Prediction of time-integrated activity coefficients in PRRT using simulated dynamic PET and a pharmacokinetic model

A PBPK model for PRRT with [177Lu]Lu-DOTA-TATE: Comparison of model implementations in SAAM II and MATLAB/SimBiology

Physiologically based pharmacokinetic (PBPK) models offer the ability to simulate and predict the biodistribution of radiopharmaceuticals and have the potential to enable individualised treatment planning in molecular radiotherapy. The objective of this study was to develop and implement a whole-body compartmental PBPK model for peptide receptor radionuclide therapy (PRRT) with [177Lu]Lu-DOTA-TATE in SimBiology to allow for more complex analyses. The correctness of the model implementation was ensured by comparing its outputs, such as the time-integrated activity (TIA), with those of a PBPK model implemented in SAAM II software.

METHODS

A combined PBPK model for [68Ga]Ga-DOTA-TATE and [177Lu]Lu-DOTA-TATE was developed and implemented in both SAAM II and SimBiology. A retrospective analysis of 12 patients with metastatic neuroendocrine tumours (NETs) was conducted. First, time-activity curves (TACs) and TIAs from the two software were calculated and compared for identical parameter values. Second, pharmacokinetic parameters were fitted to activity concentrations, analysed and compared.

RESULTS

The PBPK model implemented in SimBiology produced TIA results comparable to those generated by the model implemented in SAAM II, with a relative deviation of less than 0.5% when using the same input parameters. The relative deviation of the fitted TIAs was less than 5% when model parameter values were fitted to the measured activity concentrations.

CONCLUSION

The proposed PBPK model implemented in SimBiology can be used for dosimetry in radioligand therapy and TIA prediction. Its outputs are similar to those generated by the PBPK model implemented in SAAM II, confirming the correctness of the model implementation in SimBiology.

Predicting [177Lu]Lu-HA-DOTATATE kidney and tumor accumulation based on [68Ga]Ga-HA-DOTATATE diagnostic imaging using semi-physiological population pharmacokinetic modeling

BACKGROUND

Prediction of [177Lu]Lu-HA-DOTATATE kidney and tumor uptake based on diagnostic [68Ga]Ga-HA-DOTATATE imaging would be a crucial step for precision dosing of [177Lu]Lu-HA-DOTATATE. In this study, the population pharmacokinetic (PK) differences between [177Lu]Lu-HA-DOTATATE and [68Ga]Ga-HA-DOTATATE were assessed and subsequently [177Lu]Lu-HA-DOTATATE was predicted based on [68Ga]Ga-HA-DOTATATE imaging.

METHODS

A semi-physiological nonlinear mixed-effects model was developed for [68Ga]Ga-HA-DOTATATE and [177Lu]Lu-HA-DOTATATE, including six compartments (representing blood, spleen, kidney, tumor lesions, other somatostatin receptor expressing organs and a lumped rest compartment). Model parameters were fixed based on a previously developed physiologically based pharmacokinetic model for [68Ga]Ga-HA-DOTATATE. For [177Lu]Lu-HA-DOTATATE, PK parameters were based on literature values or estimated based on scan data (four time points post-injection) from nine patients. Finally, individual [177Lu]Lu-HA-DOTATATE uptake into tumors and kidneys was predicted based on individual [68Ga]Ga-HA-DOTATATE scan data using Bayesian estimates. Predictions were evaluated compared to observed data using a relative prediction error (RPE) for both area under the curve (AUC) and absorbed dose. Lastly, to assess the predictive value of diagnostic imaging to predict therapeutic exposure, individual prediction RPEs (using Bayesian estimation) were compared to those from population predictions (using the population model).

RESULTS

Population uptake rate parameters for spleen, kidney and tumors differed by a 0.29-fold (15% relative standard error (RSE)), 0.49-fold (15% RSE) and 1.43-fold (14% RSE), respectively, for [177Lu]Lu-HA-DOTATATE compared to [68Ga]Ga-HA-DOTATATE. Model predictions adequately described observed data in kidney and tumors for both peptides (based on visual inspection of goodness-of-fit plots). Individual predictions of tumor uptake were better (RPE AUC -40 to 28%) compared to kidney predictions (RPE AUC -53 to 41%). Absorbed dose predictions were less predictive for both tumor and kidneys (RPE tumor and kidney -51 to 44% and -58 to 82%, respectively). For most patients, [177Lu]Lu-HA-DOTATATE tumor accumulation predictions based on individual PK parameters estimated from diagnostic imaging outperformed predictions based on population parameters.

CONCLUSION

Our semi-physiological PK model indicated clear differences in PK parameters for [68Ga]Ga-HA-DOTATATE and [177Lu]Lu-HA-DOTATATE. Diagnostic images provided additional information to individually predict [177Lu]Lu-HA-DOTATATE tumor uptake compared to using a population approach. In addition, individual predictions indicated that many aspects, apart from PK differences, play a part in predicting [177Lu]Lu-HA-DOTATATE distribution.

