Darunavir Population Pharmacokinetic Model Based on HIV Outpatient Data.

BACKGROUND: Darunavir is a second-generation protease inhibitor and is registered for the treatment of HIV-1 infection. The aim of this study was to develop and validate a darunavir population pharmacokinetic model based on data from daily practice. METHODS: Data sets were obtained from 2 hospitals: ASST Fatebenefratelli Sacco University Hospital, Italy (hospital A), and University Medical Center Groningen, the Netherlands (hospital B). A pharmacokinetic model was developed using data from the largest data set using the iterative two-stage Bayesian procedure within the MWPharm software package. External validation was conducted using data from the smaller data set with Passing-Bablok regression and Bland-Altman analyses. RESULTS: In total, data from 198 patients from hospital A and 170 patients from hospital B were eligible for inclusion. A 1-compartment model with first-order absorption and elimination resulted in the best model. The Passing-Bablok analysis demonstrated a linear correlation between measured concentration and predicted concentration with r = 0.97 (P < 0.05). The predicted values correlated well with the measured values as determined by a Bland-Altman analysis and were overestimated by a mean value of 0.12 mg/L (range 0.23-0.94 mg/L). A total of 98.2% of the predicted values were within the limits of agreement. CONCLUSIONS: A robust population pharmacokinetic model was developed, which can support therapeutic drug monitoring of darunavir in daily outpatient settings.

An Allometric Model of Remifentanil Pharmacokinetics and Pharmacodynamics.

BACKGROUND: Pharmacokinetic and pharmacodynamic models are used to predict and explore drug infusion schemes and their resulting concentration profiles for clinical application. Our aim was to develop a pharmacokinetic-pharmacodynamic model for remifentanil that is accurate in patients with a wide range of age and weight. METHODS: Remifentanil pharmacokinetic data were obtained from three previously published studies of adults and children, one of which also contained pharmacodynamic data from adults. NONMEM was used to estimate allometrically scaled compartmental pharmacokinetic and pharmacodynamic models. Weight, age, height, sex, and body mass index were explored as covariates. Predictive performance was measured across young children, children, young adults, middle-aged, and elderly. RESULTS: Overall, 2,634 remifentanil arterial concentration and 3,989 spectral-edge frequency observations from 131 individuals (55 male, 76 female) were analyzed. Age range was 5 days to 85 yr, weight range was 2.5 to 106 kg, and height range was 49 to 193 cm. The final pharmacokinetic model uses age, weight, and sex as covariates. Parameter estimates for a 35-yr-old, 70-kg male (reference individual) are: V1, 5.81 l; V2, 8.82 l; V3, 5.03 l; CL, 2.58 l/min; Q2, 1.72 l/min; and Q3, 0.124 l/min. Parameters mostly increased with fat-free mass and decreased with age. The pharmacodynamic model effect compartment rate constant (ke0) was 1.09 per minute (reference individual), which decreased with age. CONCLUSIONS: We developed a pharmacokinetic-pharmacodynamic model to predict remifentanil concentration and effect for a wide range of patient ages and weights. Performance exceeded the Minto model over a wide age and weight range.

A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations.

PURPOSE: Predicting target site drug concentration in the brain is of key importance for the successful development of drugs acting on the central nervous system. We propose a generic mathematical model to describe the pharmacokinetics in brain compartments, and apply this model to predict human brain disposition. METHODS: A mathematical model consisting of several physiological brain compartments in the rat was developed using rich concentration-time profiles from nine structurally diverse drugs in plasma, brain extracellular fluid, and two cerebrospinal fluid compartments. The effect of active drug transporters was also accounted for. Subsequently, the model was translated to predict human concentration-time profiles for acetaminophen and morphine, by scaling or replacing system- and drug-specific parameters in the model. RESULTS: A common model structure was identified that adequately described the rat pharmacokinetic profiles for each of the nine drugs across brain compartments, with good precision of structural model parameters (relative standard error <37.5%). The model predicted the human concentration-time profiles in different brain compartments well (symmetric mean absolute percentage error <90%). CONCLUSIONS: A multi-compartmental brain pharmacokinetic model was developed and its structure could adequately describe data across nine different drugs. The model could be successfully translated to predict human brain concentrations.

