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

Uncertainty Analysis of Time-Integrated Activity Coefficient in Single-Time-Point Dosimetry Using Bayesian Fitting Method

Purpose

Calculation of the uncertainty of the individual time-integrated activity coefficient (TIACs) is desirable in molecular radiotherapy. However, the calculation of TIAC's uncertainty in single-time-point (STP) method has never been reported in the literature. This study presents a method based on the Bayesian fitting (BF) to calculate the standard deviation (SD) of individual TIACs in the STP dosimetry.

Methods

Biokinetic data of 177Lu-DOTATATE in kidneys were obtained from PMID33443063. BF methods with extended objective function, which optimize the fitting using prior knowledge of the function's parameters, were used. Reference TIACs (rTIACs) were calculated by fitting a mono-exponential function to the all-time-point data. The goodness of fit was checked based on the visual inspection and the coefficient of variations (CV) of the fitted parameters < 0.5. BF with relative (BFr) and absolute-based (BFa) variance methods were used to obtain the calculated TIACs (cTIACs) from the STP dosimetry. Performance of the STP method was obtained by calculating the relative deviation (RD) between cTIACs and rTIACs.

Results

Visual inspection showed a good fit for all patients with CV of fitted parameters less than 50%. The mean ± SD of cTIAC's %RD were 7.0 ± 25.2 for BFr and 2.6 ± 8.9 for BFa. The range of %CV of the individual cTIAC's SD for BFr and BFa methods was 36-78% and 22-33%, respectively, while the %CV of the rTIAC SD was 0.8-49%.

Conclusion

We introduce the BF method to calculate the SD of individual TIACs in STP dosimetry. The presented method might be used as an alternative method for uncertainty analysis in STP dosimetry.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13139-024-00851-8.

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.

Physiologically based radiopharmacokinetic (PBRPK) modeling to simulate and analyze radiopharmaceutical therapies: studies of non-linearities, multi-bolus injections, and albumin binding

BACKGROUND

We aimed to develop a publicly shared computational physiologically based pharmacokinetic (PBPK) model to reliably simulate and analyze radiopharmaceutical therapies (RPTs), including probing of hot-cold ligand competitions as well as alternative injection scenarios and drug designs, towards optimal therapies.

RESULTS

To handle the complexity of PBPK models (over 150 differential equations), a scalable modeling notation called the "reaction graph" is introduced, enabling easy inclusion of various interactions. We refer to this as physiologically based radiopharmacokinetic (PBRPK) modeling, fine-tuned specifically for radiopharmaceuticals. As three important applications, we used our PBRPK model to (1) study the effect of competition between hot and cold species on delivered doses to tumors and organs at risk. In addition, (2) we evaluated an alternative paradigm of utilizing multi-bolus injections in RPTs instead of prevalent single injections. Finally, (3) we used PBRPK modeling to study the impact of varying albumin-binding affinities by ligands, and the implications for RPTs. We found that competition between labeled and unlabeled ligands can lead to non-linear relations between injected activity and the delivered dose to a particular organ, in the sense that doubling the injected activity does not necessarily result in a doubled dose delivered to a particular organ (a false intuition from external beam radiotherapy). In addition, we observed that fractionating injections can lead to a higher payload of dose delivery to organs, though not a differential dose delivery to the tumor. By contrast, we found out that increased albumin-binding affinities of the injected ligands can lead to such a differential effect in delivering more doses to tumors, and this can be attributed to several factors that PBRPK modeling allows us to probe.

CONCLUSIONS

Advanced computational PBRPK modeling enables simulation and analysis of a variety of intervention and drug design scenarios, towards more optimal delivery of RPTs.

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.

Innovation in Radionuclide Therapy for the Treatment of Prostate Cancers: Radiochemical Perspective and Recent Therapeutic Practices

