{"title":"Machine learning of Raman spectra predicts drug release from polysaccharide coatings for targeted colonic delivery","authors":"","doi":"10.1016/j.jconrel.2024.08.010","DOIUrl":null,"url":null,"abstract":"<div><p>Colonic drug delivery offers numerous pharmaceutical opportunities, including direct access to local therapeutic targets and drug bioavailability benefits arising from the colonic epithelium's reduced abundance of cytochrome P450 enzymes and particular efflux transporters. Current workflows for developing colonic drug delivery systems involve time-consuming, low throughput <em>in vitro</em> and <em>in vivo</em> screening methods, which hinder the identification of suitable enabling materials. Polysaccharides are useful materials for colonic targeting, as they can be utilised as dosage form coatings that are selectively digested by the colonic microbiota. However, polysaccharides are a heterogeneous family of molecules with varying suitability for this purpose. To address the need for high-throughput material selection tools for colonic drug delivery, we leveraged machine learning (ML) and publicly accessible experimental data to predict the release of the drug 5-aminosalicylic acid from polysaccharide-based coatings in simulated human, rat, and dog colonic environments. For the first time, Raman spectra alone were used to characterise polysaccharides for input as ML features. Models were validated on 8 unseen drug release profiles from new polysaccharide coatings, demonstrating the generalisability and reliability of the method. Further, model analysis facilitated an understanding of the chemical features that influence a polysaccharide's suitability for colonic drug delivery. This work represents a major step in employing spectral data for forecasting drug release from pharmaceutical formulations and marks a significant advancement in the field of colonic drug delivery. It offers a powerful tool for the efficient, sustainable, and successful development and pre-ranking of colon-targeted formulation coatings, paving the way for future more effective and targeted drug delivery strategies.</p></div>","PeriodicalId":15450,"journal":{"name":"Journal of Controlled Release","volume":null,"pages":null},"PeriodicalIF":10.5000,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0168365924005492/pdfft?md5=13e24be66904c5ef5cc0baa47da289d4&pid=1-s2.0-S0168365924005492-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Controlled Release","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168365924005492","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
Colonic drug delivery offers numerous pharmaceutical opportunities, including direct access to local therapeutic targets and drug bioavailability benefits arising from the colonic epithelium's reduced abundance of cytochrome P450 enzymes and particular efflux transporters. Current workflows for developing colonic drug delivery systems involve time-consuming, low throughput in vitro and in vivo screening methods, which hinder the identification of suitable enabling materials. Polysaccharides are useful materials for colonic targeting, as they can be utilised as dosage form coatings that are selectively digested by the colonic microbiota. However, polysaccharides are a heterogeneous family of molecules with varying suitability for this purpose. To address the need for high-throughput material selection tools for colonic drug delivery, we leveraged machine learning (ML) and publicly accessible experimental data to predict the release of the drug 5-aminosalicylic acid from polysaccharide-based coatings in simulated human, rat, and dog colonic environments. For the first time, Raman spectra alone were used to characterise polysaccharides for input as ML features. Models were validated on 8 unseen drug release profiles from new polysaccharide coatings, demonstrating the generalisability and reliability of the method. Further, model analysis facilitated an understanding of the chemical features that influence a polysaccharide's suitability for colonic drug delivery. This work represents a major step in employing spectral data for forecasting drug release from pharmaceutical formulations and marks a significant advancement in the field of colonic drug delivery. It offers a powerful tool for the efficient, sustainable, and successful development and pre-ranking of colon-targeted formulation coatings, paving the way for future more effective and targeted drug delivery strategies.
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.