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.

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.

Quantitative analysis of regional distribution of tau pathology with 11C-PBB3-PET in a clinical setting.

PURPOSE: The recent developments of tau-positron emission tomography (tau-PET) enable in vivo assessment of neuropathological tau aggregates. Among the tau-specific tracers, the application of 11C-pyridinyl-butadienyl-benzothiazole 3 (11C-PBB3) in PET shows high sensitivity to Alzheimer disease (AD)-related tau deposition. The current study investigates the regional tau load in patients within the AD continuum, biomarker-negative individuals (BN) and patients with suspected non-AD pathophysiology (SNAP) using 11C-PBB3-PET. MATERIALS AND METHODS: A total of 23 memory clinic outpatients with recent decline of episodic memory were examined using 11C-PBB3-PET. Pittsburg compound B (11C-PIB) PET was available for 17, 18F-flurodeoxyglucose (18F-FDG) PET for 16, and cerebrospinal fluid (CSF) protein levels for 11 patients. CSF biomarkers were considered abnormal based on Aß42 (< 600 ng/L) and t-tau (> 450 ng/L). The PET biomarkers were classified as positive or negative using statistical parametric mapping (SPM) analysis and visual assessment. Using the amyloid/tau/neurodegeneration (A/T/N) scheme, patients were grouped as within the AD continuum, SNAP, and BN based on amyloid and neurodegeneration status. The 11C-PBB3 load detected by PET was compared among the groups using both atlas-based and voxel-wise analyses. RESULTS: Seven patients were identified as within the AD continuum, 10 SNAP and 6 BN. In voxel-wise analysis, significantly higher 11C-PBB3 binding was observed in the AD continuum group compared to the BN patients in the cingulate gyrus, tempo-parieto-occipital junction and frontal lobe. Compared to the SNAP group, patients within the AD continuum had a considerably increased 11C-PBB3 uptake in the posterior cingulate cortex. There was no significant difference between SNAP and BN groups. The atlas-based analysis supported the outcome of the voxel-wise quantification analysis. CONCLUSION: Our results suggest that 11C-PBB3-PET can effectively analyze regional tau load and has the potential to differentiate patients in the AD continuum group from the BN and SNAP group.

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 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.

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.

Comparison of MRI-based and PET-based image pre-processing for quantification of 11C-PBB3 uptake in human brain.

PURPOSE: Quantification of tau load using 11C-PBB3-PET has the potential to improve diagnosis of neurodegenerative diseases. Although MRI-based pre-processing is used as a reference method, not all patients have MRI. The feasibility of a PET-based pre-processing for the quantification of 11C-PBB3 tracer was evaluated and compared with the MRI-based method. MATERIALS AND METHODS: Fourteen patients with decreased recent memory were examined with 11C-PBB3-PET and MRI. The PET scans were visually assessed and rated as either PBB3(+) or PBB3(-). The image processing based on the PET-based method was validated against the MRI-based approach. The regional uptakes were quantified using the Mesial-temporal/Temporoparietal/Rest of neocortex (MeTeR) regions. SUVR values were calculated by normalizing to the cerebellar reference region to compare both methods within the patient groups. RESULTS: Significant correlations were observed between the SUVRs of the MRI-based and the PET-based methods in the MeTeR regions (rMe=0.91; rTe=0.98; rR=0.96; p<0.0001). However, the Bland-Altman plot showed a significant bias between both methods in the subcortical Me region (bias: -0.041; 95% CI: -0.061 to -0.024; p=0.003). As in the MRI-based method, the 11C-PBB3 uptake obtained with the PET-based method was higher for the PBB3(+) group in each of the cortical regions and for the whole brain than for the PBB3(-) group (PET-basedGlobal: 1.11 vs. 0.96; Cliff's Delta (d)=0.68; p=0.04; MRI-basedGlobal: 1.11 vs. 0.97; d=0.70; p=0.03). To differentiate between positive and negative scans, Youden's index estimated the best cut-off of 0.99 from the ROC curve with good accuracy (AUC: 0.88±0.10; 95% CI: 0.67-1.00) and the same sensitivity (83%) and specificity (88%) for both methods. CONCLUSION: The PET-based pre-processing method developed to quantify the tau burden with 11C-PBB3 provided comparable SUVR values and effect sizes as the MRI-based reference method. Furthermore, both methods have a comparable discrimination accuracy between PBB3(+) and PBB3(-) groups as assessed by visual rating. Therefore, the presented PET-based method can be used for clinical diagnosis if no MRI image is available.