Prostate cancer represents the second cause of death by cancer in males in western countries. While early-stage diseases are accessible to surgery and/or external radiotherapy, advanced metastatic prostate cancers are primarily treated with androgen deprivation therapy, to which new generation androgen receptor antagonists or taxane-based chemotherapies are added in the case of tumor relapse. Nevertheless, patients become invariably resistant to castration with a median survival that rarely exceeds 3 years. This fostered the search for alternative strategies, independent of the androgen receptor signaling pathway. In this line, radionuclide therapies may represent an interesting option as they could target either the microenvironment of sclerotic bone metastases with the use of radiopharmaceuticals containing samarium-153, strontium-89 or radium-223 or tumor cells expressing the prostate-specific membrane antigen (PSMA), a protein found at the surface of prostate cancer cells. This review gives highlights the chemical properties of radioligands targeting prostate cancer cells and recapitulates the clinical trials evaluating the efficacy of radionuclide therapies, alone or in combination with other approved treatments, in patients with castration-resistant prostate tumors. It discusses some of the encouraging results obtained, especially the benefit on overall survival that was reported with [¹⁷⁷Lu]-PSMA-617. It also addresses the specific requirements for the use of this particular class of drugs, both in terms of medical staff coordination and adapted infrastructures for efficient radioprotection.

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 ± 11) %. Visual inspection of the fitted graphs and the RMSE values show that the NLME model selection method has a relatively better or equivalent performance than the IBMS or SP-PBMS methods. The RMSEs of the IBMS, SP-PBMS, and NLME-PBMS (f3a) methods are 7.4%, 8.8%, and 2.4%, respectively.

CONCLUSION

A procedure including fitting function selection in a population-based method was developed to determine the best fit function for calculating TIAs in MRT for a given radiopharmaceutical, organ and set of biokinetic data. The technique combines standard practice approaches in pharmacokinetics, i.e. an Akaike-weight-based model selection and the NLME model framework.

Single-time-point 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 TIA_NLME-PBMS and TIA_3-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 ± 0.6), T2 = (4.6 ± 0.4), T3 = (22.8 ± 1.6), T4 = (46.7 ± 1.7) and T5 = (70.9 ± 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 RMSE_joint 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 RMSE_joint 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.

Pharmacological Optimization of PSMA-Based Radioligand Therapy

Prostate cancer (PCa) is the most common malignancy in men of middle and older age. The standard treatment strategy for PCa ranges from active surveillance in low-grade, localized PCa to radical prostatectomy, external beam radiation therapy, hormonal treatment and chemotherapy. Recently, the use of prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT) for metastatic castration-resistant PCa has been approved. PSMA is predominantly, but not exclusively, expressed on PCa cells. Because of its high expression in PCa, PSMA is a promising target for diagnostics and therapy. To understand the currently used RLT, knowledge about pharmacokinetics (PK) and pharmacodynamics (PD) of the PSMA ligand and the PSMA protein itself is crucial. PK and PD properties of the ligand and its target determine the duration and extent of the effect. Knowledge on the concentration-time profile, the target affinity and target abundance may help to predict the effect of RLT. Increased specific binding of radioligands to PSMA on PCa cells may be associated with better treatment response, where nonspecific binding may increase the risk of toxicity in healthy organs. Optimization of the radioligand, as well as synergistic effects of concomitant agents and an improved dosing strategy, may lead to more individualized treatment and better overall survival.

Application of machine learning to pretherapeutically estimate dosimetry in men with advanced prostate cancer treated with 177Lu-PSMA I&T therapy

Purpose

Although treatment planning and individualized dose application for emerging prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT) are generally recommended, it is still difficult to implement in practice at the moment. In this study, we aimed to prove the concept of pretherapeutic prediction of dosimetry based on imaging and laboratory measurements before the RLT treatment.

Methods

Twenty-three patients with metastatic castration-resistant prostate cancer (mCRPC) treated with ¹⁷⁷Lu-PSMA I&T RLT were included retrospectively. They had available pre-therapy ⁶⁸ Ga-PSMA-HEBD-CC PET/CT and at least 3 planar and 1 SPECT/CT imaging for dosimetry. Overall, 43 cycles of ¹⁷⁷Lu-PSMA I&T RLT were applied. Organ-based standard uptake values (SUVs) were obtained from pre-therapy PET/CT scans. Patient dosimetry was calculated for the kidney, liver, spleen, and salivary glands using Hermes Hybrid Dosimetry 4.0 from the planar and SPECT/CT images. Machine learning methods were explored for dose prediction from organ SUVs and laboratory measurements. The uncertainty of these dose predictions was compared with the population-based dosimetry estimates. Mean absolute percentage error (MAPE) was used to assess the prediction uncertainty of estimated dosimetry.

