Evidence map›Paper›PMID 27144597›Full record

ArticlePloS one2016

Unravelling the Secrets of Mycobacterial Cidality through the Lens of Antisense.

Parvinder Kaur, Santanu Datta, Radha Krishan Shandil, Naveen Kumar, Nanduri Robert, Upneet K Sokhi, Supreeth Guptha, Shridhar Narayanan, Anand Anbarasu, Sudha Ramaiah

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.9field-weighted citation impact, top 25% 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

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. 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

10 authors at 3 institutions in 2 countries.

Parvinder KaurResearch Area, Drug Discovery, AstraZeneca India Private Limited, Bangalore, India.ORCID 0000-0001-9154-3278
Santanu DattaDrug Discovery, Bugworks, C-CAMP, Bangalore, India.
Radha Krishan ShandilResearch Area, Drug Discovery, AstraZeneca India Private Limited, Bangalore, India.
Naveen KumarResearch Area, Drug Discovery, AstraZeneca India Private Limited, Bangalore, India.
Nanduri RobertResearch Area, Drug Discovery, AstraZeneca India Private Limited, Bangalore, India.
Upneet K SokhiArthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, New York, United States of America.
Supreeth GupthaResearch Area, Drug Discovery, AstraZeneca India Private Limited, Bangalore, India.
Shridhar NarayananResearch Area, Drug Discovery, AstraZeneca India Private Limited, Bangalore, India.
Anand AnbarasuSchool of Biosciences and Technology, VIT University, Vellore, India.
Sudha RamaiahSchool of Biosciences and Technology, VIT University, Vellore, India.
AstraZeneca (India) · INVellore Institute of Technology University · INHospital for Special Surgery · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the major impediments in anti-tubercular drug discovery is the lack of a robust grammar that governs the in-vitro to the in-vivo translation of efficacy. Mycobacterium tuberculosis (Mtb) is capable of growing both extracellular as well as intracellular; encountering various hostile conditions like acidic milieu, free radicals, starvation, oxygen deprivation, and immune effector mechanisms. Unique survival strategies of Mtb have prompted researchers to develop in-vitro equivalents to simulate in-vivo physiologies and exploited to find efficacious inhibitors against various phenotypes. Conventionally, the inhibitors are screened on Mtb under the conditions that are unrelated to the in-vivo disease environments. The present study was aimed to (1). Investigate cidality of Mtb targets using a non-chemical inhibitor antisense-RNA (AS-RNA) under in-vivo simulated in-vitro conditions.(2). Confirm the cidality of the targets under in-vivo in experimental tuberculosis. (3). Correlate in-vitro vs. in-vivo cidality data to identify the in-vitro condition that best predicts in-vivo cidality potential of the targets. Using cidality as a metric for efficacy, and AS-RNA as a target-specific inhibitor, we delineated the cidality potential of five target genes under six different physiological conditions (replicating, hypoxia, low pH, nutrient starvation, nitrogen depletion, and nitric oxide).In-vitro cidality confirmed in experimental tuberculosis in BALB/c mice using the AS-RNA allowed us to identify cidal targets in the rank order of rpoB>aroK>ppk>rpoC>ilvB. RpoB was used as the cidality control. In-vitro and in-vivo studies feature aroK (encoding shikimate kinase) as an in-vivo mycobactericidal target suitable for anti-TB drug discovery. In-vitro to in-vivo cidality correlations suggested the low pH (R = 0.9856) in-vitro model as best predictor of in-vivo cidality; however, similar correlation studies in pathologically relevant (Kramnik) mice are warranted. In the acute infection phase for the high fidelity translation, the compound efficacy may also be evaluated in the low pH, in addition to the standard replication condition.

Indexed as

AnimalsAntitubercular AgentsBacterial LoadDrug DiscoveryGene SilencingGenome, BacterialHost-Pathogen InteractionsHumansMaleMiceMice, Inbred BALB CMycobacterium tuberculosisRNA, AntisenseTuberculosis, PulmonaryAntitubercular AgentsRNA, Antisense

Identifiers

PMID27144597
PMCPMC4856384
OpenAlexW2346622332

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.