[177Lu]Lu-DOTA-TATE tumour and organ time-activity curves: prediction from a single-time-point [68Ga]Ga-DOTA-TATE PET/CT measurement

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.