Inter-species model selection for translational analysis of [¹⁷⁷Lu]Lu-OPS201 biokinetic parameters

Robust estimation of time-integrated activity coefficients (TIACs) is essential for internal dosimetry in radiopharmaceutical therapy. This study developed a structured translational framework for predicting the kidney biokinetics and TIAC of [¹⁷⁷Lu]Lu-OPS201 in humans from preclinical biodistribution data in mice and pigs. Candidate sum-of-exponential functions (SoEFs) were fitted to the mean kidney time-activity data from mice, pigs, and humans. The most suitable SoEF was selected using sequential goodness-of-fit criteria, including visual assessment of the fitted curves and parameter coefficients of variation (CV) below 50%, followed by comparison of the weighted corrected Akaike information criterion (%wAICc) among the evaluable models. The reference human TIAC (rTIAC) was calculated from the selected model fitted to the human data. Human TIACs were then predicted using 5 previously reported interspecies scaling methods and a proposed approach based on mass scaling of the SoEF parameters derived from the mouse and pig data. Predictive performance was evaluated using the relative deviation (RD) between each predicted TIAC (pTIAC) and the rTIAC. A 2-parameter SoEF provided the most suitable description of the kidney time-activity data, with all parameters estimated precisely (CV < 50%) and %wAICc ∼100%. The resulting rTIAC for human kidneys was 5.41 ± 0.36 h. The proposed SoEF-parameter mass-scaling method showed the closest agreement with the human reference, yielding a pTIAC of 5.93 ± 2.08 h, corresponding to a RD of 9.67%, the smallest deviation among all 6 translational approaches evaluated. Combining systematic biokinetic model selection with SoEF-parameter mass scaling may improve preclinical-to-clinical TIAC prediction for [¹⁷⁷Lu]Lu-OPS201 kidney dosimetry. However, the optimal model and scaling method are likely specific to the radiopharmaceutical, target organ, and species evaluated. This reproducible framework provides a hypothesis-generating approach for early-stage human dosimetry estimation and supports further validation of translational methods in radiopharmaceutical therapy.