[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125772-en":3,"doc-seo-125772-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"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":27,"seo_description":14,"update_tm":28,"read_time":29},125772,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predicting pharmaceutical inkjet printing outcomes using machine learning","Inkjet printing has been widely studied for producing personalized medicines because of its low cost and versatility. Yet achieving reliable outcomes remains difficult due to the process’s multifactorial drivers, including formulation properties and printing parameters that require extensive empirical optimization. Leveraging 687 formulations compiled from internal and literature-mined data, this study builds machine learning models (random forest, multilayer perceptron, and support vector machine) to predict printability and drug dose. The optimized models reach 97.22% accuracy for printability and 97.14% for print quality, enabling predictive insights before formulation preparation and reducing time and resources.","International Journal of Pharmaceutics: X 5 (2023) 100181  \nContents lists available at ScienceDirect  \nInternational Journal of Pharmaceutics: X  \njournal [homepage:](homepage: www.sciencedirect.com/journal/international-journal-of-pharmaceutics-x)[ www.sciencedirect.com/journal/international-journal-of-pharmaceutics-x](homepage: www.sciencedirect.com/journal/international-journal-of-pharmaceutics-x)  \n| Predicting pharmaceutical inkjet printing outcomes using machine learning\u003Cbr>Paola Carou-Senra a, 1, Jun Jie Ongb, 1, Brais Mu˜niz Castro c, Iria Seoane-Via˜no b,\u003Cbr>Lucía Rodríguez-Pomboa, Pedro Cabalard, Carmen Alvarez-Lorenzo a, Abdul W. Basit b, e, *, Gilberto P´erez c, **, Alvaro Goyanes a, b, e, f, *\u003Cbr>a Departamento de Farmacología, Farmacia y Tecnología Farmac´eutica, I+D Farma (GI-1645), Facultad de Farmacia, Instituto de Materiales (iMATUS) and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, 15782, Spain\u003Cbr>b Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK c IRLab, CITIC Research Center, Department of Computer Science, University of A Coru˜na, Spain\u003Cbr>d IRLab, Department of Computer Science, University of A Coru˜na, Spain e FabRx Ltd., Henwood House, Henwood, Ashford TN24 8DH, UK\u003Cbr>f Fabrx Artificial Intelligence, Carretera de Escair´on, 14, Currelos (O Savi˜nao) CP 27543, Spain |  |\n| --- | --- |\n| A R T I C L E I N F O\u003Cbr>Keywords:\u003Cbr>Additive manufacturing and personalized medications\u003Cbr>2D and 3D printed drug products\u003Cbr>Artificial intelligence and digital health Desktop inkjet printing of pharmaceuticals and drug delivery systems\u003Cbr>Design and fabrication of medicinal products Rational formulation development | A B S T R A C T |\n|  | Inkjet printing has been extensively explored in recent years to produce personalised medicines due to its low cost and versatility. Pharmaceutical applications have ranged from orodispersible films to complex polydrug implants. However, the multi-factorial nature of the inkjet printing process makes formulation (e.g., composition, surface tension, and viscosity) and printing parameter optimization (e.g., nozzle diameter, peak voltage, and drop spacing) an empirical and time-consuming endeavour. Instead, given the wealth of publicly available data on pharmaceutical inkjet printing, there is potential for a predictive model for inkjet printing outcomes to be developed. In this study, machine learning (ML) models (random forest, multilayer perceptron, and support vector machine) to predict printability and drug dose were developed using a dataset of 687 formulations, consolidated from in-house and literature-mined data on inkjet-printed formulations. The optimized ML models predicted the printability of formulations with an accuracy of 97.22%, and predicted the quality of the prints with an accuracy of 97.14%. This study demonstrates that ML models can feasibly provide predictive insights to inkjet printing outcomes prior to formulation preparation, affording resource- and time-savings. |\n\n1. Introduction  \nInkjet printing is a manufacturing technology based on material jetting, wherein droplets of ink are deposited onto a substrate. Inkjet printing has garnered considerable attention amongst pharmaceutical scientist for its versatility in producing personalised medicines and unique dosage forms (Alomari et al., 2015; Scoutaris et al., 2016a). Notably, inkjet printing has been used to load drugs onto orodispersible films (Alomari et al., 2018; Arshad et al., 2020; Jachowicz, 2017; Kiefer et al., 2021; Vuddanda et al., 2018), bioadhesive films for cervical administration (Varan et al., 2017), transdermal microneedles (Boehm et al., 2014; Uddin et al., 2015), coronary metal stents (Scoutaris et al., 2016b), contact lenses (Pollard et al., 2023; Tetyczka et al., 2022), and even nails (Pollard et al., 2022). Inkjet printing has also been used to  \ndispense dr","cbCaimciL7LsBqZr","https://ap.wps.com/l/cbCaimciL7LsBqZr","pdf",6369747,1,12,"English","en",105,"# Introduction\n## Inkjet printing as a pharmaceutical manufacturing technology\n## Applications across dosage forms and devices\n## Rationale for predictive machine learning models","[{\"question\":\"Why is optimizing inkjet pharmaceutical printing considered time-consuming?\",\"answer\":\"Inkjet printing depends on many interacting factors, including formulation variables (e.g., composition, surface tension, viscosity) and instrument/printing parameters (e.g., nozzle diameter, peak voltage, drop spacing). This makes optimization largely empirical.\"},{\"question\":\"How many formulations were used to train the machine learning models?\",\"answer\":\"The models were trained using a dataset of 687 formulations consolidated from in-house data and literature-mined inkjet-printed formulation reports.\"},{\"question\":\"What performance did the optimized ML models achieve?\",\"answer\":\"The optimized models predicted printability with 97.22% accuracy and predicted print quality with 97.14% accuracy.\"}]","Predicting pharmaceutical inkjet printing outcomes using machine learning | PDF",1785901122,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-pharmaceutical-inkjet-printing-outcomes-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-pharmaceutical-inkjet-printing-outcomes-using-machine-learning/125772/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is optimizing inkjet pharmaceutical printing considered time-consuming?","Question",{"text":75,"@type":76},"Inkjet printing depends on many interacting factors, including formulation variables (e.g., composition, surface tension, viscosity) and instrument/printing parameters (e.g., nozzle diameter, peak voltage, drop spacing). This makes optimization largely empirical.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How many formulations were used to train the machine learning models?",{"text":80,"@type":76},"The models were trained using a dataset of 687 formulations consolidated from in-house data and literature-mined inkjet-printed formulation reports.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance did the optimized ML models achieve?",{"text":84,"@type":76},"The optimized models predicted printability with 97.22% accuracy and predicted print quality with 97.14% accuracy.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]