[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126931-en":3,"doc-seo-126931-105":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":4,"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},126931,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","MLAPI - A framework for developing machine learning-guided drug particle syntheses in automated continuous flow platforms","Recently, machine learning (ML) models have been increasingly used within process analytical technology (PAT) for pharmaceutical manufacturing, but data-hungry requirements limit broader deployment. This work presents a computational, data-efficient framework for guiding drug particle synthesis in an automated continuous-flow precipitation platform. It combines classification to find feasible, fouling-free operating regions, a multi-output Gaussian process model linking key process parameters to drug particle size, and active learning to select informative new experiments. The framework is demonstrated using ibuprofen microparticle synthesis, enabling GP-guided tuning for targeted particle bioavailability and processability.","Chemical Engineering Science 302 (2025) 120780  \nContents lists available at ScienceDirect  \nChemical Engineering Science  \njournal [homepage:](homepage: www.elsevier.com/locate/ces)[ www.elsevier.com/locate/ces](homepage: www.elsevier.com/locate/ces)  \n| MLAPI: A framework for developing machine learning-guided drug particle   syntheses in automated continuous flow platforms\u003Cbr>Arun Pankajakshan a, Sayan Pal a, Nicholas Snead a, Juan Almeida b, Maximilian O. Besenhard a, Shorooq Abukhamees c, Duncan Q.M. Craig d, Asterios Gavriilidis a, Luca Mazzei a,\u003Cbr>Federico Galvanin a,∗\u003Cbr>a Department of Chemical Engineering, University College London, Torrington Place, London, WC1E 7JE, United Kingdom b Perceptive Engineering, Applied Materials, Vanguard House, Cheshire, WA4 4AB, United Kingdom\u003Cbr>c Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmaceutical Sciences, The Hashemite University, Zarqa, 13115, Jordan d Faculty of Science, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom |  |\n| --- | --- |\n| A R T I C L E I N F O\u003Cbr>Keywords:\u003Cbr>Machine learning\u003Cbr>Drug Active Pharmaceutical Ingredient (API) Continuous flow\u003Cbr>Automation | A B S T R A C T |\n|  | Recently, machine learning (ML) models are increasingly being used in process analytical technology (PAT) frameworks for pharmaceutical manufacturing. Yet, the applications of ML-integrated PAT frameworks are limited by big data requirements. This work introduces a computational framework to develop data-efficient ML models to guide drug particle synthesis in an automated continuous flow precipitation platform. The framework incorporates classification algorithms to identify feasible (fouling-free) operating regions of the precipitation platform, a multiple-output Gaussian process (GP) regression model to relate key process parameters to the drug particle size, and active learning to optimally generate new data for training and validation of the GP model. The usefulness of the proposed framework is demonstrated on the synthesis of ibuprofen microparticles in an automated flow precipitation platform. We envision that properly trained GP models developed using the proposed framework can be employed to fine tune the drug particle size, targeting desired particle bioavailability and processability. |\n\n1. Introduction  \nPoor bioavailability of solid formulations of pharmaceutical drugs caused by low aqueous solubility and dissolution rate of Active Pharmaceutical Ingredients (APIs) is a persistent problem within the pharmaceutical industry. Among several methods to address this problem, nanoparticle or microparticle API formulations are an attractive solution (Mohammadi et al., 2023). Preparing drugs as nano or microsuspensions markedly increases their specific surface area, speeding up drug dissolution and improving their bioavailability. However, making drugs as sub-micron particles is challenging. It requires significant fine-tuning to control particle size, stop particles from clumping together, and prevent the formation of unwanted structures. These challenges have impeded widespread adoption of this technology within the pharmaceutical sector (Jia et al., 2022).  \nContinuous flow antisolvent precipitation (Sinha et al., 2013) is an energy-efficient and scalable approach for the continuous synthesis of drug particles at the nano or micro scale (Jia et al., 2022). From a process standpoint, the main engineering obstacle in producing drug particles continuously is to manage suspensions in flow effectively. Conversely, when considering product quality, the primary engineering challenge in manufacturing drug particles in the sub-micron range is achieving precise control over the size distribution of the drug particles (Benyahia et al., 2012; Lakerveld et al., 2015). This control is vital for ensuring efficient drug delivery. Moreover, the size distribution plays a critical role in various subsequent pharmaceutical procedures, such as filtration, washing, drying,","cbCaisQ6S9BARek9","https://ap.wps.com/l/cbCaisQ6S9BARek9","pdf",4439004,1,14,"English","en",105,"# Abstract\n# Introduction\n## Bioavailability challenges of solid APIs\n## Nanoparticle/microparticle formulation and control hurdles\n## Continuous flow antisolvent precipitation and engineering obstacles\n## Process Analytical Technology (PAT) for automation and chemometrics","[{\"question\":\"What problem does the framework address in ML-guided pharmaceutical particle synthesis?\",\"answer\":\"It targets the limitation that ML-integrated PAT approaches often require large data sets, which restricts practical adoption.\"},{\"question\":\"How does the framework model and guide drug particle synthesis?\",\"answer\":\"It uses classification to identify fouling-free operating regions, a multi-output Gaussian process regression model to relate process parameters to particle size, and active learning to generate new training and validation data efficiently.\"},{\"question\":\"What demonstration and application does the paper present?\",\"answer\":\"The framework is demonstrated on ibuprofen microparticle synthesis in an automated flow precipitation platform, with the goal of tuning particle size toward desired bioavailability and processability.\"}]","MLAPI - A framework for developing machine learning-guided drug particle syntheses in automated continuous flow platforms | PDF",1785935736,35,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"mlapi-a-framework-for-developing-machine-learning-guided-drug-particle-syntheses-in-automated-continuous-flow-platforms","",{"@graph":36,"@context":85},[37,54,68],{"@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/mlapi-a-framework-for-developing-machine-learning-guided-drug-particle-syntheses-in-automated-continuous-flow-platforms/126931/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the framework address in ML-guided pharmaceutical particle synthesis?","Question",{"text":75,"@type":76},"It targets the limitation that ML-integrated PAT approaches often require large data sets, which restricts practical adoption.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the framework model and guide drug particle synthesis?",{"text":80,"@type":76},"It uses classification to identify fouling-free operating regions, a multi-output Gaussian process regression model to relate process parameters to particle size, and active learning to generate new training and validation data efficiently.",{"name":82,"@type":73,"acceptedAnswer":83},"What demonstration and application does the paper present?",{"text":84,"@type":76},"The framework is demonstrated on ibuprofen microparticle synthesis in an automated flow precipitation platform, with the goal of tuning particle size toward desired bioavailability and processability.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]