Abstract
2821
Introduction: The field of radiopharmaceutical therapy (RPT) is experiencing a tremendous renaissance. In this paradigm, the intention is to use radiopharmaceuticals, not in tracer amounts that do not perturb biological systems, but in fact to cause cellular damage. This can lead to significant nonlinear phenomena that need to be properly studied and understood in the transition to optimal, personalized, precision RPTs. In the present work, we study non-linearities related to varying injected radioactivity as well as specific-activity (SA) of the radiopharmaceutical for RPT. We look into how they contribute to time-integrated activities (TIAs) for different targets. For this, we investigated the competition between radiolabeled (hot) and unlabeled (cold) pharmaceuticals. The present effort focuses specifically on 177Lu-DOTATATE therapy.
Methods: For this study we developed and refined a python-based physiologically based Radiopharmacokinetic (PBRPK) model. The model contains 19 organs including organs-at-risk such as kidneys and bone marrow as well as tumors. Fundamental concepts related to radiopharmaceuticals such as radioactive decay and competition between radiolabeled and unlabeled molecules for available receptors were included in this model. The parameters used to describe the model (e.g. K_D value for DOTATATE, volume and flow of each organ, internalization rate, receptor densities, etc) were selected based on physiological data available in the literature [1]. To get the results, we considered different values of injected activity (form 3 to 20 MBq) and for each value in this range we did a parametric sweep on different SA values (from 1x10-4 to 7x10-4 MBq/μg). For each of these combinations, we calculated time integrated activity (TIA) values for different organs. Also we studies iso time integrated activity curves (isoTIA) in which different combinations of injected activity and SA results in the same TIA values
Results: Our results revealed significant competition between radiolabeled and unlabeled species. We observed that in the lower SA values of 1x10-4 to 3x10-4 MBq/μg, the dominance of the unlabeled molecule population is the limiting factor for TIAs (i.e. no matter what the value of injected radioactivity, if SA is low, TIA will have little dependence on the injected activity). But as SA values increase beyond 3x10-4 MBq/μg, the radioactive molecules effectively enter the competition. With increasing injected activity the TIA also increases. This increase, however, is not always linear (i.e. doubling the injected activity will not necessarily result in doubled TIA), and the SA is the parameter that controls the linearity of the dependence. In addition, the study of isoTIA curves, revealed that by tuning SA, one may achieve high TIA values with significantly reduced injected activities.
Conclusions: Significant non-linearities can occur when non-tracer amounts of radiopharmaceuticals are injected, and SA plays a significant role as a control parameter. These effects need to be considered when adjusting injections to achieve desired TIAs (and subsequently radiation doses). Utilizing PBRPK models that appropriately model the impact of varying SA and amount of labeled ligand values can enable assessment and quantification of relevant nonlinearities, in turn, facilitating approaches to personalized therapy planning and higher tumor control probabilities in clinical applications.
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