From Phantoms to Patients: Improved Fusion and Voxel-Wise Analysis of Diffusion-Weighted Imaging and FDG-Positron Emission Tomography in Positron Emission Tomography/Magnetic Resonance Imaging for Combined Metabolic-Diffusivity Index (cDMI).

Hybrid positron emission tomography/magnetic resonance imaging (PET/MR) opens new possibilities in multimodal multiparametric (m2p) image analyses. But even the simultaneous acquisition of positron emission tomography (PET) and magnetic resonance imaging (MRI) does not guarantee perfect voxel-by-voxel co-registration due to organs and distortions, especially in diffusion-weighted imaging (DWI), which would be, however, crucial to derive biologically meaningful information. Thus, our aim was to optimize fusion and voxel-wise analyses of DWI and standardized uptake values (SUVs) using a novel software for m2p analyses. Using research software, we evaluated the precision of image co-registration and voxel-wise analyses including the rigid and elastic 3D registration of DWI and [18F]-Fluorodeoxyglucose (FDG)-PET from an integrated PET/MR system. We analyzed DWI distortions with a volume-preserving constraint in three different 3D-printed phantom models. A total of 12 PET/MR-DWI clinical datasets (bronchial carcinoma patients) were referenced to the T1 weighted-DIXON sequence. Back mapping of scatterplots and voxel-wise registration was performed and compared to the non-optimized datasets. Fusion was rated using a 5-point Likert scale. Using the 3D-elastic co-registration algorithm, geometric shapes were restored in phantom measurements; the measured ADC values did not change significantly (F = 1.12, p = 0.34). Reader assessment showed a significant improvement in fusion precision for DWI and morphological landmarks in the 3D-registered datasets (4.3 ± 0.2 vs. 4.6 ± 0.2, p = 0.009). Most pronounced differences were noted for the chest wall (p = 0.006), tumor (p = 0.007), and skin contour (p = 0.014). Co-registration increased the number of plausible ADC and SUV combinations by 25%. The volume-preserving elastic 3D registration of DWI significantly improved the precision of fusion with anatomical sequences in phantom and clinical datasets. The research software allowed for a voxel-wise analysis and visualization of [18F]FDG-PET/MR data as a "combined diffusivity-metabolic index" (cDMI). The clinical value of the optimized PET/MR biomarker can thus be tested in future PET/MR studies.

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.

Single-Time-Point Renal Dosimetry Using Nonlinear Mixed-Effects Modeling and Population-Based Model Selection in [177Lu]Lu-PSMA-617 Therapy.

The aim of this study was to investigate the accuracy of single-time-point (STP) renal dosimetry imaging using SPECT/CT data, a nonlinear mixed-effects (NLME) model, and a population-based model selection (PBMS) in a large population for 177Lu-labeled prostate-specific membrane antigen therapy. Methods: Biokinetic data (mean ± SD) of [177Lu]Lu-PSMA-617 in kidneys at time points 1 (1.8 ± 0.8 h), 2 (18.7 ± 0.9 h), 3 (42.6 ± 1.0 h), 4 (66.3 ± 0.9 h), and 5 (160.3 ± 24.2 h) after injection were obtained from 63 patients with metastatic castration-resistant prostate cancer using SPECT/CT. Thirteen functions were derived from various parameterizations of 1- to 5-exponential functions. The function's parameters were fitted in the NLME framework to the all-time-point (ATP) data. The PBMS NLME method was performed using the goodness-of-fit test and Akaike weight to select the best function fitting the data. The best function from ATP fitting was used to calculate the reference time-integrated activity and absorbed doses. In STP dosimetry, the parameters of a particular patient with STP data were fitted simultaneously to the STP data at different time points of that patient with ATP data of all other patients. The parameters from STP fitting were used to calculate the STP time-integrated activity and absorbed doses. Relative deviations (RDs) and root-mean-square errors (RMSEs) were used to analyze the accuracy of the calculated STP absorbed dose compared with the reference absorbed dose obtained from the best-fit ATP function. The performance of STP dosimetry using PBMS NLME modeling was compared with the Hänscheid and Madsen methods. Results: The function [Formula: see text] was selected as the best-fit ATP function, with an Akaike weight of 100%. For STP dosimetry, the STP measurement by SPECT/CT at time point 3 (42.6 ± 1.0 h) showed a relatively low mean RD of -4.4% ± 9.4% and median RD of -0.7%. Time point 3 had the lowest RMSE value compared with those at the other 4 time points. The RMSEs of the absorbed dose RDs for time points 1-5 were 23%, 16%, 10%, 20%, and 53%, respectively. The STP dosimetry using the PBMS NLME method outperformed the Hänscheid and Madsen methods for all investigated time points. Conclusion: Our results show that a single measurement of SPECT/CT at 2 d after injection might be used to calculate accurate kidney-absorbed doses using the NLME method and PBMS.

