The role of patient-based treatment planning in peptide receptor radionuclide therapy.

BACKGROUND: Accurate treatment planning is recommended in peptide-receptor radionuclide therapy (PRRT) to minimize the toxicity to organs at risk while maximizing tumor cell sterilization. The aim of this study was to quantify the effect of different degrees of individualization on the prediction accuracy of individual therapeutic biodistributions in patients with neuroendocrine tumors (NETs). METHODS: A recently developed physiologically based pharmacokinetic (PBPK) model was fitted to the biokinetic data of 15 patients with NETs after pre-therapeutic injection of (111)In-DTPAOC. Mathematical phantom patients (MPP) were defined using the assumed true (true MPP), mean (MPP 1A) and median (MPP 1B) parameter values of the patient group. Alterations of the degree of individualization were introduced to both mean and median patients by including patient-specific information as a priori knowledge: physical parameters and hematocrit (MPP 2A/2B). Successively, measurable individual biokinetic parameters were added: tumor volume V tu (MPP 3A/3B), glomerular filtration rate GFR (MPP 4A/4B), and tumor perfusion f tu (MPP 5A/5B). Furthermore, parameters of MPP 5A/5B and a simulated (68)Ga-DOTATATE PET measurement 60 min p.i. were used together with the population values used as Bayesian parameters (MPP 6A/6B). Therapeutic biodistributions were simulated assuming an infusion of (90)Y-DOTATATE (3.3 GBq) over 30 min to all MPPs. Time-integrated activity coefficients were predicted for all MPPs and compared to the true MPPs for each patient in tumor, kidneys, spleen, liver, remainder, and whole body to obtain the relative differences RD. RESULTS: The large RD values of MPP 1A [RDtumor = (625 ± 1266)%, RDkidneys = (11 ± 38)%], and MPP 1B [RDtumor = (197 ± 505)%, RDkidneys = (11 ± 39)%] demonstrate that individual treatment planning is needed due to large physiological differences between patients. Although addition of individual patient parameters reduced the deviations considerably [MPP 5A: RDtumor = (-2 ± 27)% and RDkidneys = (16 ± 43)%; MPP 5B: RDtumor = (2 ± 28)% and RDkidneys = (7 ± 40)%] errors were still large. For the kidneys, prediction accuracy was considerably improved by including the PET measurement [MPP 6A/MPP 6B: RDtumor = (-2 ± 22)% and RDkidneys = (-0.1 ± 0.5)%]. CONCLUSION: Individualized treatment planning is needed in the investigated patient group. The use of a PBPK model and the inclusion of patient specific data, e.g., weight, tumor volume, and glomerular filtration rate, do not suffice to predict the therapeutic biodistribution. Integrating all available a priori information in the PBPK model and using additionally PET data measured at one time point for tumor, kidneys, spleen, and liver could possibly be sufficient to perform an individualized treatment planning.

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.

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.

Differences in predicted and actually absorbed doses in peptide receptor radionuclide therapy.

PURPOSE: An important assumption in dosimetry prior to radionuclide therapy is the equivalence of pretherapeutic and therapeutic biodistribution. In this study the authors investigate if this assumption is justified in sst2-receptor targeting peptide therapy, as unequal amounts of peptide and different peptides for pretherapeutic measurements and therapy are commonly used. METHODS: Physiologically based pharmacokinetic models were developed. Gamma camera and serum measurements of ten patients with metastasizing neuroendocrine tumors were conducted using (111)In-DTPAOC. The most suitable model was selected using the corrected Akaike information criterion. Based on that model and the estimated individual parameters, predicted and measured (90)Y-DOTATATE excretions during therapy were compared. The residence times for the pretherapeutic (measured) and therapeutic scenarios (simulated) were calculated. RESULTS: Predicted and measured therapeutic excretion differed in three patients by 10%, 31%, and 7%. The measured pretherapeutic and therapeutic excretion differed by 53%, 56%, and 52%. The simulated therapeutic residence times of kidney and tumor were 3.1 ± 0.6 and 2.5 ± 1.2 fold higher than the measured pretherapeutic ones. CONCLUSIONS: To avoid the introduction of unnecessary inaccuracy in dosimetry, using the same substance along with the same amount for pretherapeutic measurements and therapy is recommended.

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.

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.

Optimal preloading in radioimmunotherapy with anti-cD45 antibody.

PURPOSE: Anti-CD45 antibody is predominantly used in the treatment of acute leukemia. CD45 is stably expressed on all leukocytes and their precursors, and therefore the liver and spleen constitute major antigen sinks. Thus, as the red marrow is the target organ, in radioimmunotherapy with anti-CD45 antibody, preloading with unlabeled antibody is a method to increase the absorbed dose to the target cells. In a previous study, a method to individually determine the optimal preload for five patients with acute leukemia was developed. Here, this method is examined and improved using two pretherapeutic measurement series and a refined pharmacokinetic model. METHODS: To obtain the biodistribution of 111In-labeled anti-CD45 antibody under different saturation conditions, two measurement series one with and one without preloading were conducted in five patients. For each patient, two physiologically based pharmacokinetic models were fitted to the data and the corrected Akaike information criterion was used to identify the model, which was empirically most supported. The resultant parameter values were compared to values reported in the literature. To individually determine the optimal amount of unlabeled antibody for therapy, computer simulations for preloads ranging from 0 to 60 mg were performed based on the estimated parameters of each patient. The prediction power of the model was assessed by comparing the simulated therapeutic serum curves to the actual 90Y measurements. RESULTS: Visual inspection showed good fits and the adjusted R2 was >0.90 for all patients. All parameters were in a physiologically reasonable range. The relative deviation of the predicted area under the therapeutic serum curve and the measured curve was 15%-33%. The optimal preloading increased the marrow-over-liver selectivity up to 3.9 fold compared to the simulated biodistribution using a standard dose (0.5 mg/kg). CONCLUSIONS: The presented method can be used to individually determine the optimal preload and the corresponding residence times in radioimmunotherapy with anti-CD45 antibody.