Important pharmacokinetic parameters for individualization of 177 Lu-PSMA therapy: A global sensitivity analysis for a physiologically-based pharmacokinetic model.

PURPOSE: The knowledge of the contribution of anatomical and physiological parameters to interindividual pharmacokinetic differences could potentially be used to improve individualized treatment planning for radionuclide therapy. The aim of this study was therefore to identify the physiologically based pharmacokinetic (PBPK) model parameters that determine the interindividual variability of absorbed doses (ADs) to kidneys and tumor lesions in therapy with 177 Lu-labeled PSMA-targeting radioligands. METHODS: A global sensitivity analysis (GSA) with the extended Fourier Amplitude Sensitivity Test (eFAST) algorithm was performed. The whole-body PBPK model for PSMA-targeting radioligand therapy from our previous studies was used in this study. The model parameters of interest (input of the GSA) were the organ receptor densities [R0 ], the organ blood flows f, and the organ release rates λ. These parameters were systematically sampled NE times according to their distribution in the patient population. The corresponding pharmacokinetics were simulated and the ADs (model output) to kidneys and tumor lesions were collected. The main effect S i and total effect S Ti were calculated using the eFAST algorithm based on the variability of the model output: The main effect S i of input parameter i represents the reduction in variance of the output if the "true" value of parameter i would be known. The total effect S Ti of an input parameter i represents the proportion of variance remaining if the "true" values of all other input parameters except for i are known. The numbers of samples NE were increased up to 8193 to check the stability (i.e., convergence) of the calculated main effects S i and total effects S Ti . RESULTS: From the simulations, the relative interindividual variability of ADs in the kidneys (coefficient of variation CV = 31%) was lower than that of ADs in the tumors (CV up to 59%). Based on the GSA, the most important parameters that determine the ADs to the kidneys were kidneys flow ( S i  = 0.36, S Ti  = 0.43) and kidneys receptor density ( S i  = 0.25, S Ti  = 0.30). Tumor receptor density was identified as the most important parameter determining the ADs to tumors ( S i and S Ti up to 0.72). CONCLUSIONS: The results suggest that an accurate measurement of receptor density and flow before therapy could be a promising approach for developing an individualized treatment with 177 Lu-labeled PSMA-targeting radioligands.

An in silico study on the effect of the radionuclide half-life on PET/CT imaging with PSMA-targeting radioligands.

AIM: The aim of this work was to systematically investigate the influence of the radionuclide half-life and affinity of prostate-specific membrane antigen (PSMA)-targeting ligands on the activity concentration for PET/CT imaging. METHODS: A whole-body physiologically-based pharmacokinetic (PBPK) model with individually estimated parameters of 13 patients with metastatic castration-resistant prostate cancer (mCRPC) was used to simulate the pharmacokinetics of PSMA-targeting radioligands. The simulations were performed with 68Ga (T1/2 = 1.13 h), 18F (T1/2 = 1.83 h), 64Cu (T1/2 = 12.7 h) and for different affinities (dissociation constants KD of 1-0.01 nM) and a commonly used ligand amount of 3 nmol. The activity concentrations were calculated at 1, 2, 3, 4, 8, 12, and 16 h after injection. RESULTS: The highest tumor uptake was achieved 1 h p. i. for 68Ga-PSMA. For 18F-PSMA, the highest tumor uptake was at 1 h p. i. and 2 h p.i for dissociation constants KD  = 1 nM and KD  = 0.1-0.01 nM, respectively. For 64Cu-PSMA, the highest tumor uptake was at 4 h p. i. for dissociation constant KD  = 1 nM and at 4 h p. i. (9 patients) and 8 h p. i. (4 patients) for higher affinities. Compared to 68Ga-PSMA (1 h p. i.), the activity concentrations in the tumor for 18F-PSMA (2 h p. i.) increased maximum 1.3-fold with minor differences for all affinities. For 64Cu-PSMA (4 h p. i.), the improvements were in the range of 2.8 to 3.2-fold for all affinities. CONCLUSIONS: The simulations indicate that the highest tumor-to-background ratio can be achieved after 4 hours in PET/CT using high-affinity 64Cu-PSMA.

