Evidence map›Paper›PMID 29447150›Full record

ArticlePLoS computational biology2018

Life cycle synchronization is a viral drug resistance mechanism.

Iulia A Neagu, Jason Olejarz, Mark Freeman, Daniel I S Rosenbloom, Martin A Nowak, Alison L Hill

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2018. 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
3.3field-weighted citation impact, top 8% 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, 26 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Host-Directed Antiviral Therapy.Clinical microbiology reviews · 2020
    Review
  6. VariousTurkish journal of pharmaceutical sciences · 2020
    Article
  7. Article
  8. Review
  9. Article
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

6 authors at 2 institutions in 1 country.

Iulia A NeaguProgram for Evolutionary Dynamics, Department of Mathematics and Department of Organismic & Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America.ORCID 0000-0002-0757-0853
Jason OlejarzProgram for Evolutionary Dynamics, Department of Mathematics and Department of Organismic & Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America.ORCID 0000-0003-3571-7709
Mark FreemanProgram for Evolutionary Dynamics, Department of Mathematics and Department of Organismic & Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America.
Daniel I S RosenbloomDepartment of Biomedical Informatics, Columbia University Medical Center, New York, New York, United States of America.ORCID 0000-0003-1413-3907
Martin A NowakProgram for Evolutionary Dynamics, Department of Mathematics and Department of Organismic & Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America.
Alison L HillProgram for Evolutionary Dynamics, Department of Mathematics and Department of Organismic & Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America.ORCID 0000-0002-6583-3623
Harvard University · USColumbia University Irving Medical Center · US

Funding

Quantification and prediction of treatment efficacy for HIV cure strategiesDP5OD019851 · OD · JOHNS HOPKINS UNIVERSITY · PI HILL, ALISON LYNN · 2014 to 2018
$2.1M
NIH HHS DP5 OD019851NIH HHS DP5OD019851
6 · The paper itself

Abstract

Viral infections are one of the major causes of death worldwide, with HIV infection alone resulting in over 1.2 million casualties per year. Antiviral drugs are now being administered for a variety of viral infections, including HIV, hepatitis B and C, and influenza. These therapies target a specific phase of the virus's life cycle, yet their ultimate success depends on a variety of factors, such as adherence to a prescribed regimen and the emergence of viral drug resistance. The epidemiology and evolution of drug resistance have been extensively characterized, and it is generally assumed that drug resistance arises from mutations that alter the virus's susceptibility to the direct action of the drug. In this paper, we consider the possibility that a virus population can evolve towards synchronizing its life cycle with the pattern of drug therapy. The periodicity of the drug treatment could then allow for a virus strain whose life cycle length is a multiple of the dosing interval to replicate only when the concentration of the drug is lowest. This process, referred to as "drug tolerance by synchronization", could allow the virus population to maximize its overall fitness without having to alter drug binding or complete its life cycle in the drug's presence. We use mathematical models and stochastic simulations to show that life cycle synchronization can indeed be a mechanism of viral drug tolerance. We show that this effect is more likely to occur when the variability in both viral life cycle and drug dose timing are low. More generally, we find that in the presence of periodic drug levels, time-averaged calculations of viral fitness do not accurately predict drug levels needed to eradicate infection, even if there is no synchronization. We derive an analytical expression for viral fitness that is sufficient to explain the drug-pattern-dependent survival of strains with any life cycle length. We discuss the implications of these findings for clinically relevant antiviral strategies.

Indexed as

Anti-HIV AgentsAntiviral AgentsBasic Reproduction NumberComputational BiologyComputer SimulationDrug Resistance, ViralDrug ToleranceGenotypeHIV-1HIV InfectionsHumansModels, TheoreticalPhenotypeStochastic ProcessesVirus DiseasesVirus ReplicationAnti-HIV AgentsAntiviral Agents

Identifiers

PMID29447150
PMCPMC5813899
OpenAlexW2794343437

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.