Active Materials & Manufacturing Food & Agriculture

Next-Gen Biopharma Manufacturing 5.0

In plain English

AI plain-English summary

AstraZeneca and UCL are using AI to redesign how complex biologic drugs are manufactured, from cell-line development to factory-floor digital twins. This matters because next-generation medicines—multi-specific antibodies, antibody-drug conjugates, and protein nanoparticles—are notoriously difficult to produce. Traditional manufacturing methods yield too little product, require too many steps, and generate a large carbon footprint. Without fundamental changes in production, these promising therapies remain slow to reach patients and expensive to make. If the partnership succeeds, it could cut the development time for new biologics and push the industry toward net-zero carbon emissions in manufacturing. The project builds digital twins of production lines to simulate and optimise every step, uses AI to screen cell lines for higher yields, and evaluates the economic and environmental trade-offs of each new process. The result would be faster patient access to advanced treatments, lower manufacturing costs, and a global benchmark for sustainable drug production. The partnership also trains the next generation of scientists in both bioprocessing and digital tools, strengthening the UK’s biotechnology workforce.

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Context: In the rapidly evolving world of healthcare, it is crucial for the UK to maintain its edge in developing and delivering cutting-edge medicines swiftly and sustainably. AstraZeneca, the UK’s largest biopharmaceutical company, in partnership with UCL Biochemical Engineering, aims to tackle these challenges through a strategic Prosperity Partnership. Building on a decade-long collaboration, this partnership leverages a rich history of joint achievements, including significant contributions to bioprocessing research and the training of key industry and academic leaders. The Prosperity Partnership initiative focuses on next-generation therapies - such as multi-specific antibodies, antibody-drug conjugates (ADCs), and protein nanoparticles - that offer tremendous potential but come with complex manufacturing demands. Challenge: The production of these advanced therapies faces significant hurdles, including low yields, intricate processes, and stringent quality requirements. Traditional methods are often insufficient for these novel treatments, demanding innovative approaches to streamline their development and manufacturing processes. Aims and Objectives: The aim of our partnership is to transform the way we make medicines. By combining advanced AI technology and digital tools, we plan to make the development of biopharmaceuticals quicker, more cost-effective, and environmentally friendly. Our goal is to ensure that new treatments reach patients faster and to significantly reduce the carbon footprint of manufacturing these medicines, thereby setting new global standards for how drugs are produced. The project is structured into four interconnected work packages (WPs): WP1: Focuses on using AI and engineering biology for rapid screening and optimization of cell lines, directly influencing the quality and quantity of biopharmaceutical drug production. WP2: Aims to improve the ease of manufacture of novel biopharmaceuticals, ensuring scalability and robustness in production. WP3: Utilizes digital twins and Process Analytical Technologies (PAT) to simulate and optimize manufacturing processes, enhancing decision-making and efficiency. WP4: Evaluates the economic and environmental impacts of new manufacturing technologies, promoting sustainable practices. Potential Applications and Benefits: Our partnership is poised to make a significant impact on healthcare and the environment. Quicker Access to New Treatments: By streamlining the development and manufacturing processes with AI and digital technologies, new therapies can be produced faster. This means patients might have access to the latest treatments sooner than ever before, potentially saving more lives and improving the quality of life for those with chronic conditions. Environmental Impact: Our efforts to achieve net-zero carbon emissions in biopharmaceutical medicines manufacture represent a significant step towards sustainable healthcare. Reducing the environmental footprint of medicine production not only aligns with global sustainability goals but also sets a new standard for the pharmaceutical industry worldwide. Economic Growth and Innovation: This project not only supports the growth of the UK’s biotechnology sector by enhancing its competitiveness globally but also fosters innovation through the development of cutting-edge manufacturing technologies. As these technologies are adopted more widely, they will drive down costs and improve efficiency, benefiting the entire sector. Educational and Professional Development: The partnership ensures that the next generation of scientists and engineers are trained in the latest digital and bioprocessing techniques. This prepares them for high-impact careers in the biopharmaceutical industry, contributing to the overall skill level and expertise within the UK and beyond. Our partnership demonstrates a commitment not only to transforming biopharmaceutical development but also to doing so in a way that is responsible and forward-thinking, ensuring benefits for society at large and the environment.

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Researchers

Brenda Parker (Co-Investigator)Daniel Bracewell (Co-Investigator)Paul Dalby (Co-Investigator)Stefanie Frank (Co-Investigator)Stephen Goldrick (Co-Investigator)Suzanne Farid (Principal Investigator)

Related Research

Grants with similar aims, by meaning.

Accelerated Discovery and Development of New Medicines: Prosperity Partnership for a Healthier Nation
Integrating Continuous Technologies Rapid Delivery of Cost Effective Biotherapeutics to Patients
A Prosperity Partnership for the Design, Synthesis, and Evaluation of Next Generation Molecules Beyond Lipinski Space
Transforming synthetic drug manufacturing: novel processes, methods and tools
Smart biomanufacturing for genomic medicines

Original classification

Research and Innovation

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