Biopharmaceutical Characteristics of Nifurtimox Tablets for Age‐ and Body Weight‐Adjusted Dosing in Patients With Chagas Disease
Bibliographic record
Abstract
Abstract Treatment of Chagas disease with nifurtimox requires age‐ and body weight‐adjusted dosing, resulting in complex dosing instructions. Appropriate formulations are needed for precise and compliant dosing, especially in pediatric patients. We characterized the biopharmaceutical features of a standard nifurtimox 120‐mg tablet and a 30‐mg tablet developed to improve dose accuracy. Two open‐label, randomized crossover studies were conducted in adult outpatients with Chagas disease. One study investigated whether 4 × 30‐mg tablets and 1 × 120‐mg tablet were bioequivalent and whether tablets can be administered as an aqueous slurry without affecting bioavailability. The second study investigated the effect of a high‐calorie/high‐fat diet versus fasting on the absorption of nifurtimox after a single 4 × 30‐mg dose. Interventions were equivalent if the 90% confidence interval (CI) of their least‐squares (LS) mean ratios for both AUC 0‐tlast and C max were in the range of 80%‐125%. The 4 × 30‐mg and 1 × 120‐mg tablet doses were bioequivalent (AUC 0‐tlast : LS mean ratio, 104.7%; 90%CI, 99.1%‐110.7%; C max : LS mean ratio, 101.7%; 90%CI, 89.4%‐115.6%; n = 24). Exposure when giving the 4 × 30‐mg dose as a slurry or as tablets was comparable, with an AUC 0‐tlast ratio of 93.2% (84.2%‐103.1%; n = 12) and a slightly decreased C max ratio for the slurry of 76.5% (68.8%‐85.1%). Food improved the bioavailability of nifurtimox substantially (AUC 0‐tlast ratio fed/fasted , 172%; 90%CI, 154%‐192%; C max ratio fed/fasted , 168%; 90%CI, 150%‐187%). The data indicate that the 30‐ and 120‐mg tablets are suitable for dosing adult and pediatric patients accurately; nifurtimox should be administered under fed conditions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".