Periodic Reporting for period 2 - Biorapid (Rapid Bioprocess Development)
Reporting period: 2017-01-01 to 2018-12-31
Biorapid successfully adapted early screening methods to identify potential undesired hypersensitivity and toxicity effects and the ESRs involved in this work are currently developing novel toxicological tests for large biomolecules that present a particular challenge in early stage testing. A range of modelling techniques, based on fundamental process knowledge and on process data collected during processing have been successfully used to characterise a range of bioprocesses, from large scale lactic acid fermentations, microbial recombinant protein production to the production of monoclonal antibodies (mAbs) using cell culture processing, both upstream and downstream. Successful high-throughput (HTP) experimentation protocols and optimal design of experiments methodologies incorporating uncertainties associated with bioprocessing have been developed and tested on model systems selected by the academic and industrial consortium partners. Printed sensors and softsensors are currently being tested as demonstrators for effective monitoring of cultivations and downstream process (DSP) purification of a range of bioproducts. We have also successfully developed a range of host strains that would increase product quality through rational design. The outcomes of the individual ESR projects are now being formulated into a MultiAgent System (MAS) that will enable rapid bioprocess development and effective monitoring at the end of the project. At this stage, the MAS framework is capable of rapidly importing data from various sources for numerous processes and perform the pre-processing identified by individual ESRs projects as most appropriate. Agents capable of carrying out databased and first principle modelling have also been implemented and the demonstrator version tested with data from an industrial partner of the consortium.
In the next stages of the project these approaches will be further refined and fully tested on the processes/products of our industrial consortium partners to ensure wide applicability across the bioprocessing sector.
WP2 – innovative methods of dealing with sparse data-sets from various stages of bioprocess development, reliable scale-down models (based on CFD and multivariate data analysis (MVDA) methods) that mimic large-scale behaviour of units such as microbioreactors and freeze-dryers of manufacturing capacity have been developed and are currently being validated. New approaches including optimal experimental design and incorporating uncertainty advanced the state of the art beyond the current developments are being developed and tested. Modelling methodologies within WP1 (QSAR, MD and MVDA) also provide an exciting route to speeding up bioprocess development through predicting most appropriate processing route for novel large molecules based on their structure.
WP3 – HTP process development methodologies accounting for heterogeneities both in terms of growth of the organism and product quality resulting from process operation particularly at large scale are being developed to enable effective scale up both in terms of optimal feeding strategies and effective scale up of DSP technologies demonstrated through large scale freeze dryer unit. New recombinant E.coli strains have been constructed to improve product quality by rational design and the proposed optimal production strategies will now be validated within industrial context. This not only pushes the scientific state-of-the-art in population heterogeneity assessment, but also in scale-up from microscale through to large scale manufacturing.
WP4 – novel softsensors are being developed for cultivation and DSP combining high resolution analytical methods with advanced MVDA and first principles modelling for enhanced monitoring of bioprocesses. This is supplemented by new versatile printed biosensors for both media composition and protein concentration measurement. The output of all the WPs is implemented in a novel Multi Agent System (MAS) framework capable of synchronously handling data from numerous process units, pre-processing them and preparing them for advanced monitoring of a range of industrial bioprocess. This has not been demonstrated or validated on real industrial process to the extent that the consortium is aiming to achieve by the end of the project. The resulting framework therefore has the potential to achieve significant socio-economic impact in the bioprocess industries and human health sector.