Results

An optimal machine learning method achieved a dosimetry prediction MAPE of 15.8 ± 13.2% for the kidney, 29.6% ± 13.7% for the liver, 23.8% ± 13.1% for the salivary glands, and 32.1 ± 31.4% for the spleen. In contrast, the prediction based on literature population mean has significantly larger MAPE (p < 0.01), 25.5 ± 17.3% for the kidney, 139.1% ± 111.5% for the liver, 67.0 ± 58.3% for the salivary glands, and 54.1 ± 215.3% for the spleen.

Conclusion

The preliminary results confirmed the feasibility of pretherapeutic estimation of treatment dosimetry and its added value to empirical population-based estimation. The exploration of dose prediction may support the implementation of treatment planning for RLT.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-022-05883-w.

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

Background

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

Methods

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

Results

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

Conclusions

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

Drug Exposure to Establish Pharmacokinetic-Response Relationships in Oncology

In the oncology field, understanding the relationship between the dose administered and the exerted effect is particularly important because of the narrow therapeutic index associated with anti-cancer drugs and the high interpatient variability. Therefore, in this review, we provide a critical perspective of the different methods of characterising treatment exposure in the oncology setting. The increasing number of modelling applications in oncology reflects the applicability and the impact of pharmacometrics on all phases of the drug development process and patient management as well. Pharmacometric modelling is a worthy component within the current paradigm of model-based drug development, but pharmacometric modelling techniques are also accessible for the clinician in the optimisation of current oncology therapies. Consequently, the application of population models in a hospital setting by generating close collaborations between physicians and pharmacometricians is highly recommended, providing a systematic means of developing and assessing model-based metrics as 'drivers' for various responses to treatments, which can then be evaluated as predictors for treatment success. Characterising the key determinants of variability in exposure is of particular importance for anticancer agents, as efficacy and toxicity are associated with exposure. We present the different strategies to describe and predict drug exposure that can be applied depending on the data available, with the objective of obtaining the most useful information in the patients' favour throughout the full drug cycle. Therefore, the objective of the present article is to review the different approaches used to characterise a patient's exposure to oncology drugs, which will result in a better understanding of the time course of the response and the magnitude of interpatient variability.

Radiolabeled Peptides and Antibodies in Medicine

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

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.

Biodistribution and Multicompartment Pharmacokinetic Analysis of a Targeted α Particle Therapy

Targeted α particle therapy (TAT) is ideal for treating disease while minimizing damage to surrounding nontargeted tissues due to short path length and high linear energy transfer (LET). We developed a TAT for metastatic uveal melanoma, targeting the melanocortin-1 receptor (MC1R), which is expressed in 94% of uveal melanomas. Two versions of the therapy are being investigated: ²²⁵Ac-DOTA-Ahx-MC1RL (²²⁵Ac-Ahx) and ²²⁵Ac-DOTA-di-d-Glu-MC1RL (²²⁵Ac-di-d-Glu). The biodistribution (BD) from each was studied and a multicompartment pharmacokinetic (PK) model was developed to describe drug distribution rates. Two groups of 16 severe combined immunodeficient (SCID) mice bearing high MC1R expressing tumors were intravenously injected with ²²⁵Ac-Ahx or ²²⁵Ac-di-d-Glu. After injection, four groups (n = 4) were euthanized at 24, 96, 144, and 288 h time points for each cohort. Tumors and 13 other organs were harvested at each time point. Isomeric γ spectra were measured in tissue samples using a scintillation γ detector and converted to α activity using factors for γ ray abundance per α decay. Time activity curves were calculated for each organ. A five-compartment PK model was built with the following compartments: blood, tumor, normal tissue, kidney, and liver. This model is characterized by a system of five ordinary differential equations using mass action kinetics, which describe uptake, intercompartmental transitions, and clearance rates. The ordinary differential equations were simultaneously solved and fit to experimental data using a genetic algorithm for optimization. The BD data show that both compounds have minimal distribution to organs at risk other than the kidney and liver. The PK parameter estimates had less than 5% error. From these data, ²²⁵Ac-Ahx showed larger and faster uptake in the liver. Both compounds had comparable uptake and clearance rates for other compartments. The BD and PK behavior for two targeted radiopharmaceuticals were investigated. The PK model fit the experimental data and provided insight into the kinetics of the compounds systematically.

