TOPAS a tool to evaluate the impact of cell geometry and radionuclide on alpha particle therapy

Large-scale in vitro microdosimetry via live cell microscopy imaging: implications for radiosensitivity and RBE evaluations in alpha-emitter radiopharmaceutical therapy

BACKGROUND

Alpha-emitter radiopharmaceutical therapy (αRPT) has shown promising outcomes in metastatic disease. However, the short range of the alpha particles necessitates dosimetry on a near-cellular spatial scale. Current knowledge on cellular dosimetry is primarily based on in vitro experiments using cell monolayers. The goal of such experiments is to establish cell sensitivity to absorbed dose (AD). However, AD cannot be measured directly and needs to be modeled. Current models, often idealize cells as spheroids in a regular grid (geometric model), simplify binding kinetics and ignore the stochastic nature of radioactive decay. It is unclear what the impact of such simplifications is, but oversimplification results in inaccurate and non-generalizable results, which hampers the rigorous study of the underlying radiobiology.

METHODS

We systematically mapped out 3D cell geometries, clustering behavior, agent binding, internalization, and subcellular trafficking kinetics for a large cohort of live cells under representative experimental conditions using confocal microscopy. This allowed for realistic Monte Carlo-based (micro)dosimetry. Experimentally established surviving fractions of the HER2 + breast cancer cell line treated with a ²¹²Pb-labelled anti-HER2 conjugate or external beam radiotherapy, anchored a rigorous statistical approach to cell sensitivity and relative biological effectiveness (RBE) estimation. All outcomes were compared to a reference geometric model, which allowed us to determine which aspects are crucial model components for the proper study of the underlying radiobiology.

RESULTS

In total, 567 cells were measured up to 26 h post-incubation. Realistic cell clustering had a large (2x), and cell geometry a small (16.4% difference) impact on AD, compared to the geometric model. Microdosimetry revealed that more than half of the cells do not receive any dose for most of the tested conditions, greatly impacting cell sensitivity estimates. Including these stochastic effects in the model, resulted in significantly more accurate predictions of surviving fraction and RBE (permutation test; p < .01).

CONCLUSIONS

This comprehensive integration of the biological and physical aspects resulted in a more accurate method of cell survival modelling in αRPT experiments. Specifically, including realistic stochastic radiation effects and cell clustering behavior is crucial to obtaining generalizable radiobiological parameters.

Differential responses to 223Ra and Alpha-particles exposure in prostate cancer driven by mitotic catastrophe

Introduction

Radium-223 (223Ra) has been shown to have an overall survival benefit in metastatic castration-resistant prostate cancer (mCRPC) involving bone. Despite its increased clinical usage, relatively little is known regarding the mechanism of action of 223Ra at the cellular level.

Methods

We evaluated the effects of 223Ra irradiation in a panel of cell lines and then compared them with standard X-ray and external alpha-particle irradiation, with a particular focus on cell survival and DNA damage repair kinetics.

Results

223Ra exposures had very high, cell-type-dependent RBE50% ranging from 7 to 15. This was significantly greater than external alpha irradiations (RBE50% from 1.4 to 2.1). These differences were shown to be partially related to the volume of 223Ra solution added, independent of the alpha-particle dose rate, suggesting a radiation-independent mechanism of effect. Both external alpha particles and 223Ra exposure were associated with delayed DNA repair, with similar kinetics. Additionally, the greater treatment efficacy of 223Ra was associated with increased levels of residual DNA damage and cell death by mitotic catastrophe.

Conclusions

These results suggest that 223Ra exposure may be associated with greater biological effects than would be expected by direct comparison with a similar dose of external alpha particles, highlighting important challenges for future therapeutic optimization.

Dosimetry in targeted alpha therapy. A systematic review: current findings and what is needed

Objective. A systematic review of dosimetry in Targeted Alpha Therapy (TAT) has been performed, identifying the common issues. Approach. The systematic review was performed in accordance with the PRISMA guidelines, and the literature was searched using the Scopus and PubMed databases. Main results. From the systematic review, three key points should be considered when performing dosimetry in TAT. (1) Biodistribution/Biokinetics: the accuracy of the biodistribution data is a limit to accurate dosimetry in TAT. The biodistribution of alpha-emitting radionuclides throughout the body is difficult to image directly, with surrogate radionuclide imaging, blood/faecal sampling, and animal studies able to provide information. (2) Daughter radionuclides: the decay energy of the alpha-emissions is sufficient to break the bond to the targeting vector, resulting in a release of free daughter radionuclides in the body. Accounting for daughter radionuclide migration is essential. (3) Small-scale dosimetry and microdosimetry: due to the short path length and heterogeneous distribution of alpha-emitters at the target site, small-scale/microdosimetry are important to account for the non-uniform dose distribution in a target region, organ or cell and for assessing the biological effect of alpha-particle radiation. Significance. TAT is a form of cancer treatment capable of delivering a highly localised dose to the tumour environment while sparing the surrounding healthy tissue. Dosimetry is an important part of treatment planning and follow up. Being able to accurately predict the radiation dose to the target region and healthy organs could guide the optimal prescribed activity. Detailed dosimetry models accounting for the three points mentioned above will help give confidence in and guide the clinical application of alpha-emitting radionuclides in targeted cancer therapy.

Alpha particle microdosimetry calculations using a shallow neural network

A shallow neural network was trained to accurately calculate the microdosimetric parameters, 〈z 1〉 and 〈z 1 2〉 (the first and second moments of the single-event specific energy spectra, respectively) for use in alpha-particle microdosimetry calculations. The regression network of four inputs and two outputs was created in MATLAB and trained on a data set consisting of both previously published microdosimetric data and recent Monte Carlo simulations. The input data consisted of the alpha-particle energies (3.97–8.78 MeV), cell nuclei radii (2–10 μm), cell radii (2.5–20 μm), and eight different source-target configurations. These configurations included both single cells in suspension and cells in geometric clusters. The mean square error (MSE) was used to measure the performance of the network. The sizes of the hidden layers were chosen to minimize MSE without overfitting. The final neural network consisted of two hidden layers with 13 and 20 nodes, respectively, each with tangential sigmoid transfer functions, and was trained on 1932 data points. The overall training/validation resulted in a MSE = 3.71 × 10−7. A separate testing data set included input values that were not seen by the trained network. The final test on 892 separate data points resulted in a MSE = 2.80 × 10−7. The 95th percentile testing data errors were within ±1.4% for 〈z 1〉 outputs and ±2.8% for 〈z 1 2〉 outputs, respectively. Cell survival was also predicted using actual versus neural network generated microdosimetric moments and showed overall agreement within ±3.5%. In summary, this trained neural network can accurately produce microdosimetric parameters used for the study of alpha-particle emitters. The network can be exported and shared for tests on independent data sets and new calculations.