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Disintegration And Dissolution Of Solid Oral Dose Forms In The Fed Stomach : Novel Insights Using MRI Imaging In Humans

Disintegration And Dissolution Of Solid Oral Dose Forms In The Fed Stomach : Novel Insights Using MRI Imaging In Humans
固体口服剂型在胃中的崩解和溶解:利用人体 MRI 成像的新见解
批准号:
2753934
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2022
资助国家:
英国
项目状态:
未结题
起止时间:
2022 至 --

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中文摘要
翻译
在我们对固体口服剂型在美联储状态下的行为的基本知识中仍然存在显著的差距。核磁共振成像有可能提供发生在胃中的空间和时间过程的新信息。GSK与诺丁汉大学胃肠成像专家的这项iCASE合作解决了一些关键的基本问题,有可能为口腔产品开发带来更好的体外和硅胶(建模)工具,并最终增强我们在FED状态下量身定制药物释放的能力。学生将通过以下方式优化和利用MRI成像的能力,以检测和表征喂养的胃中剂型的行为:1)识别和设计合适的测试崩解配方。2)优化MRI技术和图像分析以评价剂型崩解。体外评估将执行这项工作的关键部分。3)通过精心挑选的测试配方和测试餐对人类志愿者的MRI技术进行评估学生将受益于世界领先的研究环境,具有明确翻译应用的独特多学科方法,以及量身定制的培训机会,包括图像数据处理、生物医学成像、机器学习和人工智能培训
英文摘要
Significant gaps remain in our basic knowledge of solid oral dosage form behaviour in the fed state. MRI imaging has the potential to provide new information on spatial and temporal processes occurring in the stomach. This iCASE collaboration between GSK and gastrointestinal imaging and specialists at the University of Nottingham addresses some of these key basic issues, with the potential to lead to better in-vitro and in-silico (modelling) tools for oral product development, and ultimately enhance our ability to tailor drug release in the fed state.The student will optimise and exploit the ability of MRI imaging to detect and characterise the behaviour of dosage forms in the fed stomach by:1) Identification and design of suitable test disintegrating formulations. 2) Optimisation of MRI techniques and image analysis for the evaluation of dosage form disintegration. In-vitro assessment will perform a key part of this work. 3) Evaluation of MRI techniques in human volunteers with carefully selected test formulations and test mealsThe student will benefit from a world-leading research environment, a unique multi-disciplinary approach with clear translational applications, and tailored training opportunities including training in image data processing, biomedical imaging, machine learning and artificial intelligence
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