Generation and application of avatars in pharmacometric modelling.

Simulations from population models have critical applications in drug discovery and development. Avatars or digital twins, defined as individual simulations matching clinical criteria of interest compared to observations from a real subject within a predefined margin of accuracy, may be a better option for simulations performed to inform future drug development stages in cases where an adequate model is not achievable. The aim of this work was to (1) investigate methods for generating avatars with pharmacometric models, and (2) explore the properties of the generated avatars to assess the impact of the different selection settings on the number of avatars per subject, their closeness to the individual observations, and the properties of the selected samples subset from the theoretical model parameters probability density function. Avatars were generated using different combinations of nature and number of clinical criteria, accuracy of agreement, and/or number of simulations for two examples models previously published (hemato-toxicity and integrated glucose-insulin model). The avatar distribution could be used to assess the appropriateness of the models assumed parameter distribution. Similarly it could be used to assess the models ability to properly describe the trajectories of the observations. Avatars can give nuanced information regarding the ability of a model to simulate data similar to the observations both at the population and at the individual level. Further potential applications for avatars may be as a diagnostic tool, an alternative to simulations with insurance to replicate key clinical features, and as an individual measure of model fit.

Population Pharmacokinetics and Dosing of Ethionamide in Children with Tuberculosis.

Ethionamide has proven efficacy against both drug-susceptible and some drug-resistant strains of Mycobacterium tuberculosis Limited information on its pharmacokinetics in children is available, and current doses are extrapolated from weight-based adult doses. Pediatric doses based on more robust evidence are expected to improve antituberculosis treatment, especially in small children. In this analysis, ethionamide concentrations in children from 2 observational clinical studies conducted in Cape Town, South Africa, were pooled. All children received ethionamide once daily at a weight-based dose of approximately 20 mg/kg of body weight (range, 10.4 to 25.3 mg/kg) in combination with other first- or second-line antituberculosis medications and with antiretroviral therapy in cases of HIV coinfection. Pharmacokinetic parameters were estimated using nonlinear mixed-effects modeling. The MDR-PK1 study contributed data for 110 children on treatment for multidrug-resistant tuberculosis, while the DATiC study contributed data for 9 children treated for drug-susceptible tuberculosis. The median age of the children in the studies combined was 2.6 years (range, 0.23 to 15 years), and the median weight was 12.5 kg (range, 2.5 to 66 kg). A one-compartment, transit absorption model with first-order elimination best described ethionamide pharmacokinetics in children. Allometric scaling of clearance (typical value, 8.88 liters/h), the volume of distribution (typical value, 21.4 liters), and maturation of clearance and absorption improved the model fit. HIV coinfection decreased the ethionamide bioavailability by 22%, rifampin coadministration increased clearance by 16%, and ethionamide administration by use of a nasogastric tube increased the rate, but the not extent, of absorption. The developed model was used to predict pediatric doses achieving the same drug exposure achieved in 50- to 70-kg adults receiving 750-mg once-daily dosing. Based on model predictions, we recommend a weight-banded pediatric dosing scheme using scored 125-mg tablets.

Maturation of Oxycodone Pharmacokinetics in Neonates and Infants: a Population Pharmacokinetic Model of Three Clinical Trials.

PURPOSE: The aim of the current population pharmacokinetic study was to quantify oxycodone pharmacokinetics in children ranging from preterm neonates to children up to 7 years of age. METHODS: Data on intravenous or intramuscular oxycodone administration were obtained from three previously published studies (n = 119). The median [range] postmenstrual age of the subjects was 299 days [170 days-7.8 years]. A population pharmacokinetic model was built using 781 measurements of oxycodone plasma concentration. The model was used to simulate repeated intravenous oxycodone administration in four representative infants covering the age range from an extremely preterm neonate to 1-year old infant. RESULTS: The rapid maturation of oxycodone clearance was best described with combined allometric scaling and maturation function. Central and peripheral volumes of distribution were nonlinearly related to bodyweight. The simulations on repeated intravenous administration in virtual patients indicated that oxycodone plasma concentration can be kept between 10 and 50 ng/ml with a high probability when the maintenance dose is calculated using the typical clearance and the dose interval is 4 h. CONCLUSIONS: Oxycodone clearance matures rapidly after birth, and between-subject variability is pronounced in neonates. The pharmacokinetic model developed may be used to evaluate different multiple dosing regimens, but the safety of repeated doses should be ensured.