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.

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.

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.

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.

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.

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.

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.

Improving anti-CD45 antibody radioimmunotherapy using a physiologically based pharmacokinetic model.

UNLABELLED: Radioimmunotherapy is a method to selectively deliver radioactivity to cancer cells via specific antibodies. A strategy to enhance the efficacy of radioimmunotherapy is the prior application of unlabeled antibody, resulting in an increase in the dose to the target tissue and a decrease in the burden to other organs. It was suggested that optimizing this approach might considerably improve radioimmunotherapy with anti-CD45 antibody. The present work develops a physiologically based pharmacokinetic model to individually determine the optimal preload for radioimmunotherapy with the YAML568 anti-CD45 antibody for each patient. METHODS: A physiologically based pharmacokinetic model was developed to describe the biodistribution of anti-CD45 antibody. The transport of antibody to the organs of interest via blood flow, competitive binding of unlabeled and labeled antibody, degradation and excretion of antibody, and physical decay were included in the model. The model was fitted to the biokinetics data of 5 patients with acute myeloid leukemia. On the basis of the estimated parameters, simulations for a 0- to 534-nmol preload of unlabeled antibody were conducted and the organ residence times were calculated. RESULTS: The measured data could be adequately described by the constructed model. The estimated numbers of accessible antigens in the respective organ, in nanomoles, were 97+/-33 for red marrow, 49+/-24 for liver, 34+/-18 for spleen, 38+/-31 for lymph nodes, and 0.9+/-0.4 for blood. These ranges indicate high interpatient variability. The optimal amount of unlabeled antibody identified by simulations would improve the ratio of residence time in red marrow to residence time in liver by a factor of 1.6-2.4. CONCLUSION: The efficacy of radioimmunotherapy using anti-CD45 antibody can be considerably increased with the presented model. A more selective delivery of radioactivity to the target organ and a reduction in the toxicity to normal tissue are achieved by determining the optimal preload. Furthermore, the adverse effects of radioimmunotherapy might be drastically reduced while saving antibody expenses. The validation of the model is ongoing. The model is easily extendible and therefore most probably applicable to radioimmunotherapy of other hematologic malignancies, such as antibodies targeted to CD20, CD33, or CD66.

Modelling radioimmunotherapy with anti-CD45 antibody to obtain a more favourable biodistribution.

UNLABELLED: Radioimmunotherapy (RIT) is a method to selectively deliver radiation to malignant haematological cells by addressing specific antigens. One approach to improve the biodistribution is to administer a preload of unlabelled antibodies. The aim of this study was to develop a model, which describes distribution of labelled and unlabelled antibodies based on the tissue blood flow and the competing binding behaviour of the antibodies. Such a model can be used to improve biodistribution in the particular case of RIT using anti-CD45 antibodies. METHODS: A compartmental model for the interconnected organs was developed. Reaction constants and organ specific flow, antigen concentrations and distribution volumes were taken from the literature. The organ residence times were calculated for different amounts of given labelled and unlabelled antibodies and the time delay between their administrations. RESULTS: The model is capable to describe the preloading effect. The biodistribution of labelled or unlabelled antibodies depends essentially on the specific blood flow to the organ and its antigen expression. The dose ratio of bone marrow to liver is maximized by applying sufficient unlabelled monoclonal antibody (mAb) to saturate antibody binding in the competing organs and by applying the labelled mAb with a delay of more than one hour. CONCLUSIONS: The developed model qualitatively describes how a preload can considerably increase selectivity of RIT due to different blood flows and antigen distribution in relevant organs. In addition, simulations can identify the optimal delay between the application of labelled and unlabelled antibody. For future analyses, i.e., to fit patient data, degradation and excretion should be incorporated into the model.

Model selection for time-activity curves: the corrected Akaike information criterion and the F-test.

Data analysis often requires a multi model approach, i.e. the best model or models are selected from a well chosen set of candidate models and subsequent parameter inference is conducted. The selection of the model or models which are best supported by the data can be accomplished using various criteria. The present work focuses on the comparison of two approaches namely the corrected Akaike information criterion (AICc) and the F-test for sparse data sets, which are common in medical research. The selection of the true model and the determination of relevant pharmacokinetic parameters as the clearance, the volume of distribution and the mean residence time are examined using Monte Carlo simulations with 10000 replications. The data (N = 10 per replication) are generated from a sum of two exponentials, which parameters were determined by fitting to time-concentration data of 111In labelled anti-CD66 antibody in blood serum. Four different normal distributed multiplicative statistical errors (0.05, 0.1, 0.15, 0.2) were examined. The set of candidate models consists of sums of up to 3 exponentials. Comparisons with two different model set sizes were conducted. All candidate models are fitted to the generated data and selected according to the AICc and the F-test. Both selection criteria perform well for our data. The selection frequency of functions of lower dimension increases proportionally to the statistical error for both criteria, while for higher errors, the AICc tends to choose a model of lower dimension more frequently than the F-test. In addition, the overfitted fraction decreases proportionally to the statistical error for both methods but selection frequency of function of higher dimension is larger using the F-test. The choice of the adequate model set is important for the positive effect of model averaging concerning the bias and the variability of the estimated parameters. It is in general assumed and has been confirmed in this study that parameter estimation using the AICc has clear advantages over the F-test.

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.

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.