[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126347-en":3,"doc-seo-126347-105":31,"detail-sidebar-cat-0-en-105":93},{"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},126347,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Machine learning strengthened formulation design of pharmaceutical suspensions","Machine learning strengthened formulation design of pharmaceutical suspensions addresses limitations of traditional milling-based formulation studies that rely heavily on prior expertise and trial-and-error. The work connects suspension characteristics with formulation parameters using full-factorial milling experiments and statistical/ML methods. Stabilizer concentration is identified as a significant driver of median suspension diameter (D50). A high-accuracy stability classification model (0.91 prediction accuracy, 0.91 F1) is built from 72 data points. Explainable ML with SHAP highlights stabilizer concentration and milling bead size as key stability contributors.","University of Southern Denmark  \nMachine learning strengthened formulation design of pharmaceutical suspensions  \nZulbeari, Nadina; Wang, Fanjin; Mustafova, Sibel Selyatinova; Parhizkar, Maryam; Holm, René  \nPublished in:  \nInternational Journal of Pharmaceutics  \nDOI:  \n10.1016/j.ijpharm.2024.124967  \nPublication date: 2025  \nDocument version:  \nFinal published version  \nDocument license: CC BY  \nCitation for pulished version (APA):  \nZulbeari, N. , Wang, F. , Mustafova, S. S. , Parhizkar, M. , & Holm, R. (2025) . Machine learning strengthened formulation design of pharmaceutical suspensions. International Journal of Pharmaceutics , 668, Article 124967. [https://doi.org/10.1016/j.ijpharm.2024.124967](https://doi.org/10.1016/j.ijpharm.2024.124967)  \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: 04. Aug. 2026  \nInternational Journal of Pharmaceutics 668 (2025) 124967  \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| Machine learning strengthened formulation design of pharmaceutical suspensions |  |  |  |\n| --- | --- | --- | --- |\n| Nadina Zulbearia,1, Fanjin Wang b,1, Sibel Selyatinova Mustafova a, Maryam Parhizkar b, Ren´e Holm a,*\u003Cbr>a Department of Physics, Chemistry, and Pharmacy, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark\u003Cbr>b Deparment ofPharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, WC1N 1AX London, United Kingdom |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Stabilizers Formulation screening Nano Microsuspensions Machine learning |  | Many different formulation strategies have been investigated to oppose suboptimal treatment of long-term or chronic conditions, one of which are the nano- and microsuspensions prepared as long-acting injectables to prolong the release of an active pharmaceutical compound for a defined period of time by regulating the size of particles by milling. Typically, surfactant and/or polymers are added in the dispersion medium of the suspension during processing for stabilization purposes. However, current formulation investigations with milling are heavily based on prior expertise and trial-and-error approaches. Various interacting parameters such as the milling bead size, stabilizer type and concentration have confounded the investigation of milling process. The present study systematically exploited statistical and machine learning (ML) strategies to understand the relationship between suspension characteristics and formulation parameters under full-factorial milling experiments. Stabilizer concentration was identified as a significant factor (p \u003C 0.001) for median suspension diameter (D50). A formulation stability classification ML model with high prediction accuracy (0.91) and F1-score (0.91) under 10-fold cross-validation was constructed based on 72 formulation datapoints. Model interpretation through Shapley additive explanations (SHAP) revealed the prominent impact of stabilizer concentration and milling bead size on formulation stability. The present work demonstrated the potential to achieve a deeper understandi","cbCaifSRzTRn3G1l","https://ap.wps.com/l/cbCaifSRzTRn3G1l","pdf",3308633,9,1,11,"English","en",105,"# Introduction\n## Long-acting injectables and controlled release\n## Wet bead media milling for size reduction\n## Stability challenges in nano- and microsuspensions\n# Methods\n## Full-factorial milling experiments\n## Statistical and machine learning modeling\n## Explainable ML using SHAP\n# Results\n## Key formulation factors for D50\n## Stability classification model performance\n## Feature importance and interpretation\n# Conclusion\n## Implications for formulation screening and optimization","[{\"question\":\"Why are nano- and microsuspensions used in long-acting injectables?\",\"answer\":\"They enable prolonged release of an active pharmaceutical compound by regulating particle size, supporting predefined plasma-concentration profiles over weeks to months.\"},{\"question\":\"What approach is used instead of trial-and-error for formulation design?\",\"answer\":\"The study applies statistical and machine learning strategies to systematically relate suspension characteristics to formulation parameters under full-factorial milling experiments.\"},{\"question\":\"Which formulation factors most influence suspension stability?\",\"answer\":\"Stabilizer concentration and milling bead size show prominent effects, with SHAP-based interpretation indicating their strong contribution to stability outcomes.\"}]","Machine learning strengthened formulation design of pharmaceutical suspensions | PDF",1785904602,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-strengthened-formulation-design-of-pharmaceutical-suspensions","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-strengthened-formulation-design-of-pharmaceutical-suspensions/126347/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why are nano- and microsuspensions used in long-acting injectables?","Question",{"text":77,"@type":78},"They enable prolonged release of an active pharmaceutical compound by regulating particle size, supporting predefined plasma-concentration profiles over weeks to months.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What approach is used instead of trial-and-error for formulation design?",{"text":82,"@type":78},"The study applies statistical and machine learning strategies to systematically relate suspension characteristics to formulation parameters under full-factorial milling experiments.",{"name":84,"@type":75,"acceptedAnswer":85},"Which formulation factors most influence suspension stability?",{"text":86,"@type":78},"Stabilizer concentration and milling bead size show prominent effects, with SHAP-based interpretation indicating their strong contribution to stability outcomes.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,122,125,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":120,"slug":121},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":123,"slug":124},30,"research-report",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]