Assessing robustness of designs for random effects parameters for nonlinear mixed-effects models.

Optimal designs for nonlinear models are dependent on the choice of parameter values. Various methods have been proposed to provide designs that are robust to uncertainty in the prior choice of parameter values. These methods are generally based on estimating the expectation of the determinant (or a transformation of the determinant) of the information matrix over the prior distribution of the parameter values. For high dimensional models this can be computationally challenging. For nonlinear mixed-effects models the question arises as to the importance of accounting for uncertainty in the prior value of the variances of the random effects parameters. In this work we explore the influence of the variance of the random effects parameters on the optimal design. We find that the method for approximating the expectation and variance of the likelihood is of potential importance for considering the influence of random effects. The most common approximation to the likelihood, based on a first-order Taylor series approximation, yields designs that are relatively insensitive to the prior value of the variance of the random effects parameters and under these conditions it appears to be sufficient to consider uncertainty on the fixed-effects parameters only.

The effect of using a robust optimality criterion in model based adaptive optimization.

Optimizing designs using robust (global) optimality criteria has been shown to be a more flexible approach compared to using local optimality criteria. Additionally, model based adaptive optimal design (MBAOD) may be less sensitive to misspecification in the prior information available at the design stage. In this work, we investigate the influence of using a local (lnD) or a robust (ELD) optimality criterion for a MBAOD of a simulated dose optimization study, for rich and sparse sampling schedules. A stopping criterion for accurate effect prediction is constructed to determine the endpoint of the MBAOD by minimizing the expected uncertainty in the effect response of the typical individual. 50 iterations of the MBAODs were run using the MBAOD R-package, with the concentration from a one-compartment first-order absorption pharmacokinetic model driving the population effect response in a sigmoidal EMAX pharmacodynamics model. The initial cohort consisted of eight individuals in two groups and each additional cohort added two individuals receiving a dose optimized as a discrete covariate. The MBAOD designs using lnD and ELD optimality with misspecified initial model parameters were compared by evaluating the efficiency relative to an lnD-optimal design based on the true parameter values. For the explored example model, the MBAOD using ELD-optimal designs converged quicker to the theoretically optimal lnD-optimal design based on the true parameters for both sampling schedules. Thus, using a robust optimality criterion in MBAODs could reduce the number of adaptations required and improve the practicality of adaptive trials using optimal design.

Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection.

Population model-based (pharmacometric) approaches are widely used for the analyses of phase IIb clinical trial data to increase the accuracy of the dose selection for phase III clinical trials. On the other hand, if the analysis is based on one selected model, model selection bias can potentially spoil the accuracy of the dose selection process. In this paper, four methods that assume a number of pre-defined model structure candidates, for example a set of dose-response shape functions, and then combine or select those candidate models are introduced. The key hypothesis is that by combining both model structure uncertainty and model parameter uncertainty using these methodologies, we can make a more robust model based dose selection decision at the end of a phase IIb clinical trial. These methods are investigated using realistic simulation studies based on the study protocol of an actual phase IIb trial for an oral asthma drug candidate (AZD1981). Based on the simulation study, it is demonstrated that a bootstrap model selection method properly avoids model selection bias and in most cases increases the accuracy of the end of phase IIb decision. Thus, we recommend using this bootstrap model selection method when conducting population model-based decision-making at the end of phase IIb clinical trials.

Accelerating Monte Carlo power studies through parametric power estimation.

Estimating the power for a non-linear mixed-effects model-based analysis is challenging due to the lack of a closed form analytic expression. Often, computationally intensive Monte Carlo studies need to be employed to evaluate the power of a planned experiment. This is especially time consuming if full power versus sample size curves are to be obtained. A novel parametric power estimation (PPE) algorithm utilizing the theoretical distribution of the alternative hypothesis is presented in this work. The PPE algorithm estimates the unknown non-centrality parameter in the theoretical distribution from a limited number of Monte Carlo simulation and estimations. The estimated parameter linearly scales with study size allowing a quick generation of the full power versus study size curve. A comparison of the PPE with the classical, purely Monte Carlo-based power estimation (MCPE) algorithm for five diverse pharmacometric models showed an excellent agreement between both algorithms, with a low bias of less than 1.2 % and higher precision for the PPE. The power extrapolated from a specific study size was in a very good agreement with power curves obtained with the MCPE algorithm. PPE represents a promising approach to accelerate the power calculation for non-linear mixed effect models.

The effect of Fisher information matrix approximation methods in population optimal design calculations.