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.

Influence of sampling schedules on [177Lu]Lu-PSMA dosimetry.

BACKGROUND: Individualized dosimetry is recommended for [177Lu]Lu-PSMA radioligand therapy (RLT) which is resource-intensive and protocols are often not optimized. Therefore, a simulation study was performed focusing on the determination of efficient optimal sampling schedules (OSS) for renal and tumour dosimetry by investigating different numbers of time points (TPs). METHODS: Sampling schedules with 1-4 TPs were investigated. Time-activity curves of the kidneys and two tumour lesions were generated based on a physiologically based pharmacokinetic (PBPK) model and biokinetic data of 13 patients who have undergone [177Lu]Lu-PSMA I&T therapy. Systematic and stochastic noise of different ratios was considered when modelling time-activity data sets. Time-integrated activity coefficients (TIACs) were estimated by simulating the hybrid planar/SPECT method for schedules comprising at least two TPs. TIACs based on one single SPECT/CT measurement were estimated using an approximation for reducing the number of fitted parameters. For each sampling schedule, the root-mean-squared error (RMSE) of the deviations of the simulated TIACs from the ground truths for 1000 replications was used as a measure for accuracy and precision. RESULTS: All determined OSS included a late measurement at 192 h p.i., which was necessary for accurate and precise tumour TIACs. OSS with three TPs were identified to be 3-4, 96-100 and 192 h with an additional SPECT/CT measurement at the penultimate TP. Kidney and tumour RMSE of 6.4 to 7.7% and 6.3 to 7.8% were obtained, respectively. Shortening the total time for dosimetry to e.g. 96 h resulted in kidney and tumour RMSE of 6.8 to 8.3% and 9.1 to 11%, respectively. OSS with four TPs showed similar results as with three TPs. Planar images at 4 and 68 h and a SPECT/CT shortly after the 68 h measurement led to kidney and tumour RMSE of 8.4 to 12% and 12 to 16%, respectively. One single SPECT/CT measurement at 52 h yielded good approximations for the kidney TIACs (RMSE of 7.0%), but led to biased tumour TIACs. CONCLUSION: OSS allow improvements in accuracy and precision of renal and tumour dosimetry for [177Lu]Lu-PSMA therapy with potentially less effort. A late TP is important regarding accurate tumour TIACs.

The effect of ligand amount, affinity and internalization on PSMA-targeted imaging and therapy: A simulation study using a PBPK model.

The aim of this work was to investigate the effect of ligand amount, affinity and internalization of prostate-specific membrane antigen (PSMA)-specific ligands on the activity concentrations for PET/CT imaging and on the absorbed doses for therapy. A physiologically-based pharmacokinetic (PBPK) model for PSMA-specific ligands was implemented. Thirteen virtual patients with metastatic castration-resistant prostate cancer were analysed. Simulations were performed for different combinations of association rates kon (0.1-0.01 L/nmol/min), dissociation rates koff (0.1-0.0001 min-1), internalization rates λint (0.01-0.0001 min-1) and ligand amounts (1-1000 nmol). For imaging the activity was normalized to volume and injected activity (68Ga-PSMA at 1 h). For therapy the absorbed dose was calculated for 7.3 ± 0.3 GBq 177Lu-PSMA. The effect of the investigated parameters on therapy were larger compared to imaging. For imaging, the combination of properties leading to the highest tumour uptake was kon = 0.1 L/nmol/min, koff = 0.01 min-1 for typical ligand amounts (1-10 nmol). For therapy, the higher the internalization rate, the larger was the required ligand amount for optimal tumour-to-kidney ratios. The higher the affinity, the more important was the choice of the optimal ligand amount. PBPK modelling provides insight into the pharmacokinetics of PSMA-specific ligands. Further in silico and in vivo studies are required to verify the influence of the analysed parameters.

