Abstract 307: Lymph node accumulation of theranostic lipid-based nanoparticles in healthy and diseased models: Preliminary results comparing nanoparticle morphology and targeting
Bibliographic record
Abstract
Abstract Background: Accumulation of systemically administered nanoparticles (NPs) in lymph nodes has been exploited clinically for diagnostic imaging (e.g., USPIOs for lymph node metastasis) and therapeutic applications (e.g., vaccine delivery). However, the combination of diagnostic and therapeutic functionalities into a single theranostic NP has obliged undesirable trade-offs between either the imaging or drug delivery of the NP and their specific accumulation in lymph nodes. To overcome these trade-offs, we conducted a screen of various lipid-based theranostic NPs focusing on differing NP design and their resulting pharmacokinetic behaviours in healthy and diseased lymph node models. Methods: Lipid-based theranostic NPs with varying physicochemical characteristics (e.g., formulation, size and morphology, surface targeting, etc.) were prepared with positron emitting Cu-64 and administered systemically at equivalent NP doses in healthy and diseased rodent models (i.e., mice and rats). NP types were assessed for time-dependent accumulation in major lymph node basins via non-invasive whole-body PET/MR imaging at two or more timepoints per animal. 72-hours post-injection the animals were sacrificed, and lymph nodes and major organs were excised for gamma counting and pathological evaluation. Pharmacokinetic behaviour of NPs in healthy versus diseased lymph nodes were calculated in individual animals and in naïvely pooled datasets using non-compartmental analysis. Results: Preliminary analysis identified a leading NP candidate with specific lymph node targeting in healthy and diseased rodents: a discoidal, 35-nm peptide-targeted HDL-mimetic. In comparison with a spherical, 100-nm PEGylated NP, the discoidal NP obtained greater absolute (%ID) and relative (%ID/g) amounts of injected dose in anatomically matched lymph nodes than the spherical NP, regardless of lymph node pathology. At greatest measured concentration in healthy lymph nodes, typically 24-hpi, the differences between the discoidal and spherical NPs were on average 3-fold greater (2.893 vs. 0.864, %ID/g). Differences in other pharmacokinetic parameters such as AUC (%ID/g*h) and MRT (h) were equally pronounced. Conclusions: Our preliminary analysis uncovered a discoidal, peptide-targeted HDL-mimetic with remarkable accumulation in lymph nodes of healthy and diseased models. Future investigations will focus on the biochemical and cellular mechanisms underlying their unique lymphatic pharmacokinetics. These preliminary results provide key insights for design of theranostic NPs for non-invasive imaging and staging lymph node pathologies, and for applications in delivery of therapeutics to lymph nodes following systemic administration. Citation Format: Michael S. Valic, Mark Zheng, Lili Ding, Michelle Lai, Chris J. Zhang, Tina Ye, Jenny Ma, Michael Halim, Pamela Schimmer, Wenlei Jiang, Juan Chen, Gang Zheng. Lymph node accumulation of theranostic lipid-based nanoparticles in healthy and diseased models: Preliminary results comparing nanoparticle morphology and targeting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 307.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".