A physiologically based pharmacokinetic model for [⁶⁸Ga]Ga-(HA-)DOTATATE to predict whole-body distribution and tumor sink effects in GEP-NET patients

Patients diagnosed with GEP-NET who received a whole-body [⁶⁸Ga]Ga-DOTATATE or [⁶⁸Ga]Ga-HA-DOTATATE PET/CT between August 2011 and April 2016 were included. Patients without apparent NET-lesions on diagnostic PET/CT were allocated to a separate cohort that was used as a control group, which represented patients that were referred with suspected NET or received treatment for prior NET. Informed consent was obtained via institutional procedures from all individual participants included in the study.

Patients were treated according to the EANM-guidelines [29], and SSA therapy was withdrawn prior to [⁶⁸Ga]Ga-(HA-)DOTATATE administration (where (HA-)DOTATATE refers to both peptides). ⁶⁸Ga-labeled (HA-)DOTATATE was produced in-house according to validated procedures described previously [30]. An intravenous injection of approximately 100 MBq ⁶⁸Ga-labeled (HA-)DOTATATE was administered, and at ~ 45 min post-injection a scan was acquired on a Gemini TF PET/CT (Philips, the Netherlands) with 2–2.5 min per bed position from skull to thighs (axial field of view of 18 cm). Image data was reconstructed using the Philips specific BLOB OS-ToF algorithm with ‘normal’ smoothing (isotropic 4 mm voxels).

SUV_peak, SUV_max and radioactivity concentrations (MBq/L) for aorta, spleen, thyroid, liver, primary tumor and metastatic lesions > 2 cm in diameter were determined by placing spherical volume of interests (VOIs) of at least 2 cm. Then, all radioactivity concentrations were corrected for decay to time of injection and subsequent peptide concentrations (µg/L) per organ or tumor were calculated based on the measured radioactivity concentrations and the administered specific activities (MBq/µg).

To assess the effect of tumor burden on biodistribution, patients were divided into three groups based on quantity and location of metastases on their clinical PET/CT report. These three groups were: (1) patients with primary tumors or with ≤ 2 metastases (limited disease), (2) patients with liver-only metastases, and (3) patients with extensive metastases (i.e. , not classified as group 1 or 2). The PBPK model predictions, for both [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE, were evaluated with clinical imaging data for these three groups separately.

A PBPK model for GEP-NET patients was developed in PK-Sim^® and MoBi^® (Open Systems Pharmacology software, version 9.0) using the standardized protein base model [31]. Compound-specific information includes the physicochemical parameters of the compound, and this was separately added for DOTATATE and HA-DOTATATE to obtain compound-specific predictions. Eventually, predictions were performed separately for each patient group, based on metastatic status, for both peptides.

All input parameters for peptide specific distribution and uptake, including intracellular degradation rates and SSTR2 amounts, were based on the previous described [⁶⁸Ga]Ga-DOTATATE PBPK model [26]. Compound-specific parameters for DOTATATE were also derived from the previous published PBPK model [26], while compound-specific input parameters of HA-DOTATATE (molecular weight and lipophilicity) were based on literature. The input parameters molecular weight and lipophilicity for [⁶⁸Ga]Ga-HA-DOTATATE were 1628.5 g/mol and −3.12, respectively [32]. Plasma protein binding for [⁶⁸Ga]Ga-HA-DOTATATE was unknown, but a major difference compared to [⁶⁸Ga]Ga-DOTATATE was not expected; thus, the fraction unbound was set at 0.69, accordingly. Renal clearance was manually scaled to a predicted 13% unchanged excretion in urine within the first 2 h post-injection for all patients [33]. For the predictions per group for both peptides, the mean estimated glomerular filtration rate (GFR) per group was used as renal function input (calculated using the Cockcroft–Gault equation [34]). In addition, all other assessed patient characteristics (i.e., age, weight and height) were used as system-specific input parameters. Internalization rates were assumed to be similar for organ and tumor tissue and were fixed to 0.268 min⁻¹. This was based on a 1.67-fold increase in internalization rate for [⁶⁸Ga]Ga-HA-DOTATATE compared to [⁶⁸Ga]Ga-DOTATATE (accumulation of [⁶⁸Ga]Ga-HA-DOTATATE in spleen plateaued 30 min post-injection compared to 50 min post-injection for [⁶⁸Ga]Ga-DOTATATE) [26, 33, 35]. Unknown input parameters for HA-DOTATATE (the equilibrium dissociation constant (K_D) and dissociation rate constant (k_off)) were optimized by using all observed spleen concentrations from all included patients at once and thus optimized values represent K_D and k_off for the entire population. By using observed data of spleen, which expresses SSTR2 but not SSTR5 [32, 36], it was ensured that the optimized affinity values of HA-DOTATATE were specific for SSTR2. Model fitting was performed using a built-in Monte Carlo algorithm for parameter identification to optimize selected input parameter to describe the data best.

