Population pharmacokinetics of 17α‐hydroxyprogesterone caproate in singleton gestation
Bibliographic record
Abstract
AIMS: 17α-hydroxyprogesterone caproate (17-OHPC) reduces the rate of preterm birth in women with a prior preterm birth. Limited data exist on the pharmacokinetics (PK) of 17-OHPC or the plasma concentrations achieved during therapy. In this study, we evaluated the population PK of 17-OHPC in pregnant subjects with singleton gestation and also evaluated intrinsic and extrinsic factors that may potentially affect 17-OHPC PK in this patient population. METHODS: Sixty-one women with singleton pregnancies participated in this trial. Subjects received weekly intramuscular injections of 250 mg 17-OHPC in 1 ml castor oil from the time of enrolment (16 0/7 weeks - 20 6/7 weeks) up to 35 weeks gestation or until delivery. Blood samples were obtained between 24 and 28 weeks, between 32 and 35 weeks and over a 28-day period beyond the last injection. Maternal and/or cord blood were obtained at delivery. Data analysis was performed by nonlinear mixed effects modelling (NONMEM(®) ). RESULTS: The 17-OHPC PK were best described by a model with one maternal compartment and one fetal compartment, with first-order absorption and elimination from the maternal compartment. Maternal body weight was a significant covariate for both clearance (CL/F) and volume of distribution (Vmaternal /F). The final population mean estimates were: CL/F 1797 l/d, Vmaternal /F 32 610 l and mother to cord rate constant 0.005 day(-1) . This report describes for the first time the population PK of 17-OHPC in singleton pregnancy. CONCLUSIONS: The population PK study reported here represents the initial steps in understanding and optimizing 17-OHPC therapy for preventing preterm birth.
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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 | 0.004 |
| 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".