Time-Activity data fitting in molecular Radiotherapy: Methodology and pitfalls.

Absorbed radiation doses are essential in assessing the effects, e.g. safety and efficacy, of radiopharmaceutical therapy (RPT). Patient-specific absorbed dose calculations in the target or the organ at risk require multiple inputs. These include the number of disintegrations in the organ, i.e. the time-integrated activities (TIAs) of the organs, as well as other parameters describing the process of radiation energy deposition in the target tissue (i.e. mean energy per disintegration, radiation dose constants, etc). TIAs are then estimated by incorporating the area under the radiopharmaceutical's time-activity curve (TAC), which can be obtained by quantitative measurements of the biokinetics in the patient (typically based on imaging data such as planar scintigraphy, SPECT/CT, PET/CT, or blood and urine samples). The process of TAC determination/calculation for RPT generally depends on the user, e.g., the chosen number and schedule of measured time points, the selection of the fit function, the error model for the data and the fit algorithm. These decisions can strongly affect the final TIA values and thus the accuracy of calculated absorbed doses. Despite the high clinical importance of the TIA values, there is currently no consensus on processing time-activity data or even a clear understanding of the influence of uncertainties and variations in personalised RPT dosimetry related to user-dependent TAC calculation. As a first step towards minimising site-dependent variability in RPT dosimetry, this work provides an overview of quality assurance and uncertainty management considerations of the TIA estimation.

Combined morphologic-metabolic biomarkers from [18F]FDG-PET/CT stratify prognostic groups in low-risk NSCLC.

AIM: The aim of this study was to derive prognostic parameters from 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG-PET/CT) in patients with low-risk NSCLC and determine their prognostic value. METHODS: 81 (21 female, mean age 66 a) therapy-naive patients that underwent [18F]FDG-PET/CT before histologic confirmation of NSCLC with stadium I and II between 2008-2016 were included. A mean follow-up time of 58 months (13-176), overall and progression free survival (OS, PFS) were registered. A volume of interest for the primary tumor was defined on PET and CT images. Parameters SUVmax, PET-solidity, PET-circularity, and CT-volume were analyzed. To evaluate the prognostic value of each parameter for OS, a minimum p-value approach was used to define cutoff values, survival analysis, and log-rank tests were performed, including subgroup analysis for combinations of parameters. RESULTS: Mean OS was 58±28 months. Poor OS was associated with a tumor CT-volume >14.3 cm3 (p=0.02, HR=7.0, CI 2.7-17.7), higher SUVmax values >12.2 (p=0.003; HR=3.0, CI 1.3-6.7) and PET-solidity >0.919 (p=0.004; HR=3.0, CI 1.0-8.9). Combined parameter analysis revealed worse prognosis in larger volume/high SUVmax tumors compared to larger volume/lower SUVmax (p=0.028; HR=2.5, CI 1.1-5.5), high PET-solidity/low volume (p=0.01; HR=2.4, CI 0.8-6.6) and low SUVmax/high PET-solidity (p=0.02, HR=4.0, CI 0.8-19.0). CONCLUSION: Even in this group of low-risk NSCLC patients, we identified a subgroup with a significantly worse prognosis by combining morphologic-metabolic biomarkers from [18F]FDG-PET/CT. The combination of SUVmax and CT-volume performed best. Based on these preliminary data, future prospective studies to validate this combined morphologic-metabolic imaging biomarker for potential therapeutic decisions seem promising.

Optimization of Radiolabeling of a [90Y]Y-Anti-CD66-Antibody for Radioimmunotherapy before Allogeneic Hematopoietic Cell Transplantation.

