课题基金 / 基金详情

PharmaCrystNet: Improving the Predictive Capabilities of Crystallisation Models in Pharma

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:
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
项目状态:
未结题
起止时间:
2024 至 --

项目摘要

项目成果

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
国内基金
海外基金
Improving modelling of compact binary evolution.
  • 批准号:
    10903001
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    20.0万元
  • 批准年份:
    2009
  • 负责人:
    史蒂芬
  • 依托单位: