Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 253

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

Identification of Novel Inhibitors of M. tuberculosis Growth Using Whole Cell Based High-Throughput Screening

Open access
Stanley, Sarah A., Grant, Sarah Schmidt, Kawate, Tomohiko, Iwase, Noriaki, Shimizu, Motohisa, Wivagg, Carl, Silvis, Melanie, Kazyanskaya, Edward, Aquadro, John, Golas, Aaron, Fitzgerald, Michael, Dai, Huanqin, Zhang, Lixin, Hung, Deborah T.·2012·ACS Chemical Biology

Abstract

Despite the urgent need for new antitubercular drugs, few are on the horizon. To combat the problem of emerging drug resistance, structurally unique chemical entities that inhibit new targets will be required. Here we describe our investigations using whole cell screening of a diverse collection of small molecules as a methodology for identifying novel inhibitors that target new pathways for Mycobacterium tuberculosis drug discovery. We find that conducting primary screens using model mycobacterial species may limit the potential for identifying new inhibitors with efficacy against M. tuberculosis. In addition, we confirm the importance of developing in vitro assay conditions that are reflective of in vivo biology for maximizing the proportion of hits from whole cell screening that are likely to have activity in vivo. Finally, we describe the identification and characterization of two novel inhibitors that target steps in M. tuberculosis cell wall biosynthesis. The first is a novel benzimidazole that targets mycobacterial membrane protein large 3 (MmpL3), a proposed transporter for cell wall mycolic acids. The second is a nitro-triazole that inhibits decaprenylphosphoryl-β-D-ribose 2'-epimerase (DprE1), an epimerase required for cell wall biosynthesis. These proteins are both among the small number of new targets that have been identified by forward chemical genetics using resistance generation coupled with genome sequencing. This suggests that methodologies currently employed for screening and target identification may lead to a bias in target discovery and that alternative methods should be explored.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/cb300151m

Cites

26 of 26 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 26 references

Cited by

See all 253

Metadata

Authors
Stanley, S. A., Grant, S. S., Kawate, T., Iwase, N., Shimizu, M., Wivagg, C., Silvis, M., Kazyanskaya, E., Aquadro, J., Golas, A., Fitzgerald, M., Dai, H., Zhang, L., & Hung, D. T.
Venue
ACS Chemical Biology
Published
2012-05-21
Volume
7
Issue
8
Pages
1377-1384
ISSN
  • 1554-8929 (ISSN-L)
  • 1554-8937
OpenAlex
W1986288809
Cited by
253
References
26
FWCI
18.01
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Identification of Novel Inhibitors of M. tuberculosis Growth Using Whole Cell Based High-Throughput Screening. https://app.cochat.ai/papers/10.1021/cb300151m
The paper
Stanley, S. A., Grant, S. S., Kawate, T., Iwase, N., Shimizu, M., Wivagg, C., Silvis, M., Kazyanskaya, E., Aquadro, J., Golas, A., Fitzgerald, M., Dai, H., Zhang, L., & Hung, D. T. (2012). Identification of Novel Inhibitors of M. tuberculosis Growth Using Whole Cell Based High-Throughput Screening. ACS Chemical Biology. https://doi.org/10.1021/cb300151m

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.