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

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

A dosimetry procedure for organs-at-risk in 177Lu peptide receptor radionuclide therapy of patients with neuroendocrine tumours

PURPOSE

Peptide receptor radionuclide therapy with ¹⁷⁷Lu-DOTATATE has become a standard treatment modality in neuroendocrine tumours (NETs). No consensus has yet been reached however regarding the absorbed dose threshold for lesion response, the absorbed dose limit to organs-at-risk, and the optimal fractionation and activity to be administered. This is partly due to a lack of uniform and comparable dosimetry protocols. The present article details the development of an organ-at-risk dosimetry procedure, which could be implemented and used routinely in a clinical context.

METHODS

Forty-seven patients with NETs underwent ¹⁷⁷Lu-DOTATATE therapy. Three SPECT/CT images were acquired at 4, 24 and 144-192 h post-injection. Three blood samples were obtained together with the SPECT/CT acquisitions and 2 additional samples were obtained around 30 min and 1 h post-injection. A bi-exponential fit was used to compute the source organ time-integrated activity coefficients. Coefficients were introduced into OLINDA/EXM software to compute organ-at-risk absorbed doses. Median values for all patients were computed for absorbed dose coefficient D/A₀ and for late effective half-life T_1/2eff for kidneys, spleen and red marrow.

RESULTS

Dosimetry resulted in a median[interquartile range] of 0.78[0.35], 1.07[0.58] and 0.028[0.010] Gy/GBq for D/A₀ and of 55[9], 71[9] and 52[18] h for T_1/2eff for kidneys, spleen and red marrow respectively.

CONCLUSIONS

A dosimetry procedure for organs-at-risk in ¹⁷⁷Lu-DOTATATE therapy based on serial SPECT/CT images and blood samples can be implemented routinely in a clinical context with limited patient burden. The results obtained were in accordance with those of other centres.

Effects of the FcRn developmental pharmacology on the pharmacokinetics of therapeutic monoclonal IgG antibody in pediatric subjects using minimal physiologically-based pharmacokinetic modelling

The aim of this study was to investigate neonatal Fc receptor (FcRn) concentration developmental pharmacology in adult and pediatric subjects using minimal physiologically-based pharmacokinetic (mPBPK) modelling. Three types of pharmacokinetic (PK) data for three agents (endogenous/exogenous native IgG, bevacizumab and palivizumab) were used. The adult group contained six subjects with weights from 50 to 100 kg. For pediatric subjects, seven age groups were assumed, with five subjects each having the weight of 95%, 75%, 50%, 25% and 5% percentile of the population. A first evidence-based rating system to evaluate the quality of the source data used to derive pediatric-specific mPBPK model parameter was proposed. A stepwise approach was used to examine the best combination of age/weight effect on the parameters of the mPBPK model in adult and pediatric subjects. IgG synthesis rate (K_syn), extravasation rate (ER) and FcRn were fitted simultaneously to the PK of bevacizumab and native-IgG in both adult and pediatric. All fitting showed good fits based on the graphs and the coefficient of variation of the fitted parameters (< 50%). Estimated weight-normalized K_syn increased while weight-normalized FcRn and ER decreased with increasing age. The age and weight effect on FcRn were successfully estimated from the data. The final mPBPK model developed with native IgG and bevacizumab was able to predict the PK of palivizumab in pediatric subjects. Implementation of the mPBPK model enables us to analyze the relationships of age, weight, FcRn, ER and K_syn in both adult and pediatric subject. This information may benefit the understanding of complex interaction between the FcRn developmental pharmacology and PK parameters, and improve the prediction of the antibody disposition in pediatric subjects.

Utility of physiologically based pharmacokinetic (PBPK) modeling in oncology drug development and its accuracy: a systematic review

PURPOSE

Physiologically based pharmacokinetic (PBPK) modeling, a mathematical modeling approach which uses a pharmacokinetic model to mimick human physiology to predict drug concentration-time profiles, has been used for the discover and development of drugs in various fields, including oncology, since 2000. There have been a few general review articles on the utilization of PBPK in the development of oncology drugs, but these do not include an evaluation of model prediction accuracy. We therefore conducted a systematic review to define the accuracy of PBPK model prediction and its utility throughout all the developmental phases of oncology drugs.