For patients with acute myeloid leukemia, myelodysplastic syndrome, or acute lymphoblastic leukemia, allogeneic hematopoietic cell transplantation (HCT) is a potentially curative treatment. In addition to standard conditioning regimens for HCT, high-dose radioimmunotherapy (RIT) offers the unique opportunity to selectively deliver a high dose of radiation to the bone marrow while limiting side effects. Modification of a CD66b-specific monoclonal antibody (mAb) with a DTPA-based chelating agent should improve the absorbed dose distribution during therapy. The stability and radioimmunoreactive fraction of the radiolabeled mAbs were determined. Before RIT, all patients underwent dosimetry to determine absorbed doses to bone marrow, kidneys, liver, and spleen. Scans were performed twenty-four hours after therapy for quality control. A radiochemical purity of >95% and acceptable radioimmunoreactivity was achieved. Absorbed organ doses for the liver and kidney were consequently improved compared to reported historical data. All patients tolerated RIT well with no treatment-related acute adverse events. Complete remission could be observed in 4/5 of the patients 3 months after RIT. Two patients developed delayed liver failure unrelated to the radioimmunotherapy. The improved conjugation and radiolabeling procedure resulted in excellent stability, radiochemical purity, and CD66-specific radioimmunoreactivity of 90Y-labeled anti-CD66 mAb. RIT followed by conditioning and HCT was well tolerated. Based on these promising initial data, further prospective studies of [90Y]Y-DTPA-Bn-CHX-A″-anti-CD66-mAb-assisted conditioning in HCT are warranted.

Organ absorbed doses in the IORT treatment of breast cancer with the INTRABEAM device: a Monte-Carlo study.

Purpose: The current prescription and the assessment of the delivered absorbed dose in intraoperative radiation therapy (IORT) with the INTRABEAM system rely mainly on depth-dose measurements in water. The accuracy of this approach is limited because tissue heterogeneity is ignored. It is also difficult to accurately determine the dose delivered to the patient experimentally as the steep dose gradient is highly sensitive to geometric errors. Our goal is to determine the dose to the target volume and the organs at risk of a clinical breast cancer patient from treatment with the system.Methods: A homogeneous water-equivalent CT dataset was derived from the preoperative CT scan of a patient by setting all materials in the patient volume as water-equivalent. This homogeneous CT data represents the current assumption of a homogenous patient, while the original CT data is considered the ground truth. An in-house Monte Carlo algorithm was used to simulate the delivered dose in both setups for a prescribed treatment dose of 20 Gy to the surface of the 3.5 cm diameter spherical applicator.Results: The doses received by 2% (D2%) of the target volume for the homogeneous and heterogeneous geometries are 16.26 Gy and 9.33 Gy, respectively. The D2% for the heart are 0.035 Gy and 0.119 Gy for the homogeneous and heterogeneous geometries, respectively. This trend is also observed for the other organs at risk.Conclusions: The assumption of a homogeneous patient overestimates the dose to the target volume and underestimates the doses to the organs at risk.

Single-time-point dosimetry using model selection and nonlinear mixed-effects modelling: a proof of concept.

PURPOSE: This project aims to develop and evaluate a method for accurately determining time-integrated activities (TIAs) in single-time-point (STP) dosimetry for molecular radiotherapy. It performs a model selection (MS) within the framework of the nonlinear mixed-effects (NLME) model (MS-NLME). METHODS: Biokinetic data of [111In]In-DOTATATE activity in kidneys at T1 = (2.9 ± 0.6) h, T2 = (4.6 ± 0.4) h, T3 = (22.8 ± 1.6) h, T4 = (46.7 ± 1.7) h, and T5 = (70.9 ± 1.0) h post injection were obtained from eight patients using planar imaging. Eleven functions were derived from various parameterisations of mono-, bi-, and tri-exponential functions. The functions' fixed and random effects parameters were fitted simultaneously (in the NLME framework) to the biokinetic data of all patients. The Akaike weights were used to select the fit function most supported by the data. The relative deviations (RD) and the root-mean-square error (RMSE) of the calculated TIAs for the STP dosimetry at T3 = (22.8 ± 1.6) h and T4 = (46.7 ± 1.7) h p.i. were determined for all functions passing the goodness-of-fit test. RESULTS: The function [Formula: see text] with four adjustable parameters and [Formula: see text] was selected as the function most supported by the data with an Akaike weight of (45 ± 6) %. RD and RMSE values show that the MS-NLME method performs better than functions with three or five adjustable parameters. The RMSEs of TIANLME-PBMS and TIA3-parameters were 7.8% and 10.9% (for STP at T3), and 4.9% and 10.7% (for STP at T4), respectively. CONCLUSION: An MS-NLME method was developed to determine the best fit function for calculating TIAs in STP dosimetry for a given radiopharmaceutical, organ, and patient population. The proof of concept was demonstrated for biokinetic 111In-DOTATATE data, showing that four-parameter functions perform better than three- and five-parameter functions.

