Predicting the aggregation propensity of mAb formulations from molecular dynamics simulations

Context & impact: Aggregation is increasingly thought to occur through the partial unfolding of protein structure to expose sites that are more prone to self-interaction. Overall, the self-association of proteins is influenced by a combination of surface properties that determine colloidal stability and propensity for surface interactions, the extent and kinetics of global and local unfolding, and the solvent accessibility and aggregation-propensity of local sequences. All of these are modulated by the physical environment provided by the formulation, which can thus alter both the kinetics and dominant pathways of aggregation.

Identifying the specific influence of formulations on aggregation kinetics and mechanism for a give protein, requires considerable experimental characterisation, while few generalities can be reliably used in the design of formulations for new proteins of interest.

There has been considerable recent growth of experimental characterisation of protein aggregation mechanisms in a range of formulations, and an increased role of computational molecular dynamics simulations to provide insights into the molecular events that lead to aggregation. Building on this, there is now significant potential for computational approaches to begin to predict the impact of formulations on protein stability and aggregation.

Aims and objectives: The aim of this project will be to develop a workflow of modelling and simulation approaches, that can provide molecular-level insights into the experimental aggregation behaviour of mAbs under a range of conditions, and provide a basis for their use in prediction of formulation stability.

Research methodology:

The project will collaborate with CSL to explore the use of all-atom and course-grain (CG) molecular dynamics simulations of mAbs under formulation conditions, and in contact with surfaces, to investigate ability of simulations to identify mAb behaviours (protein-protein and protein-surface interactions) that correlate to known stability characteristics. Statistical approaches will be used to correlate simulation features (e.g. RMSD, RMSF, APR exposure) to known experimental properties, to elucidate potential aggregation initiation mechanisms. A. hybrid approach will also be explored in which all-atom simulations are clustered to provide alternative conformations for CG simulations. Thus, the project will also train the student in the latest digital skills, including machine learning. It is anticipated that the findings and approaches could be used to predict (& understand) historical CSL mAb datasets. Anticipated outcomes: Insights into the predictive & mechanistic power of molecular dynamics in mAb formulations. A road map for further MDS platform development & future implementation into CSL workflows. Several peer-reviewed publications.

The project is aligned directly to the EPSRC Manufacturing the Future theme.

The CDT has a proven track record of delivering impact from its research and training activities and this will continue in the new Centre. The main types of impact relate to: (i) provision of highly skilled EngD and sPhD graduates; (ii) generation of intellectual property (IP) in support of collaborating companies or for spin-out company creation; (iii) knowledge exchange to the wider bioprocess-using industries; (iv) benefits to patients in terms of new and more cost effective medicines, and (v) benefits to the wider society via involvement in public engagement activities and impacts on policy.

With regard to training, provision of future bioindustry leaders is the primary output of the CDT and some 96% of previous EngD graduates have progressed to relevant bioindustry careers. These highly skilled individuals help catalyse private sector innovation and biomanufacturing activity. This is of enormous importance to capitalise on emerging markets, such as Advanced Therapy Medicinal Products (ATMPs), and to create new jobs and a skilled labour force to underpin economic growth. The CDT will deliver new, flexible on-line training modules on complex biological products manufacture that will be made available to the wider bioprocessing community. It will also provide researchers with opportunities for international company placements and cross-cohort training between UCL and SSPC via a new annual Summer School and Conference.

In terms of IP generation, each industry-collaborative EngD project will have direct impact on the industry sponsor in terms of new technology generation and improvements to existing processes or procedures. Where substantial IP is generated in EngD or sPhD programmes, this has the potential to lead to spin-out company creation and job creation with wider economic benefit. CDT research has already led to creation of a number of successful spin-out companies and licensing agreements. Once arising IP is protected the existing UCL and NIBRT post-experience training programmes provide opportunities for wider industrial dissemination and impact of CDT research and training materials.

CDT projects will address production of new ATMPs or improvements to the manufacture of the next generation of complex biological products that will directly benefit healthcare providers and patients. Examples arising from previous EngD projects have included engineered enzymes for greener pharmaceutical synthesis, novel bioprocess operations to reduce biopharmaceutical manufacturing costs and the translation of early stem cell therapies into clinical trials. In each case the individual researchers have been important champions of knowledge exchange to their collaborating companies.

Finally, in terms of wider public engagement and society, the CDT has achieved substantial impact via involvement of staff and researchers in activities with schools (e.g. STEMnet), presentations at science fairs (Big Bang, Cheltenham), delivery of high profile public lectures (Wellcome Trust, Royal Institution) as well as TV and radio presentations. The next generation of CDT researchers will receive new training on the principles of Responsible Innovation (RI) that will be embedded in their research and help inform their public engagement activities and impact on policy.