A physiologically based pharmacokinetic (PBPK) model to describe organ distribution of <sup>68</sup>Ga-DOTATATE in patients without neuroendocrine tumors.

Abstract

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.

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).

More about this publication

EJNMMI research
  • Volume 11
  • Issue nr. 1
  • Pages 73
  • Publication date 16-08-2021

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