Award details

Understanding and predicting aggregation in biopharmaceuticals

Reference	BB/I017194/1
Principal Investigator / Supervisor	Dr Robin Curtis
Co-Investigators / Co-Supervisors	Professor Jeremy Paul Derrick, Professor Alan Dickson, Dr Leo Lue, Dr James Warwicker
Institution	The University of Manchester
Department	Chem Eng and Analytical Science
Funding type	Research
Value (£)	579,057
Status	Completed
Type	Research Grant
Start date	01/11/2011
End date	30/11/2014
Duration	37 months

Abstract

The principal objective of this proposal is to develop methods for predicting protein bioprocessability based on knowledge of protein structural properties, a database of protein behaviour, and a set of high throughput data obtainable using accessible methods requiring small amounts of protein. The main emphasis is in part on predicting the intrinsic aggregation propensity of a protein, but also in predicting how the range of solvent conditions accessible in a bioprocess alters the aggregation behaviour. This tool could then be used for choosing from a set of mutants based on their bioprocessability, developing a bioprocess through informed decisions of process conditions (including excipients) chosen to minimize aggregation, improve solubility, and minimize viscosity, and in formulation, where the approach could be used as an excipient pre-screen for minimizing the solvent space to be sampled in formulation design. Our approach differs from others in that we probe the self-association step in the aggregation pathway and then integrate this knowledge into the predictor. The interactions between the aggregating precursors (partially folded proteins) are determined using static light scattering under native 'bioprocessing' conditions and using a novel approach based on probing interactions under destabilizing conditions. Using this approach we can isolate how the rate limiting steps of protein-protein association and unfolding depend upon co-solvent composition. This knowledge is then used to develop an improved aggregation predictor which incorporates the influence of different solvent conditions and protein structural properties. The tool is designed such that the predictive ability can be refined by incorporating experimental data obtained from high throughput experiments which are also developed as part of this work.

Summary

Currently, one of the bottle necks to developing cheaper protein therapeutics is the cost of the downstream bioprocessing and formulation steps. A key problem is the loss of active protein therapeutic to irreversible aggregation throughout the bioprocess. Other problems can arise during chromatography or filtration when encountering high protein concentrations which could lead to high viscosites or even precipitation. The focus of this work is to develop predictive methods for identifying problematic conditions early on in the bioprocess. These could then be used for identifying changes to the protein to minimize the problems. Alternatively, the method could be used for optimizing the solvent properties (pH, buffer type and concentration) or finding other small molecule additives to be used in order to avoid aggregation or increase protein solubility. We benchmark our approach by studying antibodies and antibody fragments due to their growing importance as human therapeutics.

Impact Summary

This application is being submitted to a call by the Bioprocessing Research Industry Club (BRIC). One of the goals of BRIC is to bring together fundamental science for solving problems in the production of biopharmaceuticals. As such the impact of the proposed work can be defined by the research priorities defined by BRIC. 3 out of the 5 of these priorities can be identified with tools utilized in our study. These areas include 1) developing predictive tools based on protein structural properties for identifying the manufacturability of a protein product early on in the bioprocess. 2) the use of high-throughput technology to guide bioprocess development 3) effective modelling of whole bioprocesses. In particular, the main focus of our work is in developing a protein aggregation propensity predictor which incorporates protein structural properties and is also benchmarked using experimental data obtained from high-throughput experiments to be developed in the proposal. Furthermore, the tool is not only linked to predicting protein aggregation, but also can be utilized for predicting protein solubility or viscosity. The advantage is that each of these solution properties are linked to each other as they all depend on protein-protein interactions. In the future, understanding this link can lead to improved bioprocess design, where knowledge is transferable across different scales (lab to pilot plant) and across different operations. More specifically, the tool we develop can have impacts at various levels in bioprocessing. These include: 1) In antibody production, very often a set of mutant proteins are now produced with similar therapeutic potential. The tool we develop could be used for choosing one of the mutant proteins based on its bioprocessability. This could lead to identifying troublesome proteins early on in bioprocess development leading to large savings. 2) The predictor could be used in developing refolding operations where protein aggregation can lead to significantly reduced yields. In this case, the tool could be used to identify troublesome proteins, suggest mutations to improve the refoldability, and identify solvent conditions which can optimize the refolding yields. Along these lines, the PI and coPI (JPD) have submitted for a BBSRC funded case studentship with MSD to study refolding. 3) The work is also especially relevant in protein formulation. The big issue is predicting long term storage stability of the protein, where one of the main degradative pathways is by aggregation which can lead to an immunogenic response. In order to prevent aggregation, certain molecules (additives) are included in the formulation. Identifying the optimal conditions requires a pre-screening step usually done with accelerated storage studies which usually takes weeks. Our method could be used as a replacement pre-screening step reducing the time for the formulation design. Each of these proposed impacts can lead to savings in downstream bioprocessing which contributes most of the cost to commercialized therapeutics. Thus, the extended impact of the work is in reducing the cost of biopharmaceuticals. The predominant mechanism for dissemination of results is via the bioprocessUK meetings which are heavily attended by both industrialists and academics. These meetings foster interactions between academics and industrialists, via various mechanisms such as 'speed-networking'. This gives a good forum for exchange of ideas and developing the projects within the industrial interests. In addition, these meetings often lead to initial collaborative studies, which can take the form of MSc and MEng based projects.

Committee	Research Committee D (Molecules, cells and industrial biotechnology)
Research Topics	Industrial Biotechnology, Pharmaceuticals, Structural Biology, Technology and Methods Development
Research Priority	X – Research Priority information not available
Research Initiative	Bioprocessing Research Industry Club (BRIC) [2006-2012]
Funding Scheme	X – not Funded via a specific Funding Scheme

I accept the terms and conditions of use (opens in new window)

export PDF file

back to list new search