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-...
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| Published in: | Nature nanotechnology Vol. 16; no. 6; pp. 725 - 733 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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London
Nature Publishing Group UK
01-06-2021
Nature Publishing Group |
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| Abstract | 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.
Self-assembly of small drugs with organic dyes represents a facile route to synthesize nanoparticles with high drug-loading capability. Here the authors combine a machine learning approach with high-throughput experimental validation to identify which combinations of drugs and excipient lead to successful nanoparticle formation and characterize the therapeutic efficacy of two of them in vitro and in animal models. |
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| AbstractList | 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.Self-assembly of small drugs with organic dyes represents a facile route to synthesize nanoparticles with high drug-loading capability. Here the authors combine a machine learning approach with high-throughput experimental validation to identify which combinations of drugs and excipient lead to successful nanoparticle formation and characterize the therapeutic efficacy of two of them in vitro and in animal models. 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.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. 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. Self-assembly of small drugs with organic dyes represents a facile route to synthesize nanoparticles with high drug-loading capability. Here the authors combine a machine learning approach with high-throughput experimental validation to identify which combinations of drugs and excipient lead to successful nanoparticle formation and characterize the therapeutic efficacy of two of them in vitro and in animal models. 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. 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. |
| Author | Traverso, Giovanni Leboeuf, Dominique von Erlach, Thomas Soule, Christian K. Smekalova, Elena M. Zhang, Rosanna M. L’Heureux, Johanna Gardner, Apolonia Tamang, Siddartha M. Rybakova, Yulia Kirtane, Ameya R. Yang, Jee Won Collins, Joy Cheah, Jaime H. Navamajiti, Natsuda Chamberlain, Paul Hess, Kaitlyn Yun, DongSoo Rogner, Jaimie Ishida, Keiko Langer, Robert Esfandiary, Tina Lytton-Jean, Abigail Hayward, Alison M. Cao, Ruonan Lopes, Aaron Reker, Daniel |
| AuthorAffiliation | 5 Biomedical Engineering Program, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330 (THA) 2 Division of Gastroenterology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 (USA) 3 Division of Engineering Science, University of Toronto, Toronto, ON M5S 2E4 (CAN) 4 Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853 (USA) 7 Skolkovo Institute of Science and Technology, Moscow, 121205 (RUS) 8 Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 (USA) 1 Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139 (USA) 6 Department of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139 (USA) 9 Current Address: Department of Biomedical Engineering, Duke University, Durham, NC, 27708 (USA) |
| AuthorAffiliation_xml | – name: 1 Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139 (USA) – name: 7 Skolkovo Institute of Science and Technology, Moscow, 121205 (RUS) – name: 3 Division of Engineering Science, University of Toronto, Toronto, ON M5S 2E4 (CAN) – name: 6 Department of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139 (USA) – name: 8 Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 (USA) – name: 2 Division of Gastroenterology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 (USA) – name: 5 Biomedical Engineering Program, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330 (THA) – name: 9 Current Address: Department of Biomedical Engineering, Duke University, Durham, NC, 27708 (USA) – name: 4 Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853 (USA) |
| Author_xml | – sequence: 1 givenname: Daniel orcidid: 0000-0003-4789-7380 surname: Reker fullname: Reker, Daniel organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Department of Biomedical Engineering, Duke University – sequence: 2 givenname: Yulia surname: Rybakova fullname: Rybakova, Yulia organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology – sequence: 3 givenname: Ameya R. orcidid: 0000-0002-3779-0363 surname: Kirtane fullname: Kirtane, Ameya R. organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School – sequence: 4 givenname: Ruonan surname: Cao fullname: Cao, Ruonan organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Division of Engineering Science, University of Toronto – sequence: 5 givenname: Jee Won orcidid: 0000-0002-3257-1823 surname: Yang fullname: Yang, Jee Won organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University – sequence: 6 givenname: Natsuda surname: Navamajiti fullname: Navamajiti, Natsuda organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Biomedical Engineering Program, Faculty of Engineering, Chulalongkorn University – sequence: 7 givenname: Apolonia surname: Gardner fullname: Gardner, Apolonia organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology – sequence: 8 givenname: Rosanna M. surname: Zhang fullname: Zhang, Rosanna M. organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology – sequence: 9 givenname: Tina orcidid: 0000-0003-0091-6310 surname: Esfandiary fullname: Esfandiary, Tina organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology – sequence: 10 givenname: Johanna surname: L’Heureux fullname: L’Heureux, Johanna organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology – sequence: 11 givenname: Thomas surname: von Erlach fullname: von Erlach, Thomas organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology – sequence: 12 givenname: Elena M. surname: Smekalova fullname: Smekalova, Elena M. organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology – sequence: 13 givenname: Dominique surname: Leboeuf fullname: Leboeuf, Dominique organization: Skolkovo Institute of Science and Technology – sequence: 14 givenname: Kaitlyn orcidid: 0000-0003-2837-0305 surname: Hess fullname: Hess, Kaitlyn organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology – sequence: 15 givenname: Aaron surname: Lopes fullname: Lopes, Aaron organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology – sequence: 16 givenname: Jaimie surname: Rogner fullname: Rogner, Jaimie organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology – sequence: 17 givenname: Joy surname: Collins fullname: Collins, Joy organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology – sequence: 18 givenname: Siddartha M. surname: Tamang fullname: Tamang, Siddartha M. organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology – sequence: 19 givenname: Keiko surname: Ishida fullname: Ishida, Keiko organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology – sequence: 20 givenname: Paul surname: Chamberlain fullname: Chamberlain, Paul organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology – sequence: 21 givenname: DongSoo orcidid: 0000-0001-9495-1613 surname: Yun fullname: Yun, DongSoo organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology – sequence: 22 givenname: Abigail orcidid: 0000-0002-1582-0066 surname: Lytton-Jean fullname: Lytton-Jean, Abigail organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology – sequence: 23 givenname: Christian K. surname: Soule fullname: Soule, Christian K. organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology – sequence: 24 givenname: Jaime H. surname: Cheah fullname: Cheah, Jaime H. organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology – sequence: 25 givenname: Alison M. surname: Hayward fullname: Hayward, Alison M. organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School – sequence: 26 givenname: Robert orcidid: 0000-0003-4255-0492 surname: Langer fullname: Langer, Robert organization: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Department of Mechanical Engineering, Massachusetts Institute of Technology – sequence: 27 givenname: Giovanni orcidid: 0000-0001-7851-4077 surname: Traverso fullname: Traverso, Giovanni email: cgt20@mit.edu organization: Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Department of Mechanical Engineering, Massachusetts Institute of Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33767382$$D View this record in MEDLINE/PubMed |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 AUTHOR CONTRIBUTIONS D.R., R.L. and G.T. conceived the study. D.R., Y.R., A.R.K., R.L, and G.T. designed experiments. D.R. and J.W.Y. performed in silico experiments. D.R., R.C., J.W.Y., N.N., R.M.Z., T.E., J.L.H. performed in vitro experiments. D.R., R.C., A.G. performed in vivo experiments. Y.R., A.R.K., T.v.E., A.L.-J., C.K.S., J.H.C supported in vitro experiments. Y.R., A.R.K., E.M.S, D.L, J.C., S.M.T, K.I., P.C. and A.M.H. supported in vivo experiments. D.S.Y. performed TEM imaging and K.H, A.L., J.R. performed pharmaceutical analytics. D.R., R.L. and G.T. wrote the manuscript with contributions from the other authors. All authors approved the final version of this manuscript. |
| ORCID | 0000-0003-0091-6310 0000-0002-3257-1823 0000-0001-7851-4077 0000-0002-1582-0066 0000-0003-2837-0305 0000-0003-4255-0492 0000-0001-9495-1613 0000-0002-3779-0363 0000-0003-4789-7380 |
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| PublicationTitle | Nature nanotechnology |
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| Snippet | Nanoformulations of therapeutic drugs are transforming our ability to effectively deliver and treat a myriad of conditions. Often, however, they are complex to... |
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| SubjectTerms | 119/118 631/61/350/354 631/61/54/152 639/301/1034/1037 639/638/898 639/925/350/2093 Animal models Animals Candida albicans - drug effects Chemistry and Materials Science Computer Simulation Drug Carriers - chemical synthesis Drug Carriers - chemistry Drug Design Drug development Drug Evaluation, Preclinical - methods Drugs Dyes Dynamic Light Scattering Excipients Excipients - chemistry Experimentation Female Glycyrrhizic Acid - chemistry Glycyrrhizin High-Throughput Screening Assays - methods Humans Learning algorithms Machine Learning Materials Science Mice Mice, Inbred Strains Nanoparticles Nanoparticles - chemistry Nanotechnology Nanotechnology and Microengineering Self-assembly Skin Absorption Sorafenib - chemistry Sorafenib - pharmacokinetics Sorafenib - pharmacology Taurocholic acid Taurocholic Acid - chemistry Terbinafine Terbinafine - chemistry Terbinafine - pharmacology Tissue Distribution Xenograft Model Antitumor Assays |
| Title | Computationally guided high-throughput design of self-assembling drug nanoparticles |
| URI | https://link.springer.com/article/10.1038/s41565-021-00870-y https://www.ncbi.nlm.nih.gov/pubmed/33767382 https://www.proquest.com/docview/2539999539 https://www.proquest.com/docview/2506285201 https://pubmed.ncbi.nlm.nih.gov/PMC8197729 |
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