Calibration and Optimisation of Biopharmaceutical Processes

Modern biopharmaceutical drugs offer novel and personalised ways for treating diseases, and the market is expected to increase to $500 billion by 2025. Controlling biopharmaceutical manufacturing processes is a challenge, however, as the process usually involves complex biological reactions. Though it is possible to build dynamic mechanistic models for the processes, and produce optimised control strategies based on the model, it requires parameter tuning for a range of parameters during the process. The traditional workflow usually starts from lab experiments, then calibrates a mechanistic model using the produced data, and finally uses optimisation for producing the best possible control strategy. However, the stages in this workflow are usually interdependent. For example, if we aim to produce the optimal control strategy, it is not necessary for the model to be perfect for each possible situation, as long as we can identify the optimal control strategy. And the control strategy will influence where the model needs to be accurate and thus what data should be collected in the lab experiment. The core research question of the project is how to detect which lab experiment would provide the most valuable information, and then iteratively extend the dataset by conducting the most informative additional experiment. This would allow us to improve the accuracy of the parameter tuning of the model, and generate better control strategies with restricted budget. We aim to develop new Bayesian Optimisation algorithms that have improvements over the existing methods. Possible approaches could include minimising expected entropy, maximising expected model change or variance reduction etc. We will start from discrete space, with fixed noise distribution, and gradually make our simple model more complex until we can work with real biopharmaceutical production processes. The problem of combined data collection, calibration and optimisation occurs in many domains, so the developed algorithms would be relevant also in a much wider domain.

In the 2018 Government Office for Science report, 'Computational Modelling: Technological Futures', Greg Clarke, the Secretary of State for Business Energy and Industrial Strategy, wrote "Computational modelling is essential to our future productivity and competitiveness, for businesses of all sizes and across all sectors of the economy". With its focus on computational models, the mathematics that underpin them, and their integration with complex data, the MathSys II CDT will generate diverse impacts beyond academia. This includes impacts on skills, on the economy, on policy and on society.

Impacts on skills.
MathSys II will produce a minimum of 50 PhD graduates to support the growing national demand for advanced mathematical modelling and data analysis skills. The CDT will provide each of them with broad core skills in the MSc, a deep knowledge of their chosen research specialisation in the PhD and a complementary qualification in transferable skills integrated throughout. Graduates will thus acquire the profiles needed to form the next generation of leaders in business, government and academia. They will be supported by an integrated pastoral support framework, including a diverse group of accessible leadership role models. The cohort based environment of the CDT provides a multiplier effect by encouraging cohorts to forge long-lasting professional networks whose value and influence will long outlast the CDT itself. MathSys II will seek to maximise the influence of these networks by providing topical training in Responsible Research and Innovation, by maintaining a robust Equality, Diversity & Inclusion policy, and by integration with Warwick's global network of international partnerships.

Economic impacts.
The research outputs from many MathSys II PhD projects will be of direct economic value to commercial, public sector and charitable external partners. Engagement with CDT partners will facilitate these impacts. This includes co-supervision of PhD and MSc projects, co-creation of Research Study Groups, and a strong commitment to provide placements/internships for CDT students. When commercial innovations or IP are generated, we will work with Warwick Ventures, the commercial arm of the University of Warwick, to commercialise/license IP where appropriate. Economic impact may also come from the creation of new companies by CDT graduates. MathSys II will present entrepreneurship as a viable career option to students. One external partner, Spectra Analytics, was founded by graduates of the preceding Complexity Science CDT, thus providing accessible role models. We will also provide in-house entrepreneurship training via Warwick Ventures and host events by external start-up accelerator Entrepreneur First.

Impacts on policy.
The CDT will influence policy at the national and international level by working with external partners operating in policy. UK examples include Department of Health, Public Health England and DEFRA. International examples include World Health Organisation (WHO) and the European Commission for the Control of Foot-and-mouth Disease (EuFMD). MathSys students will also utilise the recently announced UKRI policy internships scheme.

Impacts on society.
Public engagement will allow CDT students to promote the value of their research to society at large. Aside from social media, suitable local events include DataBeers, Cafe Scientifique, and the Big Bang Fair. MathSys will also promote a socially-oriented ethos of technology for the common good. Concretely, this includes the creation of open-source software, integration of software and data carpentry into our computational and data driven research training and championing open-access to research. We will also contribute to the 'innovation culture and science' strand of Coventry's 2021 City of Culture programme.