Translational Modeling in Schizophrenia: Predicting Human Dopamine D2 Receptor Occupancy.

OBJECTIVES: To assess the ability of a previously developed hybrid physiology-based pharmacokinetic-pharmacodynamic (PBPKPD) model in rats to predict the dopamine D2 receptor occupancy (D2RO) in human striatum following administration of antipsychotic drugs. METHODS: A hybrid PBPKPD model, previously developed using information on plasma concentrations, brain exposure and D2RO in rats, was used as the basis for the prediction of D2RO in human. The rat pharmacokinetic and brain physiology parameters were substituted with human population pharmacokinetic parameters and human physiological information. To predict the passive transport across the human blood-brain barrier, apparent permeability values were scaled based on rat and human brain endothelial surface area. Active efflux clearance in brain was scaled from rat to human using both human brain endothelial surface area and MDR1 expression. Binding constants at the D2 receptor were scaled based on the differences between in vitro and in vivo systems of the same species. The predictive power of this physiology-based approach was determined by comparing the D2RO predictions with the observed human D2RO of six antipsychotics at clinically relevant doses. RESULTS: Predicted human D2RO was in good agreement with clinically observed D2RO for five antipsychotics. Models using in vitro information predicted human D2RO well for most of the compounds evaluated in this analysis. However, human D2RO was under-predicted for haloperidol. CONCLUSIONS: The rat hybrid PBPKPD model structure, integrated with in vitro information and human pharmacokinetic and physiological information, constitutes a scientific basis to predict the time course of D2RO in man.

Pharmacokinetic Modeling and Optimal Sampling Strategies for Therapeutic Drug Monitoring of Rifampin in Patients with Tuberculosis.

Rifampin, together with isoniazid, has been the backbone of the current first-line treatment of tuberculosis (TB). The ratio of the area under the concentration-time curve from 0 to 24 h (AUC0-24) to the MIC is the best predictive pharmacokinetic-pharmacodynamic parameter for determinations of efficacy. The objective of this study was to develop an optimal sampling procedure based on population pharmacokinetics to predict AUC0-24 values. Patients received rifampin orally once daily as part of their anti-TB treatment. A one-compartmental pharmacokinetic population model with first-order absorption and lag time was developed using observed rifampin plasma concentrations from 55 patients. The population pharmacokinetic model was developed using an iterative two-stage Bayesian procedure and was cross-validated. Optimal sampling strategies were calculated using Monte Carlo simulation (n = 1,000). The geometric mean AUC0-24 value was 41.5 (range, 13.5 to 117) mg · h/liter. The median time to maximum concentration of drug in serum (Tmax) was 2.2 h, ranging from 0.4 to 5.7 h. This wide range indicates that obtaining a concentration level at 2 h (C2) would not capture the peak concentration in a large proportion of the population. Optimal sampling using concentrations at 1, 3, and 8 h postdosing was considered clinically suitable with an r(2) value of 0.96, a root mean squared error value of 13.2%, and a prediction bias value of -0.4%. This study showed that the rifampin AUC0-24 in TB patients can be predicted with acceptable accuracy and precision using the developed population pharmacokinetic model with optimal sampling at time points 1, 3, and 8 h.

Limited-sampling strategies for anidulafungin in critically ill patients.

