Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment

Journal article (2021)

Authors

Namid R. Stillman University College London, University of Bristol
Igor Balaz University of Novi Sad
Michail Antisthenis Tsompanas University of the West of England
Marina Kovacevic University of Novi Sad
Sepinoud Azimi Åbo Akademi University
Sebastien Lafond Åbo Akademi University
Andrew Adamatzky University of the West of England
Sabine Hauert University of Bristol
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Published Date

2021

Language

English

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Abstract

We present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer treatments. Our work aims to decrease both the time and cost required to develop nanoparticle designs. EVONANO includes a simulator to grow tumours, extract representative scenarios, and simulate nanoparticle transport through these scenarios in order to predict nanoparticle distribution. The nanoparticle designs are optimised using machine learning to efficiently find the most effective anti-cancer treatments. We demonstrate EVONANO with two examples optimising the properties of nanoparticles and treatment to selectively kill cancer cells over a range of tumour environments. Our platform shows how in silico models that capture both tumour and tissue-scale dynamics can be combined with machine learning to optimise nanomedicine.