[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128145-en":3,"doc-seo-128145-105":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},128145,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Optimizing Environmental Sustainability in Pharmaceutical 3D Printing through Machine Learning","3D printing (3DP) could reshape pharmaceutical manufacturing and enable personalized healthcare at scale. Rising concern about manufacturing’s environmental damage, especially carbon emissions, motivates eco-friendlier 3DP. This study evaluates environmental effects of pharmaceutical 3DP by combining Design of Experiments with machine learning, focusing on energy use in fused deposition modeling (FDM) across multiple dosage forms. Key parameters driving energy and CO2 include number of printed objects, build-plate temperature, nozzle temperature, and layer height. The ML approach predicts CO2 emissions accurately and supports experimentally validated, transferable models for future sustainable process development.","Optimizing Environmental Sustainability in Pharmaceutical 3D Printing through Machine Learning  \nHanxiang Li, Manal Alkahtani, Abdul Basit, Moe Elbadawi* , Simon Gaisford*  \nUCL School of Pharmacy, University College London, 29-39 Brunswick Square, London, WC1N 1AX, UK  \n*Co-corresponding authors  \nEmail: [s.gaisford@ucl.ac.uk](s.gaisford@ucl.ac.uk)  \n[m.elbadawi@ucl.ac.uk](m.elbadawi@ucl.ac.uk)  \nAbstract  \n3D Printing (3DP) of pharmaceuticals could radically 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 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 practices and optimize printing parameters, we can pave the way for a more environmentally sustainable future in pharmaceutical 3D printing.  \nKeyword: Sustainability; Energy consumption , Carbon footprint; 3D printing; Pharmaceutical manufacturing; Artificial intelligence; Digital manufacturing; Healthcare 5.0.  \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 (Seoane-Viaño et al. , 2021; Trenfield et al. , 2019) . This include polypills (Robles-Martinez et al. , 2019) , films (Elbadawi et al. , 2021; Sjöholm & Sandler, 2019; Xu et al. , 2023) , microneedles (Li et al. , 2021; Wu et al. , 2020) , scaffolds (Budharaju et al. , 2023; Curti et al. , 2020; Elsayedet 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 (Bastawrous et al. , 2022; Biglino et al. , 2023) , potentially reducing the amount of material waste and transportation impact on the environment. However, like all manufacturing processes, 3D printing has an environmental impact because of the carbon emissions generated during printing and the potential use of hazardous materials and chemicals (Kechagias & Chaidas, 2023; Malik et al. , 2022) . Since 3DP has huge potential as the next major method of pharmaceutical production, now is the time to develop sustainable practices and strategies that minimize its ecological footprint and promote responsible manufacturing processes.  \n3D printing is an overarching term that encompasses seven main technologies","cbCaigRsxSJ8GtRK","https://ap.wps.com/l/cbCaigRsxSJ8GtRK","pdf",1359239,3,1,28,"English","en",105,"# Abstract\n# Introduction\n## 3D printing in pharmaceutics\n## FDM and related technologies\n## Environmental sustainability drivers","[{\"question\":\"What environmental impact does the study focus on in pharmaceutical 3D printing?\",\"answer\":\"The study targets energy use and the resulting CO2 emissions from pharmaceutical 3D printing, with emphasis on carbon footprint reduction.\"},{\"question\":\"How do Design of Experiments (DoE) and machine learning (ML) contribute to the research?\",\"answer\":\"DoE identifies key parameters associated with energy consumption, while the ML pipeline is used to accurately predict CO2 emissions and guide more sustainable process development.\"},{\"question\":\"Which printing parameters were identified as most influential for energy use and CO2 emissions?\",\"answer\":\"The study finds that the number of objects printed, build plate temperature, nozzle temperature, and layer height significantly affect energy consumption and CO2 emission.\"}]","Optimizing Environmental Sustainability in Pharmaceutical 3D Printing through Machine Learning | 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