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

## TL;DR

This study shows how to predict the activity of a radioactive treatment in tumors and organs using a single scan of a diagnostic tracer, improving personalized therapy planning.

## Contribution

A novel PBPK model with a data-based error model is introduced to predict [177Lu]Lu-DOTA-TATE TACs from a single [68Ga]Ga-DOTA-TATE PET/CT measurement.

## Key findings

- The best error model was a proportional data-based model with distinct parameters for tumors and organs.
- Prediction errors for time-integrated activity were within acceptable ranges for most organs and tumors.
- The PBPK model with error analysis improves accuracy in estimating absorbed doses for PRRT.

## Abstract

Predicting the time-activity curve (TAC) of [177Lu]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 [177Lu]Lu-DOTA-TATE using a single quantitative image of [68Ga]Ga-DOTA-TATE and population data with a physiologically based pharmacokinetic (PBPK) model.

A PBPK model was developed for [68Ga]Ga-DOTA-TATE and [177Lu]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 [177Lu]Lu-DOTA-TATE from a single [68Ga]Ga-DOTA-TATE PET/CT, individual model parameters were estimated using only [68Ga]Ga-DOTA-TATE organ and tumour activity measurements. Finally, the predicted [177Lu]Lu-DOTA-TATE TACs for modelled organs and NETs were compared to the reference.

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: bT, Organ, bT, Tumour, and bD, Organ, bD, Tumour. The medians for bT, Organ, bT, Tumour and bD, Organ, bD, Tumour were determined to be 0.16, 0.39, 0.35, and 0.27, respectively. For the prediction, bD, Organ and bD, 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.

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 [177Lu]Lu-DOTA-TATE therapy, using single-time-point PET/CT imaging with [68Ga]Ga-DOTA-TATE. These results emphasise the importance of error model analysis in PBPK modelling.

The online version contains supplementary material available at 10.1186/s40658-025-00826-4.