With the increasing popularity of optimal design in drug development it is important to understand how the approximations and implementations of the Fisher information matrix (FIM) affect the resulting optimal designs. The aim of this work was to investigate the impact on design performance when using two common approximations to the population model and the full or block-diagonal FIM implementations for optimization of sampling points. Sampling schedules for two example experiments based on population models were optimized using the FO and FOCE approximations and the full and block-diagonal FIM implementations. The number of support points was compared between the designs for each example experiment. The performance of these designs based on simulation/estimations was investigated by computing bias of the parameters as well as through the use of an empirical D-criterion confidence interval. Simulations were performed when the design was computed with the true parameter values as well as with misspecified parameter values. The FOCE approximation and the Full FIM implementation yielded designs with more support points and less clustering of sample points than designs optimized with the FO approximation and the block-diagonal implementation. The D-criterion confidence intervals showed no performance differences between the full and block diagonal FIM optimal designs when assuming true parameter values. However, the FO approximated block-reduced FIM designs had higher bias than the other designs. When assuming parameter misspecification in the design evaluation, the FO Full FIM optimal design was superior to the FO block-diagonal FIM design in both of the examples.

Estimation of drug receptor occupancy when non-displaceable binding differs between brain regions ­ extending the simplified reference tissue model.

AIM: The simplified reference tissue model (SRTM) is used for estimation of receptor occupancy assuming that the non-displaceable binding in the reference region is identical to the brain regions of interest. The aim of this work was to extend the SRTM to also account for inter-regional differences in non-displaceable concentrations, and to investigate if this model allowed estimation of receptor occupancy using white matter as reference. It was also investigated if an apparent higher affinity in caudate compared with other brain regions, could be better explained by a difference in the extent of non-displaceable binding. METHODS: The analysis was based on a PET study in six healthy volunteers using the 5-HT1B receptor radioligand [(11)C]-AZ10419369. The radioligand was given intravenously as a tracer dose alone and following different oral doses of the 5-HT1B receptor antagonist AZD3783. Non-linear mixed effects models were developed where differences between regions in non-specific concentrations were accounted for. The properties of the models were also evaluated by means of simulation studies. RESULTS: The estimate (95% CI) of Ki(PL) was 10.2 ng ml(-1) (5.4, 15) and 10.4 ng ml(-1) (8.1, 13.6) based on the extended SRTM with white matter as reference and based on the SRTM using cerebellum as reference, respectively. The estimate (95% CI) of Ki(PL) for caudate relative to other brain regions was 55% (48, 62%). CONCLUSIONS: The extended SRTM allows consideration of white matter as reference region when no suitable grey matter region exists. AZD3783 affinity appears to be higher in the caudate compared with other brain regions.

Methods and software tools for design evaluation in population pharmacokinetics-pharmacodynamics studies.

Population pharmacokinetic (PK)-pharmacodynamic (PKPD) models are increasingly used in drug development and in academic research; hence, designing efficient studies is an important task. Following the first theoretical work on optimal design for nonlinear mixed-effects models, this research theme has grown rapidly. There are now several different software tools that implement an evaluation of the Fisher information matrix for population PKPD. We compared and evaluated the following five software tools: PFIM, PkStaMp, PopDes, PopED and POPT. The comparisons were performed using two models, a simple-one compartment warfarin PK model and a more complex PKPD model for pegylated interferon, with data on both concentration and response of viral load of hepatitis C virus. The results of the software were compared in terms of the standard error (SE) values of the parameters predicted from the software and the empirical SE values obtained via replicated clinical trial simulation and estimation. For the warfarin PK model and the pegylated interferon PKPD model, all software gave similar results. Interestingly, it was seen, for all software, that the simpler approximation to the Fisher information matrix, using the block diagonal matrix, provided predicted SE values that were closer to the empirical SE values than when the more complicated approximation was used (the full matrix). For most PKPD models, using any of the available software tools will provide meaningful results, avoiding cumbersome simulation and allowing design optimization.

Improved precision of exposure-response relationships by optimal dose-selection. Examples from studies of receptor occupancy using PET and dose finding for neuropathic pain treatment.

