Population plasma pharmacokinetics of 11C-flumazenil at tracer concentrations

Current use and future potential of (physiologically based) pharmacokinetic modelling of radiopharmaceuticals: a review

Rationale: Physiologically based pharmacokinetic (PBPK) and population pharmacokinetic (PK) modelling approaches are widely accepted in non-radiopharmaceutical drug development and research, while there is no major role for these approaches in radiopharmaceutical development yet. In this review, a literature search was performed to specify different research purposes and questions that have previously been answered using both PBPK and population PK modelling for radiopharmaceuticals. Methods: The literature search was performed using the databases PubMed and Embase. Wide search terms included radiopharmaceutical, tracer, radioactivity, physiologically based pharmacokinetic model, PBPK, population pharmacokinetic model and nonlinear mixed-effects model. Results: Eight articles and twenty articles were included for this review based on this literature search for population PK modelling and PBPK modelling, respectively. Included population PK analyses showed to have an added value to develop predictive models for a population and to describe individual variability sources. Main purposes of PBPK models appeared related to optimizing treatment (planning), or more specifically: to find the optimal combination of peptide amount and radioactivity, to optimize treatment planning by reducing the number of measurements, to individualize treatment, to get insights in differences between pre-therapeutic and therapeutic scans or to understand inter-patient differences. Other main research subjects were regarding radiopharmaceutical comparisons, selecting ligands based on their peptide characteristics and gaining a better understanding of drug-drug interactions. Conclusions: The use of PK modelling approaches in radiopharmaceutical research remains scarce, but can be expanded to obtain a better understanding of PK and whole-body distribution of radiopharmaceuticals in general. PK modelling of radiopharmaceuticals has great potential for the nearby future and could contribute to the evolving research of radiopharmaceuticals.

Simultaneous multifactor Bayesian analysis (SiMBA) of PET time activity curve data

Positron emission tomography (PET) is an in vivo imaging method essential for studying the neurochemical pathophysiology of psychiatric and neurological disease. However, its high cost and exposure of participants to radiation make it unfeasible to employ large sample sizes. The major shortcoming of PET imaging is therefore its lack of power for studying clinically-relevant research questions. Here, we introduce a new method for performing PET quantification and analysis called SiMBA, which helps to alleviate these issues by improving the efficiency of PET analysis by exploiting similarities between both individuals and regions within individuals. In simulated [¹¹C]WAY100635 data, SiMBA greatly improves both statistical power and the consistency of effect size estimation without affecting the false positive rate. This approach makes use of hierarchical, multifactor, multivariate Bayesian modelling to effectively borrow strength across the whole dataset to improve stability and robustness to measurement error. In so doing, parameter identifiability and estimation are improved, without sacrificing model interpretability. This comes at the cost of increased computational overhead, however this is practically negligible relative to the time taken to collect PET data. This method has the potential to make it possible to test clinically-relevant hypotheses which could never be studied before given the practical constraints. Furthermore, because this method does not require any additional information over and above that required for traditional analysis, it makes it possible to re-examine data which has already previously been collected at great expense. In the absence of dramatic advancements in PET image data quality, radiotracer development, or data sharing, PET imaging has been fundamentally limited in the scope of research hypotheses which could be studied. This method, especially combined with the recent steps taken by the PET imaging community to embrace data sharing, will make it possible to greatly improve the research possibilities and clinical relevance of PET neuroimaging.

Nonlinear Mixed-Effects Models for PET Data

OBJECTIVE

The current state-of-the-art for compartment modeling of dynamic PET data can be described as a two-stage approach. In Stage 1, individual estimates of kinetic parameters are obtained by fitting models using standard techniques, such as nonlinear least squares, to each individual's data one subject at a time. Population-level effects, such as the difference between diagnostic groups, are analyzed in Stage 2 using standard statistical methods by treating the individual estimates as if they were observed data. While this approach is generally valid, it is possible to increase efficiency and precision of the analysis, allow more complex models to be fitted, and also to permit parameter-specific investigation by fitting data across subjects simultaneously. We explore the application of nonlinear mixed-effects (NLME) models for estimation and inference in this setting.