Evolution of Portable Sensors for In-Vivo Dose and Time-Activity Curve Monitoring as Tools for Personalized Dosimetry in Molecular Radiotherapy

Treatment personalization in Molecular Radiotherapy (MRT) relies on pre- and post-treatment SPECT/ PET-based images and measurements to obtain a patient-specific absorbed dose-rate distribution map and its evolution over time. Unfortunately, the number of time points that are available per patient to investigate individual pharmacokinetics is often reduced by limited patient compliance or SPECT or PET/CT scanner availability for dosimetry in busy departments. The adoption of portable sensors for in-vivo dose monitoring during the entire treatment could improve the assessment of individual biokinetics in MRT and, thus, the treatment personalization. The evolution of portable devices, non-SPECT/PET-based options, already used for monitoring radionuclide activity transit and accumulation during therapy with radionuclides (i.e., MRT or brachytherapy), is presented to identify valuable ones, which combined with conventional nuclear medicine imaging systems could be effective in MRT. External probes, integration dosimeters and active detecting systems were included in the study. The devices and their technology, the range of applications, the features and limitations are discussed. Our overview of the available technologies encourages research and development of portable devices and dedicated algorithms for MRT patient-specific biokinetics study. This would represent a crucial advancement towards personalized treatment in MRT.

A physiologically based pharmacokinetic model for [68Ga]Ga-(HA-)DOTATATE to predict whole-body distribution and tumor sink effects in GEP-NET patients

BACKGROUND

Little is known about parameters that have a relevant impact on (dis)similarities in biodistribution between various ⁶⁸Ga-labeled somatostatin analogues. Additionally, the effect of tumor burden on organ uptake remains unclear. Therefore, the aim of this study was to describe and compare organ and tumor distribution of [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE using a physiologically based pharmacokinetic (PBPK) model and to identify factors that might cause biodistribution and tumor uptake differences between both peptides. In addition, the effect of tumor burden on peptide biodistribution in gastroenteropancreatic (GEP) neuroendocrine tumor (NET) patients was assessed.

METHODS

A PBPK model was developed for [⁶⁸Ga]Ga-(HA-)DOTATATE in GEP-NET patients. Three tumor compartments were added, representing primary tumor, liver metastases and other metastases. Furthermore, reactions describing receptor binding, internalization and recycling, renal clearance and intracellular degradation were added to the model. Scan data from GEP-NET patients were used for evaluation of model predictions. Simulations with increasing tumor volumes were performed to assess the tumor sink effect.

RESULTS

Data of 39 and 59 patients receiving [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE, respectively, were included. Evaluations showed that the model adequately described image-based patient data and that different receptor affinities caused organ uptake dissimilarities between both peptides. Sensitivity analysis indicated that tumor blood flow and blood volume impacted tumor distribution most. Tumor sink predictions showed a decrease in spleen uptake with increasing tumor volume, which seemed clinically relevant for patients with total tumor volumes higher than ~ 550 mL.

CONCLUSION

The developed PBPK model adequately predicted tumor and organ uptake for this GEP-NET population. Relevant organ uptake differences between [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE were caused by different affinity profiles, while tumor uptake was mainly affected by tumor blood flow and blood volume. Furthermore, tumor sink predictions showed that for the majority of patients a tumor sink effect is not expected to be clinically relevant.

A physiologically based pharmacokinetic (PBPK) model to describe organ distribution of 68Ga-DOTATATE in patients without neuroendocrine tumors

Background

Physiologically based pharmacokinetic (PBPK) models combine drug-specific information with prior knowledge on the physiology and biology at the organism level. Whole-body PBPK models contain an explicit representation of the organs and tissue and are a tool to predict pharmacokinetic behavior of drugs. The aim of this study was to develop a PBPK model to describe organ distribution of ⁶⁸Ga-DOTATATE in a population of patients without detectable neuroendocrine tumors (NETs).

Methods

Clinical ⁶⁸Ga-DOTATATE PET/CT data from 41 patients without any detectable somatostatin receptor (SSTR) overexpressing tumors were included. Scans were performed at 45 min (range 30–60 min) after intravenous bolus injection of ⁶⁸Ga-DOTATATE. Organ (spleen, liver, thyroid) and blood activity levels were derived from PET scans, and corresponding DOTATATE concentrations were calculated. A whole-body PBPK model was developed, including an internalization reaction, receptor recycling, enzymatic reaction for intracellular degradation and renal clearance. SSTR2 expression was added for several organs. Input parameters were fixed or estimated using a built-in Monte Carlo algorithm for parameter identification.

