Evidence map›Paper›PMID 27302286›Full record

ArticleScientific reports2016

Estimating Potency in High-Throughput Screening Experiments by Maximizing the Rate of Change in Weighted Shannon Entropy.

Keith R Shockley

Abstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  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.

Keith R ShockleyBiostatistics and Computational Biology Branch, The National Institute of Environmental Health Sciences, National Institutes of Health, 111 T. W. Alexander Drive, Research Triangle Park, NC 27709, USA.

Funding

Analysis of Quantitative High Throughput Screening DataZIAES102865 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI SHOCKLEY, KEITH · 2010 to 2025
$2.8M
Intramural NIH HHS ZIA ES102865
6 · The paper itself

Abstract

High-throughput in vitro screening experiments can be used to generate concentration-response data for large chemical libraries. It is often desirable to estimate the concentration needed to achieve a particular effect, or potency, for each chemical tested in an assay. Potency estimates can be used to directly compare chemical profiles and prioritize compounds for confirmation studies, or employed as input data for prediction modeling and association mapping. The concentration for half-maximal activity derived from the Hill equation model (i.e., AC50) is the most common potency measure applied in pharmacological research and toxicity testing. However, the AC50 parameter is subject to large uncertainty for many concentration-response relationships. In this study we introduce a new measure of potency based on a weighted Shannon entropy measure termed the weighted entropy score (WES). Our potency estimator (Point of Departure, PODWES) is defined as the concentration producing the maximum rate of change in weighted entropy along a concentration-response profile. This approach provides a new tool for potency estimation that does not depend on the assumption of monotonicity or any other pre-specified concentration-response relationship. PODWES estimates potency with greater precision and less bias compared to the conventional AC50 assessed across a range of simulated conditions.

Indexed as

EntropyModels, TheoreticalCell LineComputer SimulationDose-Response Relationship, DrugDrug Evaluation, PreclinicalEstradiolGestrinoneHigh-Throughput Screening AssaysHumansPhenolsReceptors, EstrogenReproducibility of ResultsSmall Molecule Libraries4-nonylphenolEstradiolGestrinonePhenolsReceptors, EstrogenSmall Molecule Libraries

Identifiers

PMID27302286
PMCPMC4908415

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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.