Evidence map›Paper›PMID 21753122›Full record

ArticleScience translational medicine2011

A critical subset model provides a conceptual basis for the high antiviral activity of major HIV drugs.

Lin Shen, S Alireza Rabi, Ahmad R Sedaghat, Liang Shan, Jun Lai, Sifei Xing, Robert F Siliciano

Abstract read
In one paragraph

Article in Science translational medicine, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
–field-weighted citation impact
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

38 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Multi-scale modeling of drug binding kinetics to predict drug efficacy.Cellular and molecular life sciences : CMLS · 2020
    Review
  13. Review
  14. Article
  15. Modelling Optimal Control of In-Host HIV Dynamics Using Different Control Strategies.Computational and mathematical methods in medicine · 2018
    Article
  16. Article
  17. Quantifying antiviral activity optimizes drug combinations against hepatitis C virus infection.Proceedings of the National Academy of Sciences of the United States of America · 2017
    Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Lin ShenDepartment of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
S Alireza Rabi
Ahmad R Sedaghat
Liang Shan
Jun Lai
Sifei Xing
Robert F Siliciano

Funding

Intermolecular cooperativity in the action of antiviral agentsR01AI081600 · NIAID · JOHNS HOPKINS UNIVERSITY · PI SILICIANO, ROBERT F · 2009 to 2013
$1.4M
Howard Hughes Medical InstituteNIAID NIH HHS AI081600NIAID NIH HHS R01 AI081600
6 · The paper itself

Abstract

Control of HIV-1 replication was first achieved with regimens that included a nonnucleoside reverse transcriptase inhibitor (NNRTI) or a protease inhibitor (PI); however, an explanation for the high antiviral activity of these drugs has been lacking. Indeed, conventional pharmacodynamic measures like IC(50) (drug concentration causing 50% inhibition) do not differentiate NNRTIs and PIs from less active nucleoside reverse transcriptase inhibitors (NRTIs). Drug inhibitory potential depends on the slope of the dose-response curve (m), which represents how inhibition increases as a function of increasing drug concentration and is related to the Hill coefficient, a measure of intramolecular cooperativity in ligand binding to a multivalent receptor. Although NNRTIs and PIs bind univalent targets, they unexpectedly exhibit cooperative dose-response curves (m > 1). We show that this cooperative inhibition can be explained by a model in which infectivity requires participation of multiple copies of a drug target in an individual life cycle stage. A critical subset of these target molecules must be in the unbound state. Consistent with experimental observations, this model predicts m > 1 for NNRTIs and PIs and m = 1 in situations where a single drug target/virus mediates a step in the life cycle, as is the case with NRTIs and integrase strand transfer inhibitors. This model was tested experimentally by modulating the number of functional drug targets per virus, and dose-response curves for modulated virus populations fit model predictions. This model explains the high antiviral activity of two drug classes important for successful HIV-1 treatment and defines a characteristic of good targets for antiviral drugs in general, namely, intermolecular cooperativity.

Indexed as

Models, BiologicalAnti-HIV AgentsDose-Response Relationship, DrugHIVHIV Protease InhibitorsIntegrase InhibitorsPhenotypeReproducibility of ResultsReverse Transcriptase InhibitorsVirionAnti-HIV AgentsHIV Protease InhibitorsIntegrase InhibitorsReverse Transcriptase Inhibitors

Identifiers

PMID21753122
PMCPMC3488347

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Registered trials

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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.