Separate tumor compartments were added to the model to describe distribution to these compartments and the effect of this tumor uptake on normal organ uptake. Since physiological tumor characteristics can differ between primary tumors and its metastases, three compartments were added: primary tumor, liver metastases and other metastases. All metastases other than liver metastases were gathered in one compartment since only small uptake differences were observed in clinical practice between those metastases and also for reasons of model simplicity. Tumor physiology characteristics for all three tumor compartments were based on literature. The tumor volume for the primary tumor was set at 10 mL for all groups, and liver metastases were assumed to have a total volume of 50 mL. Volumes of other tumor metastases differed for each patient group and were set at 5 mL, 0 mL and 50 mL for groups 1, 2 and 3, respectively, based on clinical observations indicating a mean total tumor volume of approximately 65 mL in 232 patients [37]. Although quantification of tumor volumes in this article was debatable, since there is no gold standard for tumor volume measurements, yet a mean total NET volume of approximately 65 mL reflected the population of patients treated in our hospital. Besides, this was comparable to median tumor volumes that were used (or fitted) in previous PBPK models [21, 27, 38, 39]. Vascular sub-compartments within tumors were estimated based on literature values for primary tumors and liver metastases, resulting in fraction vascular of 0.21 and 0.17, respectively [40‐45]. Fraction vascular for other metastases was unknown and fixed to 0.075 based on previous PBPK models [21, 22, 46]. The interstitial fraction was assumed to be similar for each tumor and was fixed to 0.3 [22]. Blood flow was set at 152 mL/min/100 g for the primary tumor compartment [45]. In absence of relevant data for distant metastatic sites, the same value was used for these sites. For liver metastases, a higher blood flow of 203 mL/min/100 g was used as an input parameter, due to their nature of hyperperfusion [42‐44]. SSTRs were added to the tumor compartments with an expression relative to spleen. Since NET metastases showed higher SSTR2 expressions compared to primary tumor sites [47], a higher relative expression was added to this compartment (relative value of 1.5 for primary tumors and 2 for all metastases). These fractional differences between tumors compared to spleen were in accordance with previous NET PBPK models [21, 22]. Transcapillary exchange of the radiopharmaceutical from plasma to the interstitial space was described by the two-pore formalism [46]. Pore radii for small and large pores were fixed to 4.5 and 500 nm, respectively, representing a leaky tumor vessel capillary [46, 48]. The flow fraction via the large pores was 0.8, reflecting a discontinuous membrane as expected in tumors [46]. Hydraulic conductivity describes passage of porous material and was fixed to a literature value of 0.00126 mL/N/min for tumors [49, 50].

Minimum and maximum ranges for administered peptide amounts per group and SSTR2 organ expression (derived from the previously published PBPK model) were imputed to derive prediction intervals for organ and tumor radionuclide distributions [26]. In addition, to take into account a high variability in tumor uptake, prediction intervals in all three tumor compartments were based on observed (inter)quartile ranges in blood flow and blood volumes of the specific compartment [42‐45]. For other metastases, blood flow was assumed to be comparable to primary tumors and fraction vascular was assumed to vary from 50 to 150% relative to the median. This resulted in minimum and maximum blood flow of 53 and 252 mL/min/100 g for primary tumors and other metastases and 139 and 363 mL/min/100 g for liver metastases. Fraction vascular ranged from 0.09 to 0.34 for primary tumors, 0.1 to 0.23 for liver metastases and 0.025 to 0.125 for other metastases.

