Remo Eugster, Markus Orsi, Giorgio Buttitta, Nicola Serafini, Mattia Tiboni, Luca Casettari, Jean-Louis Reymond, Simone Aleandri, Paola Luciani
{"title":"Leveraging machine learning to streamline the development of liposomal drug delivery systems.","authors":"Remo Eugster, Markus Orsi, Giorgio Buttitta, Nicola Serafini, Mattia Tiboni, Luca Casettari, Jean-Louis Reymond, Simone Aleandri, Paola Luciani","doi":"10.1016/j.jconrel.2024.10.065","DOIUrl":null,"url":null,"abstract":"<p><p>Drug delivery systems efficiently and safely administer therapeutic agents to specific body sites. Liposomes, spherical vesicles made of phospholipid bilayers, have become a powerful tool in this field, especially with the rise of microfluidic manufacturing during the COVID-19 pandemic. Despite its efficiency, microfluidic liposomal production poses challenges, often requiring laborious, optimization on a case-by-case basis. This is due to a lack of comprehensive understanding and robust methodologies, compounded by limited data on microfluidic production with varying lipids. Artificial intelligence offers promise in predicting lipid behaviour during microfluidic production, with the still unexploited potential of streamlining development. Herein we employ machine learning to predict critical quality attributes and process parameters for microfluidic-based liposome production. Validated models predict liposome formation, size, and production parameters, significantly advancing our understanding of lipid behaviour. Extensive model analysis enhanced interpretability and investigated underlying mechanisms, supporting the transition to microfluidic production. Unlocking the potential of machine learning in drug development can accelerate pharmaceutical innovation, making drug delivery systems more adaptable and accessible.</p>","PeriodicalId":15450,"journal":{"name":"Journal of Controlled Release","volume":null,"pages":null},"PeriodicalIF":10.5000,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Controlled Release","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.jconrel.2024.10.065","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Drug delivery systems efficiently and safely administer therapeutic agents to specific body sites. Liposomes, spherical vesicles made of phospholipid bilayers, have become a powerful tool in this field, especially with the rise of microfluidic manufacturing during the COVID-19 pandemic. Despite its efficiency, microfluidic liposomal production poses challenges, often requiring laborious, optimization on a case-by-case basis. This is due to a lack of comprehensive understanding and robust methodologies, compounded by limited data on microfluidic production with varying lipids. Artificial intelligence offers promise in predicting lipid behaviour during microfluidic production, with the still unexploited potential of streamlining development. Herein we employ machine learning to predict critical quality attributes and process parameters for microfluidic-based liposome production. Validated models predict liposome formation, size, and production parameters, significantly advancing our understanding of lipid behaviour. Extensive model analysis enhanced interpretability and investigated underlying mechanisms, supporting the transition to microfluidic production. Unlocking the potential of machine learning in drug development can accelerate pharmaceutical innovation, making drug delivery systems more adaptable and accessible.
期刊介绍:
The Journal of Controlled Release (JCR) proudly serves as the Official Journal of the Controlled Release Society and the Japan Society of Drug Delivery System.
Dedicated to the broad field of delivery science and technology, JCR publishes high-quality research articles covering drug delivery systems and all facets of formulations. This includes the physicochemical and biological properties of drugs, design and characterization of dosage forms, release mechanisms, in vivo testing, and formulation research and development across pharmaceutical, diagnostic, agricultural, environmental, cosmetic, and food industries.
Priority is given to manuscripts that contribute to the fundamental understanding of principles or demonstrate the advantages of novel technologies in terms of safety and efficacy over current clinical standards. JCR strives to be a leading platform for advancements in delivery science and technology.