Biodistribution of <sup>89</sup>Zr-Radiolabeled Nanoassemblies for Monoclonal Antibody Delivery Revealed through <i>In Vivo</i> PET Imaging
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Despite the outstanding performance of monoclonal antibodies (mAbs) in the clinic, their full potential has been hindered due to their inability to cross cell membranes and therefore reach intracellular targets. The use of nanotechnology to deliver mAbs to intracellular domains has been highlighted as a strategy with high potential. Working toward this goal, we have recently developed and validated palmitoyl hyaluronate (HAC16)-based nanoassemblies (HANAs), a novel technology for the intracellular delivery of mAbs in Kirsten Rat Sarcoma Virus (KRAS)-mutated tumors, one of the most prevalent and a challenging intracellular oncoprotein. Despite their success, the pharmacokinetics and biodistribution of these delivery vehicles are still unknown due to their chemical complexity, a challenge common to a large proportion of drug delivery nanomedicines. To support further development and clinical translation, we present an efficient radiolabeling approach with the positron emitter zirconium-89 ( 89 Zr) for the in vivo evaluation of HANAs by whole-body PET imaging. Additionally, we assessed the impact of PEGylation and size modulation on the biodistribution profile of mAbs using 89 Zr-radiolabeled PEGylated and non-PEGylated HANAs. Our PET imaging results demonstrated that HANAs significantly modify the pharmacokinetics and biodistribution of the 89 Zr-mAb. Furthermore, we established that the biodistribution of HANAs can be conveniently modulated by introducing PEG polymers on the surface, facilitating customization for cancer applications. This versatile radiolabeling strategy provides a facile approach for the in vivo evaluation of complex nanoformulations loaded with mAbs, in a quantitative manner with high sensitivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".