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

Namid R. Stillman, Igor Balaz, Michail-Antisthenis Tsompanas, Marina Kovacevic, Sepinoud Azimi, Sébastien Lafond, Andrew Adamatzky, Sabine Hauert*

*Korresponderande författare för detta arbete

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

Sammanfattning

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.
OriginalspråkEngelska
Artikelnummer150
Tidskriftnpj Computational Materials
Volym7
Utgåva1
DOI
StatusPublicerad - 21 sep 2021
MoE-publikationstypA1 Tidskriftsartikel-refererad

Fingeravtryck

Fördjupa i forskningsämnen för ”Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment”. Tillsammans bildar de ett unikt fingeravtryck.

Citera det här