Technical Note: Optimal sampling schedules for kidney dosimetry based on the hybrid planar/SPECT method in 177 Lu-PSMA therapy.

PURPOSE: Accurate and precise renal dosimetry during 177 Lu-labeled prostate-specific membrane antigen (PSMA) radioligand therapy is crucial for therapy decisions. Sampling schedules for estimating the necessary time-integrated activity coefficients (TIACs) are not optimized and standardized for clinical practice. Therefore, a simulation study to determine optimal sampling schedules (OSSs) was performed on 13 virtual 177 Lu-PSMA I&T therapy patients. METHOD: A total of 880 clinically feasible sampling schedules for planar imaging (three time points) were investigated. To simulate the hybrid planar/SPECT method, an additional quantitative SPECT/CT measurement following one planar image was considered. For each sampling schedule and patient, the activity values were generated separately. Measurement noise was modeled by drawing random numbers of log-normal distributions. The used fractional standard deviations (FSD) differed depending on the imaging modality. For activity values assigned to planar imaging, systematic noise between 25% and 75% of the total noise was simulated. After fitting with a mono-exponential function, the root-mean-squared errors of the deviations of the simulated TIACs from the ground truth for 1000 replications were used to determine the OSS. The uncertainties of the TIACs and renal dose coefficients were estimated. RESULTS: For the hybrid planar/SPECT method, OSSs were determined to be (3-4, 72-76, 124-144)  h post injection (p.i.) with the quantitative SPECT/CT scan shortly after the second measurement. The accuracy and precision of the determined TIACs were in the range of (-3.0 ±  6.2)% and (-1.0  ±â€¯ 6.5)%. This precision was improved by a factor 2-3 compared to dosimetry based on planar images only. Similar results were obtained for the renal dose coefficients. The virtual patients' renal dose coefficients were (0.68  ± 0.24)  Gy/GBq indicating that a population-based method yields an uncertainty of 35%. CONCLUSIONS: Dosimetry based on the hybrid planar/SPECT method with OSS outperforms dosimetry based on planar images. The high variability in dose coefficients between the virtual patients demonstrates the need for individualized dosimetry.

Modelling the internalisation process of prostate cancer cells for PSMA-specific ligands.

INTRODUCTION: In prostate-specific membrane antigen (PSMA)-targeting radioligand therapy, small molecules are regularly internalised by the tumour cells. To determine the effectiveness of these ligands, the internalised fraction over time is derived from cell studies. Parameters, such as the ligand concentration and the number of cells, are experiment-specific and therefore a comparison between ligands is difficult. A more objective approach that allows better comparison is desirable. Therefore, the aim of this work was to develop a compartmental model that fully describes all relevant pharmacokinetic interactions of PSMA-specific ligands with prostate cancer cells. METHODS: Internalisation studies were performed using the lymph node carcinoma of the prostate cell line LNCaP C4-2 and the prostatic carcinoma cell line PC-3. A new protocol was established for the determination of the PSMA-binding specificity by surface plasmon resonance (SPR). The experimental data in combination with parameters from literature were used for the modelling approach. RESULTS: A compartmental model which includes the relevant physiological mechanisms was developed. The basic model structure and some parameters originate from the literature. The PSMA-specific association and dissociation rates of Ga-PSMA-11 were measured using surface plasmon resonance technology. The ligand-induced internalisation and PSMA synthesis rates were estimated by fitting the developed model to experimental data obtained using LNCaP C4-2 cells. For all [68Ga]Ga-PSMA-11 concentrations and including four various incubation times, the ligand-induced internalisation was determined to be (3.6 ±â€¯0.1) % min-1. CONCLUSIONS: The presented approach is a prerequisite for better estimation and thus comparison of important ligand-cell interaction parameters, by combining SPR measurements, cell experiments and mathematical modelling. ADVANCES IN KNOWLEDGE AND IMPLICATIONS FOR PATIENT: A compartmental model was developed for evaluation and comparison of PSMA-binding small molecules. A SPR protocol was established for the determination of PSMA-binding specificity.

