Translational PKPD modeling in schizophrenia:linking receptor occupancy of antipsychotics to efficacy and safety

Schizofrenie is een ernstige psychiatrische ziekte, die ongeveer 1% van de bevolking treft en zich meestal openbaart tussen het 15de en 30e levensjaar. De symptomen als hallucinaties en stemmen horen kunnen worden verminderd door geneesmiddelen. Deze geneesmiddelen werken niet altijd voldoende tegen de verschillende symptomen van schizofrenie. Bij de ontwikkeling van nieuwe geneesmiddelen is het moeilijk te bepalen of ze werkzaam zijn, omdat de werkzaamheid onder andere afhankelijk is van de hoeveelheid van het geneesmiddel die in de hersenen komt en daar aan de dopamine-receptoren bindt. Het in dit proefschrift beschreven onderzoek heeft tot doel om een computermodel te ontwikkelen om de werking van een nieuw geneesmiddel te voorspellen met behulp van in vitro data, gegevens van proefdier-experimenten en PET scans bij vrijwilligers of patiënten van een aantal bestaande geneesmiddelen, waarbij rekening wordt gehouden met het placebo-effect. Het model toont aan dat 50-70% van de receptoren in de hersenen bezet moeten worden door het geneesmiddel om voldoende werkzaam te zijn. Als meer dan 80% van de receptoren bezet worden, neemt het risico van bijwerkingen aanzienlijk toe. Daarnaast is het model gebruikt om te onderzoeken of de vragenlijst die gebruikt wordt door psychiaters om de werking van het geneesmiddel bij patiënten te meten, verbeterd kan worden. Het model laat zien hoe gegevens van in vitro, preklinische en klinische studies gecombineerd kunnen worden om de effecten van medicijnen bij de mens te begrijpen. Het model kan worden toegepast bij de ontwikkeling van nieuwe geneesmiddelen om het tijdsverloop van klinische effecten te karakteriseren en te voorspellen

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

Outpatient treatment with AZD7442 (tixagevimab/cilgavimab) prevented Covid-19 hospitalizations over 6 months and reduced symptom progression in the TACKLE randomized trial

Introduction We assessed effects of AZD7442 (tixagevimab/cilgavimab) on deaths from any cause or hospitalizations due to coronavirus disease 2019 (COVID-19) and symptom severity and longer-term safety in the TACKLE adult outpatient treatment study. Methods Participants received 600 mg AZD7442 (n = 452) or placebo (n = 451) ≤ 7 days of COVID-19 symptom onset. Results Death from any cause or hospitalization for COVID-19 complications or sequelae through day 169 (key secondary endpoint) occurred in 20/399 (5.0%) participants receiving AZD7442 versus 40/407 (9.8%) receiving placebo [relative risk reduction (RRR) 49.1%; 95% confidence interval (CI) 14.5, 69.7; p = 0.009] or 50.7% (95% CI 17.5, 70.5; p = 0.006) after excluding participants unblinded before day 169 for consideration of vaccination). AZD7442 reduced progression of COVID-19 symptoms versus placebo through to day 29 (RRR 12.5%; 95% CI 0.5, 23.0) and improved most symptoms within 1–2 weeks. Over median safety follow-up of 170 days, adverse events occurred in 174 (38.5%) and 196 (43.5%) participants receiving AZD7442 or placebo, respectively. Cardiac serious adverse events occurred in two (0.4%) and three (0.7%) participants receiving AZD7442 or placebo, respectively. Conclusions AZD7442 was well tolerated and reduced hospitalization and mortality through 6 months, and symptom burden through 29 days, in outpatients with mild-to-moderate COVID-19. Clinical Trial Registration Clinicaltrials.gov, NCT04723394. (https://beta.clinicaltrials.gov/study/NCT04723394)

Outpatient Treatment with AZD7442 (Tixagevimab/Cilgavimab) Prevented COVID-19 Hospitalizations over 6 Months and Reduced Symptom Progression in the TACKLE Randomized Trial

INTRODUCTION: We assessed effects of AZD7442 (tixagevimab/cilgavimab) on deaths from any cause or hospitalizations due to coronavirus disease 2019 (COVID-19) and symptom severity and longer-term safety in the TACKLE adult outpatient treatment study. METHODS: Participants received 600 mg AZD7442 (n = 452) or placebo (n = 451) ≤ 7 days of COVID-19 symptom onset. RESULTS: Death from any cause or hospitalization for COVID-19 complications or sequelae through day 169 (key secondary endpoint) occurred in 20/399 (5.0%) participants receiving AZD7442 versus 40/407 (9.8%) receiving placebo [relative risk reduction (RRR) 49.1%; 95% confidence interval (CI) 14.5, 69.7; p = 0.009] or 50.7% (95% CI 17.5, 70.5; p = 0.006) after excluding participants unblinded before day 169 for consideration of vaccination). AZD7442 reduced progression of COVID-19 symptoms versus placebo through to day 29 (RRR 12.5%; 95% CI 0.5, 23.0) and improved most symptoms within 1-2 weeks. Over median safety follow-up of 170 days, adverse events occurred in 174 (38.5%) and 196 (43.5%) participants receiving AZD7442 or placebo, respectively. Cardiac serious adverse events occurred in two (0.4%) and three (0.7%) participants receiving AZD7442 or placebo, respectively. CONCLUSIONS: AZD7442 was well tolerated and reduced hospitalization and mortality through 6 months, and symptom burden through 29 days, in outpatients with mild-to-moderate COVID-19. CLINICAL TRIAL REGISTRATION: Clinicaltrials.gov, NCT04723394. ( https://beta. CLINICALTRIALS: gov/study/NCT04723394 )

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

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