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Title: Modelling immunoglobulin metabolism and its effect on prognostic utility in multiple myeloma
Author: Kendrick, Felicity
ISNI:       0000 0004 7425 6324
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2018
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Multiple myeloma is a cancer of plasma cells. In multiple myeloma, a clone of plasma cells in the bone marrow secretes a unique, monoclonal immunoglobulin (Ig), whose biological properties depend on its type and structure. The monoclonal Ig offers a convenient opportunity for clinicians to monitor the response of the tumour to therapy via the secreted protein, which is readily quantified in a blood sample. Responses to treatment are assigned based on the percentage reduction in monoclonal Ig; however, response criteria do not take into account the different metabolic half-lives of the proteins. 70% of multiple myeloma patients have either monoclonal IgA- or monoclonal IgG-producing clones. IgA and IgG have metabolic half-lives of 6 days and 23 days, at normal concentrations, respectively. The large difference in their metabolic half-lives suggests that they would respond at different rates during therapy. The elimination rate of IgG is concentration-dependent due to saturable recycling by a receptor. This could further impact upon its response during therapy, with the possibility that IgG is eliminated from the body at different rates at the beginning of therapy, when its concentration is high, and at the end of therapy, when its concentration has decreased. In this thesis compartmental models of IgG metabolism from the literature are analysed and parameter values are estimated from available data. A model of IgA metabolism is sourced in the literature. These models are used to predict the responses of monoclonal IgA and IgG during therapy. The simulations are able to replicate typical monoclonal IgA and IgG responses seen in a clinical trial of patients with relapsed and refractory multiple myeloma. Importantly, the plasma cell clone is not directly accessible to measurement and therefore not available to validate model-based predictions. However, monoclonal Ig responses are not evaluated by their ability to predict the tumour burden, but by the strength of their association with patient survival. In this thesis, a prediction is made of how the different metabolic properties of IgA and IgG may influence their association with survival outcomes. Evidence for this effect is then evaluated in data from a clinical trial using the methods of survival analysis.
Supervisor: Not available Sponsor: Biotechnology and Biological Sciences Research Council ; Midlands Integrative Biosciences Training Partnership
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: RC0254 Neoplasms. Tumors. Oncology (including Cancer)