Population-based model selection for an accurate estimation of time-integrated activity using non-linear mixed-effects modelling.

PURPOSE: Personalized treatment planning in Molecular Radiotherapy (MRT) with accurately determining the absorbed dose is highly desirable. The absorbed dose is calculated based on the Time-Integrated Activity (TIA) and the dose conversion factor. A crucial unresolved issue in MRT dosimetry is which fit function to use for the TIA calculation. A data-driven population-based fitting function selection could help solve this problem. Therefore, this project aims to develop and evaluate a method for accurately determining TIAs in MRT, which performs a Population-Based Model Selection within the framework of the Non-Linear Mixed-Effects (NLME-PBMS) model. METHODS: Biokinetic data of a radioligand for the Prostate-Specific Membrane Antigen (PSMA) for cancer treatment were used. Eleven fit functions were derived from various parameterisations of mono-, bi-, and tri-exponential functions. The functions' fixed and random effects parameters were fitted (in the NLME framework) to the biokinetic data of all patients. The goodness of fit was assumed acceptable based on the visual inspection of the fitted curves and the coefficients of variation of the fitted fixed effects. The Akaike weight, the probability that the model is the best among the whole set of considered models, was used to select the fit function most supported by the data from the set of functions with acceptable goodness of fit. NLME-PBMS Model Averaging (MA) was performed with all functions having acceptable goodness of fit. The Root-Mean-Square Error (RMSE) of the calculated TIAs from individual-based model selection (IBMS), a shared-parameter population-based model selection (SP-PBMS) reported in the literature, and the functions from NLME-PBMS method to the TIAs from MA were calculated and analysed. The NLME-PBMS (MA) model was used as the reference as this model considers all relevant functions with corresponding Akaike weights. RESULTS: The function [Formula: see text] was selected as the function most supported by the data with an Akaike weight of (54 ±â€¯11) %. Visual inspection of the fitted graphs and the RMSE values show that the NLME model selection method has a relatively better or equivalent performance than the IBMS or SP-PBMS methods. The RMSEs of the IBMS, SP-PBMS, and NLME-PBMS (f3a) methods are 7.4%, 8.8%, and 2.4%, respectively. CONCLUSION: A procedure including fitting function selection in a population-based method was developed to determine the best fit function for calculating TIAs in MRT for a given radiopharmaceutical, organ and set of biokinetic data. The technique combines standard practice approaches in pharmacokinetics, i.e. an Akaike-weight-based model selection and the NLME model framework.

Single-time-point estimation of absorbed doses in PRRT using a non-linear mixed-effects model.

INTRODUCTION: Estimation of accurate time-integrated activity coefficients (TIACs) and radiation absorbed doses (ADs) is desirable for treatment planning in peptide-receptor radionuclide therapy (PRRT). This study aimed to investigate the accuracy of a simplified dosimetry using a physiologically-based pharmacokinetic (PBPK) model, a nonlinear mixed effect (NLME) model, and single-time-point imaging to calculate the TIACs and ADs of 90Y-DOTATATE in various organs of dosimetric interest and tumors. MATERIALS & METHODS: Biokinetic data of 111In-DOTATATE in tumors, kidneys, liver, spleen, and whole body were obtained from eight patients using planar scintigraphic imaging at T1 = (2.9 ±â€¯0.6), T2 = (4.6 ±â€¯0.4), T3 = (22.8 ±â€¯1.6), T4 = (46.7 ±â€¯1.7) and T5 = (70.9 ±â€¯1.0) h post injection. Serum activity concentration was measured at 5 and 15 min; 0.5, 1, 2, and 4 h; and 1, 2, and 3 d p.i.. A published PBPK model for PRRT, NLME, and a single-time-point imaging datum at different time points were used to calculate TIACs in tumors, kidneys, liver, spleen, whole body, and serum. Relative deviations (RDs) (median [min, max]) between the calculated TIACs from single-time-point imaging were compared to the TIACs calculated from the all-time-points fit. The root mean square error (RMSE) of the difference between the computed ADs from the single-time-point imaging and reference ADs from the all-time point fittings were analyzed. A joint root mean square error RMSEjoint of the ADs was calculated with the RSME from both the tumor and kidneys to sort the time points concerning accurate results for the kidneys and tumor dosimetry. The calculations of TIACs and ADs from the single-time-point dosimetry were repeated using the sum of exponentials (SOE) approach introduced in the literature. The RDs and the RSME of the PBPK approach in our study were compared to the SOE approach. RESULTS: Using the PBPK and NLME models and the biokinetic measurements resulted in a good fit based on visual inspection of the fitted curves and the coefficient of variation CV of the fitted parameters (<50%). T4 was identified being the time point with a relatively low median and range of TIACs RDs, i.e., 5 [1, 21]% and 2 [-15, 21]% for kidneys and tumors, respectively. T4 was found to be the time point with the lowest joint root mean square error RMSEjoint of the ADs. Based on the RD and RMSE, our results show a similar performance as the SOE and NLME model approach. SUMMARY: In this study, we introduced a simplified calculation of TIACs/ADs using a PBPK model, an NLME model, and a single-time-point measurement. Our results suggest a single measurement might be used to calculate TIACs/ADs in the kidneys and tumors during PRRT.

