MétaCan
Menu
Back to cohort
Record W2003964436 · doi:10.1186/1741-7015-10-34

Novel therapeutic strategies targeting HIV integrase

2012· review· en· W2003964436 on OpenAlexafffund
Peter K. Quashie, Richard D. Sloan, Mark A. Wainberg

Bibliographic record

VenueBMC Medicine · 2012
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsIntegraseRaltegravirElvitegravirIntegrase inhibitorDolutegravirChromatinMedicineVirologyViral replicationComputational biologyBiologyHuman immunodeficiency virus (HIV)GeneticsVirusDNAAntiretroviral therapyViral load

Abstract

fetched live from OpenAlex

Integration of the viral genome into host cell chromatin is a pivotal and unique step in the replication cycle of retroviruses, including HIV. Inhibiting HIV replication by specifically blocking the viral integrase enzyme that mediates this step is an obvious and attractive therapeutic strategy. After concerted efforts, the first viable integrase inhibitors were developed in the early 2000s, ultimately leading to the clinical licensure of the first integrase strand transfer inhibitor, raltegravir. Similarly structured compounds and derivative second generation integrase strand transfer inhibitors, such as elvitegravir and dolutegravir, are now in various stages of clinical development. Furthermore, other mechanisms aimed at the inhibition of viral integration are being explored in numerous preclinical studies, which include inhibition of 3' processing and chromatin targeting. The development of new clinically useful compounds will be aided by the characterization of the retroviral intasome crystal structure. This review considers the history of the clinical development of HIV integrase inhibitors, the development of antiviral drug resistance and the need for new antiviral compounds.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.139
GPT teacher head0.378
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations73
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueBMC MedicineSame topicHIV/AIDS drug development and treatmentFrench-language works237,207