Abstract 307: Lymph node accumulation of theranostic lipid-based nanoparticles in healthy and diseased models: Preliminary results comparing nanoparticle morphology and targeting
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».