Featured Figures
By Olivia Chu
In this issue, we feature the work of Luke Heirene, a graduate student at the University of Oxford, and Yuan Yin, a graduate student at the University of Oxford, who were both poster prize winners at this year’s SMB Annual Meeting.
We asked Luke to tell us a bit more about his work here:
Immunotherapies have seen success in a variety of diseases, including cancer. An important class of immunotherapy are monoclonal antibodies (mAbs). Mabs can induce their anti-tumour effects in a variety of ways. For example, they can inhibit a tumour cell’s ability to downregulate the immune response against it as well as target and stimulate immune effector cells to kill the tumour cell. Regardless of a mAbs mechanism of action, core to their effect are their interactions with target antigens. Processes that alter the ability of a mAb to bind its target antigen, such as differences in binding affinity or antigen expression, will directly impact the resulting therapeutic effect.
In our work, we use an ODE model of bivalent antibody-antigen binding to establish the key parameters that drive mAb potency and efficacy. We utilise a global parameter sensitivity analysis to establish the parameters that most affect antigen occupancy and bound antibody number, key drivers of mAb potency and efficacy, finding that the most important parameters change with antibody dose. Another key mAb-antigen interaction is the observed increase in binding affinity due to a mAb binding multiple antigens, termed the avidity effect. We use our model to predict antibody binding affinities and antigen expression numbers which result in a large avidity effect. Our results can be used to identify key parameters and interactions that can assist in the preclinical development of mAb therapeutics.
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