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% (fΔa˜>5%) and >10% (fΔa˜>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 fΔa˜>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.

Modeling and Predicting Tumor Response in Radioligand Therapy.

The aim of this work was to develop a theranostic method that allows prediction of prostate-specific membrane antigen (PSMA)-positive tumor volume after radioligand therapy (RLT) based on a pretherapeutic PET/CT measurement and physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling at the example of RLT using 177Lu-labeled PSMA for imaging and therapy (PSMA I&T). Methods: A recently developed PBPK model for 177Lu-PSMA I&T RLT was extended to account for tumor (exponential) growth and reduction due to irradiation (linear quadratic model). Data from 13 patients with metastatic castration-resistant prostate cancer were retrospectively analyzed. Pharmacokinetic/pharmacodynamic parameters were simultaneously fitted in a Bayesian framework to PET/CT activity concentrations, planar scintigraphy data, and tumor volumes before and after (6 wk) therapy. The method was validated using the leave-one-out Jackknife method. The tumor volume after therapy was predicted on the basis of pretherapy PET/CT imaging and PBPK/PD modeling. Results: The relative deviation of the predicted and measured tumor volume for PSMA-positive tumor cells (6 wk after therapy) was 1% ± 40%, excluding 1 patient (prostate-specific antigen-negative) from the population. The radiosensitivity for the prostate-specific antigen-positive patients was determined to be 0.0172 ± 0.0084 Gy-1Conclusion: To our knowledge, the proposed method is the first attempt to solely use PET/CT and modeling methods to predict the PSMA-positive tumor volume after RLT. Internal validation shows that this is feasible with an acceptable accuracy. Improvement of the method and external validation of the model is ongoing.

Treatment planning algorithm for peptide receptor radionuclide therapy considering multiple tumor lesions and organs at risk.

PURPOSE: Peptide receptor radionuclide therapy (PRRT) has shown promising results in the treatment of tumors with high expression of somatostatin receptors such as neuroendocrine tumors (NETs) and meningioma. However, PRRT potentially produces high renal and red marrow (RM) toxicity, the kidneys usually being the dose-limiting organ. Previously, it was shown that an improved therapeutic index can be achieved by choosing an optimal combination of injected activity and peptide molar amount. The aim of this work was to develop a clinically applicable algorithm for treatment planning in PRRT. To demonstrate the applicability and possible advantages of the algorithm thus developed, an in silico clinical trial applying the algorithm to 177 Lu-DOTATATE therapy in nine virtual patients was conducted. METHODS: An algorithm for treatment planning in PRRT was developed, which simultaneously considers multiple tumor lesions, maximum tolerated biologically effective doses (BEDs) for multiple organs at risk (OARs) and a maximum achievable molar activity. The algorithm, subject to the abovementioned constraints, aims at maximizing the total number of killed tumor cells in the considered lesions/metastases. An in silico clinical trial was conducted with nine virtual patients. For each virtual patient, simulations increasing the molar dose of 177 Lu-DOTATATE from 2 to 2048 nmol by factors of 25 were performed. Maximum tolerated BEDs per cycle for the kidneys (10 Gy2.5 ) and for the RM (0.5 Gy15 ) were defined based on a planned total treatment of four cycles. A maximum achievable molar activity of 420 MBq/nmol was assumed. Optimal combinations of molar dose and activity were determined by applying the developed algorithm. For comparison, simulations for a typical plan with 177 Lu-DOTATATE (7.4 GBq, 265 nmol) were performed and BEDs for the OARs and for individual tumor lesions were calculated. Furthermore, to determine treatment efficacy, overall tumor control probability (oTCP) values after a four-cycle treatment were estimated for the optimal and typical plans. RESULTS: The conducted in silico clinical trial yielded optimal molar doses and activities ranging from 24 to 512 nmol and from 6 to 30 GBq, respectively. Tumor BEDs ranged from 2 to 107 Gy10 and from 1 to 65 Gy10 for the optimal and typical plans, respectively. The estimated oTCP values showed that the optimal plans may produce adequate tumor control in six of the nine virtual patients after four cycles of 177 Lu-DOTATATE while the typical plan may be sufficient in only two virtual patients. CONCLUSIONS: The algorithm presented can derive plans with higher tumor control than the typically delivered plan. Therefore, we propose this algorithm for clinical validation and possibly future implementation in treatment planning in molecular radiotherapy.

