ArticleScience advances2025
Machine learning-assisted exploration of multidrug-drug administration regimens for organoid arrays.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed.
- Real-time sensing-integrated organoid-on-a-chip platforms: Technological progress and emerging biomedical applications.Bioactive materials · 2026Review
- Targeting the osteoporotic bone microenvironment: Mechanistic insight and therapeutic biomaterials for accelerating bone regeneration.Bioactive materials · 2026Review
- Tumor organoids as a revolutionary platform for advancing cancer nanomedicine.Molecular cancer · 2026Review
- Smart hydrogels for overcoming cancer multidrug resistance.Molecular cancer · 2026Review
- Article
- Organoids for disease modeling and treatment: state-of-the-art.Experimental hematology & oncology · 2026Review
- Patient-derived organoids in functional precision oncology: from experimental models to clinical decision-making.Frontiers in endocrinology · 2026Review
- Lung cancer organoids for functional precision oncology: from disease modeling to clinical decision support.Frontiers in oncology · 2026Review
- Integrating artificial intelligence across the cancer drug discovery pipeline using a design-test-refine workflow.Frontiers in oncology · 2026Review
- Artificial Intelligence-Aided Microfluidic Cell Culture Systems.Biosensors · 2025Review
- Materiobiology in the omics era.Materials today. Bio · 2025Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Combination therapies enhance the therapeutic effect of cancer treatment; however, identifying effective interdependent doses, durations, and sequences of multidrug administration regimens is a time- and labor-intensive task. Here, we integrated machine learning, automation, and large microfluidic arrays of cancer spheroids or patient-derived organoids formed in a tissue-mimetic hydrogel to achieve notable acceleration of the discovery of effective multidrug administration regimens. For the clinically approved drug combination, we found a sequential administration regimen leading to a substantial reduction in the total drug dose, in comparison with concurrent drug supply, both at comparable drug efficacy. For the drugs that are currently under clinical development, we found a synergistic effect of concurrently administered drugs and showed that the synergy diminishes for the sequential drug supply. The developed strategy holds promise for the discovery of effective combination therapies for advanced cancer treatment, including personalized chemotherapies.
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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.