Efficacy of anidulafungin is driven by the area under the concentration-time curve (AUC)/MIC ratio. Determination of the anidulafungin AUC along with MIC values can therefore be useful. Since obtaining a full concentration-time curve to determine an AUC is not always feasible or appropriate, limited-sampling strategies may be useful in adequately estimating exposure. The objective of this study was to develop a model to predict the individual anidulafungin exposure in critically ill patients using limited-sampling strategies. Pharmacokinetic data were derived from 20 critically ill patients with invasive candidiasis treated with anidulafungin. These data were used to develop a two-compartment model in MW\Pharm using an iterative 2-stage Bayesian procedure. Limited-sampling strategies were subsequently investigated using two methods, a Bayesian analysis and a linear regression analysis. The best possible strategies for these two methods were evaluated by a Bland-Altman analysis for correlation of the predicted and observed AUC from 0 to 24 h (AUC0-24) values. Anidulafungin exposure can be adequately estimated with the concentration from a single sample drawn 12 h after the start of the infusion either by linear regression (R2=0.99; bias, 0.05%; root mean square error [RMSE], 3%) or using a population pharmacokinetic model (R2=0.89; bias, -0.1%; RMSE, 9%) in critically ill patients and also in less severely ill patients, as reflected by healthy volunteers. Limited sampling can be advantageous for future studies evaluating the pharmacokinetics and pharmacodynamics of anidulafungin and for therapeutic drug monitoring in selected patients. (This study has been registered at ClinicalTrials.gov under registration no. NCT01047267.).

Development of an Optimized Pharmacokinetic Model of Dexmedetomidine Using Target-controlled Infusion in Healthy Volunteers.

BACKGROUND: Several pharmacokinetic models are available for dexmedetomidine, but these have been shown to underestimate plasma concentrations. Most were developed with data from patients during the postoperative phase and/or in intensive care, making them susceptible to errors due to drug interactions. The aim of this study is to improve on existing models using data from healthy volunteers. METHODS: After local ethics committee approval, the authors recruited 18 volunteers, who received a dexmedetomidine target-controlled infusion with increasing target concentrations: 1, 2, 3, 4, 6, and 8 ng/ml, repeated in two sessions, at least 1 week apart. Each level was maintained for 30 min. If one of the predefined safety criteria was breached, the infusion was terminated and the recovery period began. Arterial blood samples were collected at preset times, and NONMEM (Icon plc, Ireland) was used for model development. RESULTS: The age, weight, and body mass index ranges of the 18 volunteers (9 male and 9 female) were 20 to 70 yr, 51 to 110 kg, and 20.6 to 29.3 kg/m, respectively. A three-compartment allometric model was developed, with the following estimated parameters for an individual of 70 kg: V1 = 1.78 l, V2 = 30.3 l, V3 = 52.0 l, CL = 0.686 l/min, Q2 = 2.98 l/min, and Q3 = 0.602 l/min. The predictive performance as calculated by the median absolute performance error and median performance error was better than that of existing models. CONCLUSIONS: Using target-controlled infusion in healthy volunteers, the pharmacokinetics of dexmedetomidine were best described by a three-compartment allometric model. Apart from weight, no other covariates were identified.

A response surface model approach for continuous measures of hypnotic and analgesic effect during sevoflurane-remifentanil interaction: quantifying the pharmacodynamic shift evoked by stimulation.

BACKGROUND: The authors studied the interaction between sevoflurane and remifentanil on bispectral index (BIS), state entropy (SE), response entropy (RE), Composite Variability Index, and Surgical Pleth Index, by using a response surface methodology. The authors also studied the influence of stimulation on this interaction. METHODS: Forty patients received combined concentrations of remifentanil (0 to 12 ng/ml) and sevoflurane (0.5 to 3.5 vol%) according to a crisscross design (160 concentration pairs). During pseudo-steady-state anesthesia, the pharmacodynamic measures were obtained before and after a series of noxious and nonnoxious stimulations. For the "prestimulation" and "poststimulation" BIS, SE, RE, Composite Variability Index, and Surgical Pleth Index, interaction models were applied to find the best fit, by using NONMEM 7.2.0. (Icon Development Solutions, Hanover, MD). RESULTS: The authors found an additive interaction between sevoflurane and remifentanil on BIS, SE, and RE. For Composite Variability Index, a moderate synergism was found. The comparison of pre- and poststimulation data revealed a shift of C50SEVO for BIS, SE, and RE, with a consistent increase of 0.3 vol%. The Surgical Pleth Index data did not result in plausible parameter estimates, neither before nor after stimulation. CONCLUSIONS: By combining pre- and poststimulation data, interaction models for BIS, SE, and RE demonstrate a consistent influence of "stimulation" on the pharmacodynamic relationship between sevoflurane and remifentanil. Significant population variability exists for Composite Variability Index and Surgical Pleth Index.

