Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment
This work addresses the challenge of personalized cancer therapy by automating the discovery of nanomedicines, though it appears incremental as it builds on existing simulation and optimization methods.
The researchers tackled the problem of designing effective nanocarriers for cancer treatment by developing the EVONANO platform, which uses machine learning to optimize nanoparticle properties and treatment strategies, demonstrating its ability to selectively kill cancer cells across various tumor environments.
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.