Evidence map›Paper›PMID 42453585›Full record

ArticleFrontiers in pharmacology2026

Tissue-autonomous pharmacological direction: how target expression landscapes convert balanced compounds into tissue-selective agents.

Huizhen Chen, Qiu Chen, Yanzhong Wang

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2026. 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

3 authors.

Huizhen ChenChengdu University of Traditional Chinese Medicine, Chengdu, China.
Qiu ChenHospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Yanzhong WangKing's College London, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multi-target drugs frequently exhibit tissue-dependent pharmacological directions: the same compound may promote a biological process in one organ while driving its functional opposite in another. Tamoxifen antagonizes estrogen receptors in breast tissue yet activates them in the endometrium; non-selective NSAIDs reduce inflammation at injury sites while damaging the gastric mucosa. Each phenomenon has been explained by drug-specific mechanisms, but no unifying principle predicts when or why such direction reversal occurs. Here we propose tissue-autonomous pharmacological direction (TAPD): when a compound simultaneously engages two functionally opposing target classes, its net pharmacological direction in any tissue is determined primarily by which class is more abundantly expressed locally. We formalize this principle through the Tissue Direction Index (TDI), a dimensionless metric (0-1) integrating binding affinity with tissue-specific expression data from transcriptomic atlases. As proof of concept, we show that bridging metabolites from traditional Chinese botanical drugs with uniform molecular-level coagulation bias undergo substantial hemostatic direction shifts between gastrointestinal and brain tissue (4.6-fold Hemostatic Index change, driven predominantly by expression differences), with two metabolites achieving complete direction reversal. We then discuss how TAPD complements existing models of tissue-selective drug effects, including SERMs, NSAIDs, kinase inhibitors, and immunomodulators, and outline translational applications in drug repurposing, predictive toxicology, and patient-specific pharmacological prediction.

Indexed as

direction reversalmulti-target drugspolypharmacologyprecision medicinetissue-autonomous pharmacological directiontissue direction indextissue-specific drug effectstranscriptomic atlas

Identifiers

PMID42453585
PMCPMC13365337

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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.