Bupropion and Escitalopram During Lactation
Bibliographic record
Abstract
OBJECTIVE: To report a case of seizure-like symptoms in an infant exposed to bupropion and escitalopram through breastfeeding. CASE SUMMARY: A 6.5-month-old female infant, breastfed by a mother treated with bupropion XL 150 mg/d and escitalopram 10 mg/d for depression, presented to our hospital with severe emesis and tonic seizure-like symptoms. The symptoms resolved with supportive therapy. Urine toxicology screen in the infant revealed bupropion and escitalopram. Bupropion, and its active metabolite, hydroxybupropion, were analyzed and quantified both in the infant's serum and in the breast milk. Diagnostic workup for seizure etiologies was otherwise negative. After being asymptomatic for 48 hours, her discharge diagnosis was adverse events associated with bupropion and escitalopram in lactation. An objective causality assessment (Naranjo assessment) revealed that this adverse effect was probable. DISCUSSION: The adverse events in our case were associated with serum concentrations of bupropion and hydroxybupropion that are lower than the reported therapeutic range, perhaps suggesting that infants, compared with adults, may have a higher susceptibility to the epileptogenic effects of bupropion and/or hydroxybupropion. Furthermore, although we do not have escitalopram serum concentrations, this drug interaction may have had a contributing role in this case, possibly because of cytochrome P4502D6 inhibition by bupropion and metabolites. CONCLUSION: As the number of reproductive-aged women requiring polytherapy to control their depression is increasing, further research is needed to establish the safety of combined antidepressants, such as selective serotonin reuptake inhibitors and bupropion, during lactation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".