Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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

Nature Nanotechnology volume 16, pages 725–733 (2021)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: High-throughput screening of solid drug nanoparticles and machine learning model development.
Fig. 2: Computationally prioritized combinations of drugs and excipients form nanoparticles.
Fig. 3: Characterization and ex vivo application of terbinafine and taurocholic acid particles.
Fig. 4: Characterization and in vivo application of sorafenib nanoparticles.

Similar content being viewed by others

Data availability

All data used in this study were extracted from publicly available sources from the DrugBank website (version 5.0, drugbank.ca), the FDA (GRAS version June 2016 and IIG version 0716 UNII 362O9ITL9D datasets, www.fda.gov) and the Shoichet laboratory (bkslab.org/takeaways/aggregator_hts). The processed and curated data are made available through the GitHub repository at https://github.com/DanReker/CoAggregators.

Code availability

All code for this study to perform simulations and machine learning is available through the GitHub repository at https://github.com/DanReker/CoAggregators.

References

  1. Hopkins, A. L., Keserü, G. M., Leeson, P. D., Rees, D. C. & Reynolds, C. H. The role of ligand efficiency metrics in drug discovery. Nat. Rev. Drug Discov. 13, 105–121 (2014).

    Article  CAS  Google Scholar 

  2. Irwin, J. J. et al. An aggregation advisor for ligand discovery. J. Med. Chem. 58, 7076–7087 (2015).

    Article  CAS  Google Scholar 

  3. Reker, D., Bernardes, G. J. L. & Rodrigues, T. Computational advances in combating colloidal aggregation in drug discovery. Nat. Chem. 11, 402–418 (2019).

    Article  CAS  Google Scholar 

  4. Owen, S. C., Doak, A. K., Wassam, P., Shoichet, M. S. & Shoichet, B. K. Colloidal aggregation affects the efficacy of anticancer drugs in cell culture. ACS Chem. Biol. 7, 1429–1435 (2012).

    Article  CAS  Google Scholar 

  5. Kipp, J. E. The role of solid nanoparticle technology in the parenteral delivery of poorly water-soluble drugs. Int. J. Pharm. 284, 109–122 (2004).

    Article  CAS  Google Scholar 

  6. Mcdonald, T. O. et al. Antiretroviral solid drug nanoparticles with enhanced oral bioavailability: production, characterization, and in vitro-in vivo correlation. Adv. Healthc. Mater. 3, 400–411 (2014).

    Article  CAS  Google Scholar 

  7. Govender, T., Stolnik, S., Garnett, M. C., Illum, L. & Davis, S. S. PLGA nanoparticles prepared by nanoprecipitation: drug loading and release studies of a water soluble drug. J. Control. Release 57, 171–185 (1999).

    Article  CAS  Google Scholar 

  8. Westesen, K., Bunjes, H. & Koch, M. H. Physicochemical characterization of lipid nanoparticles and evaluation of their drug loading capacity and sustained release potential. J. Control. Release 48, 223–236 (1997).

    Article  CAS  Google Scholar 

  9. Reker, D. et al. ‘Inactive’ ingredients in oral medications. Sci. Transl. Med. 11, eaau6753 (2019).

    Article  Google Scholar 

  10. McLaughlin, C. K. et al. Stable colloidal drug aggregates catch and release active enzymes. ACS Chem. Biol. 11, 992–1000 (2016).

    Article  CAS  Google Scholar 

  11. Shamay, Y. et al. Quantitative self-assembly prediction yields targeted nanomedicines. Nat. Mater. 17, 361–368 (2018).

    Article  Google Scholar 

  12. Inactive Ingredient Search for Approved Drug Products (FDA, 2016); https://www.accessdata.fda.gov/scripts/cder/iig/

  13. Feng, B. Y., Shelat, A., Dorman, T. N., Guy, R. K. & Shoichet, B. K. High-throughput assays for promiscuous inhibitors. Nat. Chem. Biol. 1, 146–148 (2005).

