Safety of disease-modifying drugs for multiple sclerosis in pregnancy: current challenges and future considerations for effective pharmacovigilance
Bibliographic record
Abstract
When contemplating a pregnancy, women treated for multiple sclerosis (MS) with a disease-modifying drug must decide to discontinue their medication before conception or risk exposing their unborn child to potential drug toxicity. Few studies exist as reference for patients and physicians, and of those available, the majority are less than ideal due to real-world constraints, ethical issues and methodological shortcomings. The authors provide a brief summary of existing animal and human data with current recommendations regarding the safety of IFN-β, glatiramer acetate, natalizumab, mitoxantrone, fingolimod and teriflunomide during pregnancy and lactation in women with MS. We also assess the quality, strengths and limitations of the existing studies including challenges with study design. The investigation of outcomes such as spontaneous abortion and congenital anomalies are highlighted with potential methodological improvements for future studies on drug safety in pregnancy suggested. The authors explore the pharmacokinetics and pharmacodynamics of the MS disease-modifying drugs for their possible mechanistic role in fetal harm and discuss the potential role of clinical trials. Future pharmacovigilance studies should continue to pursue multicenter collaboration with an emphasis on appropriate study design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".