[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117268-en":3,"doc-seo-117268-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},117268,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Machine learning-guided high throughput nanoparticle design","Designing nanoparticles with desired properties remains difficult due to the vast combinatorial formulation space and complex structure–function relationships. High-throughput experimentation and machine learning offer faster routes for composition discovery, yet integrating formulation, screening, and computational decision-making into one reliable workflow is still insufficiently explored. This study combines microfluidic-based formulation, high-content imaging, and active machine learning to optimize PLGA-PEG nanoparticles for high uptake in human breast cancer cells.","Machine learning-guided high throughput nanoparticle design  \nCitation for published version (APA):  \nOrtiz-Perez, A. , van Tilborg, D. , van der Meel, R. , Grisoni, F. , & Albertazzi, L. (2024) . Machine learning-guided high throughput nanoparticle design. Digital Discovery, 3(7), 1280-1291 . [https://doi.org/10.1039/D4DD00104D](https://doi.org/10.1039/D4DD00104D)  \nDocument license:  \nCC BY  \nDOI:  \n10.1039/D4DD00104D  \nDocument status and date:  \nPublished: 01/07/2024  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 22. Oct. 2024  \nOpen Access Article . Pu on 03 June 2024. Down on 7/19/2024blished loaded 3: 18: 1 1 PM .  \nDigital  \nDiscovery  \nCOMMUNICATION  \nView Article Online View Journal | View Issue  \nCite this: Digital Discovery, 2024, 3, 1280  \nReceived 12th April 2024  \nAccepted 2nd June 2024  \nDOI: 10.1039/d4dd00104d[rsc.li/digitaldiscovery](rsc.li/digitaldiscovery)  \nMachine learning-guided high throughput nanoparticle design†  \nAna Ortiz-Perez,  ‡a Derek van Tilborg,  ‡ab Roy van der Meel,  a Francesca Grisoni  *ab and Lorenzo Albertazzi  *a  \nDesigning nanoparticles with desired properties is a challenging endeavor, due to the large combinatorial space and complex structure–function relationships. High throughput methodologies and machine learning approaches are attractive and emergent strategies to accelerate nanoparticle composition design. To date, how to combine nanoparticle formulation, screening, and computational decision-making into a single effective workflow is underexplored. In this study, we showcase the integration of three key technologies, namely microfluidic-based formulation, high content imaging, and active machine learning. As a case study, we apply our approach for designing PLGA-PEG nanoparticles with high uptake in human breast cancer cells. Starting from a small set of nanoparticles for model training, our approach led to an increase in uptake from ∼5-fold to ∼15-fold in only two machine learning guided iterations, taking one week each. To the best of our knowledge, this is the first time that these three technologies have been successfully integrated to optimize a biological response through nanoparticle composition. 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nanoparticles with high uptake in human breast cancer cells, increasing uptake from about 5-fold to about 15-fold over two machine learning guided 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