    Article  CAS  Google Scholar 

  14. SCOGS (Select Committee on GRAS Substances) (FDA, 2016); https://www.accessdata.fda.gov/scripts/fdcc/?set=SCOGS

  15. Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).

    Article  CAS  Google Scholar 

  16. Reker, D. et al. Machine learning uncovers food- and excipient-drug interactions. Cell Rep. 30, 3710–3716.e4 (2020).

    Article  CAS  Google Scholar 

  17. Reker, D., Schneider, P. & Schneider, G. Multi-objective active machine learning rapidly improves structure-activity models and reveals new protein-protein interaction inhibitors. Chem. Sci. 7, 3919–3927 (2016).

    Article  CAS  Google Scholar 

  18. Gregori-Puigjane, E. & Mestres, J. A ligand-based approach to mining the chemogenomic space of drugs. Comb. Chem. High T. Scr. 11, 669–676 (2008).

    CAS  Google Scholar 

  19. Wildman, S. A. & Crippen, G. M. Prediction of physicochemical parameters by atomic contributions. J. Chem. Inf. Comp. Sci. 39, 868–873 (1999).

    Article  CAS  Google Scholar 

  20. Chuang, K. V. & Keiser, M. J. Adversarial controls for scientific machine learning. ACS Chem. Biol. 13, 2819–2821 (2018).

    Article  CAS  Google Scholar 

  21. Test no. 318: dispersion stability of nanomaterials in simulated environmental media. OECD Guidelines for the Testing of Chemicals, Section 3 https://doi.org/10.1787/9789264284142-en (2017).

  22. Lipner, S. R. & Scher, R. K. Onychomycosis: treatment and prevention of recurrence. J. Am. Acad. Dermatol. 80, 853–867 (2019).

    Article  Google Scholar 

  23. McClellan, K. J., Wiseman, L. R. & Markham, A. Terbinafine. Drugs 58, 179–202 (1999).

    Article  CAS  Google Scholar 

  24. Matteucci, M. E., Hotze, M. A., Johnston, K. P. & Williams, R. O. Drug nanoparticles by antisolvent precipitation: mixing energy versus surfactant stabilization. Langmuir 22, 8951–8959 (2006).

    Article  CAS  Google Scholar 

  25. Ganesh, A. N. et al. Colloidal drug aggregate stability in high serum conditions and pharmacokinetic consequence. ACS Chem. Biol. 14, 751–757 (2019).

    Article  CAS  Google Scholar 

  26. Jayatilake, J. A. M. S., Tilakaratne, W. M. & Panagoda, G. J. Candidal onychomycosis: a mini-review. Mycopathologia 168, 165–173 (2009).

    Article  CAS  Google Scholar 

  27. Cabral, H. et al. Accumulation of sub-100 nm polymeric micelles in poorly permeable tumours depends on size. Nat. Nanotechnol. 6, 815–823 (2011).

    Article  CAS  Google Scholar 

  28. Wong, C. et al. Multistage nanoparticle delivery system for deep penetration into tumor tissue. Proc. Natl Acad. Sci. USA 108, 2426–2431 (2011).

    Article  CAS  Google Scholar 

  29. Pavlović, N. et al. Bile acids and their derivatives as potential modifiers of drug release and pharmacokinetic profiles. Front. Pharmacol. 9, 1283 (2018).

    Article  Google Scholar 

  30. Villanueva, A. Hepatocellular carcinoma. N. Engl. J. Med. 380, 1450–1462 (2019).

    Article  CAS  Google Scholar 

  31. El-Serag, H. B. & Mason, A. C. Rising incidence of hepatocellular carcinoma in the United States. N. Engl. J. Med. 340, 745–750 (1999).