A sensitivity analysis was performed to calculate the sensitivity of the PK model output for certain parameter assumptions using a build-in algorithm, which was performed by alteration of input parameters with ± 10% [51]. This sensitivity (S_i,j) is calculated using the following equation:where PK_j is a PK parameter of a certain output to an input parameter (p_j)_. Thus, the sensitivity for the PK parameter to that input parameter was calculated as the ratio of the relative change of that PK parameter (ΔPK_j) and the relative variation of the input parameter (Δp_i). A sensitivity value of − 1 implies that a 10% increase of the input parameter resulted in a 10% decrease of the PK parameter output.

Administered patient doses of [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE are based on radioactivity (~ 100 MBq per administration). Specific activities (MBq/µg) were derived from the tracer production logs to calculate the administered absolute peptide amount. However, specific activities, and thus administered peptide amounts, differed between production batches and peptides. In general, this resulted in higher administered total peptide amounts for DOTATATE compared to HA-DOTATATE.

It is of interest to directly compare organ uptake between both peptides to assess (dis)similarities in this organ accumulation, without discrepancies in administered peptide amounts that could alter such a comparison. Due to a change in production procedures in 2017, an additional group of GEP-NET patients receiving higher peptide amounts of [⁶⁸Ga]Ga-HA-DOTATATE (comparable to administered DOTATATE amounts) could be selected for a subanalysis to exclude a potential effect of different administered peptide amounts for [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE on organ uptake. The sample size of this additional group was matched to the included patients for PBPK evaluation receiving HA-DOTATATE, and spleen was used as a reference organ to compare uptake (SUV_peak and SUV_max).

Imaging data regarding uptake (SUV_peak and SUV_max) were compared between the three patient groups (limited disease, liver-only and extensive metastases) to assess differences in organ and tumor uptake for [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE, potentially revealing a tumor sink effect. Organ uptake in GEP-NET patients was compared to the control group, to reveal any effect of tumor load and location on normal organ uptake (spleen, liver and thyroid).

Predictions to identify a relevant tumor sink effect for [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE were performed using the final PBPK model. This was performed by manually increasing tumor volumes of all separate tumor compartments and comparing the accumulation plateau (or C_max) in spleen as a reference organ.

Differences between patient characteristics and demographics receiving DOTATATE and HA-DOTATATE were evaluated using a Student’s t-test (continuous variables), a Wilcoxon test (continuous variables in case of unequal variances) or a Chi-squared test (categorical variables). An ANOVA test was used to compare patient characteristics of the patient groups for both DOTATATE and HA-DOTATATE. Kruskal–Wallis tests were performed to compare uptake in organs and tumors (SUV_max and SUV_peak) between patient groups, where a Bonferroni correction was applied to account for multiple testing when testing which groups differed significantly from each other. Statistical tests were performed in R (version 3.6.3) [52]. Continuous variables are displayed as mean ± standard deviation (range). A p-value less than 0.05 was considered statistically significant.

One patient was excluded from this analysis, because this patient erroneously received an exceptionally high DOTATATE amount (29.8 µg). This resulted in a final inclusion of 39 and 59 GEP-NET patients receiving either [⁶⁸Ga]Ga-DOTATATE or [⁶⁸Ga]Ga-HA-DOTATATE, respectively. The peptide amounts and administered radioactivity were 13.1 ± 2.18 µg (7.16–16.6 µg) and 85.8 ± 16.0 MBq (50.3–132.7 MBq) for [⁶⁸Ga]Ga-DOTATATE, and 6.06 ± 2.26 µg (2.43–11.1 µg) and 85.7 ± 14.1 MBq (43.8–106.3 MBq) for [⁶⁸Ga]Ga-HA-DOTATATE. Mean injection-acquisition interval was 44 ± 6 min (34–55 min) for [⁶⁸Ga]Ga-DOTATATE and 44 ± 6 min (29–55 min) for [⁶⁸Ga]Ga-HA-DOTATATE. Patients were classified based on disease extent as limited disease, liver-only or extensive metastases. In addition, 43 and 45 control subjects receiving [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE, respectively, were included. All patient characteristics, demographics and imaging details are shown in Tables 1 and 2; details for the subanalysis regarding patients receiving higher peptide amounts of [⁶⁸Ga]Ga-HA-DOTATATE are displayed in Additional file 1: Table S1.

