Scoping review of biosimilar disease-modifying antirheumatic drugs in pregnancy: evidence gaps and proposed outcome reporting framework
Bibliographic record
Abstract
Biologic disease-modifying antirheumatic drugs (DMARDs) have revolutionized the management of autoimmune diseases. Biosimilar DMARDs have emerged as highly similar, cost-efficient alternatives; however, the scope of their perinatal evidence remains unexplored. We conducted a scoping review to synthesize evidence on the impact of biosimilar DMARDs on pregnancy outcomes. We searched Embase, MEDLINE and CENTRAL databases in November 2023 and June 2025. Inclusion criteria were studies examining biosimilar DMARD exposure for autoimmune diseases in mothers during pregnancy, fathers prior to conception and/or fetuses/neonates in-utero. Data were extracted on sample size, study design, drug exposure (timing, duration), and pregnancy outcomes. Patterns in methodologic reporting across studies were also analyzed. Overall, 6 studies (5 descriptive, 1 cohort study) were eligible for inclusion. Biosimilars examined were tumor necrosis factor inhibitors (infliximab, n = 4; etanercept, n = 2; adalimumab, n = 1) and B-cell inhibitors (rituximab, n = 1) among 63 mothers with inflammatory bowel disease, rheumatoid arthritis, or ankylosing spondylitis. Twenty-four fetal/neonatal (i.e., congenital anomaly), fetal/neonatal-maternal (i.e., Caesarean-section, spontaneous abortion), and maternal (i.e., disease flare) outcomes were reported. For methodologic reporting, we observed inconsistencies in exposure and outcome measures. To enhance comparability and standardization, we encourage the use of our Reproductive Health Outcomes Reporting Framework. Our scoping review is the first synthesis of perinatal evidence to date on biosimilar DMARDs. Critical gaps include an overall limited number of studies and a lack of analytical research that evaluate associations between exposures and outcomes. These findings highlight key evidence gaps in understanding the perinatal impacts of these emerging drugs.
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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 | 0.301 | 0.583 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.013 |
| Bibliometrics | 0.053 | 0.049 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".