PharmaCrystNet: Improving the Predictive Capabilities of Crystallisation Models in Pharma

PharmaCrystNet：提高制药领域结晶模型的预测能力

• 批准号: EP/Z533014/1
• 负责人: Cameron Brown
• 金额: $ 19.07万
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FZ533014%2F1
• 关键词: PharmaCrystNet; Improving; Predictive; Capabilities; Crystallisation

The pharmaceutical industry plays a pivotal role in delivering life-saving medicines to people worldwide. However, the process of making these medicines is often lengthy, costly, and environmentally unsustainable, taking up to 10 years and costing £2Bn and generating up to 100 Kg of waste for every Kg of product. A crucial and ubiquitous step in developing the process of achieving pure, high quality drug substances is crystallisation, where solid drug particles are formed by nucleation and growth from solution. These fundamental process steps are highly unpredictable, sensitive to many parameters and a detailed mechanistic understanding at the molecular scale remains elusive. Developing useful predictive tools to guide the design of this step would have a significant impact with the potential to reduce the cost, time, resources, and waste involved in the design, scale-up and implementation of sustainable manufacturing processes.Current methods used for model-based design of crystallisation processes are not always accurate, failing to capture significant and commonly encountered phenomena such as polymorphism, agglomeration or fouling. This project will change that by blending cutting-edge hybrid machine learning and physics-based computing techniques with our understanding of chemistry and chemical processes.PharmaCrystNet will revolutionise the way we understand and predict crystallisation in drug manufacturing. It aims to:1) Develop a detailed understanding of the molecular attributes of drug molecules that dictate crystallisation outcomes2) Develop a new hybrid/ML/mechanistic/physics-informed computer model that can predict crystallisation outcomes under a wide range of industrially relevant process conditions at different scales with high accuracy3) Test, refine, and validate the model using real-world experiments.This new model will enable:1) Faster drug production from a 30% reduction in development time, meaning new medicines reach patients more quickly2) Huge cost savings in the drug manufacturing process, leading to lower drug prices3) A significant reduction in the environmental footprint of drug production from a 70-80% reduction in material used during development, making the industry more sustainable.By perfecting the crystallisation process, we will propel the pharmaceutical industry into a new era of efficiency and sustainability in generating engineered materials that will deliver further benefits for streamlined efficient downstream drug formulation operations. This project holds promise not just for medicine manufacturers and other specialty chemical manufacturers, but for patients, the environment, and the global community at large.

制药业在向全世界人民提供拯救生命的药物方面发挥着关键作用。然而，制造这些药物的过程通常是漫长的，昂贵的，环境不可持续的，需要长达10年的时间，成本高达20亿英镑，每公斤产品产生多达100公斤的废物。在开发获得纯净、高质量原料药的过程中，一个关键且普遍存在的步骤是结晶，其中固体药物颗粒通过溶液的成核和生长形成。这些基本的工艺步骤是高度不可预测的，对许多参数敏感，并且在分子尺度上的详细机理理解仍然难以捉摸。开发有用的预测工具来指导这一步骤的设计将具有显著的影响，可能会减少可持续制造工艺的设计、放大和实施中涉及的成本、时间、资源和浪费。目前用于结晶工艺的基于模型的设计的方法并不总是准确的，未能捕获重要和常见的现象，例如多晶型，结块或结垢。该项目将通过将尖端的混合机器学习和基于物理的计算技术与我们对化学和化学过程的理解相结合来改变这种情况。PharmaCrystNet将彻底改变我们理解和预测药物制造中结晶的方式。它旨在：1）详细了解决定结晶结果的药物分子的分子属性2）开发一种新的混合/ML/机械/物理信息计算机模型，可以在不同规模的广泛工业相关工艺条件下以高精度预测结晶结果3）使用真实世界的实验测试，改进和验证模型。1）开发时间缩短30%，从而加快药物生产，这意味着新药更快地到达患者手中2）药物制造过程中的巨大成本节约，导致更低的药品价格3）药物生产的环境足迹显著减少，从开发期间使用的材料减少70-80%，通过完善结晶工艺，我们将推动制药行业进入一个高效和可持续的新时代，生产工程材料，为精简高效的下游药物制剂操作带来进一步的好处。该项目不仅为药品制造商和其他特种化学品制造商带来了希望，也为患者、环境和整个全球社会带来了希望。