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Computational studies of antibody aggregation: Implications for bio processing and development of biologics

Reference	BB/H016627/1
Principal Investigator / Supervisor	Professor Mark Sansom
Co-Investigators / Co-Supervisors	Dr Kia Balali-Mood
Institution	University of Oxford
Department	Biochemistry
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

Aggregation and stability problems are one of the major causes of attrition in the development of biopharmaceuticals with a large impact in safety and manufacturing costs. Current methodologies do not offer detailed information on the specific interactions that mediate misfolding and aggregation, particularly in complex systems such as antibodies. The proposed project will combine state-of-the-art computational approaches with in vitro expression and stability data to gain insight into the early events that trigger protein aggregation. It will focus in the use of long (millisecond) time scale molecular dynamics simulations using monoclonal antibodies as an experimental model. Ultimately it is expected that the use of computational tools to predict the behaviour of biological drugs, particularly aggregation and stability, will have a major impact in streamlining the development of safer and better biopharmaceuticals. The proposed project will concentrate on optimising force-fields in order to run meso-scale simulations of antibody fragments with known stability problems. The ultimate goal would be to attempt a simulation on inter-chain interactions (using multiple monomers within a defined unit cell) over a millisecond timescale. This would require access to high performance computational facilities such as HPCx. Professor Mark Sansom's group at Oxford has access to such facilities as well as three powerful (~128 processor) in house clusters. Lonza Biologics plc will provide antibody models to be used in the simulations as well as experimental validation of results. The simulations will allow for elucidating a model of simulated antibody aggregate precursors in the right variant. In addition, the simulations will allow for dynamic comparison in terms of structural and energetic behaviour between different antibody variants. Current Molecular Dynamics simulations in the literature have reported a 30 ns trajectory of a whole antibody using the NAMD code (1). However, the timescale of 30 ns is a relatively short timescale when considering that typically folding events occur on a millisecond timescale, whereas misfolding and aggregation could take significantly longer. Our proposed approach will be more focused on the variable domains of antibodies, as opposed to running simulations of large antibody systems containing the conserved heavy chains. The motivation for this is that variable domains are not only responsible for the binding to antigens, but also seem to be the main contributor to aggregation and stability problems observed in antibodies. Futhermore, by reducing the complexity of the system the timescale of the simulations could be extend into the millisecond scale. Our initial simulation data show a close correlation with experimental observations made internally by Lonza. Aggregation can occur in a variety of flavours (from unfolded, surface interactions, edge interactions) it's also likely that local re-organisation could trigger or be the consequence of aggregation. Gadnell and Gunnarsson have shown cryo-EM structures revealing an Ab with a 'smashed' Fab when aggregated (2). Our proposed simulations will allow for atomic and/or coarse grained level studies of such phenomenon in silico. Another interesting approach will be the investigation of antibodies with surfaces such as membranes. Moreover there is a link between inward rectifying potassium (Kir) channels and immunoglobulins (3) . MSPS' group has a strong international reputation in modelling and simulation of Kir channels,which will allow for the potential PhD candidate to gain greater exposure to the research strengths of the group. References: 1. Chennamsetty N., et al.., J. Mol. Biol., 391, 2, 404-413 (2009). 2. Gadnell M. and Gunnarsson K., Nature Methods 2, 523-524 (2005), 3. Fallen K, et al., Channels (Austin). 3(1): 57-68. (2009)

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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