simulated from concentration input data for an inhibitory sigmoid drug efficacy (E(max)) model using Nonlinear Mixed Effect Modeling, version 5 (NONMEM). Seven designs were investigated using different concentration sampling windows ranging from 0 to 3 EC(50) (EC(50) is the drug concentration at 50% of the E(max)) units. The response data were used to estimate the PD and variability parameters in NONMEM. The accuracy and precision of parameter estimates after 100 replications were assessed using the mean and SD of percent prediction error, respectively. Four samples per individual were sufficient to provide accurate and precise estimate of almost all of the PD and variability parameters, with 100 individuals and IIV of 30%. Reduction of sample size resulted in imprecise estimates of the variability parameters; however, the PD parameter estimates were still precise. At 45% IIV, designs with 5 samples per individual behaved better than those designs with 4 samples per individual. For a moderately variable drug with a high Hill coefficient, sampling from the 0.1 to 1, 1 to 2, 2 to 2.5, and 2.5 to 3 EC(50) window is sufficient to estimate the parameters reliably in a PD study.

A pharmacokinetic/pharmacodynamic model for a platelet activating factor antagonist based on data arising from Phase I studies

A nonlinear mixed-effects modelling approach was used to analyse pharmacokinetic and pharmacodynamic data from two Phase I studies of a platelet activating factor (PAF) antagonist under development for the treatment of seasonal allergic rhinitis. Data for single-dose (8 subjects) and multiple-dose (9 subjects) administration were available for analysis with a program based on an EM algorithm. Pharmacokinetic analyses of plasma drug concentrations were performed using a biexponential model with first-order absorption. PAF response data were modelled with a hyperbolic Emax model. The drug showed nonlinear pharmacokinetics, with the clearance decreasing from 46.0 to 27.1 L h(-1) over a dose range of 160-480 mg. There was an apparent dose dependency within the C50 (concentration producing 50% of the maximum effect) but at higher doses most of the data was above the estimated C50 and when the data was analysed simultaneously a value of 17.57 ng mL(-1) was obtained for C50, with considerable intersubject variability (103%). Consistent results were obtained from the two studies and the population and individual pharmacodynamic parameter estimates from the analyses provided predicted responses that were in good agreement with the observed data. The results were used to simulate a 320-mg twice-daily dosing regimen.

Pharmacodynamic analysis of the interaction between tiagabine and midazolam with an allosteric model that incorporates signal transduction.

The objective of this study was to characterize quantitatively the pharmacodynamic interaction between midazolam (MDL), an allosteric modulator of the gamma-aminobutyric acid subtype A (GABAA) receptor, and tiagabine (TGB), an inhibitor of synaptic GABA uptake. The in vivo concentration-response relation of TGB was determined through pharmacokinetic/pharmacodynamic (PK/PD) modeling. Rats received a single intravenous dose of 10 mg/kg TGB in the absence and the presence of a steady-state plasma concentration of MDL. The EEG response in the 11.5- to 30-Hz frequency band was used as the pharmacodynamic end point. Infusion of MDL resulted in a mean steady-state plasma concentration of 66 +/- 3 ng/ml. A significant pharmacokinetic interaction with TGB was observed. MDL inhibited TGB clearance by 20 +/- 7 ml/min/kg from the original value of 89 +/- 6 ml/min/kg. However, no changes in plasma protein binding of both drugs were observed. The concentration-EEG relation of TGB was described by the sigmoid-Emax model. The pharmacodynamic parameter estimates of TGB were: Emax = 327 +/- 10 microV, EC50 = 392 +/- 20 ng/ml, and nH = 3.1 +/- 0.3. These values were not significantly different in the presence of MDL. Factors that may explain the lack of synergism were identified by a mechanism-based interaction model that separates the receptor activation from the signal-transduction process. High efficiency of signal transduction and the presence of a baseline response were shown to diminish the degree of synergism. We conclude that the in vivo pharmacodynamic interaction between MDL and TGB is additive rather than synergistic. This strongly suggests that allosteric modulation of the antiseizure activity of a GAT-1 inhibitor by a benzodiazepine does not offer a therapeutic advantage.

