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Record W2154094145 · doi:10.2147/tcrm.s1709

Combating HIV resistance – focus on darunavir

2008· article· en· W2154094145 on OpenAlexaff
Cécile Tremblay

Bibliographic record

VenueTherapeutics and Clinical Risk Management · 2008
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsDarunavirMedicineHuman immunodeficiency virus (HIV)Focus (optics)VirologyPharmacologyAntiretroviral therapyViral load

Abstract

fetched live from OpenAlex

Darunavir is a second-generation protease inhibitor designed to have antiviral efficacy against HIV-1 isolates harboring multiple resistance mutations to protease inhibitors. Pivotal trials conducted in treatment-experienced HIV-infected individuals have demonstrated significantly greater virological suppression when darunavir was added to an optimized background treatment compared with a control protease inhibitor. This virological suppression was associated with an increase in CD4 counts and was sustained over time. Darunavir resistance-associated mutations have been defined as V11I, V32I, L33F, I47V, I50V, I54L/M, G73S, L76V, I84V, and L89V. In clinical trials, baseline darunavir susceptibility was a strong predictor of virological response. Prior use of fosamprenavir was associated with darunavir resistance mutations. Darunavir has a high genetic barrier and has a distinct resistance profile. Although some cross-resistance exists with other second-generation protease inhibitors such as tipranavir, different resistance mutation patterns have been observed upon failure to these regimens. It was found that mutations at 47V, 54M, 85V, and 73T were most prevalent in isolates resistant to both PIs. Mutations 48V, 50V, and 54L were associated with resistance to darunavir but not to tipranavir. 82S and 82T were associated with resistance to tipranavir but not to darunavir. Therefore, darunavir provides potent virological efficacy as well as high genetic barrier that can be useful to preserve treatment options in HIV-infected, treatment-experienced individuals.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.354
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2008
Admission routes1
Has abstractyes

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