Results

⁶⁸Ga-DOTATATE was administered with a median peptide amount of 12.3 µg (range 8.05–16.9 µg) labeled with 92.7 MBq (range 43.4–129.9 MBq). SSTR2 amounts for spleen, liver and thyroid were estimated at 4.40, 7.80 and 0.0108 nmol, respectively. Variability in observed organ concentrations was best described by variability in SSTR2 expression and differences in administered peptide amounts.

Conclusions

To conclude, biodistribution of ⁶⁸Ga-DOTATATE was described with a whole-body PBPK model, where tissue distribution was mainly determined by variability in SSTR2 organ expression and differences in administered peptide amounts.

Radiolabeled Peptides and Antibodies in Medicine

Radiolabeled peptides are a relatively new, very specific radiotracer group, which is still expanding. This group is very diverse in terms of peptide size. It contains very small structures containing several amino acids and whole antibodies. Moreover, radiolabeled peptides are diverse in terms of the binding aim and therapeutic or diagnostic applications. The majority of this class of radiotracers is utilized in oncology, where the same structure can be used in therapy and diagnostic imaging by varying the radionuclide. In this study, we collected new reports of radiolabeled peptide applications in diagnosis and therapy in oncology and other fields of medicine. Radiolabeled peptides are also increasingly being used in rheumatology, cardiac imaging, or neurology. The studies collected in this review concern new therapeutic and diagnostic procedures in humans and new structures tested on animals. We also performed an analysis of clinical trials, which concerns application of radiolabeled peptides and antibodies that were reported in the clinicaltrials.gov database between 2008 and 2018.

Performance assessment of the ALBIRA II pre-clinical SPECT S102 system for 99mTc imaging

OBJECTIVE

The performance characteristics of the SPECT sub-system S102 of the ALBIRA II PET/SPECT/CT are analyzed for the 80 mm field of view (FOV) to evaluate the potential in-vivo imaging in rats, based on measurements of the system response for the commonly used Technetium-99 m (^99mTc) in small animal imaging.

METHODS

The ALBIRA II tri-modal µPET/SPECT/CT pre-clinical system (Bruker BioSpin, Ettlingen, Germany) was used. The SPECT modality is made up of two opposite gamma cameras (Version S102) with Sodium doped Cesium Iodide (CsI(Na)) single continuous crystal detectors coupled to position-sensitive photomultipliers (PSPMTs). Imaging was performed with the NEMA NU-4 image quality phantom (Data Spectrum Corporation, Durham, USA). Measurements were performed with a starting activity concentration of 4.76 MBq/mL ^99mTc. An energy window of 20% at 140 keV was selected in this study. The system offers a 20 mm, 40 mm, 60 mm and an 80 mm field of view (FOV) and in this study the 80 mm FOV was used for all the acquisitions. The data were reconstructed with an ordered subset expectation maximization (OSEM) algorithm. Sensitivity, spatial resolution, count rate linearity, convergence of the algorithm and the recovery coefficients (RC) were analyzed. All analyses were performed with PMOD and MATLAB software.

RESULTS

The sensitivities measured at the center of the 80 mm FOV with the point source were 23.1 ± 0.3 cps/MBq (single pinhole SPH) and 105.6 ± 5.5 cps/MBq (multi pinhole MPH). The values for the axial, tangential and radial full width at half maximum (FWHM) were 2.51, 2.54, and 2.55 mm with SPH and 2.35, 2.44 and 2.32 mm with MPH, respectively. The corresponding RC values for the 5 mm, 4 mm, 3 mm and 2 mm rods were 0.60 ± 0.28, 0.61 ± 0.24, 0.29 ± 0.11 and 0.20 ± 0.06 with SPH and 0.56 ± 0.20, 0.50 ± 0.18, 0.38 ± 0.09 and 0.23 ± 0.06 with MPH. To obtain quantitative imaging data, the image reconstructions should be performed with 12 iterations.

CONCLUSION

The ALBIRA II preclinical SPECT sub-system S102 has a favorable sensitivity and spatial resolution for the 80 mm FOV setting for both the SPH and MPH configurations and is a valuable tool for small animal imaging.

Improving individualised dosimetry in radioiodine therapy for hyperthyroidism using population biokinetic modelling

