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Correlating Digital and Experimental Chemical Space to Pharmaceutical Manufacturing Processes

将数字和实验化学空间与药品制造过程相关联

基本信息

批准号：
2898544
负责人：
金额：
--
依托单位：
University of Strathclyde
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2898544
关键词：
Correlating Digital Experimental Chemical Space

项目摘要

Bulk properties are influenced by particle attributes, such as particle size, shape, and chemistry, defined during crystallization and/or milling processes. Understanding how particle attributes affect the pharmaceutical manufacturing process performance remains a significant challenge for the industry, adding cost and time to developing robust production routes. This places strict demands on bulk material flow properties such as blend uniformity, compactability, and lubrication, which need to be satisfied. Consequently, making the flow prediction of pharmaceutical materials during early-stage development is increasingly important. Currently, the suitability of raw materials and/or formulated blends for product development requires detailed, time-consuming experimental characterisation of the bulk properties.The project aims to demonstrate the applicability of predictive models towards product manufacturing using particle informatics and to improve explainability/model confidence in the results. The project will use the novel coupling of experimental characterisation and computed molecular and particle features to achieve this. This will culminate in a framework that allows for an explainable and interpretable machine learning model.This project will deliver a novel, interpretable machine-learning model for predicting powder flow considering physical, chemical, and computed molecule/particle features. The output of this model will facilitate the rapid development of new medicines manufacturing.

整体性质受结晶和/或研磨过程中定义的颗粒属性（如粒度、形状和化学性质）的影响。了解颗粒属性如何影响制药工艺性能仍然是行业面临的重大挑战，增加了开发稳健生产路线的成本和时间。这对散装材料的流动性能提出了严格的要求，如混合均匀性、可重复性和润滑性，这些都需要得到满足。因此，在早期开发阶段对药物材料的流动进行预测变得越来越重要。目前，原材料和/或配方混合物的产品开发的适用性需要详细的，耗时的实验表征的散装properties.The项目旨在证明预测模型的适用性对产品制造使用粒子信息学和提高解释性/模型的结果的信心。该项目将使用实验表征和计算分子和粒子特征的新耦合来实现这一目标。这将最终形成一个框架，允许一个可解释和可解释的机器学习模型。该项目将提供一个新颖的，可解释的机器学习模型，用于预测粉末流动，考虑物理，化学和计算的分子/颗粒特征。该模型的输出将促进新药生产的快速发展。