Mathematical Modeling of In Vivo Alpha Particle Generators and Chelator Stability.

Background: Targeted α particle therapy using long-lived in vivo α particle generators is cytotoxic to target tissues. However, the redistribution of released radioactive daughters through the circulation should be considered. A mathematical model was developed to describe the physicochemical kinetics of 212Pb-labeled pharmaceuticals and its radioactive daughters. Materials and Methods: A bolus of 212Pb-labeled pharmaceuticals injected in a developed compartmental model was simulated. The contributions of chelated and free radionuclides to the total released energy were investigated for different dissociation fractions of 212Bi for different chelators, for example, 36% for DOTA. The compartmental model was applied to describe a 212Bi retention study and to assess the stability of the 212Bi-1,4,7,10-tetrakis(carbamoylmethyl)-1,4,7,10-tetraazacyclododecane (212Bi-DOTAM) complex after ß- decay of 212Pb. Results: The simulation of the injection showed that α emissions contribute 75% to the total released energy, mostly from 212Po (72%). The simulation of the 212Bi retention study showed that (16 ± 5)% of 212Bi atoms dissociate from the 212Bi-DOTAM complexes. The fractions of energies released by free radionuclides were 21% and 38% for DOTAM and DOTA chelators, respectively. Conclusion: The developed α particle generator model allows for simulating the radioactive kinetics of labeled and unlabeled pharmaceuticals being released from the chelating system due to a preceding disintegration.

Comparison of PSMA-TO-1 and PSMA-617 labeled with gallium-68, lutetium-177 and actinium-225.

