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.
Highlights
Cancer is known to be a complex and multiscale disease, where tumour growth is caused by multifactorial effects spanning multiple scales, such as individual cell stressors, mutations in cell signalling pathways, changes in the local tumour microenvironment, and the overall disruption of tissue homeostasis[1,2,3,4].Nanoparticle-based drug vectors have the potential for improved targeting of cancer cells when compared to free drug delivery through the design of cell-specific binding moieties and the encapsulation of drugs within nanoparticles that improve bioavailability[5,6]
We present a platform for drug discovery, EVONANO, that combines models at the scale of an individual cell to the growth dynamics of a virtual tumour, while applying machine learning to more efficiently explore the nanoparticle design space
We focus on a stochastic simulator to capture the inherent randomness that occurs within biochemical reaction networks
Summary
Cancer is known to be a complex and multiscale disease, where tumour growth is caused by multifactorial effects spanning multiple scales, such as individual cell stressors, mutations in cell signalling pathways, changes in the local tumour microenvironment, and the overall disruption of tissue homeostasis[1,2,3,4].Nanoparticle-based drug vectors have the potential for improved targeting of cancer cells when compared to free drug delivery through the design of cell-specific binding moieties and the encapsulation of drugs within nanoparticles that improve bioavailability[5,6]. Novel anti-cancer nanomedicines are possible due to the expansive range of design parameters that can be altered. Machine learning and AI methods have been used to optimise nanoparticle design, for example, by predicting the properties of nanoparticles or reducing their overall toxicity[25,26,27,28]. Many of these in silico models focus on singular aspects of the nanoparticles’ journey through the body and are unable to systematically generate virtual tumour scenarios
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