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Computationally guided high-throughput design of self-assembling drug nanoparticles
Nanoformulations of therapeutic drugs are transforming our ability to effectively deliver and treat a myriad of conditions. Often, however, they are complex to produce and exhibit low drug loading, except for nanoparticles formed via co-assembly of drugs and small molecular dyes, which display drug...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8197729/ https://www.ncbi.nlm.nih.gov/pubmed/33767382 http://dx.doi.org/10.1038/s41565-021-00870-y |
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author | Reker, Daniel Rybakova, Yulia Kirtane, Ameya R. Cao, Ruonan Yang, Jee Won Navamajiti, Natsuda Gardner, Apolonia Zhang, Rosanna M. Esfandiary, Tina L’Heureux, Johanna von Erlach, Thomas Smekalova, Elena M. Leboeuf, Dominique Hess, Kaitlyn Lopes, Aaron Rogner, Jaimie Collins, Joy Tamang, Siddartha M. Ishida, Keiko Chamberlain, Paul Yun, DongSoo Lytoon-Jean, Abigail Soule, Christian K. Cheah, Jaime H. Hayward, Alison M. Langer, Robert Traverso, Giovanni |
author_facet | Reker, Daniel Rybakova, Yulia Kirtane, Ameya R. Cao, Ruonan Yang, Jee Won Navamajiti, Natsuda Gardner, Apolonia Zhang, Rosanna M. Esfandiary, Tina L’Heureux, Johanna von Erlach, Thomas Smekalova, Elena M. Leboeuf, Dominique Hess, Kaitlyn Lopes, Aaron Rogner, Jaimie Collins, Joy Tamang, Siddartha M. Ishida, Keiko Chamberlain, Paul Yun, DongSoo Lytoon-Jean, Abigail Soule, Christian K. Cheah, Jaime H. Hayward, Alison M. Langer, Robert Traverso, Giovanni |
author_sort | Reker, Daniel |
collection | PubMed |
description | Nanoformulations of therapeutic drugs are transforming our ability to effectively deliver and treat a myriad of conditions. Often, however, they are complex to produce and exhibit low drug loading, except for nanoparticles formed via co-assembly of drugs and small molecular dyes, which display drug loading capacities of up to 95%. There is currently no understanding of which of the millions of small molecule combinations can result in the formation of these nanoparticles. Here, we report the integration of machine learning with high-throughput experimentation to enable the rapid and large-scale identification of such nanoformulations. We identified 100 self-assembling drug nanoparticles from 2.1 million pairings, each including one of 788 candidate drugs and one of 2686 approved excipients. We further characterized two nanoparticles, sorafenib-glycyrrhizin and terbinafine-taurocholic acid, both ex vivo and in vivo. We anticipate that our platform can accelerate the development of safer and more efficacious nanoformulations with high drug loading capacities for a wide range of therapeutics. |
format | Online Article Text |
id | pubmed-8197729 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
spelling | pubmed-81977292021-09-25 Computationally guided high-throughput design of self-assembling drug nanoparticles Reker, Daniel Rybakova, Yulia Kirtane, Ameya R. Cao, Ruonan Yang, Jee Won Navamajiti, Natsuda Gardner, Apolonia Zhang, Rosanna M. Esfandiary, Tina L’Heureux, Johanna von Erlach, Thomas Smekalova, Elena M. Leboeuf, Dominique Hess, Kaitlyn Lopes, Aaron Rogner, Jaimie Collins, Joy Tamang, Siddartha M. Ishida, Keiko Chamberlain, Paul Yun, DongSoo Lytoon-Jean, Abigail Soule, Christian K. Cheah, Jaime H. Hayward, Alison M. Langer, Robert Traverso, Giovanni Nat Nanotechnol Article Nanoformulations of therapeutic drugs are transforming our ability to effectively deliver and treat a myriad of conditions. Often, however, they are complex to produce and exhibit low drug loading, except for nanoparticles formed via co-assembly of drugs and small molecular dyes, which display drug loading capacities of up to 95%. There is currently no understanding of which of the millions of small molecule combinations can result in the formation of these nanoparticles. Here, we report the integration of machine learning with high-throughput experimentation to enable the rapid and large-scale identification of such nanoformulations. We identified 100 self-assembling drug nanoparticles from 2.1 million pairings, each including one of 788 candidate drugs and one of 2686 approved excipients. We further characterized two nanoparticles, sorafenib-glycyrrhizin and terbinafine-taurocholic acid, both ex vivo and in vivo. We anticipate that our platform can accelerate the development of safer and more efficacious nanoformulations with high drug loading capacities for a wide range of therapeutics. 2021-03-25 2021-06 /pmc/articles/PMC8197729/ /pubmed/33767382 http://dx.doi.org/10.1038/s41565-021-00870-y Text en http://www.nature.com/authors/editorial_policies/license.html#termsUsers may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms |
spellingShingle | Article Reker, Daniel Rybakova, Yulia Kirtane, Ameya R. Cao, Ruonan Yang, Jee Won Navamajiti, Natsuda Gardner, Apolonia Zhang, Rosanna M. Esfandiary, Tina L’Heureux, Johanna von Erlach, Thomas Smekalova, Elena M. Leboeuf, Dominique Hess, Kaitlyn Lopes, Aaron Rogner, Jaimie Collins, Joy Tamang, Siddartha M. Ishida, Keiko Chamberlain, Paul Yun, DongSoo Lytoon-Jean, Abigail Soule, Christian K. Cheah, Jaime H. Hayward, Alison M. Langer, Robert Traverso, Giovanni Computationally guided high-throughput design of self-assembling drug nanoparticles |
title | Computationally guided high-throughput design of self-assembling drug nanoparticles |
title_full | Computationally guided high-throughput design of self-assembling drug nanoparticles |
title_fullStr | Computationally guided high-throughput design of self-assembling drug nanoparticles |
title_full_unstemmed | Computationally guided high-throughput design of self-assembling drug nanoparticles |
title_short | Computationally guided high-throughput design of self-assembling drug nanoparticles |
title_sort | computationally guided high-throughput design of self-assembling drug nanoparticles |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8197729/ https://www.ncbi.nlm.nih.gov/pubmed/33767382 http://dx.doi.org/10.1038/s41565-021-00870-y |
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