Predicting [¹⁷⁷Lu]Lu-HA-DOTATATE kidney and tumor accumulation based on [⁶⁸Ga]Ga-HA-DOTATATE diagnostic imaging using semi-physiological population pharmacokinetic modeling.

Abstract

CONCLUSION

Our semi-physiological PK model indicated clear differences in PK parameters for [⁶⁸Ga]Ga-HA-DOTATATE and [¹⁷⁷Lu]Lu-HA-DOTATATE. Diagnostic images provided additional information to individually predict [¹⁷⁷Lu]Lu-HA-DOTATATE tumor uptake compared to using a population approach. In addition, individual predictions indicated that many aspects, apart from PK differences, play a part in predicting [¹⁷⁷Lu]Lu-HA-DOTATATE distribution.

RESULTS

Population uptake rate parameters for spleen, kidney and tumors differed by a 0.29-fold (15% relative standard error (RSE)), 0.49-fold (15% RSE) and 1.43-fold (14% RSE), respectively, for [¹⁷⁷Lu]Lu-HA-DOTATATE compared to [⁶⁸Ga]Ga-HA-DOTATATE. Model predictions adequately described observed data in kidney and tumors for both peptides (based on visual inspection of goodness-of-fit plots). Individual predictions of tumor uptake were better (RPE AUC -40 to 28%) compared to kidney predictions (RPE AUC -53 to 41%). Absorbed dose predictions were less predictive for both tumor and kidneys (RPE tumor and kidney -51 to 44% and -58 to 82%, respectively). For most patients, [¹⁷⁷Lu]Lu-HA-DOTATATE tumor accumulation predictions based on individual PK parameters estimated from diagnostic imaging outperformed predictions based on population parameters.

BACKGROUND

Prediction of [¹⁷⁷Lu]Lu-HA-DOTATATE kidney and tumor uptake based on diagnostic [⁶⁸Ga]Ga-HA-DOTATATE imaging would be a crucial step for precision dosing of [¹⁷⁷Lu]Lu-HA-DOTATATE. In this study, the population pharmacokinetic (PK) differences between [¹⁷⁷Lu]Lu-HA-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE were assessed and subsequently [¹⁷⁷Lu]Lu-HA-DOTATATE was predicted based on [⁶⁸Ga]Ga-HA-DOTATATE imaging.

METHODS

A semi-physiological nonlinear mixed-effects model was developed for [⁶⁸Ga]Ga-HA-DOTATATE and [¹⁷⁷Lu]Lu-HA-DOTATATE, including six compartments (representing blood, spleen, kidney, tumor lesions, other somatostatin receptor expressing organs and a lumped rest compartment). Model parameters were fixed based on a previously developed physiologically based pharmacokinetic model for [⁶⁸Ga]Ga-HA-DOTATATE. For [¹⁷⁷Lu]Lu-HA-DOTATATE, PK parameters were based on literature values or estimated based on scan data (four time points post-injection) from nine patients. Finally, individual [¹⁷⁷Lu]Lu-HA-DOTATATE uptake into tumors and kidneys was predicted based on individual [⁶⁸Ga]Ga-HA-DOTATATE scan data using Bayesian estimates. Predictions were evaluated compared to observed data using a relative prediction error (RPE) for both area under the curve (AUC) and absorbed dose. Lastly, to assess the predictive value of diagnostic imaging to predict therapeutic exposure, individual prediction RPEs (using Bayesian estimation) were compared to those from population predictions (using the population model).