Optimization of radioimmunotherapy for non-Hodgkin's lymphoma
Optimization of radioimmunotherapy for non-Hodgkin's lymphoma
Radioimmunotherapy (RIT) has emerged as a new form of effective treatment for this disease. In this form of treatment, radionuclide-labelled monoclonal antibodies (mAbs) are able to deliver selective systemic irradiation by recognising tumour-associated antigens. Most recently, the use of RIT with radiolabelled anti-CD20 antibodies in patients with B-cell lymphoma has resulted in extremely high rates of durable complete remissions. However, the optimal approach and mechanisms of action of successful RIT remain poorly understood.
The work described in this thesis has focused on clarifying some of the important determinants and mechanisms of effective RIT using syngeneic murine B-cell lymphoma models. The in vivo irradiation delivery capabilities of a panel of mAbs against mouse B cell antigens were determined by bisdistribution studies using Medical Internal Radiation Dosimetry formula and the intracellular signalling induction capabilities of these mAbs were measured by Western Blot. By comparing the radiation dosimetry with the RIT therapeutic effects of these mAbs, it has been revealed that successful RIT requires the combination of targeted irradiation and mAb mediated intracellular signalling transduction. Furthermore, by visualizing the different patterns of intratumoural distribution of intravenously administered mAbs using immunohistochemistry technique, the RIT performances of radiolabelled mAbs were found linked to microscopic dosimetry. This discovery has further confirmed that the radiation dosimetry plays critical role in the success of RIT and revealed that the conventional dosimetry methods which assume the homogeneous intratumoural distribution of radiolabelled mAb could substantially underestimate the radiation dose delivered to the tumour by RIT.
University of Southampton
Du, Yong
5ae897d7-a3db-45d7-896a-187afa95ac43
2005
Du, Yong
5ae897d7-a3db-45d7-896a-187afa95ac43
Du, Yong
(2005)
Optimization of radioimmunotherapy for non-Hodgkin's lymphoma.
University of Southampton, Doctoral Thesis.
Record type:
Thesis
(Doctoral)
Abstract
Radioimmunotherapy (RIT) has emerged as a new form of effective treatment for this disease. In this form of treatment, radionuclide-labelled monoclonal antibodies (mAbs) are able to deliver selective systemic irradiation by recognising tumour-associated antigens. Most recently, the use of RIT with radiolabelled anti-CD20 antibodies in patients with B-cell lymphoma has resulted in extremely high rates of durable complete remissions. However, the optimal approach and mechanisms of action of successful RIT remain poorly understood.
The work described in this thesis has focused on clarifying some of the important determinants and mechanisms of effective RIT using syngeneic murine B-cell lymphoma models. The in vivo irradiation delivery capabilities of a panel of mAbs against mouse B cell antigens were determined by bisdistribution studies using Medical Internal Radiation Dosimetry formula and the intracellular signalling induction capabilities of these mAbs were measured by Western Blot. By comparing the radiation dosimetry with the RIT therapeutic effects of these mAbs, it has been revealed that successful RIT requires the combination of targeted irradiation and mAb mediated intracellular signalling transduction. Furthermore, by visualizing the different patterns of intratumoural distribution of intravenously administered mAbs using immunohistochemistry technique, the RIT performances of radiolabelled mAbs were found linked to microscopic dosimetry. This discovery has further confirmed that the radiation dosimetry plays critical role in the success of RIT and revealed that the conventional dosimetry methods which assume the homogeneous intratumoural distribution of radiolabelled mAb could substantially underestimate the radiation dose delivered to the tumour by RIT.
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Published date: 2005
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Local EPrints ID: 465856
URI: http://eprints.soton.ac.uk/id/eprint/465856
PURE UUID: b55339a0-0d78-4ea6-8ccb-cf1cc951b750
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Date deposited: 05 Jul 2022 03:18
Last modified: 16 Mar 2024 20:24
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Author:
Yong Du
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