METHODS

A systematic search was performed in the PubMed, PubMed Central and Cochrane Library databases from 1980 to February 2017 for articles (1) written in English, (2) focused on oncology or antineoplastic or anticancer drugs, tumor or cancer or anticancer drugs listed in the U.S. National Institutes of Health and (3) involving a PBPK model. The absolute-average-folding-errors (AAFEs) of the area under the curve (AUC) between predicted and observed values in each article were calculated to assess model prediction accuracy.

RESULTS

Of the 2341 articles initially identified by our search of the databases, 40 were included in the review analysis. These articles reported on six types of studies, i.e. in vivo (n = 4), first-in-human (n = 5), phase II/III clinical trials (n = 9), organ impairment (n = 3), pediatrics (n = 4) and drug-drug interactions (n = 15). AAFEs of the predicted AUC for all groups of studies were within 1.3-fold of each other despite variations in experimental methodologies.

CONCLUSION

PBPK modeling is a potential tool which can be effectively applied throughout all phases of oncology drug development. The number of experimental animals and human participants enrolled in the studies can be reduced using PBPK modeling and PBPK-population-PK modeling. The limited number of publications of unsuccessful model application to date may contribute to bias toward the usefulness of modeling.

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.

Two-Pore Minimum Physiologically-based Pharmacokinetic Model to Describe the Disposition of Therapeutic Monoclonal IgG Antibody in Humans

PURPOSE

The aim of this study was to develop a two-pore minimum physiologically-based pharmacokinetic (mPBPK) model in describing the pharmacokinetic (PK) of therapeutic monoclonal antibody (TMAb) in human subjects.

METHODS

PK data used in this study were endogenous/exogenous native IgG and two TMAbs (palivizumab and Motavizumab-YTE) in normal volunteer or familial hypercatabolic hypoproteinemia (FIHH) patient. Several important components were implemented to overcome the limitations of the early mPBPK model, e.g. two-pore model to describe the transcapillary transport of IgG from vascular to interstitial space. Six mPBPK models with different osmotic reflection coefficient (OFC) of transcapillary transport, endocytosis rates (ETR) and plasma clearance for the TMAbs/IgG were tested and the best model was selected using AICc values.

RESULTS

The final model consisted of different OFC and ETR values for native IgG and TMAbs, supporting the hypothesis that the dynamics in the endosomal space had an important role in the compliant FcRn salvage mechanism to determine the clearance of TMAbs. The estimated FcRn concentration of FIHH subjects was 2.72 μmol/l. The final two-pore mPBPK model has a better performance for native IgG than previously developed mPBPK model.

CONCLUSIONS

The final two-pore mPBPK model not only overcome the limitations of the early mPBPK model but also has a better performance to describe the disposition of the IgG antibody in human subjects.

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 Gy_1.49, 6.5 ± 2.3 to 3.7 ± 1.4 Gy_2.5, 11.0 ± 2.7 to 6.4 ± 1.9 Gy_4.5, and 10.9 ± 2.7 to 6.3 ± 1.9 Gy_4.5, respectively (where the subscripts denote that an α/β 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.

Treatment planning in PRRT based on simulated PET data and a PBPK model

Aim: To investigate the accuracy of treatment planning in peptide-receptor radionuclide therapy (PRRT) based on simulated PET data (using a PET noise model) and a physiologically based pharmacokinetic (PBPK) model. Methods: The parameters of a PBPK model were fitted to the biokinetic data of 15 patients. True mathematical phantoms of patients (MPPs) were the PBPK model with the fitted parameters. PET measurements after bolus injection of 150 MBq 68Ga-DOTATATE were simulated for the true MPPs. PET noise with typical noise levels was added to the data (i.e. c=0.3 [low], 3, 30 and 300 [high]). Organ activity data in the kidneys, tumour, liver and spleen were simulated at 0.5, 1 and 4 h p.i. PBPK model parameters were fitted to the simulated noisy PET data to derive the PET-predicted MPPs. Therapy was simulated assuming an infusion of 3.3 GBq of 90Y-DOTATATE over 30 min. Time-integrated activity coefficients (TIACs) of simulated therapy in tumour, kidneys, liver, spleen and remainder were calculated from both, true MPPs (true TIACs) and predicted MPPs (predicted TIACs). Variability v between true TIACs and predicted TIACs were calculated and analysed. Variability< 10 % was considered to be an accurate prediction. Results: For all noise level, variabilities for the kidneys, liver, and spleen showed an accurate prediction for TIACs, e.g. c=300: vkidney=(5 ± 2)%, vliver=(5 ± 2)%, vspleen=(4 ± 2)%. However, tumour TIAC predictions were not accurate for all noise levels, e.g. c=0.3: vtumour=(8 ± 5)%. Conclusion: PET based treatment planning with kidneys as the dose limiting organ seems possible for all reported noise levels using an adequate PBPK model and previous knowledge about the individual patient.