BACKGROUND: PSMA-TO-1 ("Tumor-Optimized-1") is a novel PSMA ligand with longer circulation time than PSMA-617. We compared the biodistribution in subcutaneous tumor-bearing mice of PSMA-TO-1, PSMA-617 and PSMA-11 when labeled with 68Ga and 177Lu, and the survival after treatment with 225Ac-PSMA-TO-1/-617 in a murine model of disseminated prostate cancer. We also report dosimetry data of 177Lu-PSMA-TO1/-617 in prostate cancer patients. METHODS: First, PET images of 68Ga-PSMA-TO-1/-617/-11 were acquired on consecutive days in three mice bearing subcutaneous C4-2 xenografts. Second, 50 subcutaneous tumor-bearing mice received either 30 MBq of 177Lu-PSMA-617 or 177Lu-PSMA-TO-1 and were sacrificed at 1, 4, 24, 48 and 168 h for ex vivo gamma counting and biodistribution. Third, mice bearing disseminated lesions via intracardiac inoculation were treated with either 40 kBq of 225Ac-PSMA-617, 225Ac-PSMA-TO-1, or remained untreated and followed for survival. Additionally, 3 metastatic castration-resistant prostate cancer patients received 500 MBq of 177Lu-PSMA-TO-1 under compassionate use for dosimetry purposes. Planar images with an additional SPECT/CT acquisition were acquired for dosimetry calculations. RESULTS: Tumor uptake measured by PET imaging of 68Ga-labeled agents in mice was highest using PSMA-617, followed by PSMA-TO-1 and PSMA-11. 177Lu-PSMA tumor uptake measured by ex vivo gamma counting at subsequent time points tended to be greater for PSMA-TO-1 up to 1 week following treatment (p > 0.13 at all time points). This was, however, accompanied by increased kidney uptake and a 26-fold higher kidney dose of PSMA-TO-1 compared with PSMA-617 in mice. Mice treated with a single-cycle 225Ac-PSMA-TO-1 survived longer than those treated with 225Ac-PSMA-617 and untreated mice, respectively (17.8, 14.5 and 7.7 weeks, respectively; p < 0.0001). Kidney, salivary gland, bone marrow and mean ± SD tumor dose coefficients (Gy/GBq) for 177Lu-PSMA-TO-1 in patients #01/#02/#03 were 2.5/2.4/3.0, 1.0/2.5/2.3, 0.14/0.11/0.10 and 0.42 ± 0.03/4.45 ± 0.07/1.8 ± 0.57, respectively. CONCLUSIONS: PSMA-TO-1 tumor uptake tended to be greater than that of PSMA-617 in both preclinical and clinical settings. Mice treated with 225Ac-PSMA-TO-1 conferred a significant survival benefit compared to 225Ac-PSMA-617 despite the accompanying increased kidney uptake. In humans, PSMA-TO-1 dosimetry estimates suggest increased tumor absorbed doses; however, the kidneys, salivary glands and bone marrow are also exposed to higher radiation doses. Thus, additional preclinical studies are needed before further clinical use.

Blocking Studies to Evaluate Receptor-Specific Radioligand Binding in the CAM Model by PET and MR Imaging.

Inhibition studies in small animals are the standard for evaluating the specificity of newly developed drugs, including radiopharmaceuticals. Recently, it has been reported that the tumor accumulation of radiotracers can be assessed in the chorioallantoic membrane (CAM) model with similar results to experiments in mice, such contributing to the 3Rs principles (reduction, replacement, and refinement). However, inhibition studies to prove receptor-specific binding have not yet been performed in the CAM model. Thus, in the present work, we analyzed the feasibility of inhibition studies in ovo by PET and MRI using the PSMA-specific ligand [18F]siPSMA-14 and the corresponding inhibitor 2-PMPA. A dose-dependent blockade of [18F]siPSMA-14 uptake was successfully demonstrated by pre-dosing with different inhibitor concentrations. Based on these data, we conclude that the CAM model is suitable for performing inhibition studies to detect receptor-specific binding. While in the later stages of development of novel radiopharmaceuticals, testing in rodents will still be necessary for biodistribution analysis, the CAM model is a promising alternative to mouse experiments in the early phases of compound evaluation. Thus, using the CAM model and PET and MR imaging for early pre-selection of promising radiolabeled compounds could significantly reduce the number of animal experiments.

A Whole-Body Physiologically Based Pharmacokinetic Model for Alpha Particle Emitting Bismuth in Rats.

Background: α particle emitting bismuth (212Bi) as decay product of 212Pb-labeled pharmaceuticals has been effective in targeted α particle therapy (TAT). Estimating the contribution of 212Bi released from its chelator to the absorbed doses in nontarget tissues is challenging in TAT. Physiologically based pharmacokinetic (PBPK) modeling can help overcome this limitation. Therefore, a whole-body 212Bi-PBPK model was developed to describe the pharmacokinetics (PKs) of 212Bi in rats. Materials and Methods: The rat 212Bi-PBPK model was implemented using the modeling software SAAM II with data and parameter values from the literature. Besides other mechanisms, 212Bi interactions with red blood cells, high molecular weight plasma protein, and intracellular biological thiols are described. Important PK parameters were fitted to time-activity data. Absorbed dose coefficients (ADCs) were calculated for injecting 0.774 fmol of 212Bi. Results: 212Bi uptake rates of liver, bone, small intestine, bone marrow, skin, and muscle were (0.86 ± 0.13), (3.85 ± 0.63), (0.27 ± 0.05), (1.44 ± 0.29), (0.04 ± 0.01), and (0.007 ± 0.007) per min with corresponding ADCs of 0.09, 0.03, 0.03, 0.07, 0.01, and 0.003 mGy/kBq, respectively. An ADC of 0.70 mGy/kBq was determined for kidneys. Conclusions: Kidneys are the dose-limiting organs in 212Bi-based TAT. The 212Bi-PBPK model is an effective tool to investigate the 212Bi biodistribution in murine models. Integrating the 212Bi-PBPK model into other murine and human PBPK models of α particle generators can help study the efficacy and safety of TAT.

