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Details of Grant 

EPSRC Reference: EP/Z533014/1
Title: PharmaCrystNet: Improving the Predictive Capabilities of Crystallisation Models in Pharma
Principal Investigator: Brown, Dr C
Other Investigators:
Dolean Maini, Professor V Johnston, Professor BF
Researcher Co-Investigators:
Project Partners:
Cambridge Crystallographic Data Centre Pfizer UCB
Department: Inst of Pharmacy and Biomedical Sci
Organisation: University of Strathclyde
Scheme: Standard Research TFS
Starts: 01 July 2024 Ends: 30 June 2025 Value (£): 153,338
EPSRC Research Topic Classifications:
Biological & Medicinal Chem. Biomedical sciences
Drug Formulation & Delivery
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:  
Summary on Grant Application Form
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 outcomes

2) 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 accuracy

3) 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 quickly

2) Huge cost savings in the drug manufacturing process, leading to lower drug prices

3) 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.
Key Findings
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Potential use in non-academic contexts
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Further Information:  
Organisation Website: http://www.strath.ac.uk