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.

制药行业在为全球人提供挽救生命的药物方面发挥了关键作用。但是，制作这些药物的过程通常是漫长，昂贵且在环境上不可持续的，最多需要10年的时间，并且每千克产品的产品耗资200亿英镑，并产生多达100公斤的废物。在开发实现纯高品质药物的过程中，至关重要且无处不在的步骤是结晶，在该过程中，固体药物颗粒是由成核和溶液生长形成的。这些基本过程步骤是高度可预测的，对许多参数敏感，并且在分子尺度上的详细机械理解仍然难以捉摸。开发有用的预测工具来指导这一步骤的设计将产生重大影响，从而有可能降低具有可持续制造过程的设计，扩展和实施的成本，时间，资源和浪费。用于基于模型的结晶过程设计的现行方法并不总是准确，不总是准确，无法捕捉到重要的现象和常见的现象，例如Polymorphist，carlymphormphlist或fue un。该项目将通过将尖端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的结晶方式。它的目的是：1）对决定结晶结果的药物分子特性的详细了解2）开发一种新的混合/ML/ML/Magical/Physics/Physics Imformed计算机模型，该模型可以预测在与高准确性的不同范围内使用新模型的不同范围的范围的模型，可预测在各种工业相关的过程中的结晶效果。更快的药物生产从减少30％的开发时间，这意味着新药物更快地到达患者2）在药物制造过程中节省巨大的成本，导致降低药品价格3）药物生产的环境足迹从开发过程中使用的材料的70-80％减少了70-80％的环境足迹，从而使该行业更具可持续性，从而使材料的效率变得更加可持续。简化有效的下游药物配方奶粉操作的进一步好处。该项目不仅对医学制造商和其他专业化学制造商有望，还适用于患者，环境和全球社区。