An overview of the PBPK model structure is provided in Fig. 1. Optimized values for HA-DOTATATE were 0.227 nmol/L for K_D and 0.00709 min⁻¹ for k_off. An overview of compound-specific input parameters for both peptides and main system-specific input parameters for all tumor compartments is shown in Table 3. Final simulated concentration–time curves for the patient groups for both [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE are depicted in Fig. 2. The large prediction intervals were in accordance with the observed high interpatient variability in organ and tumor accumulation (Table 1). For [⁶⁸Ga]Ga-DOTATATE, the predictions of spleen uptake were adequate, with 75%, 75% and 88% of all observations within the prediction interval for group 1, 2 and 3, respectively. Evaluation with primary tumor observations showed a slight overprediction by the model, since observations were within the prediction interval for 42.9% and 66.7% in groups 1 and 3, respectively (observations were not available for group 2). Some liver metastases observations were under- or overpredicted by the model, but, in general, predictions were acceptable with 50% and 73.9% of all predictions within population ranges in groups 2 and 3, respectively. Other metastases predictions showed high accuracy with 100% and 89.2% of all observations within population ranges for groups 2 and 3, respectively. For [⁶⁸Ga]Ga-HA-DOTATATE, spleen uptake was underpredicted more often compared to [⁶⁸Ga]Ga-DOTATATE, but precision of predictions was still acceptable (52.4%, 85.7% and 80.6% for groups 1, 2 and 3, respectively). In addition, all tumor compartment predictions showed high accuracy for [⁶⁸Ga]Ga-HA-DOTATATE (83.3% (group 1), 66.7% (group 2), and 90.0% (group 3) for primary metastases, 100% (group 2) and 96.4% (group 3) for liver metastases, and 100% (group 1) and 86.2% (group 3) for other metastases), although model results showed both over- and underprediction for a few outliers.

A sensitivity analysis was performed for the tumor compartments of the [⁶⁸Ga]Ga-DOTATATE model and showed the importance of fraction vascular (sensitivity values of 0.655, 0.735 and 0.575 for primary tumor, liver metastases and other metastases, respectively) and the administered dose (sensitivity value of 1.03 for all tumor compartments) on tumor uptake (displayed as area under the curve (AUC)). All model parameters were included in the sensitivity analysis, which indicated that only seven parameters had a substantial impact (higher than 5% on tumor uptake, after a 10% adjustment of the parameter), meaning that model output was not highly reliant on input parameters. Results of the sensitivity values are shown in Table 4.

Results from PET/CT analyses showed a significant lower uptake (SUV_max and SUV_peak) in spleen, liver, thyroid (p < 0.001 for all organs) and a significantly higher uptake in aorta (p < 0.001) for [⁶⁸Ga]Ga-DOTATATE compared to [⁶⁸Ga]Ga-HA-DOTATATE. Most likely, these dissimilarities in uptake were caused by different affinities for SSTR for both peptides. To exclude a potential additional effect of administered peptide amount on uptake differences between [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE, a subanalysis was performed. Three groups were compared; patients receiving DOTATATE (mean peptide amount 13.1 µg), low amount HA-DOTATATE (mean peptide amount 6.06 µg) and high amount HA-DOTATATE (mean peptide amount 13.7 µg). Results of spleen SUV values of these three groups are shown in Fig. 3. A significant decrease in spleen uptake was observed for [⁶⁸Ga]Ga-DOTATATE compared to [⁶⁸Ga]Ga-HA-DOTATATE after administration of comparable peptide amounts, which implied that organ uptake differences were most probably caused by different affinity profiles. In PBPK model predictions, this was reflected in different values of K_D and k_off to describe differences in distribution profiles between both peptides. Moreover, different administered peptide amounts for HA-DOTATATE did not result in significant differences in spleen SUV_peak or SUV_max in this study population.

Organ uptake (SUV_peak and SUV_max) was compared between the control group and all patient subgroups. A significant decrease in spleen uptake was observed for patients with extensive metastases compared to control subjects for both [⁶⁸Ga]Ga-DOTATATE (SUV_peak: 15.2 ± 5.68 vs. 18.85 ± 3.95, p = 0.0301 and SUV_max: 17.0 ± 6.52 vs. 20.9 ± 4.20, p = 0.0290) and [⁶⁸Ga]Ga-HA-DOTATATE (SUV_peak: 22.9 ± 7.44 vs. 30.7 ± 6.86, p < 0.001 and SUV_max: 25.0 ± 7.97 vs. 33.9 ± 7.53, p < 0.001). Moreover, for [⁶⁸Ga]Ga-HA-DOTATATE a significant decrease in spleen uptake for this group compared to patients with only primary tumor and/or few metastases was noticed (SUV_peak: 22.9 ± 7.44 vs. 29.4 ± 6.58, p = 0.0418 and SUV_max: 25.0 ± 7.97 vs. 32.4 ± 7.39, p = 0.0304). This implied a probable tumor sink effect only for patients with extensive metastases. No significant differences in primary tumor and metastases uptake were observed between the patient groups. Results of spleen and tumor uptake differences between the groups are depicted in Fig. 4. Other organs (liver and thyroid) showed similar effects as for spleen (see Additional file 1: Fig. S1).

