Safety of Drugs in Breastfeeding Women With CKD
Bibliographic record
Abstract
Introduction: The benefits of breastfeeding are widely recognized. Because of lack of evidence, women may forego breastfeeding or decline treatments that impact long-term renal survival. Therefore, we systematically reviewed the evidence on the breastfeeding safety of drugs commonly prescribed to women with chronic kidney disease (CKD). Methods: We conducted bibliographic search on available databases, including PUBMED, REPROTOX, and LACTMED. Results: We reviewed a total of 81 observational studies and case reports. Among renin-angiotensin system inhibitors, enalapril and captopril are safe for breastfeeding. Based on limited evidence, quinapril, benazepril, candesartan, and valsartan are likely acceptable for use during breastfeeding. We found no compelling human data regarding the safety of other renin-angiotensin system inhibitors or sodium-glucose cotransporter type 2 (SGLT2) inhibitors, finerenone, sparsentan, or glucagon-like peptide-1 receptor agonists (GLP1RAs) in lactation. Immunosuppressive agents, including azathioprine, cyclosporine, tacrolimus, budesonide, rituximab, and eculizumab are acceptable for use during breastfeeding. Belimumab is most likely safe; however, data are limited. Data on mycophenolate use are conflicting, and the general recommendation is avoidance during lactation. No studies were found on the safety of breastfeeding while on the newer complement inhibitors, including avacopan, ravulizumab, iptacopan, and pegcetacoplan. These drugs should used with caution in breastfeeding until data become available. Conclusion: Human lactation data on the safety of most drugs used in the treatment of CKD are limited, making evidence-based recommendations challenging. Emerging pharmacometrics techniques can contribute to the safety assessment of drugs in breastfeeding, overcoming ethical and practical issues associated with clinical trials in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".