Evidence map›Paper›PMID 30906261›Full record

ArticleFrontiers in pharmacology2019

Quantitative Systems Pharmacological Analysis of Drugs of Abuse Reveals the Pleiotropy of Their Targets and the Effector Role of mTORC1.

Fen Pei, Hongchun Li, Bing Liu, Ivet Bahar

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.6field-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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. 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

4 authors at 1 institution in 1 country.

Fen PeiDepartment of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Hongchun LiDepartment of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Bing LiuDepartment of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Ivet BaharDepartment of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
University of Pittsburgh · US

Funding

TrainingP41GM103712 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BAHAR, IVET · 2012 to 2021
$16.3M
NIDA Center of Excellence OF Computational Drug Abuse Research (CDAR)P30DA035778 · NIDA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BAHAR, IVET, XIE, XIANG-QUN · 2014 to 2018
$5.4M
NIDA NIH HHS P30 DA035778NIGMS NIH HHS P41 GM103712
6 · The paper itself

Abstract

Existing treatments against drug addiction are often ineffective due to the complexity of the networks of protein-drug and protein-protein interactions (PPIs) that mediate the development of drug addiction and related neurobiological disorders. There is an urgent need for understanding the molecular mechanisms that underlie drug addiction toward designing novel preventive or therapeutic strategies. The rapidly accumulating data on addictive drugs and their targets as well as advances in machine learning methods and computing technology now present an opportunity to systematically mine existing data and draw inferences on potential new strategies. To this aim, we carried out a comprehensive analysis of cellular pathways implicated in a diverse set of 50 drugs of abuse using quantitative systems pharmacology methods. The analysis of the drug/ligand-target interactions compiled in DrugBank and STITCH databases revealed 142 known and 48 newly predicted targets, which have been further analyzed to identify the KEGG pathways enriched at different stages of drug addiction cycle, as well as those implicated in cell signaling and regulation events associated with drug abuse. Apart from synaptic neurotransmission pathways detected as upstream signaling modules that "sense" the early effects of drugs of abuse, pathways involved in neuroplasticity are distinguished as determinants of neuronal morphological changes. Notably, many signaling pathways converge on important targets such as mTORC1. The latter emerges as a universal effector of the persistent restructuring of neurons in response to continued use of drugs of abuse.

Indexed as

cellular pathwaysdrug abusedrug-target interactionsmachine learningmTOR complex 1neurotransmissionpleiotropic proteinsquantitative systems pharmacology

Identifiers

PMID30906261
PMCPMC6418047
OpenAlexW2949971796

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.