METHODS

In the NLME framework, subjects are modeled simultaneously through the inclusion of random effects of subjects for each kinetic parameter; meanwhile, population parameters are estimated directly in a joint model.

RESULTS

Simulation results indicate that NLME outperforms the two-stage approach in estimating group-level effects and also has improved power to detect differences across groups. We applied our NLME approach to clinical PET data and found effects not detected by the two-stage approach.

CONCLUSION

The proposed NLME approach is more accurate and correspondingly more powerful than the two-stage approach in compartment modeling of PET data.

SIGNIFICANCE

The NLME method can broaden the methodological scope of PET modeling because of its efficiency and stability.

Pharmacokinetic modeling of P-glycoprotein function at the rat and human blood-brain barriers studied with (R)-[11C]verapamil positron emission tomography

Background

This study investigated the influence of P-glycoprotein (P-gp) inhibitor tariquidar on the pharmacokinetics of P-gp substrate radiotracer (R)-[¹¹C]verapamil in plasma and brain of rats and humans by means of positron emission tomography (PET).

Methods

Data obtained from a preclinical and clinical study, in which paired (R)-[¹¹C]verapamil PET scans were performed before, during, and after tariquidar administration, were analyzed using nonlinear mixed effects (NLME) modeling. Administration of tariquidar was included as a covariate on the influx and efflux parameters (Q_in and Q_out) in order to investigate if tariquidar increased influx or decreased outflux of radiotracer across the blood–brain barrier (BBB). Additionally, the influence of pilocarpine-induced status epilepticus (SE) was tested on all model parameters, and the brain-to-plasma partition coefficient (V_T-NLME) was calculated.

Results

Our model indicated that tariquidar enhances brain uptake of (R)-[¹¹C]verapamil by decreasing Q_out. The reduction in Q_out in rats during and immediately after tariquidar administration (sevenfold) was more pronounced than in the second PET scan acquired 2 h after tariquidar administration (fivefold). The effect of tariquidar on Q_out in humans was apparent during and immediately after tariquidar administration (twofold reduction in Q_out) but was negligible in the second PET scan. SE was found to influence the pharmacological volume of distribution of the central brain compartment V_br1. Tariquidar treatment lead to an increase in V_T-NLME, and pilocarpine-induced SE lead to increased (R)-[¹¹C]verapamil distribution to the peripheral brain compartment.

Conclusions

Using NLME modeling, we were able to provide mechanistic insight into the effects of tariquidar and SE on (R)-[¹¹C]verapamil transport across the BBB in control and 48 h post SE rats as well as in humans.

Optimal sampling strategy development methodology using maximum a posteriori Bayesian estimation

Maximum a posteriori Bayesian (MAPB) pharmacokinetic parameter estimation is an accurate and flexible method of estimating individual pharmacokinetic parameters using individual blood concentrations and prior information. In the past decade, many studies have developed optimal sampling strategies to estimate pharmacokinetic parameters as accurately as possible using either multiple regression analysis or MAPB estimation. This has been done for many drugs, especially immunosuppressants and anticancer agents. Methods of development for optimal sampling strategies (OSS) are diverse and heterogeneous. This review provides a comprehensive overview of OSS development methodology using MAPB pharmacokinetic parameter estimation, determines the transferability of published OSSs, and compares sampling strategies determined by MAPB estimation and multiple regression analysis. OSS development has the following components: 1) prior distributions; 2) reference value determination; 3) optimal sampling time identification; and 4) validation of the OSS. Published OSSs often lack all data necessary for the OSS to be clinically transferable. MAPB estimation is similar to multiple regression analysis in terms of predictive performance but superior in flexibility.