Dopamine D2 receptor occupancy as a predictor of catalepsy in rats: a pharmacokinetic-pharmacodynamic modeling approach.

OBJECTIVES: Dopamine D2 receptor occupancy (D2RO) is the major determinant of efficacy and safety in schizophrenia drug therapy. Excessive D2RO (>80%) is known to cause catalepsy (CAT) in rats and extrapyramidal side effects (EPS) in human. The objective of this study was to use pharmacokinetic and pharmacodynamic modeling tools to relate CAT with D2RO in rats and to compare that with the relationship between D2RO and EPS in humans. METHODS: Severity of CAT was assessed in rats at hourly intervals over a period of 8 h after antipsychotic drug treatment. An indirect response model with and without Markov elements was used to explain the relationship of D2RO and CAT. RESULTS: Both models explained the CAT data well for olanzapine, paliperidone and risperidone. However, only the model with the Markov elements predicted the CAT severity well for clozapine and haloperidol. The relationship between CAT scores in rat and EPS scores in humans was implemented in a quantitative manner. Risk of EPS not exceeding 10% over placebo correlates with less than 86% D2RO and less than 30% probability of CAT events in rats. CONCLUSION: A quantitative relationship between rat CAT and human EPS was elucidated and may be used in drug discovery to predict the risk of EPS in humans from D2RO and CAT scores measured in rats.

A general purpose pharmacokinetic model for propofol.

BACKGROUND: Pharmacokinetic (PK) models are used to predict drug concentrations for infusion regimens for intraoperative displays and to calculate infusion rates in target-controlled infusion systems. For propofol, the PK models available in the literature were mostly developed from particular patient groups or anesthetic techniques, and there is uncertainty of the accuracy of the models under differing patient and clinical conditions. Our goal was to determine a PK model with robust predictive performance for a wide range of patient groups and clinical conditions. METHODS: We aggregated and analyzed 21 previously published propofol datasets containing data from young children, children, adults, elderly, and obese individuals. A 3-compartmental allometric model was estimated with NONMEM software using weight, age, sex, and patient status as covariates. A predictive performance metric focused on intraoperative conditions was devised and used along with the Akaike information criteria to guide model development. RESULTS: The dataset contains 10,927 drug concentration observations from 660 individuals (age range 0.25-88 years; weight range 5.2-160 kg). The final model uses weight, age, sex, and patient versus healthy volunteer as covariates. Parameter estimates for a 35-year, 70-kg male patient were: 9.77, 29.0, 134 L, 1.53, 1.42, and 0.608 L/min for V1, V2, V3, CL, Q2, and Q3, respectively. Predictive performance is better than or similar to that of specialized models, even for the subpopulations on which those models were derived. CONCLUSIONS: We have developed a single propofol PK model that performed well for a wide range of patient groups and clinical conditions. Further prospective evaluation of the model is needed.

Clinical relevance of pharmacological and physiological data in intrathecal baclofen therapy.

OBJECTIVE: To review all pharmacological and physiological data available on intrathecal baclofen (ITB) therapy and to evaluate its use in clinical practice and future research. DATA SOURCES: PubMed was searched for relevant anatomic, physiological, and pharmacological data available on ITB. STUDY SELECTION: All currently available data on ITB pharmacokinetics (PKs) and pharmacodynamics (PDs) in both human and animal studies were reviewed and combined with the anatomy and physiology of the intrathecal space and cerebrospinal fluid flow. DATA EXTRACTION: Only 4 studies reported PK data on ITB in humans. More studies reported PD data on ITB; however, none were combined with PK data. More detailed data on PK could be gathered from studies using an animal model. DATA SYNTHESIS: ITB does not spread equally over the intrathecal space after injection, but it diffuses according to a concentration gradient. ITB distribution can be influenced by the location of the catheter tip and by changing the infusion mode. CONCLUSIONS: The pharmacological and physiological data on ITB can be used to support decisions in clinical practice concerning drug concentration, infusion regimens, localization of the catheter tip, and management of tolerance; however, some strategies have little evidence in humans.

