Evidence map›Paper›PMID 21487477›Full record

ArticleSelf/nonself2010

The SCHOOL of nature: III. From mechanistic understanding to novel therapies.

Alexander B Sigalov

Open access · hybridAbstract read
In one paragraph

Article in Self/nonself, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.5field-weighted citation impact, top 33% 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

8 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
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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

1 author at 1 institution in 1 country.

Alexander B SigalovSignaBlok, Inc.; Shrewsbury, MA USA.
SignaBlok (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein-protein interactions play a central role in biological processes and thus represent an appealing target for innovative drug design and development. They can be targeted by small molecule inhibitors, modulatory peptides and peptidomimetics, which represent a superior alternative to protein therapeutics that carry many disadvantages. Considering that transmembrane signal transduction is an attractive process to therapeutically control multiple diseases, it is fundamentally and clinically important to mechanistically understand how signal transduction occurs. Uncovering specific protein-protein interactions critical for signal transduction, a general platform for receptor-mediated signaling, the signaling chain homooligomerization (SCHOOL) platform, suggests these interactions as universal therapeutic targets. Within the platform, the general principles of signaling are similar for a variety of functionally unrelated receptors. This suggests that global therapeutic strategies targeting key protein-protein interactions involved in receptor triggering and transmembrane signal transduction may be used to treat a diverse set of diseases. This also assumes that clinical knowledge and therapeutic strategies can be transferred between seemingly disparate disorders, such as T cell-mediated skin diseases and platelet disorders or combined to develop novel pharmacological approaches. Intriguingly, human viruses use the SCHOOL-like strategies to modulate and/or escape the host immune response. These viral mechanisms are highly optimized over the millennia, and the lessons learned from viral pathogenesis can be used practically for rational drug design. Proof of the SCHOOL concept in the development of novel therapies for atopic dermatitis, rheumatoid arthritis, cancer, platelet disorders and other multiple indications with unmet needs opens new horizons in therapeutics.

Identifiers

PMID21487477
PMCPMC3047783
OpenAlexW2417584271

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.