Author: Denis Bergeron, Ryan P Fitzgerald, Ravneet Kaur, Rao Khan 👨🔬
Affiliation: John Hopkins University, NIST Radiation Physics Division, Howard University, National Institute of Standards and Technology 🌍
Purpose: There is growing interest in short-lived alpha-emitting radionuclides for cancer therapy due to their ability to selectively target and destroy cancer cells while sparing healthy tissue. However, the decay chains of radionuclides such as Th-227, Ra-224, and Ac-225 pose challenges in activity measurement due to complex ingrowth, decay behavior of progeny, and uncertainties in nuclear decay data. Additional challenges include potential breakthrough of parent nuclides and non-equilibrium states at the time of drug delivery. The primary aim of this research is to develop a user-friendly Python-based decay calculator to address these complexities, making calculations accessible and efficient for radiopharmaceutical therapy (RPT).
Methods: This study solves the recursive form of Bateman equations for decay chains of arbitrary length, incorporating multiple branching pathways. Initial calculations focused on Ac-225, predicting time-dependent activities for all six progeny radionuclides. The next steps involved propagating uncertainties from nuclear decay data, separation times, and breakthrough levels, including correlations, to predict radionuclide activity uncertainties at delivery. The predictions were integrated with experimental detector efficiencies to simulate responses for radionuclide calibrators and liquid scintillation counters.
Results: Solution of recursive Bateman equations for Ac-225 enabled accurate predictions of ingrowth and equilibrium activity ratios within its decay chain. The results were used to combine Bateman results with Monte Carlo detector simulations to provide detection efficiencies (ion chamber current / activity of the parent nuclide).
Conclusion: A Python-based decay calculator is able to address the complexities of the Ac-225 decay chain, supporting accurate activity measurements and dose calculations. The tool solves the Bateman equations, propagates uncertainties, and enhances activity predictions, providing researchers and clinicians with key inputs for RPT dosimetry with a full treatment of uncertainties.