Evidence map›Paper›PMID 35056151›Full record

ArticlePharmaceuticals (Basel, Switzerland)2022

How Molecular Topology Can Help in Amyotrophic Lateral Sclerosis (ALS) Drug Development: A Revolutionary Paradigm for a Merciless Disease.

Maria Galvez-Llompart, Riccardo Zanni, Ramon Garcia-Domenech, Jorge Galvez

Open access · goldAbstract read
In one paragraph

Article in Pharmaceuticals (Basel, Switzerland), 2022. 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
0.7field-weighted citation impact, top 32% 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, 4 citations in OpenAlex.

  1. Article
  2. 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

4 authors at 2 institutions in 1 country.

Maria Galvez-LlompartInstituto de Tecnología Química, UPV-CSIC, Universidad Politécnica de Valencia, 46022 Valencia, Spain.ORCID 0000-0002-0718-4143
Riccardo ZanniMolecular Topology and Drug Design Unit, Department of Physical Chemistry, University of Valencia, 46100 Valencia, Spain.ORCID 0000-0002-3388-3730
Ramon Garcia-DomenechMolecular Topology and Drug Design Unit, Department of Physical Chemistry, University of Valencia, 46100 Valencia, Spain.ORCID 0000-0003-1137-0587
Jorge GalvezMolecular Topology and Drug Design Unit, Department of Physical Chemistry, University of Valencia, 46100 Valencia, Spain.ORCID 0000-0003-0928-8437
Universitat de València · ESInstituto de Tecnología Química · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Even if amyotrophic lateral sclerosis is still considered an orphan disease to date, its prevalence among the population is growing fast. Despite the efforts made by researchers and pharmaceutical companies, the cryptic information related to the biological and physiological onset mechanisms, as well as the complexity in identifying specific pharmacological targets, make it almost impossible to find effective treatments. Furthermore, because of complex ethical and economic aspects, it is usually hard to find all the necessary resources when searching for drugs for new orphan diseases. In this context, computational methods, based either on receptors or ligands, share the capability to improve the success rate when searching and selecting potential candidates for further experimentation and, consequently, reduce the number of resources and time taken when delivering a new drug to the market. In the present work, a computational strategy based on Molecular Topology, a mathematical paradigm capable of relating the chemical structure of a molecule to a specific biological or pharmacological property by means of numbers, is presented. The result was the creation of a reliable and accessible tool to help during the early in silico stages in the identification and repositioning of potential

Indexed as

amyotrophic lateral sclerosisdrug designdrug repurposingmolecular topologyorphan diseasesQSAR

Identifiers

PMID35056151
PMCPMC8781553
OpenAlexW4205584411

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.