- Title
- A computational approach for designing combination therapy in combating glioblastoma
- Creator
- Truong, Terry; Moscato, Pablo; Noman, Nasimul
- Relation
- 2019 IEEE Congress on Evolutionary Computation (CEC). 2019 IEEE Congress on Evolutionary Computation (CEC) (Wellington, New Zealand 10-13 June, 2019) p. 127-134
- Relation
- ARC.FT120100060 http://purl.org/au-research/grants/arc/FT120100060
- Publisher Link
- http://dx.doi.org/10.1109/CEC.2019.8790337
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2019
- Description
- The signalling pathways of the glioblastoma (GBM) microenvironment play critical roles in the origin and progress of the disease. Recently, it has been shown in in silico experiments that combination therapy targeting multiple key cytokines of the GBM microenvironment can significantly improve the therapeutic response. The study also revealed the inter-patient heterogeneity in response to the same combination therapy and emphasized the need for personalized treatment. This work proposes an evolutionary algorithm with a heuristic for creating combination therapies tailored to a patient's molecular profile. We investigated the effectiveness of the proposed approach using a sophisticated model of the GBM microenvironment and several virtual patients. We found that the proposed method was more successful, compared to the existing method, in designing a more effective combination therapy. Our results also underscored the importance of personalized treatment or patient stratification in designing combination therapy for GBM.
- Subject
- combination therapy; glioma treatment; evolutionary algorithm
- Identifier
- http://hdl.handle.net/1959.13/1453346
- Identifier
- uon:44655
- Identifier
- ISBN:9781728121543
- Language
- eng
- Reviewed
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