Drug–drug interactions between gender‐affirming hormone therapy and antiretrovirals for treatment/prevention of HIV
Bibliographic record
Abstract
Transgender persons face a greater burden of HIV compared to cisgender counterparts. Concerns around drug-drug interactions (DDIs) have been cited as reasons for lower engagement in HIV care and lower pre-exposure prophylaxis (PrEP) uptake among transgender populations. It is therefore imperative for hormone therapy, PrEP and antiretroviral therapy providers to understand the DDI potential between these therapies. Studies of tenofovir disoproxil fumarate (TDF)/emtricitabine (FTC) PrEP with feminizing hormone therapies (FHTs) show reduced plasma tenofovir concentrations, but intracellular concentrations of tenofovir-diphosphate are not reduced. Efficacy of PrEP is expected to be maintained despite this interaction. Masculinizing hormone therapies have no effect on tenofovir concentrations but may increase FTC to a nonclinically relevant extent. No interactions between FHT and cabotegravir or tenofovir alafenamide have been demonstrated. Administration of TDF/FTC PrEP has no effect on hormone levels in transmen or transwomen. PrEP is expected to be effective and safe in transpersons and should be provided to high-risk individuals regardless of gender affirming hormone use. Enzyme inducing/inhibiting antiretroviral therapy may decrease or increase, respectively, the concentrations of FHT and masculinizing hormone therapy. Unboosted integrase inhibitors or enzyme neutral non-nucleoside reverse transcriptase inhibitors are not expected to affect and are not affected by gender affirming hormones and can be considered in transmen and transwomen. Overlapping toxicities including weight gain, dyslipidaemia, cardiovascular disease and bone density effects should be considered, and antiretroviral modifications can be made to minimize toxicities. Interactions between supportive care medications should be assessed to avoid chelation interactions and hyperkalaemia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".