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In silico modelling of cancer nanomedicine, across scales and transport barriers

npj Computational Materials 2020年第1期6卷 899-908页

作者： namid r.stillman Marina Kovacevic Igor Balaz Sabine Hauert Department of Engineering Mathematics University of BristolBristolUK Department of Chemistry Biochemistryand Environmental ProtectionFaculty Of SciencesUniversity of Novi SadNovi SadSerbia Laboratory for Meteorology Physicsand BiophysicsFaculty of AgricultureUniversity of Novi SadNovi SadSerbia

Nanoparticles promise to improve the treatment of cancer through their increasingly sophisticated functionalisations and ability to accumulate in certain *** recent work has shown that many nanomedicines fail during clinical *** issue is the lack of understanding of how nanoparticle designs impact their ability to overcome transport barriers in the body,including their circulation in the blood stream,extravasation into tumours,transport through tumour tissue,internalisation in the targeted cells,and release of their active *** computational power,as well as improved multi-scale simulations of tumours,nanoparticles,and the biological transport barriers that affect them,now allow us to investigate the influence of a range of designs in biologically relevant scenarios.

关键词： cancer transport overcome

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

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npj Computational Materials 2021年第1期7卷 1363-1374页

作者： namid r.stillman Igor Balaz Michail-Antisthenis Tsompanas Marina Kovacevic Sepinoud Azimi Sébastien Lafond Andrew Adamatzky Sabine Hauert Department of Engineering Mathematics University of BristolBristolUK Department of Cell and Developmental Biology University College LondonLondonUK Laboratory for Meteorology Physicsand BiophysicsFaculty of AgricultureUniversity of Novi SadNovi SadSerbia Unconventional Computing Laboratory University of the West of EnglandBristolUK Department of Chemistry Biochemistryand Environmental ProtectionFaculty of SciencesUniversity of Novi SadNovi SadSerbia Faculty of Science and Engineering Åbo Akademi UniversityTurkuFinland

We present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer *** work aims to decrease both the time and cost required to develop nanoparticle *** includes a simulator to grow tumours,extract representative scenarios,and simulate nanoparticle transport through these scenarios in order to predict nanoparticle *** nanoparticle designs are optimised using machine learning to efficiently find the most effective anti-cancer *** demonstrate EVONANO with two examples optimising the properties of nanoparticles and treatment to selectively kill cancer cells over a range of tumour *** platform shows how in silico models that capture both tumour and tissue-scale dynamics can be combined with machine learning to optimise nanomedicine.

关键词： treatment distribution nanoparticle

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