Nanoparticle formulation of a poorly soluble cannabinoid receptor 1 antagonist improves absorption by rat and human intestine.

The inclusion of nanoparticles dispersed in a hydrophilic matrix is one of the formulation strategies to improve the bioavailability of orally administered Biopharmaceutics Classification System (BCS) class II and IV drugs by increasing their dissolution rate in the intestine. To confirm that the increased dissolution rate results in increased bioavailability, in vitro and in vivo animal experiments are performed, however, translation to the human situation is hazardous. In this study, we used a range of in vitro and ex vivo methods, including methods applying human tissue, to predict the in vivo oral bioavailability of a model BCS class II CB-1 antagonist, formulated as a nanoparticle solid dispersion. The enhanced dissolution rate from the nanoparticle formulation resulted in an increased metabolite formation in both rat and human precision-cut intestinal slices, suggesting increased uptake and intracellular drug concentration in the enterocytes. In Ussing chamber experiments with human tissue, both the metabolite formation and apical efflux of the metabolite were increased for the nanoparticulate solid dispersion compared with a physical mixture, in line with the results in intestinal slices. The pharmacokinetics of the different formulations was studied in rats in vivo. The nanoparticle formulation indeed improved the absorption of the cannabinoid receptor 1 (CB-1) antagonist and the delivery into the brain compared with the physical mixture. In conclusion, the combined approach provides a valuable set of tools to investigate the effects of formulation on the absorption of poorly soluble compounds in human intestine and may provide relevant information on the oral bioavailability in humans early in the development process.

Population pharmacokinetic-pharmacodynamic modeling of haloperidol in patients with schizophrenia using positive and negative syndrome rating scale.

The aim of this study was to develop a pharmacokinetic-pharmacodynamic (PKPD) model that quantifies the efficacy of haloperidol, accounting for the placebo effect, the variability in exposure-response, and the dropouts. Subsequently, the developed model was utilized to characterize an effective dosing strategy for using haloperidol as a comparator drug in future antipsychotic drug trials. The time course of plasma haloperidol concentrations from 122 subjects and the Positive and Negative Syndrome Scale (PANSS) scores from 473 subjects were used in this analysis. A nonlinear mixed-effects modeling approach was utilized to describe the time course of PK and PANSS scores. Bootstrapping and simulation-based methods were used for the model evaluation. A 2-compartment model adequately described the haloperidol PK profiles. The Weibull and Emax models were able to describe the time course of the placebo and the drug effects, respectively. An exponential model was used to account for dropouts. Joint modeling of the PKPD model with dropout model indicated that the probability of patients dropping out is associated with the observed high PANSS score. The model evaluation results confirmed that the precision and accuracy of parameter estimates are acceptable. Based on the PKPD analysis, the recommended oral dose of haloperidol to achieve a 30% reduction in PANSS score from baseline is 5.6 mg/d, and the corresponding steady-state effective plasma haloperidol exposure is 2.7 ng/mL. In conclusion, the developed model describes the time course of PANSS scores adequately, and a recommendation of haloperidol dose was derived for future antipsychotic drug trials.

Interaction between nitrous oxide, sevoflurane, and opioids: a response surface approach.

BACKGROUND: The interaction of sevoflurane and opioids can be described by response surface modeling using the hierarchical model. We expanded this for combined administration of sevoflurane, opioids, and 66 vol.% nitrous oxide (N2O), using historical data on the motor and hemodynamic responsiveness to incision, the minimal alveolar concentration, and minimal alveolar concentration to block autonomic reflexes to nociceptive stimuli, respectively. METHODS: Four potential actions of 66 vol.% N2O were postulated: (1) N2O is equivalent to A ng/ml of fentanyl (additive); (2) N2O reduces C50 of fentanyl by factor B; (3) N2O is equivalent to X vol.% of sevoflurane (additive); (4) N2O reduces C50 of sevoflurane by factor Y. These four actions, and all combinations, were fitted on the data using NONMEM (version VI, Icon Development Solutions, Ellicott City, MD), assuming identical interaction parameters (A, B, X, Y) for movement and sympathetic responses. RESULTS: Sixty-six volume percentage nitrous oxide evokes an additive effect corresponding to 0.27 ng/ml fentanyl (A) with an additive effect corresponding to 0.54 vol.% sevoflurane (X). Parameters B and Y did not improve the fit. CONCLUSION: The effect of nitrous oxide can be incorporated into the hierarchical interaction model with a simple extension. The model can be used to predict the probability of movement and sympathetic responses during sevoflurane anesthesia taking into account interactions with opioids and 66 vol.% N2O.

