Evidence map›Paper›PMID 32873881›Full record

ArticleScientific reports2020

Quantifying drug tissue biodistribution by integrating high content screening with deep-learning analysis.

Zhuyin Li, Youping Xiao, Jia Peng, Darren Locke, Derek Holmes, Lei Li, Shannon Hamilton, Erica Cook, Larnie Myer, Dana Vanderwall and 6 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2020. 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
0.2field-weighted citation impact, top 26% 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, 3 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

16 authors at 1 institution in 1 country.

Zhuyin Li *Lead Discovery and Optimization, Bristol-Myers Squibb, 3551 Lawrenceville Road, Princeton, NJ, 08540, USA. Zhuyin.Li@bms.com.
Youping Xiao *Information Technology for R&D, Bristol-Myers Squibb, Princeton, NJ, USA.
Jia PengLead Discovery and Optimization, Bristol-Myers Squibb, 3551 Lawrenceville Road, Princeton, NJ, 08540, USA.
Darren LockeTranslational Medicine, Bristol-Myers Squibb, Princeton, NJ, USA.
Derek HolmesImmunoscience Biology Discovery, Bristol-Myers Squibb, Princeton, NJ, USA.
Lei LiLead Discovery and Optimization, Bristol-Myers Squibb, 3551 Lawrenceville Road, Princeton, NJ, 08540, USA.
Shannon HamiltonLead Discovery and Optimization, Bristol-Myers Squibb, 3551 Lawrenceville Road, Princeton, NJ, 08540, USA.
Erica CookLead Discovery and Optimization, Bristol-Myers Squibb, 3551 Lawrenceville Road, Princeton, NJ, 08540, USA.
Larnie MyerLead Discovery and Optimization, Bristol-Myers Squibb, 3551 Lawrenceville Road, Princeton, NJ, 08540, USA.
Dana VanderwallInformation Technology for R&D, Bristol-Myers Squibb, Princeton, NJ, USA.
Normand CloutierInformation Technology for R&D, Bristol-Myers Squibb, Princeton, NJ, USA.
Akbar M SiddiquiInformation Technology for R&D, Bristol-Myers Squibb, Princeton, NJ, USA.
Paul WhiteheadInformation Technology for R&D, Bristol-Myers Squibb, Princeton, NJ, USA.
Richard BishopInformation Technology for R&D, Bristol-Myers Squibb, Princeton, NJ, USA.
Lei ZhaoCardiovascular Translational Research, Bristol-Myers Squibb, Hopewell, NJ, USA.
Mary Ellen CvijicLead Discovery and Optimization, Bristol-Myers Squibb, 3551 Lawrenceville Road, Princeton, NJ, 08540, USA.
Bristol-Myers Squibb (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantitatively determining in vivo achievable drug concentrations in targeted organs of animal models and subsequent target engagement confirmation is a challenge to drug discovery and translation due to lack of bioassay technologies that can discriminate drug binding with different mechanisms. We have developed a multiplexed and high-throughput method to quantify drug distribution in tissues by integrating high content screening (HCS) with U-Net based deep learning (DL) image analysis models. This technology combination allowed direct visualization and quantification of biologics drug binding in targeted tissues with cellular resolution, thus enabling biologists to objectively determine drug binding kinetics.

Indexed as

CarbocyaninesDeep LearningFluorescent DyesAnimalsCadherinsColonDrug DiscoveryHigh-Throughput Screening AssaysImage Processing, Computer-AssistedImmunoconjugatesIntestine, SmallMiceTissue DistributionAlexa Fluor 647CadherinsCarbocyaninesCdh17 protein, mouseFluorescent DyesImmunoconjugates

Identifiers

PMID32873881
PMCPMC7463244
OpenAlexW3082199857

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.