Global Medicines Regulation in Pregnancy and Lactation: Addressing Emerging Therapies, Safety, and Access Inequalities
Bibliographic record
Abstract
Safe medication use during pregnancy and lactation is fundamental to protecting maternal and neonatal health. However, global variability in regulatory frameworks, pharmacovigilance systems, and access to therapies presents major challenges-particularly for emerging treatments such as biologics, GLP-1 receptor agonists, and novel small molecules. This review aimed to synthesize international regulatory frameworks, evaluate safety monitoring practices, and explore disparities in access to medicines for pregnant and lactating populations. A narrative review was conducted using PubMed, Embase, Scopus, Web of Science, and regulatory agency websites (FDA, EMA, Health Canada, MHLW Japan, WHO), supplemented by pharmacovigilance databases (FAERS, VigiBase, EudraVigilance). Publications in English from 2000-2025 addressing regulatory policies, safety monitoring, or access inequalities were included. Extracted data were analyzed to identify global trends, gaps, and best practices in maternal-fetal pharmacotherapy. High-income countries demonstrate mature systems, including the FDA Pregnancy and Lactation Labeling Rule, EMA risk management plans, and comprehensive pharmacovigilance infrastructures. In contrast, low- and middle-income countries (LMICs) often face fragmented regulations, limited monitoring capacity, and restricted access to innovative therapies. Global initiatives such as the World Health Organization (WHO) guidelines, the International Council for Harmonization (ICH) E11(R1) guideline, and TransCelerate programs promote harmonization, yet substantial gaps remain. Active and passive surveillance mechanisms, pregnancy registries, and real-world data enhance safety assessment for emerging therapies, while access inequalities persist due to regulatory delays, cost, and sociocultural barriers. Despite meaningful advances in high-resource settings, LMICs continue to experience major challenges in ensuring safe and equitable access to medicines. Strengthening evidence-based, harmonized regulatory frameworks, expanding pharmacovigilance coverage, and integrating real-world evidence are essential to safeguarding maternal and neonatal health. Coordinated global collaboration is imperative to achieve equitable access to innovative therapies worldwide.
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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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".