Evidence map›Paper›PMID 28694997›Full record

ReviewVirus evolution2016

Antiviral drug resistance as an adaptive process.

Kristen K Irwin, Nicholas Renzette, Timothy F Kowalik, Jeffrey D Jensen

Open access · goldAbstract readReview
In one paragraph

Review in Virus evolution, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
117citing papers in PubMed, 1 pooled it
7.2field-weighted citation impact, top 3% 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

117 citing papers in PubMed, 1 synthesis or guideline pooled it, 225 citations in OpenAlex.

  1. Pooled it
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  7. The Redesign of the Molecular Scaffold of Viral Ion Channel Blockers.Computational and structural biotechnology journal · 2026
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57 more citing papers are in PubMed but not listed here.

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

4 authors at 2 institutions in 2 countries.

Kristen K IrwinSchool of Life Sciences, École Polytechnique Fédéral de Lausanne (EPFL), Lausanne, Switzerland.
Nicholas RenzetteDepartment of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
Timothy F KowalikDepartment of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
Jeffrey D JensenSchool of Life Sciences, École Polytechnique Fédéral de Lausanne (EPFL), Lausanne, Switzerland.
SIB Swiss Institute of Bioinformatics · CHUniversity of Massachusetts Chan Medical School · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antiviral drug resistance is a matter of great clinical importance that, historically, has been investigated mostly from a virological perspective. Although the proximate mechanisms of resistance can be readily uncovered using these methods, larger evolutionary trends often remain elusive. Recent interest by population geneticists in studies of antiviral resistance has spurred new metrics for evaluating mutation and recombination rates, demographic histories of transmission and compartmentalization, and selective forces incurred during viral adaptation to antiviral drug treatment. We present up-to-date summaries on antiviral resistance for a range of drugs and viral types, and review recent advances for studying their evolutionary histories. We conclude that information imparted by demographic and selective histories, as revealed through population genomic inference, is integral to assessing the evolution of antiviral resistance as it pertains to human health.

Indexed as

antiviral resistancecompensatory mutationcost of adaptationfluctuating selectiongenetic barriermutagenesis

Identifiers

PMID28694997
PMCPMC5499642
OpenAlexW2411891913

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.