Evidence map›Paper›PMID 22861052›Full record

ReviewExpert opinion on drug discovery2012

New hepatitis C virus drug discovery strategies and model systems.

Snawar Hussain, Naina Barretto, Susan L Uprichard

Abstract readReview
In one paragraph

Review in Expert opinion on drug discovery, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

3 authors.

Snawar HussainUniversity of Illinois at Chicago, Department of Medicine, Chicago, IL 60612, USA.
Naina Barretto
Susan L Uprichard

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
Elucidating the role of the NPC1L1 cholesterol uptake receptor in HCV infectionR21AI097809 · NIAID · UNIVERSITY OF ILLINOIS AT CHICAGO · PI UPRICHARD, SUSAN L. · 2012 to 2013
$424k
NIAID NIH HHS R01 - AI07881NIAID NIH HHS R01 AI078881NIAID NIH HHS R21 AI097809NIAID NIH HHS R21 - AI097809
6 · The paper itself

Abstract

introductionHepatitis C virus (HCV) is a major cause of liver disease worldwide and the leading indication for liver transplantation in the United States. Current treatment options are expensive, not effective in all patients and are associated with serious side effects. Although preclinical, anti-HCV drug screening is still hampered by the lack of readily infectable small animal models, the development of cell culture HCV experimental model systems has driven a promising new wave of HCV antiviral drug discovery. AREAS COVERED: This review contains a concise overview of current HCV treatment options and limitations with a subsequent in-depth focus on the available experimental models and novel strategies that have, and continue to enable, important advances in HCV drug development. EXPERT OPINION: With a large cohort of chronically HCV-infected patients progressively developing liver disease that puts them at risk for hepatocellular carcinoma and hepatic decompensation, there is an urgent need to develop effective therapeutics that are well tolerated and effective in all patients and against all HCV genotypes. Significant advances in HCV experimental model development have expedited drug discovery; however, additional progress is needed. Importantly, the current trends and momentum in the field suggests that we will continue to overcome critical experimental challenges to reach this end goal.

Indexed as

AnimalsAntiviral AgentsDrug DiscoveryHepacivirusHepatitis C, ChronicHumansModels, BiologicalRepliconAntiviral Agents

Identifiers

PMID22861052
PMCPMC3664227

What OpenQuestion holds

Textmetadata
LicenceTDM
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.