An understanding of the relationship between drug exposure and response is a fundamental basis for any dosing recommendation. We investigate optimal dose-selection for two different types of studies, a receptor occupancy study assessed by positron emission tomography (PET) and a dose-finding study in neuropathic pain treatment. For the PET-study, an inhibitory E-max model describes the relationship between drug exposure and displacement of a radioligand from specific receptors in the brain. The model has a mechanistic basis in the law of mass action and the affinity parameter (Ki PL ) is of primary interest. For optimization of the neuropathic pain study, the model is empirical and the exposure response curve itself is of primary interest. An alternative parameterization of the sigmoid Emax model was therefore used where the plasma concentration corresponding to the minimum relevant efficacy was estimated as a parameter. Optimal design methodology was applied using the D-optimal criterion as well as the Ds-optimal criterion where parameters of interest were defined. For the PET-study it was shown that the precision of Ki PL can be improved by inclusion of brain regions with both high and low receptor density and that the need for high doses is reduced when a brain region with low receptor density is included in the analysis. In the case of the neuropathic pain study it was shown that a Ds-optimal study design using the reparameterized Emax model can improve the precision in the minimum effective dose compared to a D-optimal design.

Simultaneous pharmacokinetic modeling of alkylresorcinols and their main metabolites indicates dual absorption mechanisms and enterohepatic elimination in humans.

BACKGROUND: Alkylresorcinols have proven to be useful biomarkers of whole-grain wheat and rye intake in many nutritional studies. To improve their utility, more knowledge regarding the fate of alkylresorcinols and their metabolites after consumption is needed. OBJECTIVE: The objective of this study was to develop a combined pharmacokinetic model for plasma concentrations of alkylresorcinols and their 2 major metabolites, 3,5-dihydroxybenzoic acid (DHBA) and 3-(3,5-dihydroxyphenyl)-propanoic acid (DHPPA). METHODS: The model was established by using plasma samples collected from 3 women and 2 men after a single dose (120 g) of rye bran and validated against fasting plasma concentrations from 8 women and 7 men with controlled rye bran intake (23, 45, or 90 g/d). Alkylresorcinols in the lymph and plasma of a pig fed a single alkylresorcinol dose (1.3 mmol) were quantified to assess absorption. Human ileostomal effluent and pig bile after high and low alkylresorcinol doses were analyzed to evaluate biliary alkylresorcinol metabolite excretion. RESULTS: The model contained 2 absorption compartments: 1 that transferred alkylresorcinols directly to the systematic circulation and 1 in which a proportion of absorbed alkylresorcinols was metabolized before reaching the systemic circulation. Plasma concentrations of alkylresorcinols and their metabolites depended on absorption and formation, respectively, and the mean ± SEM terminal elimination half-life of alkylresorcinols (1.9 ± 0.59 h), DHPPA (1.5 ± 0.26 h), and DHBA (1.3 ± 0.22 h) did not differ. The model accurately predicted alkylresorcinol and DHBA concentrations after repeated alkylresorcinol intake but DHPPA concentration was overpredicted, possibly because of poorly modeled enterohepatic circulation. During the 8 h following administration, <2% of the alkylresorcinol dose was recovered in the lymph. DHPPA was identified in both human ileostomal effluent and pig bile, indicating availability of DHPPA for absorption and enterohepatic circulation. CONCLUSION: Intact alkylresorcinols have advantages over DHBA and DHPPA as plasma biomarkers for whole-grain wheat and rye intake because of lower susceptibility to factors other than alkylresorcinol intake.

Improved utilization of ADAS-cog assessment data through item response theory based pharmacometric modeling.

PURPOSE: This work investigates improved utilization of ADAS-cog data (the primary outcome in Alzheimer's disease (AD) trials of mild and moderate AD) by combining pharmacometric modeling and item response theory (IRT). METHODS: A baseline IRT model characterizing the ADAS-cog was built based on data from 2,744 individuals. Pharmacometric methods were used to extend the baseline IRT model to describe longitudinal ADAS-cog scores from an 18-month clinical study with 322 patients. Sensitivity of the ADAS-cog items in different patient populations as well as the power to detect a drug effect in relation to total score based methods were assessed with the IRT based model. RESULTS: IRT analysis was able to describe both total and item level baseline ADAS-cog data. Longitudinal data were also well described. Differences in the information content of the item level components could be quantitatively characterized and ranked for mild cognitively impairment and mild AD populations. Based on clinical trial simulations with a theoretical drug effect, the IRT method demonstrated a significantly higher power to detect drug effect compared to the traditional method of analysis. CONCLUSION: A combined framework of IRT and pharmacometric modeling permits a more effective and precise analysis than total score based methods and therefore increases the value of ADAS-cog data.

Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model.

