Evidence map›Paper›PMID 27736015›Full record

ArticleClinical and translational science2016

Estimation of Maximum Recommended Therapeutic Dose Using Predicted Promiscuity and Potency.

T Liu, T Oprea, O Ursu, C Hasselgren, R B Altman

Open access · goldAbstract read
In one paragraph

Article in Clinical and translational science, 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
1.1field-weighted citation impact, top 19% 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, 13 citations in OpenAlex.

  1. Article
  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

5 authors at 2 institutions in 1 country.

T LiuDepartment of Genetics, Stanford University, Stanford, California, USA.
T OpreaDepartment of Internal Medicine, Translational Informatics Division, University of New Mexico, Albuquerque, New Mexico, USA.
O UrsuDepartment of Internal Medicine, Translational Informatics Division, University of New Mexico, Albuquerque, New Mexico, USA.
C HasselgrenPureInfo Discovery Corp, Albuquerque, New Mexico, USA.
R B AltmanDepartments of Bioengineering and Genetics, Stanford University, Stanford, California, USA.
University of New Mexico · USStanford University · US

Funding

Physics-based Simulation of Biological Structures(RMI)U54GM072970 · NIGMS · STANFORD UNIVERSITY · PI DELP, SCOTT L · 2004 to 2013
$33.5M
Pilot Study: POPULATION PHARMACOLOGYU54HL117798 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI FITZGERALD, GARRET A · 2012 to 2016
$18.5M
Illuminating the Druggable Genome Knowledge Management Center (IDG KMC)U54CA189205 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI OPREA, TUDOR I · 2014 to 2017
$5.7M
Undergraduate Summer Research Experiences Support for Combining systems biology and structural biology to find new therapeuticsR01GM102365 · NIGMS · STANFORD UNIVERSITY · PI ALTMAN, RUSS BIAGIO · 2012 to 2021
$3.0M
NCI NIH HHS U54 CA189205NHLBI NIH HHS U54 HL117798NIGMS NIH HHS R01 GM102365NIGMS NIH HHS U54 GM072970
6 · The paper itself

Abstract

We report a simple model that predicts the maximum recommended therapeutic dose (MRTD) of small molecule drugs based on an assessment of likely protein-drug interactions. Previously, we reported methods for computational estimation of drug promiscuity and potency. We used these concepts to build a linear model derived from 238 small molecular drugs to predict MRTD. We applied this model successfully to predict MRTDs for 16 nonsteroidal antiinflammatory drugs (NSAIDs) and 14 antiretroviral drugs. Of note, based on the estimated promiscuity of low-dose drugs (and active chemicals), we identified 83 proteins as "high-risk off-targets" (HROTs) that are often associated with low doses; the evaluation of interactions with HROTs may be useful during early phases of drug discovery. Our model helps explain the MRTD for drugs with severe adverse reactions caused by interactions with HROTs.

Indexed as

Dose-Response Relationship, DrugDrug DiscoveryHumansLinear Models

Identifiers

PMID27736015
PMCPMC5161261
OpenAlexW2530611076

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.