Analysis of a maternal health medicines pipeline database 2000–2021: New candidates for the prevention and treatment of fetal growth restriction
Bibliographic record
Abstract
OBJECTIVE: The Accelerating Innovation for Mothers project established a new database of candidate medicines under development between 2000 and 2021 for five pregnancy-related conditions, including fetal growth restriction. The objective was to assess medicines for fetal growth restriction and their potential for clinical use globally. DESIGN: Landscape analysis. SETTING: Global (focus on low- and middle-income countries, LMICs). SAMPLE: Drugs, dietary supplements and biologics under investigation for prevention or treatment of fetal growth restriction. METHODS: A research pipeline database of medicines was created through searching AdisInsight, PubMed and various grant and clinical trial databases. Analysis of clinical and preclinical candidates were descriptive. MAIN OUTCOMES MEASURES: Fetal growth restriction candidates in clinical development were identified and ranked as high, medium or low potential based on prespecified criteria, including efficacy, safety and accessibility. RESULTS: Of the 444 unique candidates in the database across all five pregnancy-related conditions, 63 were for fetal growth restriction. Of these, 31 were in clinical development (phases I, II or III) and 32 were in preclinical development. Three candidates, aspirin, l-arginine and vitamin D, were ranked as having high potential as preventive agents. There were no high-potential candidates for treating fetal growth restriction, although five candidates were ranked as having medium potential: allylestrenol, dalteparin, omega-3 fatty acids, tadalafil, and United Nations International Multiple Micronutrient Antenatal Preparation (UNIMMAP). CONCLUSIONS: l-Arginine, aspirin and vitamin D are promising, high-potential preventative agents for fetal growth restriction. Based on the medicines pipeline, new pharmacological agents for fetal growth restriction are unlikely to emerge in the near future.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.022 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".