Pharmacokinetic–pharmacodynamic modelling of the hypothermic and corticosterone effects of the 5-HT 1A receptor agonist flesinoxan

The current investigation describes the pharmacokinetic–pharmacodynamic correlation of the hypothermic and the corticosterone effect of flesinoxan in the rat simultaneously. A specific objective was to determine the influence of handling the animal. The pharmacokinetic–pharmacodynamic correlation was determined following intravenous administration of 3 and 10 mg/kg flesinoxan in 5 or 15 min. Serial blood samples were obtained for determination of the time course of the flesinoxan and corticosterone concentrations by high performance liquid chromatography. Body temperature was monitored using a telemetric probe. The pharmacokinetics of flesinoxan were described using a three-compartment model. Both the hypothermic and the corticosterone response were successfully described using a physiological indirect response model. It is shown that customizing the animal prior to the experiment has no influence on the pharmacokinetic–pharmacodynamic parameter estimates. Furthermore, the similarity in potency between the hypothermic and corticosterone effects suggests that both are mediated via tissues with a similar receptor–effector coupling efficiency.

Population pharmacokinetics and pharmacodynamics of sotalol in pediatric patients with supraventricular or ventricular tachyarrhythmia.

To derive useful pharmacokinetic (PK) and pharmacodynamic (PD) information for guiding the clinical use of sotalol in pediatric patients with supraventricular (SVT) or ventricular tachyarrhythmia (VT). Two studies were conducted in-patients with SVT or VT in the age range between birth and 12 years old. Both studies used an extemporaneously compounded formulation prepared from sotalol HCl tablets. In the PK study, following a single dose of 30 mg/m2 sotalol, extensive blood samples (n = 10) were taken. The PK-PD study used a dose escalation design with doses of 10, 30, and 70 mg/m2, each administered three times at 8-hr intervals without a washout. Six ECG recordings for determination of QT and RR were obtained prior to the initial dose of sotalol. Four blood samples were collected six ECG's were determined during the third interval at each dose level. Plasma concentrations of sotalol (C) were assayed by LC/MS/MS. The data analysis used NONMEM to obtain the population PK and PD parameter estimates. The individual PK and PD parameters were estimated with empirical Bayes methodology. A total of 611 C from 58 patients, 477 QTc and 499 RR measurements from 23 and 22 patients, respectively, were available for analysis. The PK of sotalol was best described by a linear two-compartment model. Oral clearance (CL/F) and volume of central compartment (Vc/F) were linearly correlated with body surface area (BSA), body weight or age. CL/F was also linearly correlated with creatinine clearance. The best predictor for both CL/F and Vc/F was BSA. The remaining intersubject coefficients of variation (CV's) in CL/F, and Vc/F were 21.6% and 20.3%, respectively. The relationship of QTc to C was adequately described by a linear model. The intersubject CV's in slope (SL) and intercept (E0) were 56.2 and 4.7%, respectively. The relationship of RR to C was also adequately described by a linear model in which the baseline RR and SL were related to age or BSA. The intersubject CV's for SL and E0 were 86.7 and 14.4%, respectively. BSA is the best predictor for the PK of sotalol. Both QTc and RR effects are linearly related to C. No covariates are found for the QTc-C relation, while the RR-C relation shows age or BSA dependency.

Investigations using logistic regression models on the effect of the LMA on morphine induced vomiting after tonsillectomy.