D-optimal designs for discrete-type responses have been derived using generalized linear mixed models, simulation based methods and analytical approximations for computing the fisher information matrix (FIM) of non-linear mixed effect models with homogeneous probabilities over time. In this work, D-optimal designs using an analytical approximation of the FIM for a dichotomous, non-homogeneous, Markov-chain phase advanced sleep non-linear mixed effect model was investigated. The non-linear mixed effect model consisted of transition probabilities of dichotomous sleep data estimated as logistic functions using piecewise linear functions. Theoretical linear and nonlinear dose effects were added to the transition probabilities to modify the probability of being in either sleep stage. D-optimal designs were computed by determining an analytical approximation the FIM for each Markov component (one where the previous state was awake and another where the previous state was asleep). Each Markov component FIM was weighted either equally or by the average probability of response being awake or asleep over the night and summed to derive the total FIM (FIM(total)). The reference designs were placebo, 0.1, 1-, 6-, 10- and 20-mg dosing for a 2- to 6-way crossover study in six dosing groups. Optimized design variables were dose and number of subjects in each dose group. The designs were validated using stochastic simulation/re-estimation (SSE). Contrary to expectations, the predicted parameter uncertainty obtained via FIM(total) was larger than the uncertainty in parameter estimates computed by SSE. Nevertheless, the D-optimal designs decreased the uncertainty of parameter estimates relative to the reference designs. Additionally, the improvement for the D-optimal designs were more pronounced using SSE than predicted via FIM(total). Through the use of an approximate analytic solution and weighting schemes, the FIM(total) for a non-homogeneous, dichotomous Markov-chain phase advanced sleep model was computed and provided more efficient trial designs and increased nonlinear mixed-effects modeling parameter precision.

Evaluation of bias, precision, robustness and runtime for estimation methods in NONMEM 7.

NONMEM is the most widely used software for population pharmacokinetic (PK)-pharmacodynamic (PD) analyses. The latest version, NONMEM 7 (NM7), includes several sampling-based estimation methods in addition to the classical methods. In this study, performance of the estimation methods available in NM7 was investigated with respect to bias, precision, robustness and runtime for a diverse set of PD models. Simulations of 500 data sets from each PD model were reanalyzed with the available estimation methods to investigate bias and precision. Simulations of 100 data sets were used to investigate robustness by comparing final estimates obtained after estimations starting from the true parameter values and initial estimates randomly generated using the CHAIN feature in NM7. Average estimation time for each algorithm and each model was calculated from the runtimes reported by NM7. The method giving the lowest bias and highest precision across models was importance sampling, closely followed by FOCE/LAPLACE and stochastic approximation expectation-maximization. The methods relative robustness differed between models and no method showed clear superior performance. FOCE/LAPLACE was the method with the shortest runtime for all models, followed by iterative two-stage. The Bayesian Markov Chain Monte Carlo method, used in this study for point estimation, performed worst in all tested metrics.

Optimizing the design of a pharmacokinetic trial to evaluate the dosing scheme of a novel tuberculosis drug in children living with or without HIV.

Pharmacokinetic (PK) studies in children are usually small and have ethical constraints due to the medical complexities of drawing blood in this special population. Often, population PK models for the drug(s) of interest are available in adults, and these models can be extended to incorporate the expected deviations seen in children. As a consequence, there is increasing interest in the use of optimal design methodology to design PK sampling schemes in children that maximize information using a small sample size and limited number of sampling times per dosing period. As a case study, we use the novel tuberculosis drug delamanid, and show how applications of optimal design methodology can result in highly efficient and model-robust designs in children for estimating PK parameters using a limited number of sampling measurements. Using developed population PK models based on available data from adults living with and without HIV, and limited data on children without HIV, competing designs for children living with HIV were derived and assessed based on robustness to model uncertainty.

Item performance of the scale for the assessment and rating of ataxia in rare and ultra-rare genetic ataxias.