Renal function assessment in older adults.

AIMS: The Cockcroft-Gault (CG), the Modification of Diet in Renal Disease (MDRD) and the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) formulae are often used to estimate glomerular filtration rate (GFR). The objective was to determine the best method for estimating GFR in older adults. METHODS: A cross-sectional study was conducted at the geriatric wards of two hospitals in The Netherlands. Patients aged 70 years or above with an estimated (e)GFR below 60 ml min⁻¹ 1.73 m⁻² were included. The CG, CG calculated with ideal bodyweight (IBW), MDRD and CKD-EPI formulae were compared with a criterion standard, sinistrin clearance. Renal function was classified into five stages according to the National Kidney Foundation Disease Outcomes Quality Initiative chronic kidney disease classification, as follows (in ml min⁻¹ 1.73 m)⁻²: stage 1, eGFR ≥ 90; stage 2, eGFR of 60-89; stage 3, eGFR of 30-59; stage 4, eGFR of 15-29; and stage 5, eGFR < 15. RESULTS: Sixteen patients, 50% male, with a mean age of 82 years (range 71-87 years) and mean body mass index 26 kg m⁻² (range 18-36 kg m⁻²), were included. On average, all formulae slightly overestimated GFR, as follows (in ml min⁻¹ 1.73 m⁻²: CG +0.05 [95% confidence interval (CI) -28 to +28]; CG with IBW +0.03 (95% CI -20 to +20); MDRD +9 (95% CI -16 to +34); and CKD-EPI +5 (95% CI -20 to +29). They classified kidney disease correctly in 68.8% (CG), 75% (CG with IBW), 43.8% (MDRD) and 68.8% (CKD-EPI) of the participants, respectively. CONCLUSIONS: The CG, CG with IBW, MDRD and CKD-EPI formulae estimate the mean GFR of a population rather well. In individual cases, all formulae may misclassify kidney disease by one stage.

Modelling and simulation of placebo effect: application to drug development in schizophrenia.

High and variable placebo effect (PE) within and among clinical trials can substantially affect conclusions about the efficacy of new drugs in the treatment of schizophrenia and other neuropsychiatric disorders. In recent years, it has become increasingly difficult to prove drug efficacy against placebo, and one of the reasons is that the placebo response has increased over recent years. The increased placebo response over the years is partly explained by unidentified parallel interventions, patient factors, issues with trial designs, and regional variability or demographic differences. In addition, a nocebo effect, which is undesirable effects a subject manifests after receiving placebo, e.g. extrapyramidal side effects, in placebo arms of antipsychotic trials could also influence the PE and clinical trial outcomes. Placebo effects (PEs) are a natural phenomenon and cannot be avoided completely in clinical trials. However, accounting for the PE via mixed effects modelling approaches could reduce bias in quantifying the overall effect size of the drug treatment. This review article focuses on the PE and its impact on schizophrenia clinical trial outcomes. The authors briefly describe the factors that lead to high and variable PE. Next, pharmacometric approaches to account for the PE and dropouts in schizophrenia clinical trials are described. Finally, some points are provided that could be considered while designing and optimizing antipsychotic trials via simulation approaches.

Sevoflurane remifentanil interaction: comparison of different response surface models.

