Machine learning to determine optimal conditions for controlling the size of elastin-based particles

Abstract This paper evaluates the aggregation behavior of a potential drug and gene delivery system that combines branched polyethyleneimine (PEI), a positively-charged polyelectrolyte, and elastin-like polypeptide (ELP), a recombinant polymer that exhibits lower critical solution temperature (LCST)...

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Autores principales:	Jared S. Cobb, Alexandra Engel, Maria A. Seale, Amol V. Janorkar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1716b72bf59244aba9b1b7d7fd0b7d97
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spelling	oai:doaj.org-article:1716b72bf59244aba9b1b7d7fd0b7d972021-12-02T13:17:48ZMachine learning to determine optimal conditions for controlling the size of elastin-based particles10.1038/s41598-021-85601-y2045-2322https://doaj.org/article/1716b72bf59244aba9b1b7d7fd0b7d972021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85601-yhttps://doaj.org/toc/2045-2322Abstract This paper evaluates the aggregation behavior of a potential drug and gene delivery system that combines branched polyethyleneimine (PEI), a positively-charged polyelectrolyte, and elastin-like polypeptide (ELP), a recombinant polymer that exhibits lower critical solution temperature (LCST). The LCST behavior of ELP has been extensively studied, but there are no quantitative ways to control the size of aggregates formed after the phase transition. The aggregate size cannot be maintained when the temperature is lowered below the LCST, unless the system exhibits hysteresis and forms irreversible aggregates. This study shows that conjugation of ELP with PEI preserves the aggregation behavior that occurs above the LCST and achieves precise aggregate radii when the solution conditions of pH (3, 7, 10), polymer concentration (0.1, 0.15, 0.3 mg/mL), and salt concentration (none, 0.2, 1 M) are carefully controlled. K-means cluster analyses showed that salt concentration was the most critical factor controlling the hydrodynamic radius and LCST. Conjugating ELP to PEI allowed crosslinking the aggregates and achieved stable particles that maintained their size below LCST, even after removal of the harsh (high salt or pH) conditions used to create them. Taken together, the ability to control aggregate sizes and use of crosslinking to maintain stability holds excellent potential for use in biological delivery systems.Jared S. CobbAlexandra EngelMaria A. SealeAmol V. JanorkarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jared S. Cobb Alexandra Engel Maria A. Seale Amol V. Janorkar Machine learning to determine optimal conditions for controlling the size of elastin-based particles
description	Abstract This paper evaluates the aggregation behavior of a potential drug and gene delivery system that combines branched polyethyleneimine (PEI), a positively-charged polyelectrolyte, and elastin-like polypeptide (ELP), a recombinant polymer that exhibits lower critical solution temperature (LCST). The LCST behavior of ELP has been extensively studied, but there are no quantitative ways to control the size of aggregates formed after the phase transition. The aggregate size cannot be maintained when the temperature is lowered below the LCST, unless the system exhibits hysteresis and forms irreversible aggregates. This study shows that conjugation of ELP with PEI preserves the aggregation behavior that occurs above the LCST and achieves precise aggregate radii when the solution conditions of pH (3, 7, 10), polymer concentration (0.1, 0.15, 0.3 mg/mL), and salt concentration (none, 0.2, 1 M) are carefully controlled. K-means cluster analyses showed that salt concentration was the most critical factor controlling the hydrodynamic radius and LCST. Conjugating ELP to PEI allowed crosslinking the aggregates and achieved stable particles that maintained their size below LCST, even after removal of the harsh (high salt or pH) conditions used to create them. Taken together, the ability to control aggregate sizes and use of crosslinking to maintain stability holds excellent potential for use in biological delivery systems.
format	article
author	Jared S. Cobb Alexandra Engel Maria A. Seale Amol V. Janorkar
author_facet	Jared S. Cobb Alexandra Engel Maria A. Seale Amol V. Janorkar
author_sort	Jared S. Cobb
title	Machine learning to determine optimal conditions for controlling the size of elastin-based particles
title_short	Machine learning to determine optimal conditions for controlling the size of elastin-based particles
title_full	Machine learning to determine optimal conditions for controlling the size of elastin-based particles
title_fullStr	Machine learning to determine optimal conditions for controlling the size of elastin-based particles
title_full_unstemmed	Machine learning to determine optimal conditions for controlling the size of elastin-based particles
title_sort	machine learning to determine optimal conditions for controlling the size of elastin-based particles
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1716b72bf59244aba9b1b7d7fd0b7d97
work_keys_str_mv	AT jaredscobb machinelearningtodetermineoptimalconditionsforcontrollingthesizeofelastinbasedparticles AT alexandraengel machinelearningtodetermineoptimalconditionsforcontrollingthesizeofelastinbasedparticles AT mariaaseale machinelearningtodetermineoptimalconditionsforcontrollingthesizeofelastinbasedparticles AT amolvjanorkar machinelearningtodetermineoptimalconditionsforcontrollingthesizeofelastinbasedparticles
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Machine learning to determine optimal conditions for controlling the size of elastin-based particles

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