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Details of Grant 

EPSRC Reference: EP/J019798/1
Title: EPSRC Fellowships in Manufacturing - Macromolecular Manufacturing
Principal Investigator: Velayudhan, Professor A
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Biochemical Engineering
Organisation: UCL
Scheme: EPSRC Fellowship
Starts: 01 September 2012 Ends: 30 November 2018 Value (£): 1,047,150
EPSRC Research Topic Classifications:
Biochemical engineering Macro-molecular delivery
Manufact. Enterprise Ops& Mgmt
EPSRC Industrial Sector Classifications:
Pharmaceuticals and Biotechnology
Related Grants:
Panel History:
Panel DatePanel NameOutcome
21 Feb 2012 Fellowships in Manufacturing Announced
Summary on Grant Application Form
The bioprocess industry manufactures novel macromolecular drugs, proteins, to address a broad range of chronic and debilitating human diseases. The UK holds a leading position by virtue of its science base and has unique university capabilities underpinning the sector. Whilst revenues are large, ~£110bn in 2009 on a worldwide basis, there are huge pressures on the industry for change if demands for cost reduction and waste minimisation are to be met, and populations are to benefit from the potent drugs becoming available. A sea change in manufacturing will be needed over the next decade or so if the potential of modern drugs are to make their way through to widespread distribution. It has been estimated that an economic return of 200-1000% can be gained by applying modelling and optimisation tools to bioprocessing.

In this fellowship I will address a series of related issues which will be necessary in order to achieve the transformation in manufacturing necessary for the UK to build a world lead. Broadly, there are two goals:-

i) to create mathematical models of complete manufacturing processes used for macromolecule production; and

ii) to use these mathematical descriptions as tools for the faster prediction of efficient and reliable operation so that industry can be more flexible in the way it runs intrinsically costly process plants.

Research to address this first goal will require collaborations with a range of scientists, mathematical, engineers and computer scientists in order to work out the most effective way to describe complex processes in mathematical equations. Of particular importance will be the need to devise methods which allow us to capture how the process for making a particular material actually changes the material properties. This "process memory" is a poorly understood phenomena but has a profound impact on manufacturing performance.

The second goal will be addressed via an industrial forum providing materials and data for testing of our wrk. The most crucial aspect will be the need to use the mathematical description of the process as a framework for setting up control strategies designed to be reliable even if the manufacturing process is perturbed. Such events are frequent in biological systems where inherent levels of variability mean that the processes are never truly identical.

The project, as a whole, will therefore lead to the rational development of robust processes that should work with minimal change during manufacturing. This, in turn, will lead to reductions in manufacturing costs; such reductions are critical to producing the next generation of protein and vaccine medicines in quantities sufficient to meet worldwide demand.
Key Findings
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