Suppr超能文献

固体剂型设计、优化与制造的预测及相关技术

Predictive and correlative techniques for the design, optimisation and manufacture of solid dosage forms.

作者信息

Hardy Ian J, Cook Walter G

机构信息

Pharmaceutical and Analytical R&D, AstraZeneca R&D Charnwood, Bakewell Road, Loughborough, Leicestershire, LE11 5RH, UK.

出版信息

J Pharm Pharmacol. 2003 Jan;55(1):3-18. doi: 10.1211/002235702423.

Abstract

There is much interest in predicting the properties of pharmaceutical dosage forms from the properties of the raw materials they contain. Achieving this with reasonable accuracy would aid the faster development and manufacture of dosage forms. A variety of approaches to prediction or correlation of properties are reviewed. These approaches have variable accuracy, with no single technique yet able to provide an accurate prediction of the overall properties of the dosage form. However, there have been some successes in predicting trends within a formulation series based on the physicochemical and mechanical properties of raw materials, predicting process scale-up through mechanical characterisation of materials and predicting product characteristics by process monitoring. Advances in information technology have increased predictive capability and accuracy by facilitating the analysis of complex multivariate data, mapping formulation characteristics and capturing past knowledge and experience.

摘要

从药物剂型所含原料的性质预测其性质引起了广泛关注。以合理的准确度实现这一点将有助于剂型的更快开发和生产。本文综述了多种预测或关联性质的方法。这些方法的准确度各不相同,尚无单一技术能够准确预测剂型的整体性质。然而,基于原料的物理化学和机械性质预测配方系列中的趋势、通过材料的机械表征预测工艺放大以及通过过程监测预测产品特性方面已经取得了一些成功。信息技术的进步通过促进复杂多变量数据的分析、绘制配方特征以及获取过去的知识和经验,提高了预测能力和准确度。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验