The application of an individualised dosimetric procedure for radioiodine therapy requires the intensive use of resources in nuclear medicine facilities. In practice, the amount of data taken per patient is too limited to obtain an accurate estimate of the absorbed dose in the thyroid. The individualised absorbed dose estimates can be enhanced using statistical tools for population-based approaches. The aim of this work was to build a population biokinetic model of thyroid uptake and elimination of radioiodine using a nonlinear mixed-effects approach in patients with Graves' disease. Input data for the model development were taken from a dosimetric method based on ¹²³I imaging data. ¹²³I decay-corrected uptake values were estimated at 4, 24, and 96 h post-administration and for 58 patients. The root mean squared error (RMSE) for predicted ¹²³I uptake values by the fitted model was 4%. The root mean squared error of prediction (RMSEP) for out-of-sample ¹²³I uptake values, computed by a leave-one-out cross-validation, was 12%. We calculated ¹³¹I activity to administer from out-of-sample predicted ¹²³I uptake values and compared the result with that calculated from observed ¹²³I uptake values. RMSEP values for therapeutic activity revealed that there were measuring points with higher weight than others in the model. The mixed-effects approach can be used to enhance the accuracy of dosimetric calculations in therapies using ¹³¹I. Assessing the accuracy of the predictive model enables choosing among different time-sampling schedules of the radioiodine thyroid uptake curve. This methodology can also be applied in other areas of radiation dosimetry.

A simulation-based method to determine optimal sampling schedules for dosimetry in radioligand therapy

AIM

For dosimetry in radioligand therapy, the time-integrated activity coefficients (TIACs) for organs at risk and for tumour lesions have to be determined. The used sampling scheme affects the TIACs and therefore the calculated absorbed doses. The aim of this work was to develop a general and flexible method, which analyses numerous clinically applicable sampling schedules using true time-activity curves (TACs) of virtual patients.

METHOD

Nine virtual patients with true TACs of the tumours were created using a physiologically-based pharmacokinetic (PBPK) model and individual biokinetic data of five patients with neuroendocrine tumours and four with meningioma. ¹¹¹In-DOTATATE was used for pre-therapeutic dosimetry. In total, 15,120 sampling schemes, each consisting of 4 time points, were investigated. Gaussian noise of different levels was added to the corresponding true time-activity points. A bi-exponential function was used to fit the simulated time-activity data. For each investigated sampling schedule, 1000 replications were performed. Patient-specific and population-specific optimal sampling schedules were determined using the relative root-mean-square error (rRMSE). Furthermore, the fractions of TIACs a˜ deviating >5% (f_Δa˜>5%) and >10% (f_Δa˜>10%) from the true TIAC a˜_true were used for additional evaluations e.g. to investigate the effect of varying single time points.

RESULTS

Almost all patient-specific and all population-specific optimal sampling schedules have t₄≥96h for all noise levels. Changing the latest time point from the population-specific optimal time to e.g. 48h leads to a median increase of f_Δa˜>10% from 0.1% to 88% for the lowest investigated noise level. Using the determined population-specific optimal schedules, results in more accurate and precise results than established schedules from the literature.

CONCLUSION

A method of determining the optimal sampling schedule for dosimetry, which considers clinical working hours and measurement uncertainties, has been developed and applied. The simulation study shows that optimised sampling schedules result in high accuracy and precision of the determined TIACs.

A five-compartment biokinetic model for 90 Y-DOTATOC therapy

PURPOSE

Neuroendocrine tumors (NETs) are now routinely treated by radiopeptide targeted therapy using somatostatin receptor-binding peptides such as ⁹⁰ Y- and ¹⁷⁷ Lu-DOTATOC. The objective of this work was to develop a biokinetics model of ⁹⁰ Y labelled DOTATOC, which is applied in the therapy of NETs to estimate doses in kidney and tumor.

METHODS

A multi-compartment model described by two sets of differential equations, one set for the actual 30-min infusion and the other set for the post-infusion period was developed and activities were measured by liquid scintillation counting in blood (compartment 1) and the urine (compartment 3). The inter-compartment transfer coefficients, λ_ij , were varied to yield the best fit of the calculated to the measured time-activity data and the ⁹⁰ Y-DOTATOC time-activity data in the five-compartments comprising the human body were thus determined. The resulting time-activity curves were integrated over the interval from 0 to 72 h post administration to obtain the number of radioactive decays in each compartment and, in case of the kidneys and tumor, then multiplied by the self-dose ⁹⁰ Y beta particle absorbed fraction, determined by Monte Carlo (MC) simulation, the kidney and tumor absorbed doses.

RESULTS

Transfer coefficients λ_ij , were determined for five-compartments for all patients. Time- activity curves of ⁹⁰ Y-DOTATOC in 14 patients were determined, and two typical ones are shown graphically. Absorbed doses in the tumor and kidneys, obtained by the developed method, were determined. The mean absorbed dose in a kidney per unit of administered activity is 1.43 mGy/MBq (range 0.73-2.42 mGy/MBq). The tumor dose was determined as 30.94 mGy/MBq (range 20.05-42.31 mGy/MBq).

CONCLUSION

Analytical solution of a biokinetic model for ⁹⁰ Y-DOTATOC therapy enabled determination of the transfer coefficients and derivation of time-activity curves and kidney and tumor absorbed doses for 14 treated patients. The model can be applied to other radionuclides where elimination is predominantly through urine, which is often the case in radiopharmaceuticals.