[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126483-en":3,"doc-seo-126483-105":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},126483,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Precise Control of Drug Release in Machine Learning-Designed Antibody-Eluting Implants for Postoperative Scarring Inhibition in Glaucoma - Research Article","A scalable subconjunctival micro-cylindrical implant platform using polycaprolactone (PCL) and polyethylene glycol (PEG) is developed for sustained delivery of basic fibroblast growth factor monoclonal antibody (FGFb mAb) to inhibit postoperative scarring in glaucoma filtration surgery. Machine learning predicts drug release profiles from fabrication parameters including polymer composition, implant geometry, and drug loading, with LightGBM achieving the strongest performance for release kinetics. The optimized PCL-PEG system with PLGA bio-coating shows synergistic diffusion-degradation behavior and suppresses fibroblast-mediated collagen contraction and fibrotic gene expression in vitro. Rat in vivo GFS studies and histology further confirm reduced fibrosis markers, along with good biocompatibility and no significant toxicity.","King’s Research Portal  \nDOI:  \n10.1002/adhm.202502689  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication record in King's Research Portal  \nCitation for published version (APA):  \nQin, M. , Jiang, W. , Thong, K. X. , Ulker, Z. , Patel, B. , & Yu-Wai-Man, C. (2026) . Precise Control of Drug Release in Machine Learning-Designed Antibody-Eluting Implants for Postoperative Scarring Inhibition in Glaucoma. Advanced Healthcare Materials, 15(13), Article e02689 . [https://doi.org/10.1002/adhm.202502689](https://doi.org/10.1002/adhm.202502689)  \nCiting this paper  \nPlease note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections.  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights.  \n•Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research.  \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 the publication in the Research Portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact [librarypure@kcl.ac.uk](librarypure@kcl.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 04. Aug. 2026  \nAdvanced Healthcare Materials   \n[www.advhealthmat.de](www.advhealthmat.de)  \n RESEARCH ARTICLE   \nPrecise Control of Drug Release in Machine Learning-Designed Antibody-Eluting Implants for Postoperative Scarring Inhibition in Glaucoma  \nMengqi Qin  Wenbing Jiang  Kai Xin Thong  Zeynep Ulker  Brihitejas Patel  Cynthia Yu-Wai-Man   \nFaculty of Life Sciences and Medicine, King’s College London, London, UK  \nCorrespondence: Cynthia Yu-Wai-Man ([cynthia.yu-wai-man@kcl.ac.uk](cynthia.yu-wai-man@kcl.ac.uk))  \nReceived: 29 May 2025  Revised: 21 December 2025  Accepted: 4 January 2026  \nKeywords: drug delivery | glaucoma filtration surgery | machine learning | monoclonal antibodies | ophthalmology | pharmacology | polymers  \nABSTRACT  \nWe have developed a scalable, smart subconjunctival micro-cylindrical implant system composed of polycaprolactone (PCL) and polyethylene glycol (PEG) for sustained delivery of basic fibroblast growth factor monoclonal antibody (FGFb mAb), a promising anti-fibrotic agent. This delivery platform allows precise prediction of drug release profiles through machine learning analysis based on key fabrication parameters, such as polymer composition, implant dimension, and drug content. Among the tested machine learning algorithms, LightGBM outperforms others in predicting drug release kinetics (R2 = 0.9000 ± 0.0058) . Besides, this model effectively elucidates the combined roles of various implant parameters in controlling release behavior and provides insights to guide formulation selection to maximize drug release. The optimized PCL-PEG implant, combined with 0.1% w/v poly (lactic-co-glycolic acid) (PLGA) bio-coating, exhibits sustained antibody release through synergistic diffusion-degradation kinetics. In vitro evaluation demonstrates the PCL-PEG/FGFb mAb@PLGA implant’s ability to effectively inhibit 3D-fibroblastmediated collagen contraction and fibrotic genes. In vivo studies in a rat GFS model, along with histological analysis, further validate the im","cbCaia8XUj7XisUA","https://ap.wps.com/l/cbCaia8XUj7XisUA","pdf",12761184,15,1,23,"English","en",105,"# Abstract\n# Introduction\n## Problem: postoperative fibrosis in glaucoma filtration surgery\n## Rationale: limitations of nonspecific antifibrotic drugs and rapid mAb clearance\n## Objective: sustained-release implant to reduce repeated injections\n# Key approach and modeling (machine learning-guided release prediction)\n# Experimental validation (in vitro and in vivo efficacy, biocompatibility)","[{\"question\":\"What implant system is developed for antibody delivery in glaucoma filtration surgery?\",\"answer\":\"A scalable subconjunctival micro-cylindrical implant made from PCL and PEG delivers sustained basic fibroblast growth factor monoclonal antibody (FGFb mAb).\"},{\"question\":\"How does machine learning contribute to controlling drug release?\",\"answer\":\"Machine learning predicts drug release profiles using fabrication parameters such as polymer composition, implant dimension, and drug content; LightGBM performs best for release kinetics prediction.\"},{\"question\":\"What evidence supports the implant’s effectiveness and safety?\",\"answer\":\"In vitro assays show inhibition of fibroblast-mediated collagen contraction and fibrotic gene expression, while rat in vivo GFS histology confirms reduced fibrosis markers and demonstrates good biocompatibility with no significant toxicity.\"}]","Precise Control of Drug Release in Machine Learning-Designed Antibody-Eluting Implants for Postoperative Scarring Inhibition in Glaucoma - Research Article | PDF",1785905306,58,{"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},"precise-control-of-drug-release-in-machine-learning-designed-antibody-eluting-implants-for-postoperative-scarring-inhibition-in-glaucoma-research-article","",{"@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/precise-control-of-drug-release-in-machine-learning-designed-antibody-eluting-implants-for-postoperative-scarring-inhibition-in-glaucoma-research-article/126483/",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","2026-08-24","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},"What implant system is developed for antibody delivery in glaucoma filtration surgery?","Question",{"text":77,"@type":78},"A scalable subconjunctival micro-cylindrical implant made from PCL and PEG delivers sustained basic fibroblast growth factor monoclonal antibody (FGFb mAb).","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does machine learning contribute to controlling drug release?",{"text":82,"@type":78},"Machine learning predicts drug release profiles using fabrication parameters such as polymer composition, implant dimension, and drug content; 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