Dataset Information

Improved modeling of in vivo kinetics of slowly diffusing radiotracers for tumor imaging.

ABSTRACT: Large-molecule tracers, such as labeled antibodies, have shown success in immuno-PET for imaging of specific cell surface biomarkers. However, previous work has shown that localization of such tracers shows high levels of heterogeneity in target tissues, due to both the slow diffusion and the high affinity of these compounds. In this work, we investigate the effects of subvoxel spatial heterogeneity on measured time-activity curves in PET imaging and the effects of ignoring diffusion limitation on parameter estimates from kinetic modeling.Partial differential equations (PDE) were built to model a radially symmetric reaction-diffusion equation describing the activity of immuno-PET tracers. The effects of slower diffusion on measured time-activity curves and parameter estimates were measured in silico, and a modified Levenberg-Marquardt algorithm with Bayesian priors was developed to accurately estimate parameters from diffusion-limited data. This algorithm was applied to immuno-PET data of mice implanted with prostate stem cell antigen-overexpressing tumors and injected with (124)I-labeled A11 anti-prostate stem cell antigen minibody.Slow diffusion of tracers in linear binding models resulted in heterogeneous localization in silico but no measurable differences in time-activity curves. For more realistic saturable binding models, measured time-activity curves were strongly dependent on diffusion rates of the tracers. Fitting diffusion-limited data with regular compartmental models led to parameter estimate bias in an excess of 1,000% of true values, while the new model and fitting protocol could accurately measure kinetics in silico. In vivo imaging data were also fit well by the new PDE model, with estimates of the dissociation constant (Kd) and receptor density close to in vitro measurements and with order of magnitude differences from a regular compartmental model ignoring tracer diffusion limitation.Heterogeneous localization of large, high-affinity compounds can lead to large differences in measured time-activity curves in immuno-PET imaging, and ignoring diffusion limitations can lead to large errors in kinetic parameter estimates. Modeling of these systems with PDE models with Bayesian priors is necessary for quantitative in vivo measurements of kinetics of slow-diffusion tracers.

SUBMITTER: Wilks MQ

PROVIDER: S-EPMC4334373 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Improved modeling of in vivo kinetics of slowly diffusing radiotracers for tumor imaging.

Wilks Moses Q MQ Knowles Scott M SM Wu Anna M AM Huang Sung-Cheng SC

Journal of nuclear medicine : official publication, Society of Nuclear Medicine 20140703 9

<h4>Unlabelled</h4>Large-molecule tracers, such as labeled antibodies, have shown success in immuno-PET for imaging of specific cell surface biomarkers. However, previous work has shown that localization of such tracers shows high levels of heterogeneity in target tissues, due to both the slow diffusion and the high affinity of these compounds. In this work, we investigate the effects of subvoxel spatial heterogeneity on measured time-activity curves in PET imaging and the effects of ignoring di ...[more]

PMID: 24994929

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Project description:Cancer-associated fibroblasts constitute a vital subpopulation of the tumor stroma and are present in more than 90% of epithelial carcinomas. The overexpression of the serine protease fibroblast activation protein (FAP) allows a selective targeting of a variety of tumors by inhibitor-based radiopharmaceuticals (FAPIs). Of these compounds, FAPI-04 has been recently introduced as a theranostic radiotracer and demonstrated high uptake into different FAP-positive tumors in cancer patients. To enable the delivery of higher doses, thereby improving the outcome of a therapeutic application, several FAPI variants were designed to further increase tumor uptake and retention of these tracers. Methods: Novel quinoline-based radiotracers were synthesized by organic chemistry and evaluated in radioligand binding assays using FAP-expressing HT-1080 cells. Depending on their in vitro performance, small-animal PET imaging and biodistribution studies were performed on HT-1080-FAP tumor-bearing mice. The most promising compounds were used for clinical PET imaging in 8 cancer patients. Results: Compared with FAPI-04, 11 of 15 FAPI derivatives showed improved FAP binding in vitro. Of these, 7 compounds demonstrated increased tumor uptake in tumor-bearing mice. Moreover, tumor-to-normal-organ ratios were improved for most of the compounds, resulting in images with higher contrast. Notably two of the radiotracers, FAPI-21 and -46, displayed substantially improved ratios of tumor to blood, liver, muscle, and intestinal uptake. A first diagnostic application in cancer patients revealed high intratumoral uptake of both radiotracers already 10 min after administration but a higher uptake in oral mucosa, salivary glands, and thyroid for FAPI-21. Conclusion: Chemical modification of the FAPI framework enabled enhanced FAP binding and improved pharmacokinetics in most of the derivatives, resulting in high-contrast images. Moreover, higher doses of radioactivity can be delivered while minimizing damage to healthy tissue, which may improve therapeutic outcome.

Project description:BackgroundWe suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our approach molecules possess a location in the reactor as well as an orientation and geometry, while the reactions are carried out according to a list of implicitly specified reaction rules. Because the reaction rules can contain patterns for molecules, a combinatorially complex or even infinitely sized reaction network can be defined. For our implementation (based on LAMMPS), we have chosen an already existing formalism (BioNetGen) for the implicit specification of the reaction network. This compatibility allows to import existing models easily, i.e., only additional geometry data files have to be provided.ResultsOur simulations show that the obtained dynamics can be fundamentally different from those simulations that use classical reaction-diffusion approaches like Partial Differential Equations or Gillespie-type spatial stochastic simulation. We show, for example, that the combination of combinatorial complexity and geometric effects leads to the emergence of complex self-assemblies and transportation phenomena happening faster than diffusion (using a model of molecular walkers on microtubules). When the mentioned classical simulation approaches are applied, these aspects of modeled systems cannot be observed without very special treatment. Further more, we show that the geometric information can even change the organizational structure of the reaction system. That is, a set of chemical species that can in principle form a stationary state in a Differential Equation formalism, is potentially unstable when geometry is considered, and vice versa.ConclusionsWe conclude that our approach provides a new general framework filling a gap in between approaches with no or rigid spatial representation like Partial Differential Equations and specialized coarse-grained spatial simulation systems like those for DNA or virus capsid self-assembly.

Dataset Information

Improved modeling of in vivo kinetics of slowly diffusing radiotracers for tumor imaging.

Publications

Improved modeling of in vivo kinetics of slowly diffusing radiotracers for tumor imaging.

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