The intersection of drug interactions and adverse reactions in contemporary antiretroviral therapy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Advances in antiretroviral therapy (ART) have transformed HIV infection into a chronic and manageable condition. The introduction of potent and more tolerable antiretrovirals (ARVs) with favorable pharmacokinetic profiles has changed the prevalence and nature of drug-drug interactions (DDIs). Here, we review the relevance of DDIs in the era of contemporary ART. RECENT FINDINGS: Management of DDIs remains an important challenge with modern ART, primarily due to increased polypharmacy in older persons living with HIV. Significant DDIs exist between boosted ARVs or older nonnucleoside reverse transcriptase inhibitors and comedications for chronic comorbidities (e.g., anticoagulants, antiplatelets, statins) or complex conditions (e.g., anticancer agents, immunosuppressants). Newer ARVs such as unboosted integrase inhibitors, doravirine, and fostemsavir have reduced DDI potential, but there are clinically relevant DDIs that warrant consideration. Potential consequences of DDIs include increased toxicity and/or reduced efficacy of ARVs and/or comedications. Management approaches include switching to an ARV with less DDI potential, changing comedications, or altering medication dosage or dosing frequency. Deprescribing strategies can reduce DDIs and polypharmacy, improve adherence, minimize unnecessary adverse effects, and prevent medication-related errors. SUMMARY: Management of DDIs requires close interdisciplinary collaboration from multiple healthcare disciplines (medicine, nursing, pharmacy) across a spectrum of care (community, outpatient, inpatient).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".