PharmaCrystNet: Improving the Predictive Capabilities of Crystallisation Models in Pharma

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往往漫长、昂贵，而且在环境上不可持续，需要长达10年的时间，耗资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，一个关键和普遍的步骤是结晶，其中固体药物颗粒由溶液成核和生长形成。这些基本的过程步骤是高度不可预测的，对许多参数敏感，在分子尺度上的详细机制理解仍然难以捉摸。开发有用的预测工具来指导这一步骤的设计，将对减少可持续制造过程的设计、扩展和实施所涉及的成本、时间、资源和浪费产生重大影响。目前用于基于模型的结晶过程设计的方法并不总是准确的，无法捕获重要的和常见的现象，如多态性，团聚或结垢。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一现状。PharmaCrystNet将彻底改变我们理解和预测药物生产中结晶的方式。它的目标是：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，可以在不同规模的各种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，完善和验证模型。这种新模式将使：1)药物生产速度更快，开发时间缩短30%，这意味着新药更快地到达患者手中；2)在药物制造过程中节省大量成本，从而降低药物价格；3)药物生产的环境足迹显著减少，开发过程中使用的材料减少70-80%，使该行业更具可持续性。通过完善结晶过程，我们将推动制药行业进入一个生产工程材料的效率和可持续性的新时代，这将为简化高效的下游药物配方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。

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.