Correlating Digital and Experimental Chemical Space to Pharmaceutical Manufacturing Processes

块体属性受结晶和/或球磨过程中定义的颗粒属性(如颗粒大小、形状和化学)的影响。了解颗粒属性如何影响制药制造过程性能仍然是该行业面临的重大挑战，这增加了开发强大的生产路线的成本和时间。这对混合均匀性、紧凑性和润滑性等散装材料流动特性提出了严格的要求，需要满足这些要求。因此，在药物开发的早期阶段对其进行流动预测变得越来越重要。目前，原材料和/或配方混合物对产品开发的适宜性需要详细、耗时的块体特性实验表征。该项目旨在展示预测模型对使用粒子信息学的产品制造的适用性，并提高结果的可解释性/模型置信度。该项目将使用实验表征和计算的分子和粒子特征的新型耦合来实现这一点。这个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，考虑到物理、化学和计算的分子/颗粒特征。该模式的产出将促进新药制造的快速发展。

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.