[18F]flumazenil binding to central benzodiazepine receptor studies by PET--quantitative analysis and comparisons with [11C]flumazenil

[(11)C]flumazenil is the reference radioligand for Positron Emission Tomography (PET) studies of central benzodiazepine (BZ) receptors. Fluorine is available in the flumazenil molecule and [(18)F]flumazenil has recently been prepared. The aim of the present PET-study in 8 male subjects was to examine the binding of [(18)F]flumazenil in the human brain by direct comparison with [(11)C]flumazenil. Each subject participated in two 93-minute PET-measurements with [(11)C]flumazenil and [(18)F]flumazenil, respectively. Data were analyzed using compartment models with metabolite-corrected arterial plasma input and reference tissue models using the pons as reference region. There was no evident difference between the kinetic behaviors of the two ligands. Overall, the noise in the time activity curves for [(18)F]flumazenil was lower at late time points, and the variance of the kinetic parameters was lower than for [(11)C]flumazenil. In BZ receptor rich regions, such as the neocortex, the 3-compartment model was statistically favored, whereas the 2-compartment model was favored in the pons. Binding potential values obtained by the reference tissue models were in good agreement with those obtained by the kinetic analysis. There was no support for the presence of specific binding in the pons. In conclusion, the binding and the kinetic behavior of [(11)C]flumazenil and [(18)F]flumazenil were similar. The present analysis supports the use of pons as reference region in simplified protocols without arterial blood sampling. [(18)F]flumazenil should thus be an excellent choice for applied studies at centers not having a cyclotron.

Accuracy of 3-dimensional reconstruction algorithms for the high-resolution research tomograph

The high-resolution research tomograph (HRRT) is a dedicated human brain PET scanner. At present, iterative reconstruction methods are preferred for reconstructing HRRT studies. However, these iterative reconstruction algorithms show bias in short-duration frames. New algorithms such as the shifted Poisson ordered-subsets expectation maximization (SP-OSEM) and ordered-subsets weighted least squares (OSWLS) showed promising results in bias reduction, compared with the recommended ordinary Poisson OSEM (OP-OSEM). The goal of this study was to evaluate quantitative accuracy of these iterative reconstruction algorithms, compared with 3-dimensional filtered backprojection (3D-FBP).

METHODS

The 3 above-mentioned 3D iterative reconstruction methods were implemented for the HRRT. To evaluate the various 3D iterative reconstruction techniques quantitatively, several phantom studies and a human brain study (n=5) were performed.

RESULTS

OSWLS showed a low and almost linearly increasing coefficient of variation (SD over average activity concentration), with decreasing noise-equivalent count rates. In decay studies, OSWLS showed good agreement with the 3D-FBP gray matter (GM)-to-white matter (WM) contrast ratio (<4%), and OP-OSEM and SP-OSEM showed agreement within 6% and 7%, respectively. For various frame durations, both SP-OSEM and OP-OSEM showed the fewest errors in GM-to-WM contrast ratios, varying 75% between different noise-equivalent count rates; this variability was much higher for other iterative methods (>92%). 3D-FBP showed the least variability (34%). Visually, OSWLS hardly showed any artifacts in parametric images and showed good agreement with 3D-FBP data for parametric images, especially in the case of reference-tissue kinetic methods (slope, 1.02; Pearson correlation coefficient, 0.99).

CONCLUSION

OP-OSEM, SP-OSEM, and OSWLS showed good performance for phantom studies. In addition, OSWLS showed better results for parametric analysis of clinical studies and is therefore recommended for quantitative HRRT brain PET studies.

Brain histamine H1 receptor occupancy of orally administered antihistamines, bepotastine and diphenhydramine, measured by PET with 11C-doxepin

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

'Bepotastine besilate' is a novel second-generation antihistamine developed in Japan and its antiallergic effects have already been demonstrated by various studies. However, only a few clinical studies regarding its sedative property are available. In addition, histamine H(1) receptor occupancy (H(1)RO) of this new antihistamine has never been measured by positron emission tomography (PET).

