- Title
- Designing optimal combination therapy for personalised glioma treatment
- Creator
- Noman, Nasimul; Moscato, Pablo
- Relation
- Memetic Computing Vol. 12, Issue 4, p. 317-329
- Publisher Link
- http://dx.doi.org/10.1007/s12293-020-00312-7
- Publisher
- Springer
- Resource Type
- journal article
- Date
- 2020
- Description
- Background: Like it happens in other tumours, glioma cells co-evolve in a microenvironment consisting of bona fide tumour cells as well as a range of parenchymal cells, which produces numerous signalling molecules. Recently, the results of an in silico experiment suggested that a combination therapy that would target multiple key cytokines at the same time may be more effective for suppressing the growth of a tumour. The in silico experiments also showed that the optimal combination therapy is very much dependent on a patient’s molecular profile. Method: In this work, we employ evolutionary algorithms for designing optimal combination therapy tailored to the patient’s tumour microenvironment. Experiments were performed using a state-of-the-art glioma microenvironment model, capable of imitating many characteristics of human glioma development, and many virtual patient profiles. Conclusions: Results show that the therapies designed by the presented memetic algorithm were very effective in impeding tumour growth and were tailored to the patient’s personal tumour microenvironment.
- Subject
- combination therapy; glioma treatment; personalised treatment; evolutionary algorithm; memetic algorithm; differential evolution
- Identifier
- http://hdl.handle.net/1959.13/1432892
- Identifier
- uon:39130
- Identifier
- ISSN:1865-9284
- Language
- eng
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