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

Stillman, Namid R.; Balaz, Igor; Tsompanas, Michail Antisthenis; Kovacevic, Marina; Azimi, Sepinoud; Lafond, S�bastien; Adamatzky, Andrew; Hauert, Sabine

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.

• Journal Article Type: Article
• Acceptance Date: Jul 29, 2021
• Online Publication Date: Sep 21, 2021
• Publication Date: Sep 21, 2021
• Deposit Date: Feb 25, 2022
• Publicly Available Date: Feb 25, 2022
• Journal: npj Computational Materials
• Electronic ISSN: 2057-3960
• Publisher: Nature Research (part of Springer Nature)
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 1
• Article Number: 150
• DOI: https://doi.org/10.1038/s41524-021-00614-5
• Keywords: Computer Science Applications; Mechanics of Materials; General Materials Science; Modelling and Simulation
• Public URL: https://uwe-repository.worktribe.com/output/8581274
• Publisher URL: https://www.nature.com/articles/s41524-021-00614-5
• Additional Information: Received: 28 February 2021; Accepted: 29 July 2021; First Online: 21 September 2021; : The authors declare no competing interests.