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Details of Grant 

EPSRC Reference: EP/V051458/1
Title: EPSRC-SFI: Table Top Manufacturing of Tailored Silica for Personalised Medicine [SiPM]
Principal Investigator: Patwardhan, Professor SV
Other Investigators:
Brown, Dr S
Researcher Co-Investigators:
Project Partners:
Asynt Glantreo Ltd Queen's University of Belfast
Seda Pharmaceutical Development Services Texas A and M University TIGERSi Technologies Ltd.
University of Limerick University of Nottingham
Department: Chemical & Biological Engineering
Organisation: University of Sheffield
Scheme: Standard Research
Starts: 01 October 2021 Ends: 30 September 2024 Value (£): 649,763
EPSRC Research Topic Classifications:
Design & Testing Technology
EPSRC Industrial Sector Classifications:
Manufacturing
Related Grants:
Panel History:
Panel DatePanel NameOutcome
08 Jun 2021 Engineering Prioritisation Panel Meeting 8 and 9 June 2021 Announced
Summary on Grant Application Form
Personalised medicine (PM) is gaining significant attention in recent years as it has the potential to transform healthcare across the globe by moving away from the "one-size-fits-all" model to utilise personal circumstances, medical history and needs to deliver individually suitable treatment. Current bulk manufacturing technologies are unable to meet most of these demands as they are slow in responding to changes, capital intensive, use unsustainable methods and are not flexible to meet PM needs.



A recent white paper from the EPSRC funded Redistributed Manufacturing in Healthcare has identified that small-scale, localised, high-speed and automated manufacturing platforms are urgently needed to realise PM. They identified that such "factory-in-a-box" should be:



- able to manufacture on-demand,

- flexible to deliver multiple products with desired properties,

- sustainable (energy efficient and using mild conditions) and

- able to integrate various unit operations using data science tools.



Given the future needs for PM, recent research efforts have been directed towards redefining the manufacturing of active pharmaceutical ingredient (API) and their formulations into e.g. tablets for oral dosages using advanced methods such as microfluidics, Hot Melt Extrusion or 3D printing. However, as a medicine is a carefully designed formulation of an API with non-active components such as excipients or drug delivery systems (DDS), challenges in manufacturing of the non-active components for PM are also equally important, but have not been addressed.

The non-active components improve physicochemical properties and bioavailability of APIs. In its many forms silica is one of the most commonly used component of many current and future API formulations, yet their manufacturing to meet the PM requirements do not exist. Specifically, despite tremendous progress made on the use of silica in pharmaceutical formulations, currently, their on-demand, automated and flexible manufacture to produce silica of desired properties for PM is non-existent. A key reason for this is that the vast majority of promising silicas require synthesis conditions that are prohibitive for any meaningful scale-up and for implementation in a 'factory in a box' platform. Hence, this missing piece, despite the recent developments in manufacturing of API and formulations, creates a significant barrier to making PM a reality.

We have shown the potential of bioinspired silica (BIS) as an alternate drug delivery system, which is scalable, economical and sustainable - an ideal candidate for on-demand and flexible manufacturing. This research will rely on a close synergy between computational modelling and experimental synthesis. Green synthesis processes and research on intensified reactors by the applicants will be used as a starting point. A range of intensified reactors and Gaussian Process-based modelling will be used to achieve process intensification of particulate manufacturing processes. Comprehensive models will be used to create digital twins of fluidic devices and recipes of green synthesis of silica particles using those devices. Machine learning approaches based on results of simulations of reactors will be developed to relate quality attributes of silica produced with key process and operating parameters. Device geometry and process parameters will be manipulated to achieve the desired Critical Quality Attributes (CQAs).

The work will contribute to revolutionising PM and help deliver table top pharmaceutical manufacturing equipment in hospitals and pharmacies. Ultimately, the impact will include significant improvements in treatments and quality of life as well as the formation of new companies to build such units.

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Organisation Website: http://www.shef.ac.uk