Simulations, based on the developed PBPK model, with varying total tumor volumes of 0 to 1500 mL resulted in more detailed insights in the relevance of the tumor sink effect by comparing maximum accumulation concentrations (C_max) in spleen. All tumor compartments and population ranges for tumor blood flow and blood volume were included in the simulations, to include possible tumor-specific effects and to reveal potential minimum and maximum ranges for the tumor sink effect for different tumor volumes. Predictions showed a decrease in C_max in spleen after increasing total tumor burden, with a maximum decrease of 41% for 1500 mL primary tumor volume. Predictions showed that a relevant tumor sink effect (> 20%) was only present in patients with an extensive tumor load above approximately 550 mL. However, at lower tumor volumes (< 200 mL, representing the majority of patients in our clinical population) only a modest decrease in spleen uptake was predicted (8.5% for primary tumors (range 3.7–12.7%)). Results of the tumor sink predictions are displayed in Fig. 5. This prediction of the existence of a tumor sink effect particularly in patients with high total tumor burden was comparable to the observed uptake data within the patient subgroups.

In the current study, patients were divided based on their tumor load into three groups: (1) patients with primary tumors or with ≤ 2 metastases (limited disease), (2) patients with liver-only metastases and (3) patients with extensive metastases. Our classification roughly follows the work of Riihimaki et al. [53] and reflects the three main categories of patients. For this modeling study, it was important to have a simplified resemblance of a clinical population, which also provided a subgroups with comparable tumor burden. Lastly, liver metastases have a high incidence in metastatic NET and are known to have different uptake compared to other metastases and primary tumors [12, 53, 54]. The latter two arguments were both important factors for the identification of a potential tumor sink effect.

Because of the retrospective nature of this study, the total included patients differed between [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE groups (n = 39 vs. n = 59, respectively). However, patient data were only used for model evaluation and we believe that our data were sufficient to evaluate biodistribution for both peptides in this population. Also, since parameter optimization was performed based on total patients rather than smaller patient groups, this difference in group size was not likely to affect model predictions. The control group was created from patients without apparent DOTATATE-positive lesions (i.e., signal intensity higher than local background) on diagnostic PET/CT. This group was assumed to only have physiological tracer accumulation, but actually consisted of patients that were referred with suspected GEP-NET or received treatment for prior NET. Still, we believe that this assumption did not influence our findings and model predictions.

By calculating peptide concentrations based on measured radioactivity and administered specific activities, the assumption was made that peptide degradation did not occur during circulation. Intracellular degradation of peptides was described with a fixed degradation rate constant, but it was unlikely that this impacted our assumptions of observed data. Firstly because the rate constant is very small and thus degradation is limited, but also because most degradation occurs at points in time after the observed data.

Despite evidence that SSTR expression is highly variable between individuals [55], changing SSTR2 expression for tumors did not result in substantial different tumor uptake predictions. Results of the sensitivity analysis supported this finding (the sensitivity value of SSTR2 amount was 0.06 for primary tumor) and, therefore, SSTR2 expression on tumors was fixed in further analyses. Sensitivity analysis results also showed the importance of blood volume (fraction vascular) on tumor uptake. Tumor blood flow only slightly affected tumor uptake as shown by the sensitivity analysis (0.237 for primary tumor). However, it should be mentioned that a 10% change in tumor blood flow is not representative for the known inter-individual variability [42‐45]. Manual adjustments of tumor blood flow based on reported literature variability values clearly affected tumor uptake and, therefore, these values were included as population ranges in the final PBPK tumor models.

