Extremely short bioavailability and fast pharmacodynamic effects of pMHC-based nanomedicines
Bibliographic record
Abstract
Nanoparticles (NPs) coated with autoimmune disease-relevant peptide-major histocompatibility complexes (pMHCs) can blunt autoimmune diseases by re-programming cognate effector T-lymphocytes into disease-suppressing regulatory T-cells, followed by massive expansion. Here, a method to quantify the absolute amounts of the active drug product is developed, to understand the relationship between bioavailability and pharmacodynamics. Incubation with plasma results in the formation of a protein corona that stabilizes the directional pMHC coat, shielding it from proteolysis or anti-drug antibody recognition, without any appreciable loss in biological potency. A quantitative method that harnesses these features indicates that the half-life of these compounds in the circulation and organs is an order of magnitude shorter (minutes vs. hours) than that measured using commonly-used semi-quantitative methods. Extensive transmission electron microscopy-based organ scanning and flow cytometry-based enumeration of pMHCII-NP capturing cells confirmed that these compounds are rapidly captured (within 1 min) by liver sinusoidal endothelial cells, Kupffer cells, splenic phagocytes and cognate T-cells, leading to a fast decline in the circulation. Therefore, the powerful pharmacodynamic effects of these compounds are dissociated from long bioavailability, implying a hit-and-run event. Collectively, these data provide a detailed view of the life-cycle of a nanoimmunomedicine, and suggest that the real half-lives of intact nanomedicines may be much shorter than those estimated using indirect approaches.
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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".