BACKGROUND: Various pharmacodynamic response surface models have been developed to quantitatively describe the relationship between two or more drug concentrations with their combined clinical effect. We examined the interaction of remifentanil and sevoflurane on the probability of tolerance to shake and shout, tetanic stimulation, laryngeal mask airway insertion, and laryngoscopy in patients to compare the performance of five different response surface models. METHODS: Forty patients preoperatively received different combined concentrations of remifentanil (0-12 ng/ml) and sevoflurane (0.5-3.5 vol.%) according to a criss-cross design (160 concentration pairs, four per patient). After having reached pseudosteady state, the response to shake and shout, tetanic stimulation, laryngeal mask airway insertion, and laryngoscopy was recorded. For the analysis of the probability of tolerance, five different interaction models were tested: Greco, Reduced Greco, Minto, Scaled C50(O) Hierarchical, and Fixed C50(O) Hierarchical model. All calculations were performed with NONMEM VI (Icon Development Solutions, Ellicott City, MD). RESULTS: The pharmacodynamic interaction between sevoflurane and remifentanil was strongly synergistic for both the hypnotic and the analgesic components of anesthesia. The Greco model did not result in plausible parameter estimates. The Fixed C50(O) Hierarchical model performed slightly better than the Scaled C50(O) Hierarchical and Reduced Greco models, whereas the Minto model fitted less well. CONCLUSION: We showed the importance of exploring various surface model approaches when studying drug interactions. The Fixed C50(O) Hierarchical model fits our data on sevoflurane remifentanil interaction best and appears to be an appropriate model for use in hypnotic-opioid drug interaction.

Pharmacokinetic-pharmacodynamic modeling of the D2 and 5-HT (2A) receptor occupancy of risperidone and paliperidone in rats.

PURPOSE: A pharmacokinetic-pharmacodynamic (PK-PD) model was developed to describe the time course of brain concentration and dopamine D2 and serotonin 5-HT(2A) receptor occupancy (RO) of the atypical antipsychotic drugs risperidone and paliperidone in rats. METHODS: A population approach was utilized to describe the PK-PD of risperidone and paliperidone using plasma and brain concentrations and D2 and 5-HT(2A) RO data. A previously published physiology- and mechanism-based (PBPKPD) model describing brain concentrations and D2 receptor binding in the striatum was expanded to include metabolite kinetics, active efflux from brain, and binding to 5-HT(2A) receptors in the frontal cortex. RESULTS: A two-compartment model best fit to the plasma PK profile of risperidone and paliperidone. The expanded PBPKPD model described brain concentrations and D2 and 5-HT(2A) RO well. Inclusion of binding to 5-HT(2A) receptors was necessary to describe observed brain-to-plasma ratios accurately. Simulations showed that receptor affinity strongly influences brain-to-plasma ratio pattern. CONCLUSION: Binding to both D2 and 5-HT(2A) receptors influences brain distribution of risperidone and paliperidone. This may stem from their high affinity for D2 and 5-HT(2A) receptors. Receptor affinities and brain-to-plasma ratios may need to be considered before choosing the best PK-PD model for centrally active drugs.

Influence of erroneous patient records on population pharmacokinetic modeling and individual bayesian estimation.

BACKGROUND: Observational data sets can be used for population pharmacokinetic (PK) modeling. However, these data sets are generally less precisely recorded than experimental data sets. This article aims to investigate the influence of erroneous records on population PK modeling and individual maximum a posteriori Bayesian (MAPB) estimation. METHODS: A total of 1123 patient records of neonates who were administered vancomycin were used for population PK modeling by iterative 2-stage Bayesian (ITSB) analysis. Cut-off values for weighted residuals were tested for exclusion of records from the analysis. A simulation study was performed to assess the influence of erroneous records on population modeling and individual MAPB estimation. Also the cut-off values for weighted residuals were tested in the simulation study. RESULTS: Errors in registration have limited the influence on outcomes of population PK modeling but can have detrimental effects on individual MAPB estimation. A population PK model created from a data set with many registration errors has little influence on subsequent MAPB estimates for precisely recorded data. A weighted residual value of 2 for concentration measurements has good discriminative power for identification of erroneous records. CONCLUSIONS: ITSB analysis and its individual estimates are hardly affected by most registration errors. Large registration errors can be detected by weighted residuals of concentration.

An evaluation of using population pharmacokinetic models to estimate pharmacodynamic parameters for propofol and bispectral index in children.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.