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Owing to their distinct physical and chemical properties, inorganic nanoparticles (NPs) have shown promising results in preclinical cancer therapy, but designing and engineering them for effective therapeutic purposes remains a challenge. Although a comprehensive database of inorganic NP research is not currently available, it is crucial for developing effective cancer therapies. In this context, machine learning (ML) has emerged as a transformative tool, but its adaptation to nanomedicine is hindered by inexistent or small datasets. Here we assembled a large database of inorganic NPs, comprising experimental datasets from 745 preclinical studies in cancer nanomedicine. Using descriptive statistics and explainable ML models we mined this database to gain knowledge of inorganic NP design patterns and inform future NP research for cancer treatment. Our analyses suggest that NP shape and therapy type are prominent features in determining in vivo efficacy, measured as a percentage of tumour reduction. Moreover, our database provides a large-scale open-access resource for discriminative ML that the broader nanotechnology community can utilize. Our work blueprints data mining for translational cancer research and offers evidence for standardizing NP reporting to accelerate and de-risk inorganic NP-based drug delivery, which may help to improve patient outcomes in clinical settings.

Titel:	A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research.
Autor/in / Beteiligte Person:	Mendes, BB ; Zhang, Z ; Conniot, J ; Sousa, DP ; Ravasco, JMJM ; Onweller, LA ; Lorenc, A ; Rodrigues, T ; Reker, D ; Conde, J
Zeitschrift:	Nature nanotechnology, Jg. 19 (2024-06-01), Heft 6, S. 867-878
Veröffentlichung:	London : Nature Pub. Group, 2006-, 2024
Medientyp:	academicJournal
ISSN:	1748-3395 (electronic)
DOI:	10.1038/s41565-024-01673-7
Schlagwort:	Humans Animals Mice Databases, Factual Antineoplastic Agents chemistry Antineoplastic Agents therapeutic use Antineoplastic Agents administration & dosage Machine Learning Nanoparticles chemistry Neoplasms drug therapy Nanomedicine methods
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Nat Nanotechnol] 2024 Jun; Vol. 19 (6), pp. 867-878. <i>Date of Electronic Publication: </i>2024 May 15. MeSH Terms: Machine Learning* ; Nanoparticles* / chemistry ; Neoplasms* / drug therapy ; Nanomedicine* / methods ; Humans ; Animals ; Mice ; Databases, Factual ; Antineoplastic Agents / chemistry ; Antineoplastic Agents / therapeutic use ; Antineoplastic Agents / administration & dosage References: Mendes, B. B., Sousa, D. P., Conniot, J. & Conde, J. Nanomedicine-based strategies to target and modulate the tumor microenvironment. Trends Cancer 7, 847–862 (2021). (PMID: 10.1016/j.trecan.2021.05.00134090865) ; Bobo, D., Robinson, K. J., Islam, J., Thurecht, K. J. & Corrie, S. R. Nanoparticle-based medicines: a review of FDA-approved materials and clinical trials to date. Pharm. Res. 33, 2373–2387 (2016). (PMID: 10.1007/s11095-016-1958-527299311) ; Anselmo, A. C. & Mitragotri, S. 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Grant Information: ERC-StG-2019-848325 EC \| EU Framework Programme for Research and Innovation H2020 \| H2020 Priority Excellent Science \| H2020 European Research Council (H2020 Excellent Science - European Research Council); R21EB034443 U.S. Department of Health & Human Services \| NIH \| National Institute of Biomedical Imaging and Bioengineering (NIBIB) Substance Nomenclature: 0 (Antineoplastic Agents) Entry Date(s): Date Created: 20240515 Date Completed: 20240619 Latest Revision: 20240619 Update Code: 20240620

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A large-scale machine learning analysis of inorganic nanoparticles in preclinical cancer research.