Bridging the Evidence Gap: A Systematic Review and Model-based Meta-analysis of Medication Safety and Pharmacokinetics in Pregnancy
Bibliographic record
Abstract
Abstract The safe and effective use of medications during pregnancy remains a critical challenge in perinatal medicine, where physiological changes alter maternal pharmacokinetics (PKs) and fetal exposure in ways that are poorly captured by current clinical guidance. We conducted a systematic review to evaluate the safety, PKs, and model-informed dosing of medications during pregnancy, bridging mechanistic, clinical, and epidemiologic evidence. Comprehensive searches of MEDLINE, Embase, CENTRAL, Web of Science, ClinicalTrials.gov, European Union EU Clinical Trials Register, World Health Organization International Clinical Trials Registry Platform, Food and Drug Administration/European Medicines Agency labeling, and teratology information services were performed for the years 2000–2025, with final searches in September 2025. Eligible studies included systematic reviews, population or physiologically based PK (PBPK) models, prospective and retrospective cohorts, and clinical PK studies reporting maternal or fetal drug exposure and safety outcomes. Screening, extraction, and risk of bias assessments (AMSTAR-2, ROBIS, Newcastle–Ottawa Scale, and PBPK reporting checklist) were conducted in duplicate with conflict adjudication. Forty-one studies met the inclusion criteria, comprising nine systematic reviews, eight large epidemiologic cohorts, seven clinical PK investigations, and seventeen model-based studies. Core evidence synthesized across 20 high-quality studies revealed consistent reductions in drug exposure for antiepileptics, antivirals, antihypertensives, and psychotropics during pregnancy, with implications for therapeutic drug monitoring, dose adjustment, and maternal–fetal safety. Modeling frameworks increasingly informed dosing recommendations, but external validation remained limited. Evidence gaps were identified in antihypertensives, immunotherapies, and pregnancy-specific drug–drug interactions. This review highlights the urgent need for integrated model-informed strategies, standardized maternal–fetal pharmacology endpoints, and coordinated international data resources to ensure safe and effective medication use in pregnancy.
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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 | 0.041 | 0.102 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.042 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".