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

TL;DR

The paper introduces EVONANO, an automated platform that combines simulation and machine learning to design effective nanocarriers for targeted cancer therapy, streamlining the discovery process.

Contribution

It presents a novel integrated platform that automates the design and optimization of nanomedicines for cancer treatment using evolutionary algorithms and simulations.

Findings

Successfully optimized nanoparticle properties for selective cancer cell targeting

Demonstrated platform's ability to adapt to different tumour environments

Achieved efficient identification of effective nanocarrier designs

Abstract

We present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer treatments. EVONANO includes a simulator to grow tumours, extract representative scenarios, and then simulate nanoparticle transport through these scenarios to predict nanoparticle distribution. The nanoparticle designs are optimised using machine learning to efficiently find the most effective anti-cancer treatments. We demonstrate our platform with two examples optimising the properties of nanoparticles and treatment to selectively kill cancer cells over a range of tumour environments.