[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127569-en":3,"doc-seo-127569-105":30,"detail-sidebar-cat-0-en-105":96},{"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},127569,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predicting pharmaceutical inkjet printing outcomes using machine learning - Article","Inkjet printing enables low-cost, versatile production of personalised pharmaceutical dosage forms, yet outcome quality depends on many formulation and process factors, making optimization empirical and time-intensive. This study builds machine learning models—random forest, multilayer perceptron, and support vector machine—using 687 inkjet-printed formulations mined from literature and in-house sources. The optimized models achieve 97.22% accuracy for predicting printability and 97.14% accuracy for predicting print quality, providing actionable preformulation guidance that reduces resource and development time.","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","cbCaivwfUJDlqV8e","https://ap.wps.com/l/cbCaivwfUJDlqV8e","pdf",6457592,1,12,"English","en",105,"# Introduction\n## Inkjet printing applications in pharmaceuticals\n## Motivation for predictive modeling\n# Methods and Models\n## Dataset construction (687 formulations)\n## Machine learning approaches (random forest, MLP, SVM)\n# Results\n## Printability prediction accuracy\n## Print quality prediction accuracy\n# Conclusions\n## Preformulation predictive insights and efficiency gains","[{\"question\":\"Why is inkjet printing optimization usually time-consuming in pharmaceutical development?\",\"answer\":\"Inkjet printing outcomes depend on multiple formulation variables (e.g., composition, surface tension, viscosity) and printing parameters (e.g., nozzle diameter, peak voltage, drop spacing). Managing these coupled factors typically requires empirical iteration.\"},{\"question\":\"What data and machine learning methods were used to predict inkjet outcomes?\",\"answer\":\"Models were trained on a consolidated dataset of 687 inkjet-printed formulations mined from literature and in-house sources. The study developed random forest, multilayer perceptron, and support vector machine models.\"},{\"question\":\"How accurate were the optimized models for printability and print quality?\",\"answer\":\"The optimized models predicted formulation printability with 97.22% accuracy and predicted print quality with 97.14% accuracy.\"},{\"question\":\"How can these models help before formulation preparation?\",\"answer\":\"The ML models provide predictive insights into inkjet printing outcomes prior to formulation preparation, supporting more efficient resource use and reduced development time.\"}]","Predicting pharmaceutical inkjet printing outcomes using machine learning - Article | PDF",1785940032,30,{"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":91,"head_meta":93,"extra_data":95,"updated_unix":28},"predicting-pharmaceutical-inkjet-printing-outcomes-using-machine-learning-article","",{"@graph":36,"@context":90},[37,54,69],{"@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-article/127569/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82,86],{"name":73,"@type":74,"acceptedAnswer":75},"Why is inkjet printing optimization usually time-consuming in pharmaceutical development?","Question",{"text":76,"@type":77},"Inkjet printing outcomes depend on multiple formulation variables (e.g., composition, surface tension, viscosity) and printing parameters (e.g., nozzle diameter, peak voltage, drop spacing). Managing these coupled factors typically requires empirical iteration.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data and machine learning methods were used to predict inkjet outcomes?",{"text":81,"@type":77},"Models were trained on a consolidated dataset of 687 inkjet-printed formulations mined from literature and in-house sources. The study developed random forest, multilayer perceptron, and support vector machine models.",{"name":83,"@type":74,"acceptedAnswer":84},"How accurate were the optimized models for printability and print quality?",{"text":85,"@type":77},"The optimized models predicted formulation printability with 97.22% accuracy and predicted print quality with 97.14% accuracy.",{"name":87,"@type":74,"acceptedAnswer":88},"How can these models help before formulation preparation?",{"text":89,"@type":77},"The ML models provide predictive insights into inkjet printing outcomes prior to formulation preparation, supporting more efficient resource use and reduced development time.","https://schema.org",{"og:url":52,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":97},[98,102,106,110,115,120,125,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},"Exam",70,"exam",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},5,"Comic",60,"comic",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},6,"Technology",50,"technology",{"id":121,"doc_module":4,"doc_module_name":46,"category_name":122,"show_sort_weight":123,"slug":124},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":126},"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":111,"slug":142},19,"General","general"]