Fetal Defects in Mice Treated With Integrase Strand Transfer Inhibitors: Comparison of Dolutegravir, Raltegravir, Bictegravir, and Cabotegravir
Bibliographic record
Abstract
BACKGROUND: Integrase strand transfer inhibitors (INSTIs) are preferred for treatment of human immunodeficiency virus (HIV). However, safety data in pregnancy are limited for newer INSTIs. METHODS: Pregnant C57BL/6 mice were randomly allocated to control (water), dolutegravir (DTG), raltegravir (RAL), bictegravir (BIC), or cabotegravir (CAB) at clinically relevant doses, administered orally with tenofovir disoproxil fumarate and emtricitabine, once daily from gestational day (GD) 0.5 to sacrifice (GD 15.5). Fetuses were assessed for gross anomalies. Descriptive statistics were used to compare proportions of gross anomalies. RESULTS: In total, 550 litters (115 in the control group, 150 for DTG, 113 for RAL, 79 for BIC, and 93 for CAB) were assessed. RAL was associated with the highest fetal weight, placental weight, and fetal-placental weight ratio (placental efficiency). Fetal weight, placental efficiency, and litter size were lowest in BIC and CAB. Neural tube defects were observed only in INSTI groups, with litter prevalence rates of 0.66% for RAL, 0.45% for DTG, 0.39% for BIC, 0.15% for CAB, and 0% for the control. Tail defects, eye defects, bleeding defects, cranial swelling, and growth restriction were significantly more common in all INSTI groups than in the control group. Overall rates of defects were lowest with DTG. Compared with the DTG group, limb and tail defects (indicative of spinal dysraphism) were significantly more prevalent in the RAL group, while bleeding defects were significantly more prevalent in the BIC and CAB groups. CONCLUSIONS: While INSTIs represent a critical advance in the management of HIV infection, the findings of the current study demonstrate a link between INSTI therapy and adverse fetal outcomes. This highlights the need for continued surveillance of pregnancy outcomes in women exposed to INSTIs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".