A Physiologically Based Pharmacokinetic Model for In Vivo Alpha Particle Generators Targeting Neuroendocrine Tumors in Mice.

In vivo alpha particle generators have great potential for the treatment of neuroendocrine tumors in alpha-emitter-based peptide receptor radionuclide therapy (α-PRRT). Quantitative pharmacokinetic analyses of the in vivo alpha particle generator and its radioactive decay products are required to address concerns about the efficacy and safety of α-PRRT. A murine whole-body physiologically based pharmacokinetic (PBPK) model was developed for 212Pb-labeled somatostatin analogs (212Pb-SSTA). The model describes pharmacokinetics of 212Pb-SSTA and its decay products, including specific and non-specific glomerular and tubular uptake. Absorbed dose coefficients (ADC) were calculated for bound and unbound radiolabeled SSTA and its decay products. Kidneys received the highest ADC (134 Gy/MBq) among non-target tissues. The alpha-emitting 212Po contributes more than 50% to absorbed doses in most tissues. Using this model, it is demonstrated that α-PRRT based on 212Pb-SSTA results in lower absorbed doses in non-target tissue than α-PRRT based on 212Bi-SSTA for a given kidneys absorbed dose. In both approaches, the energies released in the glomeruli and proximal tubules account for 54% and 46%, respectively, of the total energy absorbed in kidneys. The 212Pb-SSTA-PBPK model accelerates the translation from bench to bedside by enabling better experimental design and by improving the understanding of the underlying mechanisms.

A population-based method to determine the time-integrated activity in molecular radiotherapy.

BACKGROUND: The calculation of time-integrated activities (TIAs) for tumours and organs is required for dosimetry in molecular radiotherapy. The accuracy of the calculated TIAs is highly dependent on the chosen fit function. Selection of an adequate function is therefore of high importance. However, model (i.e. function) selection works more accurately when more biokinetic data are available than are usually obtained in a single patient. In this retrospective analysis, we therefore developed a method for population-based model selection that can be used for the determination of individual time-integrated activities (TIAs). The method is demonstrated at an example of [177Lu]Lu-PSMA-I&T kidneys biokinetics. It is based on population fitting and is specifically advantageous for cases with a low number of available biokinetic data per patient. METHODS: Renal biokinetics of [177Lu]Lu-PSMA-I&T from thirteen patients with metastatic castration-resistant prostate cancer acquired by planar imaging were used. Twenty exponential functions were derived from various parameterizations of mono- and bi-exponential functions. The parameters of the functions were fitted (with different combinations of shared and individual parameters) to the biokinetic data of all patients. The goodness of fits were assumed as acceptable based on visual inspection of the fitted curves and coefficients of variation CVs < 50%. The Akaike weight (based on the corrected Akaike Information Criterion) was used to select the fit function most supported by the data from the set of functions with acceptable goodness of fit. RESULTS: The function [Formula: see text] with shared parameter [Formula: see text] was selected as the function most supported by the data with an Akaike weight of 97%. Parameters [Formula: see text] and [Formula: see text] were fitted individually for every patient while parameter [Formula: see text] was fitted as a shared parameter in the population yielding a value of 0.9632 ± 0.0037. CONCLUSIONS: The presented population-based model selection allows for a higher number of parameters of investigated fit functions which leads to better fits. It also reduces the uncertainty of the obtained Akaike weights and the selected best fit function based on them. The use of the population-determined shared parameter for future patients allows the fitting of more appropriate functions also for patients for whom only a low number of individual data are available.

Comparison of Quantification of Target-Specific Accumulation of [18F]F-siPSMA-14 in the HET-CAM Model and in Mice Using PET/MRI.

