[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121188-en":3,"doc-seo-121188-105":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},121188,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Advancing algorithmic drug product development - Recommendations for machine learning approaches in drug formulation","Artificial intelligence has strong potential to reshape pharmaceutical workflows, from discovery through clinical translation. Machine learning enables streamlined in silico modelling and supports more efficient formulation development, yet practical modelling guidance remains limited for drug product work. This review analyzes data-driven modelling practices, highlights risks of unreliable predictions from suboptimal approaches, and addresses the dominance of benchtop experimental targets. Recommendations are provided to improve model trustworthiness, transparency, reliability, and to guide future directions toward robust tools for formulators.","University of Southern Denmark  \nAdvancing algorithmic drug product development  \nRecommendations for machine learning approaches in drug formulation  \nMurray, Jack D. ; Lange, Justus J. ; Bennett-Lenane, Harriet; Holm, René; Kuentz, Martin; O'Dwyer, Patrick J. ; Griffin, Brendan T.  \nPublished in:  \nEuropean Journal of Pharmaceutical Sciences  \nDOI:  \n10.1016/j.ejps.2023.106562  \nPublication date: 2023  \nDocument version:  \nFinal published version  \nDocument license: CC BY  \nCitation for pulished version (APA):  \nMurray, J. D. , Lange, J. J. , Bennett-Lenane, H. , Holm, R. , Kuentz, M. , O'Dwyer, P. J. , & Griffin, B. T. (2023) . Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation. European Journal of Pharmaceutical Sciences, 191, Article 106562.  \n[https://doi.org/10.1016/j.ejps.2023.106562](https://doi.org/10.1016/j.ejps.2023.106562)  \nGo to publication entry in University of Southern Denmark's Research Portal  \nTerms of use  \nThis work is brought to you by the University of Southern Denmark.  \nUnless otherwise specified it has been shared according to the terms for self-archiving.  \nIf no other license is stated, these terms apply:  \n• You may download this work for personal use only.  \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 this open access version  \nIf you believe that this document breaches copyright please contact us providing details and we will investigate your claim. Please direct all enquiries to [puresupport@bib.sdu.dk](puresupport@bib.sdu.dk)  \nDownload date: 02. Aug. 2026  \nEuropean Journal of Pharmaceutical Sciences 191 (2023) 106562  \nContents lists available at ScienceDirect  \nEuropean Journal of Pharmaceutical Sciences  \njournal [homepage: www.elsevier.com/locate/ejps](homepage: www.elsevier.com/locate/ejps)  \n| Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation\u003Cbr>Jack D. Murray a, Justus J. Lange a, b, Harriet Bennett-Lenanea, Ren´e Holm c, Martin Kuentz d, Patrick J. O’Dwyer a, Brendan T. Griffin a, *\u003Cbr>a School of Pharmacy, University College Cork, Cork, Ireland\u003Cbr>b Roche Pharmaceutical Research & Early Development, Pre-Clinical CMC, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Basel, Switzerland\u003Cbr>c Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, Odense 5230, Denmark d School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz CH 4132, Switzerland |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Machine learning\u003Cbr>Artificial intelligence Computational pharmaceutics Drug formulation\u003Cbr>Data-driven modelling Property prediction |  | Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on blackbox methods over interpretable models. This further limits the widespread adoption of machin","cbCaib76sv4PKZfL","https://ap.wps.com/l/cbCaib76sv4PKZfL","pdf",1237362,1,14,"English","en",105,"# Introduction\n## Data-driven modelling in drug product development\n## Challenges in model reliability and interpretability\n## Recommendations for trustworthy machine learning\n## Future directions for robust guidance","[{\"question\":\"Why is machine learning important for drug product development?\",\"answer\":\"Machine learning improves in silico modelling and can streamline clinical translation by enabling data-driven approaches for formulation development.\"},{\"question\":\"What limitations in current drug product ML modelling does the review highlight?\",\"answer\":\"It notes limited formulation-specific guidance, a tendency toward suboptimal practices, and models that may not produce reliable predictions in real-world use.\"},{\"question\":\"How does the review suggest improving trustworthiness of ML models?\",\"answer\":\"It presents recommendations aimed at ensuring trustworthiness, transparency, and reliability, reducing dependence on black-box methods and improving interpretability for formulators.\"}]","Advancing algorithmic drug product development - 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