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.

EANM practical guidance on uncertainty analysis for molecular radiotherapy absorbed dose calculations.

A framework is proposed for modelling the uncertainty in the measurement processes constituting the dosimetry chain that are involved in internal absorbed dose calculations. The starting point is the basic model for absorbed dose in a site of interest as the product of the cumulated activity and a dose factor. In turn, the cumulated activity is given by the area under a time-activity curve derived from a time sequence of activity values. Each activity value is obtained in terms of a count rate, a calibration factor and a recovery coefficient (a correction for partial volume effects). The method to determine the recovery coefficient and the dose factor, both of which are dependent on the size of the volume of interest (VOI), are described. Consideration is given to propagating estimates of the quantities concerned and their associated uncertainties through the dosimetry chain to obtain an estimate of mean absorbed dose in the VOI and its associated uncertainty. This approach is demonstrated in a clinical example.

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.

In vivo micro-CT imaging of untreated and irradiated orthotopic glioblastoma xenografts in mice: capabilities, limitations and a comparison with bioluminescence imaging.

Small animal imaging is of increasing relevance in biomedical research. Studies systematically assessing the diagnostic accuracy of contrast-enhanced in vivo micro-CT of orthotopic glioma xenografts in mice do not exist. NOD/SCID/γc(-/-) mice (n = 27) underwent intracerebral implantation of 2.5 × 10(6) GFP-Luciferase-transduced U87MG cells. Mice underwent bioluminescence imaging (BLI) to detect tumor growth and afterwards repeated contrast-enhanced (300 µl Iomeprol i.v.) micro-CT imaging (80 kV, 75 µAs, 360° rotation, 1,000 projections, 33 s scan time, resolution 40 × 40 × 53 µm, 0.5 Gy/scan). Presence of tumors, tumor diameter and tumor volume in micro-CT were rated by two independent readers. Results were compared with histological analyses. Six mice with tumors confirmed by micro-CT received fractionated irradiation (3 × 5 Gy every other day) using the micro-CT (5 mm pencil beam geometry). Repeated micro-CT scans were tolerated well. Tumor engraftment rate was 74 % (n = 20). In micro-CT, mean tumor volume was 30 ± 33 mm(3), and the smallest detectable tumor measured 360 × 620 µm. The inter-rater agreement (n = 51 micro-CT scans) for the item tumor yes/no was excellent (Spearman-Rho = 0.862, p < 0.001). Sensitivity and specificity of micro-CT were 0.95 and 0.71, respectively (PPV = 0.91, NPV = 0.83). BLI on day 21 after tumor implantation had a sensitivity and specificity of 0.90 and 1.0, respectively (PPV = 1.0, NPV = 0.5). Maximum tumor diameter and volume in micro-CT and histology correlated excellently (tumor diameter: 0.929, p < 0.001; tumor volume: 0.969, p < 0.001, n = 17). Irradiated animals showed a large central tumor necrosis. Longitudinal contrast enhanced micro-CT imaging of brain tumor growth in live mice is feasible at high sensitivity levels and with excellent inter-rater agreement and allows visualization of radiation effects.

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.

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.

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.

Radioimmunotherapy-based conditioning for hematopoietic cell transplantation in children with malignant and nonmalignant diseases.

Targeted irradiation of the bone marrow with radiolabeled monoclonal antibodies (radioimmunotherapy) represents a novel therapeutic approach with both myeloablative and antileukemic potential. In an open-label, single-center pilot study, 30 pediatric and adolescent patients undergoing hematopoietic cell transplantation for malignant (n = 16) and nonmalignant (n = 14) disorders received treatment with a 9°Y-labeled anti-CD66 monoclonal antibody. Patients with a high risk of relapse (n = 7) received additional treatment with standard conditioning based on either total body irradiation or busulfan to intensify the antileukemic effect. In patients with comorbidities (n = 23), radioimmunotherapy was combined with a reduced-intensity conditioning regimen to reduce systemic toxicity. Preferential irradiation of the bone marrow was achieved in all patients. Nonrelapse mortality was 4 (13%) of 30 patients. In patients with malignant diseases, the probabilities of overall and disease-free survival at 2 years were 0.69 (95% confidence interval 0.37-0.87) and 0.46 (95% confidence interval 0.19-0.70), respectively. In patients with nonmalignant diseases, the probability of both overall and disease-free survival at 2 years was 0.94 (95% confidence interval 0.63-0.99). This pilot study demonstrates that radioimmunotherapy is effective in achieving myeloablation with low additional toxicity when used in combination with standard or reduced-intensity conditioning in young patients.

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.

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.

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.

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.

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.

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.

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.

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.