Evidence map›Paper›PMID 41731528›Full record

ReviewMolecular cancer2026

The application of experimental models for the drug discovery for digestive tumors.

Linxiao Zheng, Wen Shuai, Yinyang Liu, Yang Deng, Ji Bao, Xiuying Hu, Guan Wang

Abstract readReview
In one paragraph

Review in Molecular cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Linxiao Zheng *Innovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, China.
Wen Shuai *Innovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, China.
Yinyang Liu *Innovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, China.
Yang DengInnovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, China.
Ji BaoInnovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, China. baoji@scu.edu.cn.
Xiuying HuInnovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, China. huxiuying@scu.edu.cn.
Guan WangInnovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, China. guan8079@163.com.

Funding

The National Natural Science Foundation of China ,the China Postdoctoral Science Foundation ,the Natural Science Foundation of Sichuan Province No.82404407,No. 2024M752278,No.2025ZNSFSC1721The National Natural Science Foundation of China ,the Natural Science Foundation of Sichuan Province, the Sichuan University Science Research Foundationand the Chengdu Municipal Science and Technology Program No. 22177083, 22477089,No. 2025ZNSFSC0690,No. 2023SCUH0053,No. 2024-YF05-00246-SN
6 · The paper itself

Abstract

Drug development for digestive tumors depends on various preclinical models to evaluate their efficacy and safety. Traditional models, such as 2D cell lines and animal models, fail to recapitulate the complex pathology in the human body. Recently, novel models, such as 3D organoids, tumor spheroids, and organ-on-a-chip systems, have undergone rapid growth. These models can recapitulate tissue architecture and the microenvironment in a more faithful way, enhancing the translational relevance of in vitro experiments to clinical outcomes. Moreover, patient-derived xenografts and genetically engineered models retain the genetic heterogeneity and immune contexture of original tumors, which play critical roles in assessing drug efficacy and resistance mechanisms. This review aims to explore the strengths and limitations of diverse models in drug discovery for digestive tumors. In addition, we delineate their utility in target discovery or validation, lead compound screening, and preclinical mechanistic profiling. Current studies indicate that drug failure rates can be minimized by the integration of 2D high-throughput screening, 3D organoid/tumor spheroid screening, and in vivo animal validation. Multimodal synergy and personalized models can lead to the development of more efficient and precise approaches for treating digestive tumors.

Indexed as

Antineoplastic AgentsDigestive System NeoplasmsDrug DiscoveryAnimalsHumansMicrophysiological SystemsOrganoidsSpheroids, CellularAntineoplastic AgentsDigestive tumorsDrug developmentGenetically engineered animalsOrganoidsOrgan-on-a-ChipPreclinical models

Identifiers

PMID41731528
PMCPMC13020270

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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.