Evidence map›Paper›PMID 34008121›Full record

ArticleThe AAPS journal2021

Direct Comparison of the Prediction of the Unbound Brain-to-Plasma Partitioning Utilizing Machine Learning Approach and Mechanistic Neuropharmacokinetic Model.

Yohei Kosugi, Kunihiko Mizuno, Cipriano Santos, Sho Sato, Natalie Hosea, Michael Zientek

Open access · hybridAbstract readComparative Study
In one paragraph

Article in The AAPS journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-weighted citation impact, top 25% 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

5 citing papers in PubMed, 12 citations in OpenAlex.

  1. Review
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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

6 authors at 2 institutions in 2 countries.

Yohei KosugiGlobal DMPK, Takeda California Inc., San Diego, California, 92121, USA. youhei.kosugi@takeda.com.ORCID http://orcid.org/0000-0001-9318-4723
Kunihiko MizunoGlobal DMPK, Takeda California Inc., San Diego, California, 92121, USA.
Cipriano SantosGlobal DMPK, Takeda California Inc., San Diego, California, 92121, USA.
Sho SatoGlobal DMPK, Takeda Pharmaceutical Company Limited, 26-1 Muraoka-Higashi, 2-Chome, Fujisawa, Kanagawa, 251-8555, Japan.
Natalie HoseaGlobal DMPK, Takeda California Inc., San Diego, California, 92121, USA.
Michael ZientekGlobal DMPK, Takeda California Inc., San Diego, California, 92121, USA.
Takeda (United States) · USTakeda (Japan) · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The mechanistic neuropharmacokinetic (neuroPK) model was established to predict unbound brain-to-plasma partitioning (K

Indexed as

Machine LearningModels, BiologicalAnimalsATP Binding Cassette Transporter, Subfamily BATP Binding Cassette Transporter, Subfamily G, Member 2Blood-Brain BarrierDatasets as TopicDogsMadin Darby Canine Kidney CellsMaleModels, AnimalPredictive Value of TestsRatsAbcg2 protein, ratATP Binding Cassette Transporter, Subfamily BATP Binding Cassette Transporter, Subfamily G, Member 2multidrug resistance protein 3BCRPbrain-to-plasma unbound concentration ratioin silicomachine learningMDR1

Identifiers

PMID34008121
PMCPMC8131289
OpenAlexW3161384012

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.