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

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 simul...

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Autores principales:	Namid R. Stillman, Igor Balaz, Michail-Antisthenis Tsompanas, Marina Kovacevic, Sepinoud Azimi, Sébastien Lafond, Andrew Adamatzky, Sabine Hauert
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/49ab49b7d5c1485abf5565b9443d2620
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spelling	oai:doaj.org-article:49ab49b7d5c1485abf5565b9443d26202021-12-02T18:14:22ZEvolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment10.1038/s41524-021-00614-52057-3960https://doaj.org/article/49ab49b7d5c1485abf5565b9443d26202021-09-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00614-5https://doaj.org/toc/2057-3960Abstract 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.Namid R. StillmanIgor BalazMichail-Antisthenis TsompanasMarina KovacevicSepinoud AzimiSébastien LafondAndrew AdamatzkySabine HauertNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Namid R. Stillman Igor Balaz Michail-Antisthenis Tsompanas Marina Kovacevic Sepinoud Azimi Sébastien Lafond Andrew Adamatzky Sabine Hauert Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment
description	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.
format	article
author	Namid R. Stillman Igor Balaz Michail-Antisthenis Tsompanas Marina Kovacevic Sepinoud Azimi Sébastien Lafond Andrew Adamatzky Sabine Hauert
author_facet	Namid R. Stillman Igor Balaz Michail-Antisthenis Tsompanas Marina Kovacevic Sepinoud Azimi Sébastien Lafond Andrew Adamatzky Sabine Hauert
author_sort	Namid R. Stillman
title	Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment
title_short	Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment
title_full	Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment
title_fullStr	Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment
title_full_unstemmed	Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment
title_sort	evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/49ab49b7d5c1485abf5565b9443d2620
work_keys_str_mv	AT namidrstillman evolutionarycomputationalplatformfortheautomaticdiscoveryofnanocarriersforcancertreatment AT igorbalaz evolutionarycomputationalplatformfortheautomaticdiscoveryofnanocarriersforcancertreatment AT michailantisthenistsompanas evolutionarycomputationalplatformfortheautomaticdiscoveryofnanocarriersforcancertreatment AT marinakovacevic evolutionarycomputationalplatformfortheautomaticdiscoveryofnanocarriersforcancertreatment AT sepinoudazimi evolutionarycomputationalplatformfortheautomaticdiscoveryofnanocarriersforcancertreatment AT sebastienlafond evolutionarycomputationalplatformfortheautomaticdiscoveryofnanocarriersforcancertreatment AT andrewadamatzky evolutionarycomputationalplatformfortheautomaticdiscoveryofnanocarriersforcancertreatment AT sabinehauert evolutionarycomputationalplatformfortheautomaticdiscoveryofnanocarriersforcancertreatment
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Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment

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