Evidence map›Paper›PMID 20368406›Full record

ArticleAntimicrobial agents and chemotherapy2010

Predicting tipranavir and darunavir resistance using genotypic, phenotypic, and virtual phenotypic resistance patterns: an independent cohort analysis of clinical isolates highly resistant to all other protease inhibitors.

Annie Talbot, Philip Grant, Jonathan Taylor, Jean-Guy Baril, Tommy Fulisma Liu, Hugues Charest, Bluma Brenner, Michel Roger, Robert Shafer, Régis Cantin and 1 more

Open access · bronzeAbstract read
In one paragraph

Article in Antimicrobial agents and chemotherapy, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.8field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 31 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 5 institutions in 2 countries.

Annie TalbotClinique du Quartier Latin and Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada.
Philip Grant
Jonathan Taylor
Jean-Guy Baril
Tommy Fulisma Liu
Hugues Charest
Bluma Brenner
Michel Roger
Robert Shafer
Régis Cantin
Andrew Zolopa
Stanford University · USCentre Hospitalier de l’Université de Montréal · CAInstitut National de Santé Publique du Québec · CAHôpital Notre-Dame · CAMcGill University Health Centre · CA

Funding

PUBLIC HIV DRUG RESISTANCE DATABASER01AI068581 · NIAID · STANFORD UNIVERSITY · PI SHAFER, ROBERT WILLIAM · 2006 to 2017
$6.2M
6 · The paper itself

Abstract

Genotypic interpretation systems (GISs) for darunavir and tipranavir susceptibility are rarely tested by the use of independent data sets. The virtual phenotype (the phenotype determined by Virco [the "Vircotype"]) was used to interpret all genotypes in Québec, Canada, and phenotypes were determined for isolates predicted to be resistant to all protease inhibitors other than darunavir and tipranavir. We used multivariate analyses to predict relative phenotypic susceptibility to darunavir and tipranavir. We compared the performance characteristics of the Agence Nationale de Recherche sur le Sida scoring algorithm, the Stanford HIV database scoring algorithm (with separate analyses of the discrete and numerical scores), the Vircotype, and the darunavir and tipranavir manufacturers' scores for prediction of the phenotype. Of the 100 isolates whose phenotypes were determined, 89 and 72 were susceptible to darunavir and tipranavir, respectively. In multivariate analyses, the presence of I84V and V82T and the lack of L10F predicted that the isolates would be more susceptible to darunavir than tipranavir. The presence of I54L, V32I, and I47V predicted that the isolates would be more susceptible to tipranavir. All GISs except the system that provided the Stanford HIV database discrete score performed well in predicting the darunavir resistance phenotype (R(2) = 0.61 to 0.69); the R(2) value for the Stanford HIV database discrete scoring system was 0.38. Other than the system that provided the Vircotype (R(2) = 0.80), all GISs performed poorly in predicting the tipranavir resistance phenotype (R(2) = 0.00 to 0.31). In this independent cohort harboring highly protease inhibitor-resistant HIV isolates, reduced phenotypic susceptibility to darunavir and tipranavir was rare. Generally, GISs predict susceptibility to darunavir substantially better than they predict susceptibility to tipranavir.

Indexed as

AlgorithmsAmino Acid SubstitutionCohort StudiesDarunavirDatabases, FactualDrug Resistance, Multiple, ViralGenotypeHIV-1HIV InfectionsHIV ProteaseHIV Protease InhibitorsHumansIn Vitro TechniquesMultivariate AnalysisMutationPhenotypeDarunavirHIV ProteaseHIV Protease InhibitorsPyridinesPyronesSulfonamidestipranavir

Identifiers

PMID20368406
PMCPMC2876425
OpenAlexW2165910865

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.