Assessment of biodistribution and specific tumor accumulation is essential for the development of new radiopharmaceuticals and requires animal experiments. The HET-CAM (hens-egg test-chorioallantoic membrane) model can be used in combination with the non-invasive imaging modalities PET and MRI for pre-selection during radiopharmaceutical development to reduce the number of animal experiments required. Critical to the acceptance of this model is the demonstration of the quantifiability and reproducibility of these data compared to the standard animal model. Tumor accumulation and biodistribution of the PSMA-specific radiotracer [18F]F-siPSMA-14 was analyzed in the chick embryo and in an immunodeficient mouse model. Evaluation was based on MRI and PET data in both models. γ-counter measurements and histopathological analyses complemented these data. PSMA-specific accumulation of [18F]F-siPSMA-14 was successfully demonstrated in the HET-CAM model, similar to the results obtained by mouse model studies. The combination of MR and PET imaging allowed precise quantification of peptide accumulation, initial assessment of biodistribution, and accurate determination of tumor volume. Thus, the use of the HET-CAM model is suitable for the pre-selection of new radiopharmaceuticals and potentially reduces animal testing in line with the 3Rs principles of animal welfare.

EANM position paper on the role of radiobiology in nuclear medicine.

With an increasing variety of radiopharmaceuticals for diagnostic or therapeutic nuclear medicine as valuable diagnostic or treatment option, radiobiology plays an important role in supporting optimizations. This comprises particularly safety and efficacy of radionuclide therapies, specifically tailored to each patient. As absorbed dose rates and absorbed dose distributions in space and time are very different between external irradiation and systemic radionuclide exposure, distinct radiation-induced biological responses are expected in nuclear medicine, which need to be explored. This calls for a dedicated nuclear medicine radiobiology. Radiobiology findings and absorbed dose measurements will enable an improved estimation and prediction of efficacy and adverse effects. Moreover, a better understanding on the fundamental biological mechanisms underlying tumor and normal tissue responses will help to identify predictive and prognostic biomarkers as well as biomarkers for treatment follow-up. In addition, radiobiology can form the basis for the development of radiosensitizing strategies and radioprotectant agents. Thus, EANM believes that, beyond in vitro and preclinical evaluations, radiobiology will bring important added value to clinical studies and to clinical teams. Therefore, EANM strongly supports active collaboration between radiochemists, radiopharmacists, radiobiologists, medical physicists, and physicians to foster research toward precision nuclear medicine.

Effect of Tumor Perfusion and Receptor Density on Tumor Control Probability in 177Lu-DOTATATE Therapy: An In Silico Analysis for Standard and Optimized Treatment.

The aim of this work was to determine a minimal tumor perfusion and receptor density for 177Lu-DOTATATE therapy using physiologically based pharmacokinetic (PBPK) modeling considering, first, a desired tumor control probability (TCP) of 99% and, second, a maximal tolerated biologically effective dose (BEDmax) for organs at risk (OARs) in the treatment of neuroendocrine tumors and meningioma. Methods: A recently developed PBPK model was used. Nine virtual patients (i.e., individualized PBPK models) were used to perform simulations of pharmacokinetics for different combinations of perfusion (0.001-0.1 mL/g/min) and receptor density (1-100 nmol/L). The TCP for each combination was determined for 3 different treatment strategies: a standard treatment (4 cycles of 7.4 GBq and 105 nmol), a treatment maximizing the number of cycles based on BEDmax for red marrow and kidneys, and a treatment having 4 cycles with optimized ligand amount and activity. The red marrow and the kidneys (BEDmax of 2 Gy15 and 40 Gy2.5, respectively) were assumed to be OARs. Additionally, the influence of varying glomerular filtration rates, kidney somatostatin receptor densities, tumor volumes, and release rates was investigated. Results: To achieve a TCP of at least 99% in the standard treatment, a minimal tumor perfusion of 0.036 ± 0.023 mL/g/min and receptor density of 34 ± 20 nmol/L were determined for the 9 virtual patients. With optimization of the number of cycles, the minimum values for perfusion and receptor density were considerably lower, at 0.022 ± 0.012 mL/g/min and 21 ± 11 nmol/L, respectively. However, even better results (perfusion, 0.018 ± 0.009 mL/g/min; receptor density, 18 ± 10 nmol/L) were obtained for strategy 3. The release rate of 177Lu (or labeled metabolites) from tumor cells had the strongest effect on the minimal perfusion and receptor density for standard and optimized treatments. Conclusion: PBPK modeling and simulations represent an elegant approach to individually determine the minimal tumor perfusion and minimal receptor density required to achieve an adequate TCP. This computational method can be used in the radiopharmaceutical development process for ligand and target selection for specific types of tumors. In addition, this method could be used to optimize clinical trials.