Two main considerations should be pointed out about the model not being able to predict all data observations, especially for liver metastases and other metastases. Firstly, segmentation of tumor lesions was based on lesions with highest uptake and, therefore, were probably not representative for average expected tumor uptake. Secondly, interquartile ranges of tumor blood flow and blood volume were used to predict minimum and maximum uptake ranges. Data observations outside of these predicted ranges most probably represented patients with, for example, extensive tumor blood flow and blood volume.

Higher organ uptake of [⁶⁸Ga]Ga-HA-DOTATATE compared to [⁶⁸Ga]Ga-DOTATATE was described previously, although these differences seemed on average minor and tumor uptake was found to be comparable for [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE [5, 32]. Sensitivity analysis of the PBPK model showed that slight differences in chemical properties, such as molecular weight and lipophilicity, could not fully explain the observed organ uptake dissimilarities between the tracers. In the subanalysis regarding administered peptide amount, it was shown that this peptide amount did not cause the difference in spleen uptake. These findings together lead to the conclusion that differences in receptor affinity are the likely cause for increased organ uptake for HA-DOTATATE. This was taken into account in the PBPK model by the optimization of affinity (K_D and k_off) of HA-DOTATATE to describe uptake differences between both peptides.

Apparently, the increased affinity of HA-DOTATATE did not affect (primary) tumor uptake, since primary tumor uptake was not significantly different between both peptides (Table 1). Results of the PBPK model were in agreement with these observations, because tumor blood flow and volume showed most effect on tumor uptake and not SSTR binding or expression of the receptor. In addition, the subanalysis showed that administered peptide amount did not alter spleen uptake (for HA-DOTATATE), probably indicating that receptor saturation was not likely to occur within these relatively low ranges of peptide amounts (< 20 µg).

To the best of our knowledge, this is the first study to compare organ uptake in patient groups with varying tumor burden. Scan analysis showed no significant effect of total tumor burden on organ uptake for groups 1 and 2 compared to the control group; thus, a tumor sink effect was not present within the range of observed total tumor burden in these patients. PBPK model predictions also showed a small and probably clinically irrelevant tumor sink effect for tumors with rather low-to-moderate volumes (which represents the majority of patients) [37]. To clarify, for patients with a median NET volume (approximately 65 mL) [37], a decrease in spleen uptake of ~ 3% was predicted. However, PBPK model predictions showed a clear decrease in spleen accumulation for patients with increasing total tumor loads (Fig. 5). Although variability in predictions was high, it was expected that liver metastases play an import part for patients with extensive NET volumes and predictions within the minimum decrease range in spleen uptake (based on high tumor load of other metastases with low tumor blood flow and blood volume) will be less likely to occur. When looking at the day-to-day variability in DOTATATE spleen accumulation, differences of approximately 15% can be observed according to previous work of Aalbersberg et al. [56]. Therefore, we believe that a tumor sink effect of > 20% may be considered clinically relevant and, based on the model predictions, this only occurs in patients with total tumor loads higher than ~ 550 mL. These predictions were in agreement with scan observations, since for patients with extensive metastases (group 3), a measurable tumor sink effect was indeed demonstrated, where a significant lower organ uptake (spleen, liver, thyroid) was observed compared to the control groups (Fig. 4 and Additional file 1: Fig. S1).

Personalizing therapy by increasing injected activity for patients with extensive tumor burden will probably result in greater radiation delivery to tumor sites, while not exceeding maximum organ doses. Such a similar individualized approach was suggested by Beauregard et al.; however, their observed decrease in organ uptake was caused by increased tumor sequestration or increasing body size [28]. Unfortunately, tumor volumes were not analyzed as potential cause of this effect and, therefore, results remain difficult to compare. Still, increasing activity doses for patients with high total tumors loads remains an interesting topic for future research. The characterization of the tumor sink effect as a disease-drug interaction will allow the further use of PBPK models to personalize dosing in the near future. However, based on these results, it seems that only a small part of all treated NET patients would benefit from individualized dosing based on tumor volume, since a relevant tumor sink effect was only predicted for patients with total tumor loads higher than ~ 550 mL. Regarding peptide selection, model predictions demonstrated only slight organ and tumor uptake differences for DOTATATE compared to HA-DOTATATE despite their differences in physicochemical parameters and receptor affinity, and thus there seems no clear preference of one peptide over the other. Future model predictions could help to select optimal dosing regimens to increase tumor lesion uptake while limiting organ uptake in patients receiving these radiolabeled SSAs for both diagnosis and therapy.