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Computationally guided high-throughput design of self-assembling drug nanoparticles.

Publication ,  Journal Article
Reker, D; Rybakova, Y; Kirtane, AR; Cao, R; Yang, JW; Navamajiti, N; Gardner, A; Zhang, RM; Esfandiary, T; L'Heureux, J; von Erlach, T; Yun, D ...
Published in: Nature nanotechnology
June 2021

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 2,686 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.

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Published In

Nature nanotechnology

DOI

EISSN

1748-3395

ISSN

1748-3387

Publication Date

June 2021

Volume

16

Issue

6

Start / End Page

725 / 733

Related Subject Headings

  • Xenograft Model Antitumor Assays
  • Tissue Distribution
  • Terbinafine
  • Taurocholic Acid
  • Sorafenib
  • Skin Absorption
  • Nanoscience & Nanotechnology
  • Nanoparticles
  • Mice, Inbred Strains
  • Mice
 

Citation

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Reker, D., Rybakova, Y., Kirtane, A. R., Cao, R., Yang, J. W., Navamajiti, N., … Traverso, G. (2021). Computationally guided high-throughput design of self-assembling drug nanoparticles. Nature Nanotechnology, 16(6), 725–733. https://doi.org/10.1038/s41565-021-00870-y
Reker, Daniel, Yulia Rybakova, Ameya R. Kirtane, Ruonan Cao, Jee Won Yang, Natsuda Navamajiti, Apolonia Gardner, et al. “Computationally guided high-throughput design of self-assembling drug nanoparticles.Nature Nanotechnology 16, no. 6 (June 2021): 725–33. https://doi.org/10.1038/s41565-021-00870-y.
Reker D, Rybakova Y, Kirtane AR, Cao R, Yang JW, Navamajiti N, et al. Computationally guided high-throughput design of self-assembling drug nanoparticles. Nature nanotechnology. 2021 Jun;16(6):725–33.
Reker, Daniel, et al. “Computationally guided high-throughput design of self-assembling drug nanoparticles.Nature Nanotechnology, vol. 16, no. 6, June 2021, pp. 725–33. Epmc, doi:10.1038/s41565-021-00870-y.
Reker D, Rybakova Y, Kirtane AR, Cao R, Yang JW, Navamajiti N, Gardner A, Zhang RM, Esfandiary T, L’Heureux J, von Erlach T, Smekalova EM, Leboeuf D, Hess K, Lopes A, Rogner J, Collins J, Tamang SM, Ishida K, Chamberlain P, Yun D, Lytton-Jean A, Soule CK, Cheah JH, Hayward AM, Langer R, Traverso G. Computationally guided high-throughput design of self-assembling drug nanoparticles. Nature nanotechnology. 2021 Jun;16(6):725–733.

Published In

Nature nanotechnology

DOI

EISSN

1748-3395

ISSN

1748-3387

Publication Date

June 2021

Volume

16

Issue

6

Start / End Page

725 / 733

Related Subject Headings

  • Xenograft Model Antitumor Assays
  • Tissue Distribution
  • Terbinafine
  • Taurocholic Acid
  • Sorafenib
  • Skin Absorption
  • Nanoscience & Nanotechnology
  • Nanoparticles
  • Mice, Inbred Strains
  • Mice