Abstract
AIM: Predicting the time-activity curve (TAC) of [(177)Lu]Lu-DOTA-TATE for organs at risk and neuroendocrine tumours (NETs) is an essential element in the calculation of the absorbed dose (AD) and a critical step for individualising peptide receptor radionuclide therapy (PRRT) treatment planning. This study aims to predict the TAC of [(177)Lu]Lu-DOTA-TATE using a single quantitative image of [(68)Ga]Ga-DOTA-TATE and population data with a physiologically based pharmacokinetic (PBPK) model. METHODS: A PBPK model was developed for [(68)Ga]Ga-DOTA-TATE and [(177)Lu]Lu-DOTA-TATE, including organs and NETs. To generate reference TACs, general physiological parameters were taken from the literature, while individual model parameters were estimated using pre-therapy (PET/CT) and post-therapy (planar and SPECT/CT) image-based organ activity measurements from patients with NETs. Different error models were evaluated to determine the best one. To predict the TAC of [(177)Lu]Lu-DOTA-TATE from a single [(68)Ga]Ga-DOTA-TATE PET/CT, individual model parameters were estimated using only [(68)Ga]Ga-DOTA-TATE organ and tumour activity measurements. Finally, the predicted [(177)Lu]Lu-DOTA-TATE TACs for modelled organs and NETs were compared to the reference. RESULTS: The best error model was the proportional data-based error model, where the proportionality parameter b differs between diagnostic and therapeutic data, and between tumours and organs: b(T, Organ), b(T, Tumour), and b(D, Organ), b(D, Tumour). The medians for b(T, Organ), b(T, Tumour) and b(D, Organ), b(D, Tumour) were determined to be 0.16, 0.39, 0.35, and 0.27, respectively. For the prediction, b(D, Organ) and b(D, Tumour) were used as patient-specific proportional errors. The relative prediction error (RPE) was calculated for the predicted time-integrated activity (TIA). The mean and standard deviation for the RPEs were found to be (- 5 ± 51)%, (- 4 ± 22)%, (- 13 ± 40)%, and (- 10 ± 21)% for tumours, kidneys, liver, and spleen, respectively. The mean absolute percentage errors (MAPEs) were 43%, 18%, 31% and 17% for tumours, kidney, liver, and spleen, respectively. CONCLUSION: The integration of the PBPK model with a data-based proportional error model represents a significant improvement in predicting TACs for estimating tumour and organ ADs following [(177)Lu]Lu-DOTA-TATE therapy, using single-time-point PET/CT imaging with [(68)Ga]Ga-DOTA-TATE. These results emphasise the importance of error model analysis in PBPK modelling.