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 E-max 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

Alternative Magnesium Sulfate Dosing Regimens for Women With Preeclampsia: A Population Pharmacokinetic Exposure-Response Modeling and Simulation Study

Magnesium sulfate is the anticonvulsant of choice for eclampsia prophylaxis and treatment; however, the recommended dosing regimens are costly and cumbersome and can be administered only by skilled health professionals. The objectives of this study were to develop a robust exposure-response model for the relationship between serum magnesium exposure and eclampsia using data from large studies of women with preeclampsia who received magnesium sulfate, and to predict eclampsia probabilities for standard and alternative (shorter treatment duration and/or fewer intramuscular injections) regimens. Exposure-response modeling and simulation were applied to existing data. A total of 10 280 women with preeclampsia who received magnesium sulfate or placebo were evaluated. An existing population pharmacokinetic model was used to estimate individual serum magnesium exposure. Logistic regression was applied to quantify the serum magnesium area under the curve-eclampsia rate relationship. Our exposure-response model-estimated eclampsia rates were comparable to observed rates. Several alternative regimens predicted magnesium peak concentration < 3.5 mmol/L (empiric safety threshold) and eclampsia rate ≤ 0.7% (observed response threshold), including 4 g intravenously plus 10 g intramuscularly followed by either 8 g intramuscularly every 6 hours × 3 doses or 10 g intramuscularly every 8 hours × 2 doses and 10 g intramuscularly every 8 hours × 3 doses. Several alternative magnesium sulfate regimens with comparable model-predicted efficacy and safety were identified that merit evaluation in confirmatory clinical trials

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.</p

Immunophenotypic measurable residual disease (MRD) in acute myeloid leukemia: Is multicentric MRD assessment feasible?

Flow-cytometric detection of now termed measurable residual disease (MRD) in acute myeloid leukemia (AML) has proven to have an independent prognostic impact. In a previous multicenter study we developed protocols to accurately define leukemia-associated immunophenotypes (LAIPs) at diagnosis. It has, however, not been demonstrated whether the use of the defined LAIPs in the same multicenter setting results in a high concordance between centers in MRD assessment. In the present paper we evaluated whether interpretation of list-mode data (LMD) files, obtained from MRD assessment of previously determined LAIPs during and after treatment, could reliably be performed in a multicenter setting. The percentage of MRD positive cells was simultaneously determined in totally 173 LMD files from 77 AML patients by six participating centers. The quantitative concordance between the six participating centers was meanly 84%, with slight variation of 75%–89%. In addition our data showed that the type and number of LAIPs were of influence on the performance outcome. The highest concordance was observed for LAIPs with cross-lineage expression, followed by LAIPs with an asynchronous antigen expression. Our results imply that immunophenotypic MRD assessment in AML will only be feasible when fully standardized methods are used for reliable multicenter assessment

Mechanism-based pharmacokinetic-pharmacodynamic modeling of the dopamine D-2 receptor occupancy of olanzapine in rats

A mechanism-based PK-PD model was developed to predict the time course of dopamine D-2 receptor occupancy (D2RO) in rat striatum following administration of olanzapine, an atypical antipsychotic drug. A population approach was utilized to quantify both the pharmacokinetics and pharmacodynamics of olanzapine in rats using the exposure (plasma and brain concentration) and D2RO profile obtained experimentally at various doses (0.01-40 mg/kg) administered by different routes. A two-compartment pharmacokinetic model was used to describe the plasma pharmacokinetic profile. A hybrid physiology- and mechanism-based model was developed to characterize the D-2 receptor binding in the striatum and was fitted sequentially to the data. The parameters were estimated using nonlinear mixed-effects modeling . Plasma, brain concentration profiles and time course of D2RO were well described by the model; validity of the proposed model is supported by good agreement between estimated association and dissociation rate constants and in vitro values from literature. This model includes both receptor binding kinetics and pharmacokinetics as the basis for the prediction of the D2RO in rats. Moreover, this modeling framework can be applied to scale the in vitro and preclinical information to clinical receptor occupancy

Pharmacokinetic-Pharmacodynamic Modeling of the D2 and 5-HT2A Receptor Occupancy of Risperidone and Paliperidone in Rats

A pharmacokinetic-pharmacodynamic (PK-PD) model was developed to describe the time course of brain concentration and dopamine D-2 and serotonin 5-HT2A receptor occupancy (RO) of the atypical antipsychotic drugs risperidone and paliperidone in rats. A population approach was utilized to describe the PK-PD of risperidone and paliperidone using plasma and brain concentrations and D-2 and 5-HT2A RO data. A previously published physiology- and mechanism-based (PBPKPD) model describing brain concentrations and D-2 receptor binding in the striatum was expanded to include metabolite kinetics, active efflux from brain, and binding to 5-HT2A receptors in the frontal cortex. A two-compartment model best fit to the plasma PK profile of risperidone and paliperidone. The expanded PBPKPD model described brain concentrations and D-2 and 5-HT2A RO well. Inclusion of binding to 5-HT2A receptors was necessary to describe observed brain-to-plasma ratios accurately. Simulations showed that receptor affinity strongly influences brain-to-plasma ratio pattern. Binding to both D-2 and 5-HT2A receptors influences brain distribution of risperidone and paliperidone. This may stem from their high affinity for D-2 and 5-HT2A receptors. Receptor affinities and brain-to-plasma ratios may need to be considered before choosing the best PK-PD model for centrally active drugs

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

<p>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.</p>