Martin Johnson

University of Groningen, Groningen, Province of Groningen, Netherlands

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Publications (8)40.56 Total impact

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    ABSTRACT: 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.
    Journal of clinical psychopharmacology 10/2013; · 5.09 Impact Factor
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    ABSTRACT: BACKGROUND: To develop a pharmacokinetic-pharmacodynamic (PK-PD) model using individual-level data of Positive and Negative Syndrome Scale (PANSS) total score to characterize the antipsychotic drug effect taking into account the placebo effect and dropout rate. In addition, a clinical utility (CU) criterion that describes the usefulness of a drug therapy was calculated using the efficacy of the drug and dropout rates. METHODS: Data from 12 clinical trials in schizophrenia patients was used to quantify the effects of the antipsychotic drugs (APs), namely, haloperidol, risperidone, olanzapine, ziprasidone and paliperidone. Compartmental PK models were used to describe the time course of plasma drug concentrations. The combination of an Emax and the Weibull model was used to describe the drug and placebo effects. The steady-state drug concentrations were assumed to be the drivers of the exposure-response relationship. An exponential model was utilized to identify the predictors of probability of dropout. Simulations were performed to check the predictability of the model, and to calculate the CU of the drugs based on PANSS scores and dropout rates. RESULTS: The maximal drug effect (Emax) was highest for olanzapine whilst it was lowest for ziprasidone. Higher observed PANSS scores resulted in a greater likelihood of dropout. Taking into account the efficacy and the drop-out rate, all APs possessed a comparable CU at the therapeutic doses. The resulting PK-PD model parameters were used to compute the effective concentration and dose required to produce a clinically meaningful 30% drop in PANSS score from the baseline. CONCLUSIONS: The developed PK-PD model and the associated CU score allow the evaluation of the time course of the PANSS scores of the different APs and a proper comparison of their clinically relevant treatments effects.
    Schizophrenia Research 03/2013; · 4.59 Impact Factor
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    ABSTRACT: BACKGROUND AND OBJECTIVES: The superiority of atypical antipsychotics (also known as second-generation antipsychotics (SGAs)) over typical antipsychotics (first generation antipsychotics (FGAs)) for negative symptom control in schizophrenic patients is widely debated. The objective of this study was to characterize the time course of the scores of the 3 subscales (positive, negative, general) of the Positive and Negative Syndrome Scale (PANSS) after treatment of patients with antipsychotics, and to compare the control of negative symptom by SGAs versus a FGA (haloperidol) using pharmacokinetic and pharmacodynamic (PKPD) modelling. In addition, to obtain insight in the relationship between the clinical efficacy and the in vitro and in vivo receptor pharmacology profiles, the D2 and 5-HT2A receptor occupancy levels of antipsychotics were related to the effective concentrations. METHODS: The PKPD model structure developed earlier (part I) was used to quantify the drug effect using the 3 PANSS subscales. The maximum drug effect sizes (Emax) of oral SGAs (risperidone, olanzapine, ziprasidone, and paliperidone) across PANSS subscales were compared with that of haloperidol, while accounting for the placebo effect. Using the estimates of PKPD model parameters, the effective concentrations (Ceff) needed to achieve 30% reduction in the PANSS subscales were computed. Calculated effective concentrations were then correlated with receptor pharmacology profiles. RESULTS: Positive symptoms of schizophrenia responded well to all antipsychotics. Olanzapine showed a better effect towards negative symptoms than the other SGAs and haloperidol. Dropout modelling results showed that the probability of a patient dropping out from a trial was associated with all subscales, but was more strongly correlated with the positive subscale than with the negative or the general subscales. Our results suggest that different levels of D2 or 5-HT2A receptor occupancy are required to achieve improvement in PANSS subscales. CONCLUSIONS: This PKPD modelling approach can be helpful to differentiate the effect of antipsychotics across the different symptom domains of schizophrenia. Our analysis revealed that olanzapine seems to be superior in treating the negative symptoms compared to other non-clozapine SGAs. The relationship between receptor pharmacology profiles of the antipsychotics and their clinical efficacy is not yet fully understood.
    Schizophrenia Research 03/2013; · 4.59 Impact Factor
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    ABSTRACT: Lack of hard clinical endpoints is an essential problem in schizophrenia research. Disease state and treatment outcomes are measured using rating scales, e.g. Positive and Negative Syndrome Scale (PANSS). However, the PANSS score cannot always differentiate between placebo and drug treatment, even for established antipsychotics. The goal of this study was to identify the individual items of PANSS and subscales of selected items which are most sensitive to differentiate between placebo and drug effect. We analysed data from seven clinical trials of different antipsychotics. "Mini-PANSS" scales consisting of the most sensitive items were created and analysed statistically. The power of these scales to show a significant difference between placebo and drug treatment was compared with the power of total PANSS. Furthermore, pharmacokinetic-pharmacodynamic analysis was performed to determine which of these scales shows the highest drug effect on top of the placebo effect. The results reveal that all 30 items of the PANSS scale show a therapeutic drug effect. The magnitude of placebo effect was not predictive for the power to detect drug effect. Mini-PANSS scales consisting of items with the largest drug treatment response and the scale with the largest mean-to-SD ratio are somewhat better in differentiating between placebo and drug treatment than the total PANSS. However, the difference between these scales and total PANSS is small. Therefore, our analysis does not support replacement of the total PANSS by a reduced scale in the analysis of primary endpoints.
