3D printed hydroxyurea for pediatric use: toward personalized formulations and reduced exposure risk
Bibliographic record
Abstract
Sickle cell anemia is one of the most frequent severe monogenic disorders in the world, with the highest prevalence in Sub-Saharan Africa, India, the Middle East, and in populations with ancestry from these regions. Currently, hydroxyurea is recommended as a primary prevention from 9 to 12 months of age in infants affected by this condition, with doses adjusted upon weight and biological parameters. Because treatment is initiated early and maintained throughout life, the lack of age-adapted, palatable formulations poses major challenges to long-term adherence. To address this issue in children able to chew solid dosage forms, we developed a customizable, pediatric-friendly 3D printed hydroxyurea formulation. Hydroxyurea was incorporated at 30 % weight/weight into a pectin-based chewable "pharma-ink" and successfully printed into gummy-like chewable tablets containing 200-600 mg hydroxyurea using a semi-solid extrusion 3D printer. The resulting printlets were comprehensively characterized according to the United States Pharmacopeia recommendations. They demonstrated nearly 100 % drug loading with no detectable degradation, ensuring accurate dose delivery. In vitro release testing confirmed compliance with the United States Pharmacopeia specifications for immediate-release hydroxyurea. In vivo pharmacokinetic profiles in Beagle dogs were comparable to those of conventional hydroxyurea suspensions. A 90-day stability study under ambient conditions revealed no degradation or change in hydroxyurea release. Although occupational exposure was not directly quantified, the workflow design plausibly reduces preparation risks compared with current compounding practices, an aspect that will be addressed in future clinical studies.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".