[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127967-en":3,"doc-seo-127967-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127967,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Optimizing environmental sustainability in pharmaceutical 3D printing through machine learning","3D printing (3DP) could reshape pharmaceutical manufacturing and enable personalized healthcare, yet it must minimize environmental damage, especially carbon emissions. This study examined the environmental effects of pharmaceutical 3DP using Design of Experiments (DoE) and Machine Learning (ML) to analyze energy use in fused deposition modeling (FDM). Across 136 runs from four dosage forms, key drivers were number of objects, build plate and nozzle temperatures, and layer height. Reducing build plate temperature and cutting trial-and-error lowered CO2 emissions, while ML most accurately predicted CO2 and maintained high accuracy on new dosage forms of varying complexity. Results support sustainable digitalization aligned with Industry 5.0.","International Journal of Pharmaceutics 648 (2023) 123561  \nContents lists available at ScienceDirect  \nInternational Journal of Pharmaceutics  \njournal [homepage:](homepage: www.elsevier.com/locate/ijpharm)[ www.elsevier.com/locate/ijpharm](homepage: www.elsevier.com/locate/ijpharm)  \n| Optimizing environmental sustainability in pharmaceutical 3D printing through machine learning |  |  |  |\n| --- | --- | --- | --- |\n| Hanxiang Lia, Manal E. Alkahtania, b, Abdul W. Basita, Moe Elbadawia, c, *, Simon Gaisford a, *\u003Cbr>a UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK\u003Cbr>b Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia c School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End Road, London E1 4DQ, UK |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Sustainability\u003Cbr>Energy consumption, Carbon footprint 3D printing\u003Cbr>Pharmaceutical manufacturing Artificial intelligence\u003Cbr>Digital manufacturing\u003Cbr>Healthcare 5.0 |  | 3D Printing (3DP) of pharmaceuticals could drastically transform the manufacturing of medicines and facilitate the widespread availability of personalised healthcare. However, with increasing awareness of the environmental damage of manufacturing, 3DP must be eco-friendly, especially when it comes to carbon emissions. This study investigated the environmental effects of pharmaceutical 3DP. Using Design of Experiments (DoE) and Machine Learning (ML), we looked at energy use in pharmaceutical Fused Deposition Modeling (FDM). From 136 experimental runs across four common dosage forms, we identified several key parameters that contributed to energy consumption, and consequently CO2 emission. These parameters, identified by both DoE and ML, were the number of objects printed, build plate temperature, nozzle temperature, and layer height. Our analysis revealed that minimizing trial-and-error by being more efficient in R&D and reducing the build plate temperature can significantly decrease CO2 emissions. Furthermore, we demonstrated that only the ML pipeline could accurately predict CO2 emissions, suggesting ML could be a powerful tool in the development of more sustainable manufacturing processes. The models were validated experimentally on new dosage forms of varying geometric complexities and were found to maintain high accuracy across all three dosage forms. The study underscores the potential of merging sustainability and digitalization in the pharmaceutical sector, aligning with the principles of Industry 5.0. It highlights the comparable learning traits between DoE and ML, indicating a promising pathway for wider adoption of ML in pharmaceutical manufacturing. Through focused efforts to reduce wasteful practicesand optimize printing parameters, we can pave the way for a more environmentally sustainable future in pharmaceutical 3DP. |  |\n\n1. Introduction  \nThree-dimensional (3D) printing, also known as additive manufacturing, has emerged as a transformative technology in various industries, including pharmaceutics. Its ability to generate complex structures and customized formulations has created new opportunities in drug delivery and personalized medicine (Govender et al., 2021; Seoane-Via˜no et al., 2021; Tracy et al., 2023; Yang et al., 2023a). This include polypills (Robles-Martinez et al., 2019), films (Elbadawi et al., 2021; Sj¨oholm and Sandler, 2019; Xu et al., 2023), microneedles (Liet al., 2021; Wu et al., 2020), scaffolds (Budharaju et al., 2023; Curtiet al., 2020; Elsayed et al., 2019), and medical devices (Tan et al., 2022; Xu et al., 2021). The advantages of 3D printing extend to the potential of point of care manufacturing and production of small batches  \n(Bastawrous et al., 2022; Biglino et al., 2023; O’Donovan et al., 2023; Yang et al., 2023b), potentially reducing the amount of material waste and transportation impact on the en","cbCaim3lCMGWYXIE","https://ap.wps.com/l/cbCaim3lCMGWYXIE","pdf",5964535,2,1,11,"English","en",105,"# Introduction\n## Environmental impact of pharmaceutical 3D printing\n## Role of machine learning and design of experiments\n# Methods and optimization approach\n## Energy and CO2 drivers in FDM\n## Model validation across dosage forms","[{\"question\":\"What problem does the study address in pharmaceutical 3D printing?\",\"answer\":\"The study addresses environmental impact from 3D printing, focusing on carbon emissions during pharmaceutical fabrication.\"},{\"question\":\"Which factors were identified as key contributors to energy use and CO2 emissions?\",\"answer\":\"The study found important parameters including number of objects printed, build plate temperature, nozzle temperature, and layer height.\"},{\"question\":\"How do the DoE and ML approaches compare in predicting CO2 emissions?\",\"answer\":\"Both approaches identified drivers, but only the ML pipeline could accurately predict CO2 emissions, maintaining high accuracy across all tested dosage forms.\"}]","Optimizing environmental sustainability in pharmaceutical 3D printing through machine learning | 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problem does the study address in pharmaceutical 3D printing?","Question",{"text":76,"@type":77},"The study addresses environmental impact from 3D printing, focusing on carbon emissions during pharmaceutical fabrication.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which factors were identified as key contributors to energy use and CO2 emissions?",{"text":81,"@type":77},"The study found important parameters including number of objects printed, build plate temperature, nozzle temperature, and layer height.",{"name":83,"@type":74,"acceptedAnswer":84},"How do the DoE and ML approaches compare in predicting CO2 emissions?",{"text":85,"@type":77},"Both approaches identified drivers, but only the ML pipeline could accurately predict CO2 emissions, maintaining high accuracy across all tested dosage 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