It takes up to ten years and hundreds of millions of pounds to bring a new biologic drug from a lab discovery to a patient’s bedside, and the next generation of medicines will be even harder to manufacture. This project tackles the bottleneck between small-scale lab experiments and full-scale industrial production, where changes in how materials are handled can ruin a promising drug’s properties. The researchers will combine automated robotic systems with computer modelling to simulate large-scale manufacturing using tiny amounts of precious early-stage drug material. This allows them to identify scale-up problems long before a drug reaches clinical trials, when failure is still cheap and fast. If successful, the approach could slash development timelines for complex biopharmaceuticals—including personalised medicines—by making manufacturing data available at the discovery stage. That would let companies recover their investment costs more quickly and bring affordable treatments to patients sooner. The work is applied, not fundamental: it aims to change how the pharmaceutical industry designs and tests its production processes, not to uncover new biology.
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It is now widely accepted that up to ten years are needed to take a drug from discovery to availability for general healthcare treatment. This means that only a limited time is available where a company is able to recover its very high investment costs in making a drug available via exclusivity in the market and via patents. The next generation drugs will be even more complex and difficult to manufacture. If these are going to be available at affordable costs via commercially viable processes then the speed of drug development has to be increased while ensuring robustness and safety in manufacture. The research in this proposal addresses the challenging transition from bench to large scale where the considerable changes in the way materials are handled can severely affect the properties and ways of manufacture of the drug. The research will combine novel approaches to scale down with automated robotic methods to acquire data at a very early stage of new drug development. Such data will be relatable to production at scale, a major deliverable of this programme. Computer-based bioprocess modelling methods will bring together this data with process design methods to explore rapidly the best options for the manufacture of a new biopharmaceutical. By this means those involved in new drug development will, even at the early discovery stage, be able to define the scale up challenges. The relatively small amounts of precious discovery material needed for such studies means they must be of low cost and that automation of the studies means they will be applicable rapidly to a wide range of drug candidates. Hence even though a substantial number of these candidates may ultimately fail clinical trials it will still be feasible to explore process scale up challenges as safety and efficency studies are proceeding. For those drugs which prove to be effective healthcare treatments it will be possible then to go much faster to full scale operation and hence recoup the high investment costs.As society moves towards posing even greater demands for effective long-term healthcare, such as personalised medicines, these radical solutions are needed to make it possible to provide the new treatments which are going to be increasingly demanding to manufature.
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