Few-time-points time-integrated activity coefficients calculation using non-linear mixed-effects modeling: Proof of...

Area of Science:

• Nuclear Medicine
• Radiopharmaceutical Therapy
• Pharmacokinetics and Dosimetry

Background:

• Accurate dosimetry is crucial for peptide-receptor radionuclide therapy (PRRT) effectiveness and safety.
• Time-integrated activity coefficients (TIACs) are essential for calculating organ radiation doses.
• Few-time-points (FTP) approaches can simplify dosimetry calculations compared to full pharmacokinetic modeling.

Purpose of the Study:

• To evaluate the accuracy of few-time-points (FTP) time-integrated activity coefficients (TIACs) in peptide-receptor radionuclide therapy (PRRT).
• To assess the performance of non-linear mixed-effects (NLME) modeling for deriving accurate TIACs from limited data points.
• To determine the optimal time points for FTP TIAC calculations in [¹¹¹In]In-DOTA-TATE renal dosimetry.

Main Methods:

• Collected renal biokinetic data of [¹¹¹In]In-DOTA-TATE from eight patients at five time points (2.9 to 70.9 h) using planar imaging.
• Utilized a Sum-Of-Exponentials (SOE) function within a non-linear mixed-effects (NLME) framework to model renal biokinetics and derive reference TIACs (rTIACs).
• Calculated estimated TIACs (eTIACs) using all combinations of FTPs and assessed accuracy by comparing relative deviations (RDs) and root-mean-square errors (RMSEs) against rTIACs.

Main Results:

• FTP-NLME modeling demonstrated high accuracy for eTIACs across various time point combinations.
• The lowest relative deviations (RDs) were achieved with four time points (T-2, T-3, T-4, T-5) at (0 ± 2) %.
• The lowest root-mean-square errors (RMSEs) were 2% for four time points (T-2, T-3, T-4, T-5), with significant accuracy also observed using T-4 alone (8% RMSE).

Conclusions:

• FTP-NLME modeling provides a highly accurate method for estimating TIACs in [¹¹¹In]In-DOTA-TATE PRRT.
• The T-4 time point (46.7 ± 1.7 h) is particularly valuable for achieving accurate TIAC estimations.
• This approach simplifies dosimetry while maintaining a high degree of accuracy, supporting clinical application.