Hydroxychloroquine PK and exposure-response in pregnancies with lupus: the importance of adherence for neonatal outcomes
Bibliographic record
Abstract
OBJECTIVE: Evaluate the impact of pregnancy physiology and medication non-adherence on serum hydroxychloroquine (HCQ) pharmacokinetics (PK) and exposure-response in SLE. METHODS: We conducted a PK analysis using data from two observational pregnancy registries. We enrolled pregnant women with SLE taking HCQ at least 3 months prior to, and throughout pregnancy, and excluded those with multiple gestations. Using the PK model, we conducted dosing simulations and imputed 0%/20%/40%/60% non-adherence to evaluate the impact of adherence versus physiological changes on HCQ concentrations. We compared the effect of pregnancy-average non-adherent concentrations (≤100 ng/mL vs >100 ng/mL) on preterm birth using adjusted logistic regression. RESULTS: We enrolled 56 women who had 61 pregnancies. By the third trimester, mean apparent HCQ clearance increased by 59.6%. At a dosage of 400 mg/day, fully adherent patients are expected to have HCQ concentrations ≤100 ng/mL only 0.3% of the time, compared with 24.2% when 60% of doses are missed. Persistently low HCQ concentrations throughout pregnancy were associated with a significantly higher odds of preterm birth, controlling for lupus nephritis and race (OR 11.2; 95% CI 2.3 to 54.2; p=0.003). CONCLUSIONS: We observed significant changes in HCQ PK during pregnancy, resulting in a shortening in the drug's half-life by 10 days; however, medication non-adherence had a more pronounced effect on HCQ exposure compared with physiological changes alone. Moreover, pregnant women with non-adherent HCQ concentrations had significantly higher rates of preterm birth. Accordingly, optimising adherence in pregnancy may be more clinically meaningful than adjusting HCQ dosage to account for physiological changes. PK modelling indicates that serum HCQ concentrations ≤100 ng/mL are suggestive of non-adherence regardless of trimester and may help identify pregnancies at risk for poor outcomes.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".