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.

Common strategic research agenda for radiation protection in medicine

Reflecting the change in funding strategies for European research projects, and the goal to jointly improve medical radiation protection through sustainable research efforts, five medical societies involved in the application of ionising radiation (European Association of Nuclear Medicine, EANM; European Federation of Organizations for Medical Physics. EFOMP; European Federation of Radiographer Societies, EFRS; European Society of Radiology, ESR; European Society for Radiotherapy and Oncology, ESTRO) have identified research areas of common interest and developed this first edition of the Common Strategic Research Agenda (SRA) for medical radiation protection. The research topics considered necessary and most urgent for effective medical care and efficient in terms of radiation protection are summarised in five main themes: 1. Measurement and quantification in the field of medical applications of ionising radiation 2. Normal tissue reactions, radiation-induced morbidity and long-term health problems 3. Optimisation of radiation exposure and harmonisation of practices 4. Justification of the use of ionising radiation in medical practice 5. Infrastructures for quality assurance The SRA is a living document; thus comments and suggestions by all stakeholders in medical radiation protection are welcome and will be dealt with by the European Alliance for Medical Radiation Protection Research (EURAMED) established by the above-mentioned societies.

MAIN MESSAGES

• Overcome the fragmentation of medical radiation protection research in Europe • Identify research areas of joint interest in the field of medical radiation protection • Improve the use of ionising radiation in medicine • Collect stakeholder feedback and seek consensus • Emphasise importance of clinical translation and evaluation of research results.

Time-integrated activity coefficient estimation for radionuclide therapy using PET and a pharmacokinetic model: A simulation study on the effect of sampling schedule and noise

PURPOSE

The aim of this study was to investigate the accuracy of PET-based treatment planning for predicting the time-integrated activity coefficients (TIACs).

METHODS

The parameters of a physiologically based pharmacokinetic (PBPK) model were fitted to the biokinetic data of 15 patients to derive assumed true parameters and were used to construct true mathematical patient phantoms (MPPs). Biokinetics of 150 MBq (68)Ga-DOTATATE-PET was simulated with different noise levels [fractional standard deviation (FSD) 10%, 1%, 0.1%, and 0.01%], and seven combinations of measurements at 30 min, 1 h, and 4 h p.i. PBPK model parameters were fitted to the simulated noisy PET data using population-based Bayesian parameters to construct predicted MPPs. Therapy simulations were performed as 30 min infusion of (90)Y-DOTATATE of 3.3 GBq in both true and predicted MPPs. Prediction accuracy was then calculated as relative variability vorgan between TIACs from both MPPs.

RESULTS

Large variability values of one time-point protocols [e.g., FSD = 1%, 240 min p.i., vkidneys = (9 ± 6)%, and vtumor = (27 ± 26)%] show inaccurate prediction. Accurate TIAC prediction of the kidneys was obtained for the case of two measurements (1 and 4 h p.i.), e.g., FSD = 1%, vkidneys = (7 ± 3)%, and vtumor = (22 ± 10)%, or three measurements, e.g., FSD = 1%, vkidneys = (7 ± 3)%, and vtumor = (22 ± 9)%.

CONCLUSIONS

(68)Ga-DOTATATE-PET measurements could possibly be used to predict the TIACs of (90)Y-DOTATATE when using a PBPK model and population-based Bayesian parameters. The two time-point measurement at 1 and 4 h p.i. with a noise up to FSD = 1% allows an accurate prediction of the TIACs in kidneys.

