Opportunistic dried blood spot sampling validates and optimizes a pediatric population pharmacokinetic model of metronidazole
Bibliographic record
Abstract
Pharmacokinetic models rarely undergo external validation in vulnerable populations such as critically ill infants, thereby limiting the accuracy, efficacy, and safety of model-informed dosing in real-world settings. Here, we describe an opportunistic approach using dried blood spots (DBS) to evaluate a population pharmacokinetic model of metronidazole in critically ill preterm infants of gestational age (GA) ≤31 weeks from the Metronidazole Pharmacokinetics in Premature Infants (PTN_METRO, NCT01222585) study. First, we used linear correlation to compare 42 paired DBS and plasma metronidazole concentrations from 21 preterm infants [mean (SD): post natal age 28.0 (21.7) days, GA 26.3 (2.4) weeks]. Using the resulting predictive equation, we estimated plasma metronidazole concentrations (ePlasma) from 399 DBS collected from 122 preterm and term infants [mean (SD): post natal age 16.7 (15.8) days, GA 31.4 (5.1) weeks] from the Antibiotic Safety in Infants with Complicated Intra-Abdominal Infections (SCAMP, NCT01994993) trial. When evaluating the PTN_METRO model using ePlasma from the SCAMP trial, we found that the model generally predicted ePlasma well in preterm infants with GA ≤31 weeks. When including ePlasma from term and preterm infants with GA >31 weeks, the model was optimized using a sigmoidal Emax maturation function of postmenstrual age on clearance and estimated the exponent of weight on volume of distribution. The optimized model supports existing dosing guidelines and adds new data to support a 6-hour dosing interval for infants with postmenstrual age >40 weeks. Using an opportunistic DBS to externally validate and optimize a metronidazole population pharmacokinetic model was feasible and useful in this vulnerable population.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".