Call to action: Harmonization of pharmacovigilance regulations for post‐marketing pregnancy and breastfeeding safety studies
Bibliographic record
Abstract
Globally, more than 200 million women become pregnant each year, most of whom receive medications despite limited information on their safe use during pregnancy. The paucity of drug safety data on pregnant and breastfeeding women stems from the routine exclusion of this population from clinical trials due to scientific, ethical, regulatory and legal concerns. Consequently, at the time of initial drug approval, there may be scant safety data to inform the drug benefit-risk balance to the mother, foetus or infant. Although momentum is growing to include this underrepresented population in clinical trials, most information on drug exposure outcomes comes from data collected in the postmarketing setting. Regulatory guidance and legislation on medication use in pregnancy and breastfeeding were reviewed globally by the TransCelerate IGR PV Pregnancy and Breastfeeding Team. The International Conference of Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) standards and Council for International Organizations of Medical Sciences guidelines served as benchmarks for national safety regulations and guidance. The landscape assessment identified a lack of harmonization of global regulations on research in pregnant and breastfeeding women and a lack of specific regulations on this topic in the majority of the territories included in the assessment. This article focuses on the ambiguities and lack of harmonization in global regulations on postmarketing pregnancy and breastfeeding safety studies. There is currently no ICH standard to guide these types of safety studies and, in most regions reviewed, there are no clear regulations or guidance on when and how to conduct them. While a challenging undertaking, greater clarity and harmonization would facilitate more timely completion of postmarketing pregnancy safety studies that would ultimately generate the critical data needed to optimize benefit-risk decisions for women who may conceive, as well as pregnant and breastfeeding women.
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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 | 0.454 | 0.416 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.020 | 0.017 |
| Research integrity | 0.043 | 0.054 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".