Radiation Dosimetry for 177Lu-PSMA I&T in Metastatic Castration-Resistant Prostate Cancer: Absorbed Dose in Normal Organs and Tumor Lesions

Prostate-specific membrane antigen (PSMA)-targeted radioligand therapy is increasingly used in metastatic castration-resistant prostate cancer. We aimed to estimate the absorbed doses for normal organs and tumor lesions using ¹⁷⁷Lu-PSMA I&T (I&T is imaging and therapy) in patients undergoing up to 4 cycles of radioligand therapy. Results were compared with pretherapeutic Glu-NH-CO-NH-Lys-(Ahx)-[⁶⁸Ga(HBEDCC)] (⁶⁸Ga-PSMA-HBED-CC) PET. Methods: A total of 34 cycles in 18 patients were analyzed retrospectively. In 15 patients the first, in 9 the second, in 5 the third, and in 5 the fourth cycle was analyzed, respectively. Whole-body scintigraphy was performed at least between 30-120 min, 24 h, and 6-8 d after administration. Regions of interest covering the whole body, organs, and up to 4 tumor lesions were drawn. Organ and tumor masses were derived from pretherapeutic ⁶⁸Ga-PSMA-HBED-CC PET/CT. Absorbed doses for individual cycles were calculated using OLINDA/EXM. SUVs from pretherapeutic PET were compared with absorbed doses and with change of SUV. Results: The mean whole-body effective dose for all cycles was 0.06 ± 0.03 Sv/GBq. The mean absorbed organ doses were 0.72 ± 0.21 Gy/GBq for the kidneys; 0.12 ± 0.06 Gy/GBq for the liver; and 0.55 ± 0.14 Gy/GBq for the parotid, 0.64 ± 0.40 Gy/GBq for the submandibular, and 3.8 ± 1.4 Gy/GBq for the lacrimal glands. Absorbed organ doses were relatively constant among the 4 different cycles. Tumor lesions received a mean absorbed dose per cycle of 3.2 ± 2.6 Gy/GBq (range, 0.22-12 Gy/GBq). Doses to tumor lesions gradually decreased, with 3.5 ± 2.9 Gy/GBq for the first, 3.3 ± 2.5 Gy/GBq for the second, 2.7 ± 2.3 Gy/GBq for the third, and 2.4 ± 2.2 Gy/GBq for the fourth cycle. SUVs of pretherapeutic PET moderately correlated with absorbed dose (r = 0.44, P < 0.001 for SUV_max; r = 0.43, P < 0.001 for SUV_mean) and moderately correlated with the change of SUV (r = 0.478, P < 0.001 for SUV_max, and r = 0.50, P < 0.001 for SUV_mean). Conclusion: Organ- and tumor-absorbed doses for ¹⁷⁷Lu-PSMA I&T are comparable to recent reports and complement these with information on an excellent correlation between the 4 therapy cycles. With the kidneys representing the critical organ, a cumulative activity of 40 GBq of ¹⁷⁷Lu-PSMA I&T appears to be safe and justifiable. The correlation between pretherapeutic SUV and absorbed tumor dose emphasizes the need for PSMA-ligand PET imaging for patient selection.

Investigating the Effect of Ligand Amount and Injected Therapeutic Activity: A Simulation Study for 177Lu-Labeled PSMA-Targeting Peptides

In molecular radiotherapy with ¹⁷⁷Lu-labeled prostate specific membrane antigen (PSMA) peptides, kidney and/or salivary glands doses limit the activity which can be administered. The aim of this work was to investigate the effect of the ligand amount and injected activity on the tumor-to-normal tissue biologically effective dose (BED) ratio for ¹⁷⁷Lu-labeled PSMA peptides. For this retrospective study, a recently developed physiologically based pharmacokinetic model was adapted for PSMA targeting peptides. General physiological parameters were taken from the literature. Individual parameters were fitted to planar gamma camera measurements (¹⁷⁷Lu-PSMA I&T) of five patients with metastasizing prostate cancer. Based on the estimated parameters, the pharmacokinetics of tumor, salivary glands, kidneys, total body and red marrow was simulated and time-integrated activity coefficients were calculated for different peptide amounts. Based on these simulations, the absorbed doses and BEDs for normal tissue and tumor were calculated for all activities leading to a maximal tolerable kidney BED of 10 Gy_2.5/cycle, a maximal salivary gland absorbed dose of 7.5 Gy/cycle and a maximal red marrow BED of 0.25 Gy₁₅/cycle. The fits yielded coefficients of determination > 0.85, acceptable relative standard errors and low parameter correlations. All estimated parameters were in a physiologically reasonable range. The amounts (for 2⁵−2⁹ nmol) and pertaining activities leading to a maximal tumor dose, considering the defined maximal tolerable doses to organs of risk, were calculated to be 272±253 nmol (452±420 μg) and 7.3±5.1 GBq. Using the actually injected amount (235±155 μg) and the same maximal tolerable doses, the potential improvement for the tumor BED was 1–3 fold. The results suggest that currently given amounts for therapy are in the appropriate order of magnitude for many lesions. However, for lesions with high binding site density or lower perfusion, optimizing the peptide amount and activity might improve the tumor-to-kidney and tumor-to-salivary glands BED ratio considerably.

