Hyaluronic Acid Molecular Weight Determines Lung Clearance and Biodistribution after Instillation
Bibliographic record
Abstract
Hyaluronic acid (HA) has emerged as a versatile polymer for drug delivery. Multiple commercial products utilize HA, it can be obtained in a variety of molecular weights, and it offers chemical handles for cross-linkers, drugs, or imaging agents. Previous studies have investigated multiple administration routes, but the absorption, biodistribution, and pharmacokinetics of HA after delivery to the lung is relatively unknown. Here, pharmacokinetic parameters were investigated by delivering different molecular weights of HA (between 7 and 741 kDa) to the lungs of mice. HA was labeled with either a near-infrared dye or with iodine-125 conjugated to HA using a tyrosine linker. In initial studies, dye-labeled HA was instilled into the lungs and fluorescent images of organs were collected at 1, 8, and 24 h post administration. Data suggested longer lung persistence of higher molecular weight HA, but signal diminished for all molecular weights at 8 h. To better quantitate pharmacokinetic parameters, different molecular weights of iodine-125 labeled HA were instilled and organ radioactivity was determined after 1, 2, 4, 6, and 8 h. The data showed that, after instillation, the lungs contained the highest levels of HA, as expected, followed by the gastrointestinal tract. Smaller molecular weights of HA showed more rapid systemic distribution, while 67 and 215 kDa HA showed longer persistence in the lungs. Lung exposure appeared to be optimum in this size range due to the rapid absorption of <67 kDa HA and the poor lung penetration and mucociliary clearance of viscous solutions of HA > 215 kDa. The versatility of HA molecular weight and conjugation chemistries may, therefore, provide new opportunities to extend pulmonary drug exposure and potentially facilitate access to lymph nodes draining the pulmonary bed.
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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 | 0.000 |
| 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) | 0.000 | 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".