The effect of intraoperative airway management on postoperative vomiting after tonsillectomy is unknown. Logistic regression was used in a retrospective study to investigate the effect of the laryngeal mask airway (LMA) on a morphine dose-vomiting response curve. Charts were reviewed in 351 children in whom the airway was managed with either a LMA (n=177) or a tracheal tube (n=174). A mean perioperative morphine dose of 0.10 mg.kg(-1) (SD 0.09) was used in 248 children and a further 103 children were given no opioid. One hundred and eighteen of these 248 children vomited (47.6%) compared to 14 of 103 children given no morphine (13.6%). The probability of vomiting was related to morphine dose using logistic regression with both a linear and an E(max) model. Both the calibration (Hosmer-Lemishow goodness of fit chi-squared test lambda(2), P=0.81) and discrimination (area under the receiver operating characteristic plot, AUC ROC=0.67) of the E(max) model were better than the linear model (lambda(2), P=0.49; AUC ROC=0.64). Pharmacodynamic parameter estimates for the Emax model were P(0) (the baseline probability of vomiting) 0.139, P(max) (the maximal probability of vomiting due to morphine) 0.96, ED(50) (morphine dose that induces an effect equivalent to 50% of the logit P(max)) 0.09 mg.kg-1. The probability of vomiting was 50% after morphine 0.125 mg.kg-1. The use of the LMA had no effect on this dose-response curve. A covariate analysis investigating propofol for induction or isoflurane for the intraoperative maintenance of anaesthesia, however, showed that both drugs shifted the curve to the right. The probability of vomiting was 50% after morphine 0.17 mg.kg(-1) and 0.21 mg.kg(-1) for the isoflurane and propofol use curves, respectively. The concomitant use of propofol and isoflurane, but not the use of the LMA, decreases the probability of vomiting due to morphine.

The dose-effect relationship for morphine and vomiting after day-stay tonsillectomy in children.

A dose-response curve for intravenous morphine and vomiting was investigated in children having day-stay tonsillectomy. A retrospective chart review was performed for the 164 children fulfilling the inclusion criteria. Morphine (mean 0.09 mg/kg SD 0.05) was used in 108 children in the perioperative period and a further 56 children were given no opioid. Fifty-five of these 164 children vomited and 20 children required an overnight stay in hospital because of vomiting. The probability of vomiting or overnight stay in hospital was related to morphine dose (by logistic regression). The overall probability of vomiting after morphine 0.1 mg/kg was 50% and the probability of admission for vomiting with this dose was 10%. Pharmacodynamic parameter estimates for postoperative vomiting were P0 (the baseline probability of vomiting, with no opioid) 0.115, Pmax (the maximal probability of vomiting due to morphine) 0.997, ED50 (morphine dose that induces an effect equivalent to 50% of the logit Pmax) 0.18 mg/kg. Parameter estimates for overnight stay because of vomiting after morphine administration were P0 0.038, Pmax 0.999, ED50 0.369 mg/kg. Satisfactory postoperative analgesia in children has been reported with morphine 0.05 to 0.15 mg/kg. Doses above 0.1 mg/kg are associated with a greater than 50% incidence of vomiting. Our data suggests that lower doses of morphine are associated with a decreased incidence of emesis after tonsillectomy in children.

Rate of change of blood concentrations is a major determinant of the pharmacodynamics of midazolam in rats.

The objective of this investigation was to characterize quantitatively the influence of the rate of increase in blood concentrations on the pharmacodynamics of midazolam in rats. The pharmacodynamics of midazolam were quantified by an integrated pharmacokinetic-pharmacodynamic modelling approach. Using a computer controlled infusion technique, a linear increase in blood concentrations up to 80 ng ml(-1) was obtained over different time intervals of 16 h, resulting in rates of rise of the blood concentrations of respectively, 1.25, 1.00, 0.87, 0.46, 0.34 and 0.20 ng ml(-1) min(-1). In one group of rats the midazolam concentration was immediately brought to 80 ng ml(-1) and maintained at that level for 4 h. Immediately after the pretreatment an intravenous bolus dose was given to determine the time course of the EEG effect in conjunction with the decline of midazolam concentrations. The increase in beta activity (11.5-30 Hz) of the EEG was used as pharmacodynamic endpoint. For each individual animal the relationship between blood concentration and the EEG effect could be described by the sigmoidal Emax model. After placebo, the values of the pharmacodynamic parameter estimates were Emax = 82+/-5 microV, EC50,u = 6.4+/-0.8 ng ml(-1) and Hill factor = 1.4+/-0.1. A bell-shaped relationship between the rate of change of midazolam concentration and the value of EC50,u was observed with a maximum of 21+/-5.0 ng ml(-1) at a rate of change of 0.46 ng ml(-1) min(-1); lower values of EC50,u were observed at both higher and lower rates. The findings of this study show that the rate of change in plasma concentrations is an important determinant of the pharmacodynamics of midazolam in rats.