The Effect of Total Tumor Volume on the Biologically Effective Dose to Tumor and Kidneys for 177Lu-Labeled PSMA Peptides.

The aim of this work was to simulate the effect of prostate-specific membrane antigen (PSMA)-positive total tumor volume (TTV) on the biologically effective doses (BEDs) to tumors and organs at risk in patients with metastatic castration-resistant prostate cancer who are undergoing 177Lu-PSMA radioligand therapy. Methods: A physiologically based pharmacokinetic model was fitted to the data of 13 patients treated with 177Lu-PSMA I&T (a PSMA inhibitor for imaging and therapy). The tumor, kidney, and salivary gland BEDs were simulated for TTVs of 0.1-10 L. The activity and peptide amounts leading to an optimal tumor-to-kidneys BED ratio were also investigated. Results: When the TTV was increased from 0.3 to 3 L, the simulated BEDs to tumors, kidneys, parotid glands, and submandibular glands decreased from 22 ± 15 to 11.0 ± 6.0 Gy1.49, 6.5 ± 2.3 to 3.7 ± 1.4 Gy2.5, 11.0 ± 2.7 to 6.4 ± 1.9 Gy4.5, and 10.9 ± 2.7 to 6.3 ± 1.9 Gy4.5, respectively (where the subscripts denote that an α/ß of 1.49, 2.5, or 4.5 Gy was used to calculate the BED). The BED to the red marrow increased from 0.17 ± 0.05 to 0.32 ± 0.11 Gy15 For patients with a TTV of more than 0.3 L, the optimal amount of peptide was 273 ± 136 nmol and the optimal activity was 10.4 ± 4.4 GBq. Conclusion: This simulation study suggests that in patients with large PSMA-positive tumor volumes, higher activities and peptide amounts can be safely administered to maximize tumor BEDs without exceeding the tolerable BED to the organs at risk.

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

PURPOSE: To investigate the accuracy of predicted time-integrated activity coefficients (TIACs) in peptide-receptor radionuclide therapy (PRRT) using simulated dynamic PET data and a physiologically based pharmacokinetic (PBPK) model. METHODS: PBPK parameters were estimated using biokinetic data of 15 patients after injection of (152±15)MBq of 111In-DTPAOC (total peptide amount (5.78±0.25)nmol). True mathematical phantoms of patients (MPPs) were the PBPK model with the estimated parameters. Dynamic PET measurements were simulated as being done after bolus injection of 150MBq 68Ga-DOTATATE using the true MPPs. Dynamic PET scans around 35min p.i. (P1), 4h p.i. (P2) and the combination of P1 and P2 (P3) were simulated. Each measurement was simulated with four frames of 5min each and 2 bed positions. PBPK parameters were fitted to the PET data to derive the PET-predicted MPPs. Therapy was simulated assuming an infusion of 5.1GBq of 90Y-DOTATATE over 30min in both true and PET-predicted MPPs. TIACs of simulated therapy were calculated, true MPPs (true TIACs) and predicted MPPs (predicted TIACs) followed by the calculation of variabilities v. RESULTS: For P1 and P2 the population variabilities of kidneys, liver and spleen were acceptable (v<10%). For the tumours and the remainders, the values were large (up to 25%). For P3, population variabilities for all organs including the remainder further improved, except that of the tumour (v>10%). CONCLUSION: Treatment planning of PRRT based on dynamic PET data seems possible for the kidneys, liver and spleen using a PBPK model and patient specific information.