Target product profiles for paediatric formulations of azithromycin and nitrofurantoin
Bibliographic record
Abstract
Bacterial infections are still a main cause of death in children younger than 5 years, yet few age-appropriate antibiotic formulations exist, which limits treatment options and compromises quality of care. In 2023, the World Health Organization (WHO) published its first list of priority paediatric antibiotic formulations to guide research and development for age-appropriate antibiotic formulations. Both azithromycin and nitrofurantoin are on this list. Currently, no dispersible tablets are approved or available for these drugs and existing liquid forms are poorly palatable and/or contain excipients of safety concern. To support the development of age-appropriate formulations for these two antibiotics, we produced target product profiles using WHO's methods. For azithromycin, the optimum age-appropriate formulation and dose is scored 100 mg dispersible tablets or an orodispersible 50 mg multiparticulate formulation, with dispersible 50 mg tablets as the minimum requirement. For nitrofurantoin, the optimum age-appropriate formulation is an orodispersible multiparticulate formulation or scored dispersible tablets, with dispersible tablets as the minimum requirement. Based on the WHO recommended dosage of 4 mg/kg per day for children for nitrofurantoin, the optimum unit dose is 5 mg. If scoring is feasible, a 10 mg unit dose should be developed for dosing flexibility across paediatric age groups. These profiles aim to support regulatory authorities, pharmaceutical developers, health programmes and other stakeholders in advancing safer, effective and child-appropriate antibiotic formulations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".