ReviewArchives of pharmacal research2026
Tumor organoid platform design for drug response modeling: culture architecture, microenvironmental complexity, and AI-assisted readouts.
Review in Archives of pharmacal research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Tumor organoids preserve the cellular heterogeneity and structural complexity of native tumors, providing robust platforms for mechanistic studies, preclinical drug testing, and translational oncology research. However, their predictive performance is not an intrinsic property of organoids. It is shaped by platform design, including culture format, matrix composition, medium formulation, and the extent of multicellular reconstruction. This review examines how these design variables influence model fidelity, reproducibility, tumor microenvironment reconstruction, and drug-response interpretation. We compare major culture formats and matrix systems, discuss strategies for incorporating stromal and immune components, and evaluate current and emerging uses of AI-assisted analysis for organoid-derived phenotypic data. Overall, this review highlights how integrated tumor organoid design can strengthen pharmacological modeling and oncology translation.
Indexed as
Identifiers
42347891What OpenQuestion holds
Registered trials
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.