The UK Technology Strategy Board has awarded £1.1m ($2.2m) to a group led by biotech firm Onyvax, which will combine the expertise of the vaccine specialist with two UK universities and analytics firm LGC to work on the process technology. The aim of the project is to develop technology to predict and optimise cell line performance in large-scale manufacturing, combining micro-scale process engineering technology with advanced analytical and informatics. "It is quite easy to have success [with cell lines] at the lab scale, but in making the leap to large-scale manufacturing, failure rates can be quite high," Onyvax director of development Stephen Ward told in-PharmaTechnologist.com. "[We plan to develop] a platform to screen cells very early in the development cycle to see if they can withstand the pressures of large-scale manufacturing...[and] reduce the failure rate for these innovative therapies." If successful, the technology could reduce the cost and time involved in selecting and producing new cell lines that form the basis of future cancer vaccines. The platform could also be applied to other areas of cell therapy, such as regenerative medicine and stem cell medicine, according to Onyvax. Onyvax will be working with the Advanced Centre for Biochemical Engineering at University College London, which will apply its ultra scale-down and whole bioprocessing technology to assess the impact of the bioprocessing environment on vaccine cells. LGC, which has had a contract and service relationship with Onyvax for several years, will contribute to the project by applying its advanced mass-spectrometry expertise and array-based platforms to characterise intra- and extra-cellular protein markers. The final member of the consortium is Nottingham Trent University in the UK, who will bring bioinformatics capabilities to the project. The university will provide proprietary Artificial Neural Networking algorithms to sift through datasets and identify biomarkers to indicate cell robustness. This approach means the group can look at several parameters in one go, and try to identify patterns of 'manufacturability' to help inform decisions on selection of future cell lines. A added benefit of the neural network algorithms is that as more data is produced and fed back into the network, more robust answers are generated as to the suitability and robustness of cell lines. The project will essentially mean that cell lines can be selected with a much higher degree of confidence, and a reduced chance of failure in large-scale manufacturing conditions. Ward estimated that information generated through the research project could feed into the manufacturing space within 18 months, with the grant announced today representing a significant boost in resources to develop the process technology. "[The grant] makes a very large difference in terms of manufacturing support," said Ward. "We can really drill down into the manufacturing process and development pipeline and augment our current manufacturing process." The £1.1m grant will be topped up by a further £700,000 contributed by consortia members, with the project running until March 2011.