    Article  CAS  Google Scholar 

  32. Isbrucker, R. A. A. & Burdock, G. A. A. Risk and safety assessment on the consumption of Licorice root (Glycyrrhiza sp.), its extract and powder as a food ingredient, with emphasis on the pharmacology and toxicology of glycyrrhizin. Regul. Toxicol. Pharm. 46, 167–192 (2006).

    Article  CAS  Google Scholar 

  33. Basso, U., Brunello, A., Bertuzzi, A. & Santoro, A. Sorafenib is active on lung metastases from synovial sarcoma. Ann. Oncol. 20, 386–387 (2009).

    Article  CAS  Google Scholar 

  34. Chaparro, M., González Moreno, L., Trapero-Marugán, M., Medina, J. & Moreno-Otero, R. Review article: pharmacological therapy for hepatocellular carcinoma with sorafenib and other oral agents. Aliment. Pharm. Ther. 28, 1269–1277 (2008).

    Article  CAS  Google Scholar 

  35. Zhong, J. et al. Meloxicam combined with sorafenib synergistically inhibits tumor growth of human hepatocellular carcinoma cells via ER stress-related apoptosis. Oncol. Rep. 34, 2142–2150 (2015).

    Article  CAS  Google Scholar 

  36. Auffan, M. et al. Towards a definition of inorganic nanoparticles from an environmental, health and safety perspective. Nat. Nanotechnol. https://doi.org/10.1038/nnano.2009.242 (2009).

  37. Lin, A. et al. Glycyrrhizin surface-modified chitosan nanoparticles for hepatocyte-targeted delivery. Int. J. Pharm. 359, 247–253 (2008).

    Article  CAS  Google Scholar 

  38. Tward, A. D. et al. Distinct pathways of genomic progression to benign and malignant tumors of the liver. Proc. Natl Acad. Sci. USA 104, 14771–14776 (2007).

    Article  CAS  Google Scholar 

  39. Bogorad, R. L. et al. Nanoparticle-formulated siRNA targeting integrins inhibits hepatocellular carcinoma progression in mice. Nat. Commun. 5, 3869 (2014).

    Article  CAS  Google Scholar 

  40. Lee, J. et al. Tumor stem cells derived from glioblastomas cultured in bFGF and EGF more closely mirror the phenotype and genotype of primary tumors than do serum-cultured cell lines. Cancer Cell 9, 391–403 (2006).

    Article  CAS  Google Scholar 

  41. Sharpless, N. E. & DePinho, R. A. The mighty mouse: genetically engineered mouse models in cancer drug development. Nat. Rev. Drug Discov. 5, 741–754 (2006).

    Article  CAS  Google Scholar 

  42. Waidely, E., Al-Yuobi, A. R. O., Bashammakh, A. S., El-Shahawi, M. S. & Leblanc, R. M. Serum protein biomarkers relevant to hepatocellular carcinoma and their detection. Analyst 141, 36–44 (2016).

    Article  CAS  Google Scholar 

  43. Lee, C. H. et al. Protective mechanism of glycyrrhizin on acute liver injury induced by carbon tetrachloride in mice. Biol. Pharm. Bull. 30, 1898–1904 (2007).

    Article  CAS  Google Scholar 

  44. Gelderblom, H., Verweij, J., Nooter, K., Sparreboom, A. & Cremophor, E. L. The drawbacks and advantages of vehicle selection for drug formulation. Eur. J. Cancer 37, 1590–1598 (2001).

    Article  CAS  Google Scholar 

  45. Singh, A., Iyer, A. K. & Amiji, M. M. in Handbook of Nanobiomedical Research (ed. Torchilin, V.) 199–233 (World Scientific Publishing Company, 2014); https://doi.org/10.1142/9789814520652_0006

  46. Pohjala, L., Tammela, P., Pohjala, L. & Tammela, P. Aggregating behavior of phenolic compounds—a source of false bioassay results? Molecules 17, 10774–10790 (2012).