WHAT THIS STUDY ADDS

This paper provides the first measurement result of cerebral H(1)RO of bepotastine besilate (approximately 15%) as determined by PET. This result is in accordance with the clinical classification of bepotastine as a second-generation antihistamine. In addition, the relationship between subjective sleepiness and cerebral H(1)RO of this second-generation antihistamine is demonstrated for the first time using a placebo-controlled crossover study design. AIMS Antihistamines are frequently used for treating various allergic diseases, but often induce sedation. The degree of sedation can be evaluated by measuring histamine H(1) receptor occupancy (H(1)RO) in the brain using positron emission tomography (PET). The aim was to measure H(1)RO of bepotastine, a new second-generation antihistamine, and to compare it with that of diphenhydramine.

METHODS

Eight healthy male volunteers (mean age +/- SD 24.4 +/- 3.3 years) were studied after single oral administration of bepotastine (10 mg), diphenhydramine (30 mg) or placebo, by PET imaging with (11)C-doxepin in a crossover study design. Binding potential ratio and H(1)ROs were calculated using placebo data and were compared between bepotastine and diphenhydramine in the anterior and posterior cingulate gyri (ACG and PCG, respectively), superior and inferior frontal cortices (SFC and IFC, respectively), orbitofrontal cortex (OFC), insular cortex (IC), lateral and medial temporal cortices (LTC and MTC, respectively), parietal cortex (PC), occipital cortex (OC) and sensorimotor cortex (SMC). Plasma concentration of each antihistamine was measured, and its correlation to H(1)RO was examined.

RESULTS

H(1)RO after bepotastine treatment was significantly lower than that after diphenhydramine treatment in all cortical regions (P < 0.001). Mean H(1)ROs of bepotastine and diphenhydramine were 14.7% and 56.4%, respectively. H(1)ROs of both bepotastine and diphenhydramine correlated to their respective drug plasma concentration (P < 0.001).

CONCLUSION

Oral bepotastine (10 mg), with its relatively low H(1)RO and thus minimal sedation, has the potential for use as a mildly or slightly sedative antihistamine in the treatment of various allergic disorders.

[11C]Flumazenil PET in temporal lobe epilepsy: do we need an arterial input function or kinetic modeling?

Reduced signal on [(11)C]]flumazenil (FMZ) positron emission tomography (PET) is associated with epileptogenic foci. Linear correlations within individuals between parametric and nonparametric images of FMZ binding have been shown, and various methods have been used, without comparison of diagnostic usefulness. Using hippocampal sclerosis (HS) as a test case, we formally compare the diagnostic yield of parametric images obtained either with a parent tracer arterial plasma input function and spectral analysis (yielding volume-of-distribution (VD) images), or with an image-based input function and the simplified reference tissue model (binding potential images, BP-SRTM) with the diagnostic yield of semiquantitative-integrated (ADD) images from 10 to 20 or 20 to 40 mins (ADD1020 and ADD2040). Dynamic 90-min [(11)C]FMZ PET datasets and arterial plasma input functions were available for 15 patients with medically refractory medial temporal lobe epilepsy (TLE) and histologically verified unilateral HS and for 13 control subjects. SPM2 was used for analysis. ADD1020 and ADD2040 images showed decreased FMZ uptake ipsilateral to the epileptogenic hippocampus in 13/15 cases; 6/13 had bilateral decreases in the ADD1020 analysis and 5/13 in the ADD2040 analysis. BP-SRTM images detected ipsilateral decreases in 12/15 cases, with bilateral decreases in three. In contrast, VD images showed ipsilateral hippocampal decreases in all 15 patients, with bilateral decreases in three patients. Bilateral decreases in the ADD images tended to be more symmetrical and in one case were more marked contralaterally. Full quantification with an image-independent input should ideally be used in the evaluation of FMZ PET; at least in TLE, intrasubject correlations do not predict equivalent clinical usefulness.