Machine learning to determine optimal conditions for controlling the size of elastin-based particles
- PMID: 33737605
- PMCID: PMC7973436
- DOI: 10.1038/s41598-021-85601-y
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). 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.
Conflict of interest statement
AVJ and JSC have filed a patent application based on the results reported in this paper.
Figures






Similar articles
-
Evaluation of machine learning algorithms to predict the hydrodynamic radii and transition temperatures of chemo-biologically synthesized copolymers.Comput Biol Med. 2021 Jan;128:104134. doi: 10.1016/j.compbiomed.2020.104134. Epub 2020 Nov 21. Comput Biol Med. 2021. PMID: 33249343 Free PMC article.
-
Effects of Doxorubicin on the Liquid-Liquid Phase Change Properties of Elastin-Like Polypeptides.Biophys J. 2018 Oct 16;115(8):1431-1444. doi: 10.1016/j.bpj.2018.09.006. Epub 2018 Sep 15. Biophys J. 2018. PMID: 30292393 Free PMC article.
-
Effect of Peptide Sequence on the LCST-Like Transition of Elastin-Like Peptides and Elastin-Like Peptide-Collagen-Like Peptide Conjugates: Simulations and Experiments.Biomacromolecules. 2019 Mar 11;20(3):1178-1189. doi: 10.1021/acs.biomac.8b01503. Epub 2019 Feb 4. Biomacromolecules. 2019. PMID: 30715857
-
Biotechnological applications of elastin-like polypeptides and the inverse transition cycle in the pharmaceutical industry.Protein Expr Purif. 2019 Jan;153:114-120. doi: 10.1016/j.pep.2018.09.006. Epub 2018 Sep 11. Protein Expr Purif. 2019. PMID: 30217600 Review.
-
Elastin-like polypeptides: A strategic fusion partner for biologics.Biotechnol Bioeng. 2016 Aug;113(8):1617-27. doi: 10.1002/bit.25998. Epub 2016 Jun 3. Biotechnol Bioeng. 2016. PMID: 27111242 Review.
Cited by
-
Elastin-like polypeptide-functionalized nanobody for column-free immunoaffinity purification of aflatoxin B1.Anal Bioanal Chem. 2024 Nov;416(28):6199-6208. doi: 10.1007/s00216-024-05498-0. Epub 2024 Sep 12. Anal Bioanal Chem. 2024. PMID: 39264463
-
Molecular bases for temperature sensitivity in supramolecular assemblies and their applications as thermoresponsive soft materials.Mater Horiz. 2022 Jan 4;9(1):164-193. doi: 10.1039/d1mh01091c. Mater Horiz. 2022. PMID: 34549764 Free PMC article. Review.
-
Machine Learning in Polymer Research.Adv Mater. 2025 Mar;37(11):e2413695. doi: 10.1002/adma.202413695. Epub 2025 Feb 9. Adv Mater. 2025. PMID: 39924835 Free PMC article. Review.
-
Adaptive Recombinant Nanoworms from Genetically Encodable Star Amphiphiles.Biomacromolecules. 2022 Mar 14;23(3):863-876. doi: 10.1021/acs.biomac.1c01314. Epub 2021 Dec 23. Biomacromolecules. 2022. PMID: 34942072 Free PMC article.
-
Evaluation of machine learning algorithms to predict the hydrodynamic radii and transition temperatures of chemo-biologically synthesized copolymers.Comput Biol Med. 2021 Jan;128:104134. doi: 10.1016/j.compbiomed.2020.104134. Epub 2020 Nov 21. Comput Biol Med. 2021. PMID: 33249343 Free PMC article.
References
-
- Telko M, Hickey A. Dry powder inhaler formulation. Respir. Care. 2005;50(9):1209–1227. - PubMed
Publication types
MeSH terms
Substances
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources