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.

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.

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

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

Single-time-point 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.

Minimal Physiologically-based Pharmacokinetic Model to Investigate the Effect of Charge on the Pharmacokinetics of Humanized anti-HCV-E2 IgG Antibodies in Sprague-Dawley Rats.

PURPOSE: To develop a minimal physiologically-based pharmacokinetic (mPBPK) model in quantifying the relationships between the charge and pharmacokinetics (PK) of therapeutic monoclonal IgG antibody (TMAb). METHODS: PK data used in this study were native IgG and five humanized anti-HCVE2-IgG antibodies in rats. Different models that related the effect of charge on interstitial distribution, transcapillary transport, and cellular uptake for FcRn-mediated metabolism were tested. External validation was conducted to assess if the charge-parameter relationships derived from rats could be used to predict the PK of TMAbs in mice. The final mPBPK model was used to construct the relationships between the FcRn binding and charge on the PK of TMAbs. RESULTS: Increasing the isoelectric point (pI) of IgG was associated with higher interstitial space distribution and cellular uptake. The transcapillary transport of IgG from plasma to interstitial space remains constant with pI values below 7.96 and then increased linearly with pI. The model-based simulation results suggested that improving the FcRn binding affinity can overcome the problems of low plasma/interstitial space exposures associated with TMAbs with higher pI values by reducing the FcRn-mediated metabolism and hence increasing drug exposure in the interstitial space that has close contact with many solid tumors. CONCLUSIONS: The final mPBPK model was developed and used to construct complex quantitative relationships between the pI/FcRn binding affinity and PK of TMAbs and such relationships are useful to select the discovery of a "sweet spot" of designing future generation of TMAbs with optimal PK properties to achieve desirable plasma and tissue drug exposures.

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

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

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

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

Performance assessment of the ALBIRA II pre-clinical SPECT S102 system for 99mTc imaging.

OBJECTIVE: The performance characteristics of the SPECT sub-system S102 of the ALBIRA II PET/SPECT/CT are analyzed for the 80 mm field of view (FOV) to evaluate the potential in-vivo imaging in rats, based on measurements of the system response for the commonly used Technetium-99 m (99mTc) in small animal imaging. METHODS: The ALBIRA II tri-modal µPET/SPECT/CT pre-clinical system (Bruker BioSpin, Ettlingen, Germany) was used. The SPECT modality is made up of two opposite gamma cameras (Version S102) with Sodium doped Cesium Iodide (CsI(Na)) single continuous crystal detectors coupled to position-sensitive photomultipliers (PSPMTs). Imaging was performed with the NEMA NU-4 image quality phantom (Data Spectrum Corporation, Durham, USA). Measurements were performed with a starting activity concentration of 4.76 MBq/mL 99mTc. An energy window of 20% at 140 keV was selected in this study. The system offers a 20 mm, 40 mm, 60 mm and an 80 mm field of view (FOV) and in this study the 80 mm FOV was used for all the acquisitions. The data were reconstructed with an ordered subset expectation maximization (OSEM) algorithm. Sensitivity, spatial resolution, count rate linearity, convergence of the algorithm and the recovery coefficients (RC) were analyzed. All analyses were performed with PMOD and MATLAB software. RESULTS: The sensitivities measured at the center of the 80 mm FOV with the point source were 23.1 ± 0.3 cps/MBq (single pinhole SPH) and 105.6 ± 5.5 cps/MBq (multi pinhole MPH). The values for the axial, tangential and radial full width at half maximum (FWHM) were 2.51, 2.54, and 2.55 mm with SPH and 2.35, 2.44 and 2.32 mm with MPH, respectively. The corresponding RC values for the 5 mm, 4 mm, 3 mm and 2 mm rods were 0.60 ± 0.28, 0.61 ± 0.24, 0.29 ± 0.11 and 0.20 ± 0.06 with SPH and 0.56 ± 0.20, 0.50 ± 0.18, 0.38 ± 0.09 and 0.23 ± 0.06 with MPH. To obtain quantitative imaging data, the image reconstructions should be performed with 12 iterations. CONCLUSION: The ALBIRA II preclinical SPECT sub-system S102 has a favorable sensitivity and spatial resolution for the 80 mm FOV setting for both the SPH and MPH configurations and is a valuable tool for small animal imaging.

Quantitative Systems Pharmacology Model of Chimeric Antigen Receptor T-Cell Therapy.

Chimeric antigen receptor T-cell (CART) therapy is a new and promising cancer therapy. However, severe toxicity due to cytokine release syndrome (CRS) in CART-treated patients highlighted the possible danger of this new therapy. Disease burden and CART doses are the potential factors associated with CRS but the detail relationships between these factors and the severity of the CRS remain largely unknown. In this study, the quantitative systems pharmacology (QSP) approach is used to quantify the complex relationships among CART doses, disease burden, and pro inflammatory cytokines in human subjects and to gain relevant insights into the determinant of clinical toxicity/efficacy in development of CART therapy. The expansion of CART and elimination of B cells are more highly correlated with disease burden than the administered CART doses. To our best knowledge, this is the first QSP model that can describe the observed clinical data from CART-treated patients with cancer. This QSP model is a valuable tool for deepening our understanding of how the mechanism of action connects to the clinical outcomes and, therefore, may serve as an important model-based platform to guide the development and personalized dosing of the CART therapy.

Minimal physiologically-based pharmacokinetic model to investigate the effect of pH dependent FcRn affinity and the endothelial endocytosis on the pharmacokinetics of anti-VEGF humanized IgG1 antibody in cynomolgus monkey.

In this study, we developed a first minimal physiologically-based pharmacokinetic (mPBPK) model to investigate the complex interaction effects of endocytosis rate/FcRn binding affinity at both acidic/physiological pH on the pharmacokinetics (PK) of the anti-VEGF IgG1 antibodies. The data used in this study were the PK of the native IgG and humanized anti-VEGF IgG1 antibodies with a wide range FcRn-binding at both acidic and physiological pH in the cynomolgus monkey. The basic structure of the developed mPBPK models consisted of plasma, tissue and lymph compartments. The tissue compartment was subdivided into vascular, endothelial and interstitial spaces. Non-equilibrium binding mechanism was used to describe the FcRn-IgG interaction in the endosome. The fittings in the final model with three pH systems in the endosome compartment showed a good fit based on the visualization of the fitted graphs and the coefficient of variations of the estimated parameters (CV < 50%). The quantitative endocytosis/FcRn binding affinity PK relationships was constructed using the final model to provide better understanding of complex interaction effects of endocytosis rate and FcRn binding on PK of anti-VEGF IgG1 antibodies. This result may serve as an important model-based drug discovery platform to guide the design and development of the future generation of anti-VEGF IgG1 or other therapeutic IgG1 antibodies. In addition, the mPBPK model developed in cynomolgus monkey was successfully used to predict the PK of the anti-VEGF IgG1 antibody (bevacizumab) in human subjects.

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 (Ksyn), 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 Ksyn 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 Ksyn 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.

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.

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.

Investigation of the imaging characteristics of the ALBIRA II small animal PET system for 18F, 68Ga and 64Cu.

AIM: In this study the performance characteristics of the Albira II PET sub-system and the response of the system for the following radionuclides 18F, 68Ga and 64Cu was analyzed. MATERIALS AND METHODS: The Albira II tri-modal system (Bruker BioSpin MRI GmbH, Ettlingen, Germany) is a pre-clinical device for PET, SPECT and CT. The PET sub-system uses single continuous crystal detectors of lutetium yttrium orthosilicate (LYSO). The detector assembly consists of three rings of 8 detector modules. The transaxial field of view (FOV) has a diameter of 80mm and the axial FOV is 148mm. A NEMA NU-4 image quality phantom (Data Spectrum Corporation, Durham, USA) having five rods with diameters of 1, 2, 3, 4 and 5mm and a uniform central region was used. Measurements with 18F, 68Ga and 64Cu were performed in list mode acquisition over 10h. Data were reconstructed using a maximum-likelihood expectation-maximization (MLEM) algorithm with iteration numbers between 5 and 50. System sensitivity, count rate linearity, convergence and recovery coefficients were analyzed. RESULTS: The sensitivities for the entire FOV (non-NEMA method) for 18F, 68Ga and 64Cu were (3.78±0.05)%, (3.97±0.18)% and (3.79±0.37)%, respectively. The sensitivity based on the NEMA protocol using the 22Na point source yielded (5.53±0.06)%. Dead-time corrected true counts were linear for activities ≤7MBq (18F and 68Ga) and ≤17MBq (64Cu) in the phantom. The radial, tangential and axial full widths at half maximum (FWHMs) were 1.52, 1.47 and 1.48mm. Recovery coefficients for the uniform region with a total activity of 8MBq in the phantom were (0.97±0.05), (0.98±0.06), (0.98±0.06) for 18F, 68Ga and 64Cu, respectively. CONCLUSION: The Albira II pre-clinical PET system has an adequate sensitivity range and the system linearity is suitable for the range of activities used for pre-clinical imaging. Overall, the system showed a favorable image quality for pre-clinical applications.

Treatment planning in PRRT based on simulated PET data and a PBPK model. Determination of accuracy using a PET noise 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.

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.

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.

Physiologically based pharmacokinetic modeling of (18)F-SiFAlin-Asp3-PEG1-TATE in AR42J tumor bearing mice.

PURPOSE: Peptide receptor radionuclide therapy (PRRT) is commonly performed in the treatment of neuroendocrine tumors (NET), where somatostatin analogs (DOTATATE) are radiolabeled with (90)Y, (68)Ga or (111)In for pre-therapeutic and therapeutic purposes. Quantitative evaluation of the biokinetic data can be performed by using physiologically based pharmacokinetic (PBPK) models. Knowledge about the biodistribution in a pre-clinical setting would allow optimizing the translation from bench to bedside. The aim of this study was to develop a PBPK model to describe the biodistribution of a novel sst2-targeting radiotracer. METHODS: Biokinetic data of six mice after injection of (18)F-SiFAlin-Asp3-PEG1-TATE were investigated using two PBPK models. The PBPK models describe the biodistribution of the tracer in the tumor, kidneys, liver, remainder and whole body via blood flow to these organs via absorption, distribution, metabolism and excretion. A recently published sst2 PBPK model for humans (model 1) was used to describe the data. Physiological information in this model was adapted to that of a mouse. Model 1 was further modified by implementing receptor-mediated endocytosis (model 2). Model parameters were fitted to the biokinetic data of each mouse. Model selection was performed by calculating Akaike weights wi using the corrected Akaike Information Criterion (AICc). RESULTS: The implementation of receptor-mediated endocytosis considerably improved the description of the biodistribution (Akaike weights w1=0% and w2=100% for model 1 and 2, respectively). The resulting time-integrated activity coefficients determined by model 2 were for tumor (0.05 ± 0.02) h, kidneys (0.11 ± 0.01) h and liver (0.02 ± 0.01) h. CONCLUSION: Simply downscaling a human PBPK model does not allow for an accurate description of (18)F-SiFAlin-Asp3-PEG1-TATE in mice. Biokinetics of this tracer can be accurately and adequately described using a physiologically based pharmacokinetic model including receptor-mediated endocytosis. Thus, an optimized translation from bench to bedside is possible.