Award details

Development of predictive tools and formulations for improved stability and delivery of recombinant protein formulations for bio therapeutic use

Reference	BB/H016082/1
Principal Investigator / Supervisor	Professor Christopher Smales
Co-Investigators / Co-Supervisors	Mr Shahid Uddin
Institution	University of Kent
Department	Sch of Biosciences
Funding type	Skills
Value (£)	75,281
Status	Completed
Type	Training Grants
Start date	01/10/2010
End date	30/09/2014
Duration	48 months

Abstract

unavailable

Summary

Industry currently employs formulation scientists who investigate a range of pre-formulation studies to determine formulation compositions and concentrations as required for various proteins and monoclonal antibodies as they come to market. However, current methods for determining the best formulation(s) for preservation, stability and delivery rely heavily on trial-and-error approaches. The described programme of work will test the hypothesis that 'by investigating protein sequence, use of molecular dynamic calculations to predict aggregation, and by employing a matrix of formulation reagents and conditions with model proteins, it will be possible to predict improved formulation strategies and compositions for specific recombinant protein products'. In addition, the proposed programme of work will improve our understanding of the molecular mechanisms by which proteins may be stabilised. We note that trained formulation scientists are relatively rare and that this is an area of Bioprocessing that has been highlighted through the BRIC programme as in need of attention. This project will train a formulation scientist and further our understanding of the issues preventing stable formualtion of a number of key therapeutic target molecules. This project arises as a direct result of a successful collaborative PhD studentship between the two institutes in formulation studies. The student will initially use model monoclonal antibodies provided by MedImmune to investigate the following objectives: (1) use in silico molecular dynamic simulations to predict how changes to the CDRs of antibody sequences influences the theroetical propensity of antibodies to aggregation (2) test the predicted in silico aggregation experimentally to determine how effective this approach is in highlighting potentially troublesome sequences or residues early n the antibody development stage (3) use DSC, light scattering and mass spectrometry approaches to set up a high throughput screeningmethod for formulation studies to determine the 'best' formulation for a given therapeutic protein (4) use NMR STD analysis to determine how excipients in formulations interact with the surface of a model protein (5) determine effects not only on stability and aggregation but activity through appropriate assays (6) investigate any link between the distribution of glycoforms within a therapeutic product, aggregation, formualtion and stability/activity. The initial monoclonal antibodies used in the study will have been characterised at MedImmune and be readily available. Additional therapeutic prtein products are also availabe for study should they be required. The individual subunits of the antibodies are amenable to mass spectrometry when the disulphide bonds holding these individual domains together are reduced. The outcomes of this proposal will be (1) the development of in silico methods to predict agggregation of therapeutic proteins, (2) detailed formulations for the stabilisation of monoclonals and the development of 'generic' formulations, (3) an improved understanding of the effects on, and the mechanisms that determine , preservation, stabilisation and the maintenance of protein integrity in the formulated state, and (4) further development of analytical methodology to monitor stabilisation and authenticity of monoclonal antibodies.

Committee	Not funded via Committee
Research Topics	X – not assigned to a current Research Topic
Research Priority	X – Research Priority information not available
Research Initiative	X - not in an Initiative
Funding Scheme	Training Grant - Industrial Case

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