[Importance of pharmacogenetics in antiretroviral metabolism and drug-transporters].
Bibliographic record
Abstract
Wide intra- and inter-subject variability in antiretroviral drug response is observed. Pharmacotherapy of HIV-infected patients is challenging considering the great numbers of co-morbidities increasing the risk of drug-drug interactions. Drug-metabolism enzymes and drug-transporters regulate drug access to the systemic circulation, target cells and sanctuary sites; these factors determine pharmacokinetics and could explain variability in efficacy and adverse drug reactions associated with antiretroviral drugs. Notions related to the major enzymes (CYP450s and UGTs) involved in antiretroviral metabolism and drugtransporters are reviewed with an attention paid on genetic polymorphisms. Genetic polymorphisms affecting the activity or the expression of membrane proteins in the transport of drugs would be highlighted with examples such as neurotoxicity with efavirenz, nephrotoxicity with tenofovir, hepatotoxicity with nevirapine and hyperlibirubinemia associated with indinavir and atazanavir. The objective is to provide a better understanding on mechanisms involved in drugdisposition of antiretroviral helping out health care providers in the management of pharmacotherapy of HIV-infected patients.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".