    Schizophrenia Research 02/2013; · 4.59 Impact Factor
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    ABSTRACT: The likelihood of detecting a therapeutic signal of an effective drug for schizophrenia is impeded by a high placebo effect and by high dropout of patients. Several unsuccessful trials of schizophrenia, at least partly due to highly variable placebo effects, have indicated the necessity for a robust methodology to evaluate such a placebo effect and reasons for dropout. Hence, the objectives of this analysis were to (i) develop a longitudinal placebo model that accounts for dropouts and predictors of the placebo effect, using the Positive and Negative Syndrome Scale (PANSS) score; (ii) compare the performance of empirical and semi-mechanistic placebo models; and (iii) compare different time-to-event (TTE) dropout modelling approaches used to account for dropouts. The PANSS scores from 1436 individual patients were used to develop and validate a placebo model. This pooled dataset included 16 trials (conducted between 1989 and 2009), with different study durations, in both acute and chronic schizophrenic patients. A nonlinear mixed-effects modelling approach was employed, using NONMEM VII software. Among the different tested placebo models, the Weibull model and the indirect response model adequately described the PANSS data. Covariate analysis showed that the disease condition, study duration, study year, geographic region where the trial was conducted, and route of administration were important predictors for the placebo effect. All three parametric TTE dropout models, namely the exponential, Weibull and Gompertz models, described the probability of patients dropping out from a clinical trial equally well. The study duration and trial phase were found to be predictors for high dropout rates. Results of joint modelling of the placebo effect and dropouts indicated that the probability of patients dropping out is associated with an observed high PANSS score. The indirect response model was found to be a slightly better model than the Weibull placebo model to describe the time course of the PANSS score. Our modelling approach was shown to adequately simulate the longitudinal PANSS data and the dropout trends after placebo treatment. Data analyses suggest that the Weibull and indirect response models are more robust than other placebo models to describe the nonlinear trends in the PANSS score. The developed placebo models, accounting for dropouts and predictors of the placebo effect, could be a useful tool in the evaluation of new trial designs and for better quantification of antipsychotic drug effects.
    Clinical Pharmacokinetics 04/2012; 51(4):261-75. · 6.11 Impact Factor
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    ABSTRACT: 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. 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. 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. 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.
    Pharmaceutical Research 03/2012; 29(7):1932-48. · 4.74 Impact Factor
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    ABSTRACT: Large variation in placebo response within and among clinical trials can substantially affect conclusions about the efficacy of new medications in psychiatry. Developing a robust placebo model to describe the placebo response is important to facilitate quantification of drug effects, and eventually to guide the design of clinical trials for psychiatric treatment via a model-based simulation approach. In addition, high dropout rates are very common in the placebo arm of psychiatric clinical trials. While developing models to evaluate the effect of placebo response, the data from patients who drop out of the trial should be considered for accurate interpretation of the results. The objective of this paper is to review the various empirical and semi-mechanistic models that have been used to quantify the placebo response in schizophrenia trials. Pros and cons of each placebo model are discussed. Additionally, placebo models used in other neuropsychiatric disorders like depression, Alzheimer's disease and Parkinson's disease are also reviewed with the objective of finding those placebo models that could be useful for clinical studies of both acute and chronic schizophrenic disease conditions. Better understanding of the patterns of dropout and the factors leading to dropouts are crucial in identifying the true placebo response. We therefore also review dropout models that are used in the development of models for treatment effects and in the optimization of clinical trials by simulation approaches. The use of an appropriate modelling strategy that is capable of identifying the potential sources of variable placebo responses and dropout rates is recommended for improving the sensitivity in discriminating between the effects of active treatment and placebo.
    Clinical Pharmacokinetics 07/2011; 50(7):429-50. · 6.11 Impact Factor
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    ABSTRACT: Purpose A mechanism-based PK-PD model was developed to predict the time course of dopamine D2 receptor occupancy (D2RO) in rat striatum following administration of olanzapine, an atypical antipsychotic drug. Methods 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 D2 receptor binding in the striatum and was fitted sequentially to the data. The parameters were estimated using nonlinear mixed-effects modeling . Results 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. Conclusion 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.
    Pharmaceutical Research 06/2011; · 4.74 Impact Factor

Publication Stats

24 Citations
40.56 Total Impact Points


  • 2011–2013
    • University of Groningen
      • Department of Pharmacokinetics, Toxicology and Targeting
      Groningen, Province of Groningen, Netherlands