Pharmacokinetics and Safety of the Integrase Inhibitors Elvitegravir and Dolutegravir in Pregnant Women With HIV
Bibliographic record
Abstract
Objective: To synthesize data on the pharmacokinetics and safety of dolutegravir and elvitegravir in pregnant women living with HIV. Data Sources: A PubMed, EMBASE, Web of Science, and Google Scholar literature search (January 2010 to December 2018) was performed using the search terms dolutegravir, elvitegravir, women, pregnant*, and HIV. Additional reports were identified from conference abstracts and review of reference lists. Study Selection and Data Extraction: English-language studies reporting pharmacokinetic and/or safety data in pregnant women receiving dolutegravir or elvitegravir/cobicistat were included. Data Synthesis: A total of 17 studies were selected. Studies demonstrated a modest decrease in dolutegravir concentrations in pregnancy. Preliminary data suggest an increased risk of neural tube defects when dolutegravir is used at the time of conception. Available pharmacokinetic data in pregnant women showed significantly reduced plasma concentrations of elvitegravir/cobicistat which may increase the risk of virological failure. Current guidelines recommend that dolutegravir should not be initiated in women who have the potential to become pregnant or women in their first trimester of pregnancy and elvitegravir/cobicistat should be avoided during pregnancy. Relevance to Patient Care and Clinical Practice: This review highlights pharmacokinetic and safety data for dolutegravir and elvitegravir/cobicistat in pregnant women. Clinicians need to be aware of these data to convey the risks and benefits of using these agents in women of child-bearing potential. Conclusions: Changes in guideline recommendations reflect emerging data regarding the use of dolutegravir and elvitegravir/cobicistat in pregnancy. Until further information is available, raltegravir or other first-line agents are recommended for women with HIV planning to become pregnant.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".