摘要
缓控释制剂是能降低药物毒副作用、提高患者依从性和增强疗效的理想制剂,其中口服缓控释片的应用最为广泛。制剂体外释放与药物的体内吸收密切相关,然而目前的体外释放实验均为劳动密集型和破坏性的,大数据的缺乏也导致难以建立良好的体内外相关性。计算机建模作为一种可将客观原理转化为数学模型的技术手段,在数据预测方面有很大的前景。本综述探讨了可用于口服缓控释片体外释放曲线预测的计算机建模手段,并进一步讨论了可提高模型准确度的辅助技术,为药物的开发提供了新的思路。
Sustained and controlled release preparation is ideal for reducing the side effects of drugs,improving patient compliance and enhancing efficacy,among which oral sustained-release tablets are the most widely used.The in vitro release of the preparation is closely related to the in vivo absorption of the drug.However,current in vitro release experiments are labor-intensive and destructive,and the lack of big data also makes it difficult to establish good in vivo and in vitro correlations.Computer modeling,as a technical means that can transform objective principles into mathematical models,has great prospects in data prediction.This review explores the existing computer modeling methods that can be used to predict in vitro release profiles of oral sustained-release tablets,and further discusses auxiliary technologies that can improve the accuracy of the models,providing new ideas for drug development.
作者
陈潇
郑海花
潘新彤
向柏
潘振华
党云洁
CHEN Xiao;ZHENG Hai-hua;PAN Xin-tong;XIANG Bai;PAN Zhen-hua;DANG Yun-jie(School of Pharmacy,Hebei Medical University,Shijiazhuang 050017,China;The Second Hospital of Hebei Medical University,Shijiazhuang 050000,China)
出处
《药学学报》
CAS
CSCD
北大核心
2024年第6期1593-1600,共8页
Acta Pharmaceutica Sinica
基金
国家自然科学基金资助项目(81973251)
河北省自然科学基金资助项目(H2020206610)
河北省教育厅引进留学人才项目(C20220345).
关键词
口服缓控释片
计算机建模
体外释放曲线
数学建模
机器学习
oral sustained-release tablet
computer modeling
in vitro release curve
mathematical modeling
machine learning