Sensitivity Analysis of a Physiologically Based Pharmacokinetic Model Used for Treatment Planning in Peptide Receptor Radionuclide Therapy

The aim of this work was to evaluate the sensitivity of time-integrated activity coefficients (TIACs) on the erroneously chosen prior knowledge in a physiologically based pharmacokinetic (PBPK) model used for treatment planning in peptide receptor radionuclide therapy (PRRT). Parameters of the PBPK model were fitted to the biokinetic data of 15 patients after the injection of (111)In-DTPAOC. The fittings were performed using fixed parameter values taken from literature as prior knowledge (reference case, Ref). The fixed parameters were gender, physical information (e.g., body weight), dissociation rate koff, dissociation constant KD, fraction of blood flow, and spleen and liver volumes. The fittings were repeated with changed fixed parameters (Changed). The relative deviations (RDs) of TIACs calculated from Changed and Ref were analyzed for kidneys, tumor, liver, spleen, remainder, whole body, and serum. A changed koff has the largest effect on RD, the largest RD values were found for changed koff = 0.001 L/min: RDkidneys = (3 ± 3)%, RDtumor = (0.5 ± 4)%, RDliver = (6 ± 9)%, RDspleen = (5 ± 5)%, RDremainder = (2 ± 31)%, RDserum = (-4 ± 25)%, and RDwholebody = (3 ± 16)%. For other changed parameters, the maximum RDs were <1%. The calculation of organ TIACs in PRRT using the PBPK model was little affected by assigning wrong prior knowledge to the evaluated patients. The calculation of bone marrow-absorbed doses could be affected by the inaccurate TIACs of serum and remainder in the case of an inadequate koff.

Dependence of treatment planning accuracy in peptide receptor radionuclide therapy on the sampling schedule

Background

Peptide receptor radionuclide therapy (PRRT) plays an important role in the treatment of neuroendocrine tumors (NET). Pre-therapeutic dosimetry using the area under the measured time-activity curve (AUC) is important. The sampling schedule for this dosimetry determines the accuracy and reliability of the obtained AUC.

The aim of this study was to investigate the effect of reduced number of measurement points (i.e., gamma camera image acquisition or serum measurements) on treatment planning accuracy in PRRT using ¹¹¹In-labeled-diethylenetriaminopentaacetic acid-octreotide (DTPAOC; Octreoscan™).

Methods

Pre-therapeutic biokinetic data of 15 NET patients were investigated using a recently developed physiologically based pharmacokinetic (PBPK) model. Two parameter sets were determined (standard or iterative approach) and used for calculation of time-integrated activity coefficients (TIACs) for the tumor, kidneys, liver, spleen, serum, and whole body. TIACs obtained using the full data sets were used as reference. To evaluate the effect of sampling on individual treatment planning, reduced sampling schedules were generated omitting either 1, 2, 3, or 4 organ and serum measurements or all serum measurements for each patient. Relative deviations (RDs) between these and reference TIACs were calculated and used as criterion for treatment planning accuracy. An RD < 0.1 was considered acceptable.

Results

When omitting serum measurements, TIAC accuracy remained acceptable (RD < 0.1) for the standard approach. The kidney TIACs could be estimated for both approaches with acceptable RDs using two time points (t = 4 h; 2 d); tumor RDs were <0.3. The iterative approach reduced the range of RD, but did not further reduce the number of needed measurement points (i.e., to achieve an RD <0.1). For both approaches RDs for liver, spleen and whole body were larger than 0.1. However, in the clinical setting these RDs are less relevant as liver and spleen are not organs at risk due to the low absorbed doses.

Conclusions

When using a priori information of a PBPK model structure combined with Bayesian information about PBPK model parameter distribution, the administered activity could be determined with acceptable accuracy using only two time points (4 h, 2 d) and thus allow a considerable reduction of needed data for individual dosimetry.