Evidence map›Paper›PMID 28174263›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2017

Quantifying antiviral activity optimizes drug combinations against hepatitis C virus infection.

Yoshiki Koizumi, Hirofumi Ohashi, Syo Nakajima, Yasuhito Tanaka, Takaji Wakita, Alan S Perelson, Shingo Iwami, Koichi Watashi

Erratum issuedAbstract readComparative Study
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Treatment of Respiratory Viral Coinfections.Epidemiologia (Basel, Switzerland) · 2022
    Article
  7. Review
  8. Article
  9. Drugs repurposed for COVID-19 by virtual screening of 6,218 drugs and cell-based assay.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  10. Article
  11. Review
  12. Article
  13. Review
  14. Article
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  16. Review
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  18. Article
  19. Review
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Yoshiki KoizumiSchool of Medicine, College of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Ishikawa 920-8640, Japan.
Hirofumi OhashiDepartment of Virology II, National Institute of Infectious Diseases, Tokyo 162-8640, Japan.
Syo NakajimaDepartment of Virology II, National Institute of Infectious Diseases, Tokyo 162-8640, Japan.
Yasuhito TanakaDepartment of Virology and Liver Unit, Nagoya City University Graduate School of Medicinal Sciences, Nagoya 467-8601, Japan.
Takaji WakitaDepartment of Virology II, National Institute of Infectious Diseases, Tokyo 162-8640, Japan.
Alan S PerelsonTheoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545.
Shingo IwamiMathematical Biology Laboratory, Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka 812-8581, Japan; siwami@kyushu-u.org kwatashi@nih.go.jp.
Koichi WatashiDepartment of Virology II, National Institute of Infectious Diseases, Tokyo 162-8640, Japan; siwami@kyushu-u.org kwatashi@nih.go.jp.

Funding

Collaborative Integration of HCV Molecular Virology and Mathematical ModelingR01AI078881 · NIAID · UNIVERSITY OF ILLINOIS AT CHICAGO · PI PERELSON, ALAN S, UPRICHARD, SUSAN L. · 2011 to 2025
$7.5M
Modeling Viral and T Lymphocyte DynamicsR01OD011095 · OD · TRIAD NATIONAL SECURITY, LLC · PI PERELSON, ALAN S · 2012 to 2024
$5.0M
Immune System Modeling/HIVR01AI028433 · NIAID · UNIVERSITY OF CALIF-LOS ALAMOS NAT LAB · PI PERELSON, ALAN S · 1990 to 2021
$3.7M
NIAID NIH HHS R01 AI028433NIAID NIH HHS R01 AI078881NIH HHS R01 OD011095
6 · The paper itself

Abstract

With the introduction of direct-acting antivirals (DAAs), treatment against hepatitis C virus (HCV) has significantly improved. To manage and control this worldwide infectious disease better, the "best" multidrug treatment is demanded based on scientific evidence. However, there is no method available that systematically quantifies and compares the antiviral efficacy and drug-resistance profiles of drug combinations. Based on experimental anti-HCV profiles in a cell culture system, we quantified the instantaneous inhibitory potential (IIP), which is the logarithm of the reduction in viral replication events, for both single drugs and multiple-drug combinations. From the calculated IIP of 15 anti-HCV drugs from different classes [telaprevir, danoprevir, asunaprevir, simeprevir, sofosbuvir (SOF), VX-222, dasabuvir, nesbuvir, tegobuvir, daclatasvir, ledipasvir, IFN-α, IFN-λ1, cyclosporin A, and SCY-635], we found that the nucleoside polymerase inhibitor SOF had one of the largest potentials to inhibit viral replication events. We also compared intrinsic antiviral activities of a panel of drug combinations. Our quantification analysis clearly indicated an advantage of triple-DAA treatments over double-DAA treatments, with triple-DAA treatments showing enhanced antiviral activity and a significantly lower probability for drug resistance to emerge at clinically relevant drug concentrations. Our framework provides quantitative information to consider in designing multidrug strategies before costly clinical trials.

Indexed as

Antiviral AgentsCells, CulturedDrug Evaluation, PreclinicalDrug Resistance, ViralDrug Therapy, CombinationHepacivirusHepatitis CHumansRNA-Dependent RNA PolymeraseViral Nonstructural ProteinsVirus ReplicationAntiviral AgentsNS-5 protein, hepatitis C virusRNA-Dependent RNA PolymeraseViral Nonstructural ProteinsantiviralHCVinstantaneous inhibitory potentialmathematical modelreplicon

Identifiers

PMID28174263
PMCPMC5338374

What OpenQuestion holds

Textmetadata
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.