Evidence map›Paper›PMID 40881343›Full record

Articlenpj women's health2025

Predicting oxytocin binding dynamics in receptor genetic variants through computational modeling.

Preeti Dubey, Yingye Fang, K Lionel Tukei, Shobhan Kuila, Xinming Liu, Annika Sahota, Antonina I Frolova, Erin L Reinl, Manasi Malik, Sarah K England and 1 more

Abstract read
In one paragraph

Article in npj women's health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Preeti DubeyDepartment of Bioengineering, University of Washington, Seattle, WA, USA.
Yingye FangDepartment of Bioengineering, University of Washington, Seattle, WA, USA.
K Lionel TukeiDepartment of Bioengineering, University of Washington, Seattle, WA, USA.
Shobhan KuilaDepartment of Bioengineering, University of Washington, Seattle, WA, USA.
Xinming LiuDepartment of Bioengineering, University of Washington, Seattle, WA, USA.
Annika SahotaDepartment of Bioengineering, University of Washington, Seattle, WA, USA.
Antonina I FrolovaCenter for Reproductive Health Sciences, Department of Obstetrics and Gynecology, WashU Medicine, St. Louis, MO, USA.
Erin L ReinlCenter for Reproductive Health Sciences, Department of Obstetrics and Gynecology, WashU Medicine, St. Louis, MO, USA.
Manasi MalikCenter for Reproductive Health Sciences, Department of Obstetrics and Gynecology, WashU Medicine, St. Louis, MO, USA.
Sarah K EnglandCenter for Reproductive Health Sciences, Department of Obstetrics and Gynecology, WashU Medicine, St. Louis, MO, USA.
Princess I ImoukhuedeDepartment of Bioengineering, University of Washington, Seattle, WA, USA.

Funding

Quantitative and computational characterization of oxytocin receptor signaling: Administrative supplementR01HD096737 · NICHD · WASHINGTON UNIVERSITY · PI ENGLAND, SARAH K., IMOUKHUEDE, PRINCESS IZEVBUA · 2019 to 2023
$2.8M
NICHD NIH HHS R01 HD096737
6 · The paper itself

Abstract

Approximately half of U.S. women giving birth annually receive Pitocin, a synthetic form of oxytocin (OXT), yet the optimal dosing remains challenging due to significant individual variability in response. To address this, we developed a mathematical model examining the effects of five OXT receptor (OXTR) variants (V45L, P108A, L206V, V281M, and E339K) on OXT-OXTR binding dynamics in human embryonic kidney cells (HEK293T) and myometrial smooth muscle cells. The model was parameterized using experimentally derived, cell-specific OXTR surface localization measurements and literature-reported OXT-OXTR-binding kinetics. The model revealed differences in time to equilibrium between HEK293T and myometrial cells, distinct dynamics among genetic variants, and that early increases in OXT could partially rescue diminished responses in V281M and E339K variants. This model provides key insights into how genetic variants influence OXT dose responses and offers a framework for tailoring OXT dosing to patient-specific genetic profiles.

Identifiers

PMID40881343
PMCPMC12381597

What OpenQuestion holds

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Registered trials

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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.