Paper Info
Title
Designing Optimal Combination Therapy For Personalised Glioma Treatment
Abstract
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.
Year
DOI
Venue
2020
10.1007/s12293-020-00312-7
MEMETIC COMPUTING
Keywords
DocType
Volume
Combination therapy, Glioma treatment, Personalised treatment, Evolutionary algorithm, Memetic algorithm, Differential evolution
Journal
12
Issue
ISSN
Citations 
4
1865-9284
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Nasimul Noman132321.61
Pablo Moscato233437.27