The Scale for the Assessment and Rating of Ataxia (SARA) is widely used for assessing the severity and progression of genetic cerebellar ataxias. SARA is now considered a primary end point in several ataxia treatment trials, but its underlying composite item measurement model has not yet been tested. This work aimed to evaluate the composite properties of SARA and its items using item response theory (IRT) and to demonstrate its applicability across even ultra-rare genetic ataxias. Leveraging SARA subscores data from 1932 visits from 990 patients of the Autosomal Recessive Cerebellar Ataxias (ARCA) registry, we assessed the performance of SARA using IRT methodology. The item characteristics were evaluated over the ataxia severity range of the entire ataxia population as well as the assessment validity across 115 genetic ARCA subpopulations. A unidimensional IRT model was able to describe SARA item data, indicating that SARA captures one single latent variable. All items had high discrimination values (1.5-2.9) indicating the effectiveness of the SARA in differentiating between subjects with different disease statuses. Each item contributed between 7% and 28% of the total assessment informativeness. There was no evidence for differences between the 115 genetic ARCA subpopulations in SARA applicability. These results show the good discrimination ability of SARA with all of its items adding informational value. The IRT framework provides a thorough description of SARA on the item level, and facilitates its utilization as a clinical outcome assessment in upcoming longitudinal natural history or treatment trials, across a large number of ataxias, including ultra-rare ones.

The Role of Model Master Files for Sharing, Acceptance, and Communication with FDA.

With the evolving role of Model Integrated Evidence (MIE) in generic drug development and regulatory applications, the need for improving Model Sharing, Acceptance, and Communication with the FDA is warranted. Model Master File (MMF) refers to a quantitative model or a modeling platform that has undergone sufficient model Verification & Validation to be recognized as sharable intellectual property that is acceptable for regulatory purposes. MMF provides a framework for regulatorily acceptable modeling practice, which can be used with confidence to support MIE by both the industry and the U.S. Food and Drug Administration (FDA). In 2022, the FDA and the Center for Research on Complex Generics (CRCG) hosted a virtual public workshop to discuss the best practices for utilizing modeling approaches to support generic product development. This report summarizes the presentations and panel discussions of the workshop symposium entitled "Model Sharing, Acceptance, and Communication with the FDA". The symposium and this report serve as a kick-off discussion for further utilities of MMF and best practices of utilizing MMF in drug development and regulatory submissions. The potential advantages of MMFs have garnered acknowledgment from model developers, industries, and the FDA throughout the workshop. To foster a unified comprehension of MMFs and establish best practices for their application, further dialogue and cooperation among stakeholders are imperative. To this end, a subsequent workshop is scheduled for May 2-3, 2024, in Rockville, Maryland, aiming to delve into the practical facets and best practices of MMFs pertinent to regulatory submissions involving modeling and simulation methodologies.

Methods for non-proportional hazards in clinical trials: A systematic review.

For the analysis of time-to-event data, frequently used methods such as the log-rank test or the Cox proportional hazards model are based on the proportional hazards assumption, which is often debatable. Although a wide range of parametric and non-parametric methods for non-proportional hazards has been proposed, there is no consensus on the best approaches. To close this gap, we conducted a systematic literature search to identify statistical methods and software appropriate under non-proportional hazard. Our literature search identified 907 abstracts, out of which we included 211 articles, mostly methodological ones. Review articles and applications were less frequently identified. The articles discuss effect measures, effect estimation and regression approaches, hypothesis tests, and sample size calculation approaches, which are often tailored to specific non-proportional hazard situations. Using a unified notation, we provide an overview of methods available. Furthermore, we derive some guidance from the identified articles.

A positron emission tomography study in healthy volunteers to estimate mGluR5 receptor occupancy of AZD2066 - estimating occupancy in the absence of a reference region.

AZD2066 is a new chemical entity pharmacologically characterized as a selective, negative allosteric modulator of the metabotropic glutamate receptor subtype 5 (mGluR5). Antagonism of mGluR5 has been implicated in relation to various diseases such as anxiety, depression, and pain disorders. To support translation from preclinical results and previous experiences with this target in man, a positron emission tomography study was performed to estimate the relationship between AZD2066 plasma concentrations and receptor occupancy in the human brain, using the mGluR5 radioligand [(11)C]-ABP688. The study involved PET scans on 4 occasions in 6 healthy volunteers. The radioligand was given as a tracer dose alone and following oral treatment with different doses of AZD2066. The analysis was based on the total volume of distribution derived from each PET-assessment. A non-linear mixed effects model was developed where ten delineated brain regions of interest from all PET scans were included in one simultaneous fit. For comparison the analysis was also performed according to a method described previously by Lassen et al. (1995). The results of the analysis showed that the total volume of distribution decreased with increasing drug concentrations in all regions with an estimated Kipl of 1170 nM. Variability between individuals and occasions in non-displaceable volume of distribution could explain most of the variability in the total volume of distribution. The Lassen approach provided a similar estimate for Kipl, but the variability was exaggerated and difficult to interpret.