    Article  CAS  Google Scholar 

  47. Traverso, G. & Langer, R. Perspective: special delivery for the gut. Nature 519, S19 (2015).

    Article  CAS  Google Scholar 

  48. Cheng, C. J. & Saltzman, W. M. Nanomedicine: downsizing tumour therapeutics. Nat. Nanotechnol. 7, 346–347 (2012).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

D.R. is a Swiss National Science Foundation Fellow (grants P2EZP3_168827 and P300P2_177833). Y.R. is grateful to the MIT Skoltech Initiative for financial support. A.R.K. is grateful to the PhRMA foundation postdoctoral fellowship for financial support. We thank the Koch Institute Swanson Biotechnology Center for technical support, specifically the High Throughput Sciences Facility, Peterson (1957) Nanotechnology Materials Core Facility, the Animal Imaging and Preclinical Testing core, and the Hope Babette Tang (1983) Histology Facility. This work was supported in part by the Koch Institute Support (core) grant P30-CA14051 from the National Cancer Institute and by the NIH grant EB000244. We are grateful to OpenEye for providing us with an OpenEye Academic License. We are grateful to H. Mazdiyasni for technical support and to M. Jimenez for providing access to C. albicans and helpful discussions throughout the study.

Author information

Author notes

  1. Daniel Reker

    Present address: Department of Biomedical Engineering, Duke University, Durham, NC, USA

Authors and Affiliations

  1. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Daniel Reker, Yulia Rybakova, Ameya R. Kirtane, Ruonan Cao, Jee Won Yang, Natsuda Navamajiti, Apolonia Gardner, Rosanna M. Zhang, Tina Esfandiary, Johanna L’Heureux, Thomas von Erlach, Elena M. Smekalova, Kaitlyn Hess, Aaron Lopes, Jaimie Rogner, Joy Collins, Siddartha M. Tamang, Keiko Ishida, Paul Chamberlain, DongSoo Yun, Abigail Lytton-Jean, Christian K. Soule, Jaime H. Cheah, Alison M. Hayward & Robert Langer

  2. Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Daniel Reker, Ameya R. Kirtane, Jee Won Yang, Alison M. Hayward & Giovanni Traverso

  3. Division of Engineering Science, University of Toronto, Toronto, Ontario, Canada

    Ruonan Cao

  4. Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, USA

    Jee Won Yang

  5. Biomedical Engineering Program, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand

    Natsuda Navamajiti

  6. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Apolonia Gardner & Rosanna M. Zhang

  7. Skolkovo Institute of Science and Technology, Moscow, Russia

    Dominique Leboeuf

  8. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Robert Langer & Giovanni Traverso

Authors
  1. Daniel Reker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yulia Rybakova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ameya R. Kirtane
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ruonan Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jee Won Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Natsuda Navamajiti
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Apolonia Gardner
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Rosanna M. Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Tina Esfandiary
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Johanna L’Heureux
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Thomas von Erlach
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Elena M. Smekalova
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Dominique Leboeuf
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Kaitlyn Hess
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Aaron Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Jaimie Rogner
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Joy Collins
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Siddartha M. Tamang
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Keiko Ishida
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Paul Chamberlain
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. DongSoo Yun
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Abigail Lytton-Jean
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Christian K. Soule
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Jaime H. Cheah
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Alison M. Hayward
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Robert Langer
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Giovanni Traverso
    View author publications

    You can also search for this author in PubMed Google Scholar

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. and J.L. performed in vitro experiments. D.R., R.C. and A.G. performed in vivo experiments. Y.R., A.R.K., T.v.E., A.L.-J., C.K.S. and 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.Y. performed TEM imaging, and K.H., A.L. and 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.

Corresponding author

Correspondence to Giovanni Traverso.

Ethics declarations

Competing interests

D.R., Y.R., A.R.K., R.C., J.W.Y., N.N., A.G., R.M.Z., R.L. and G.T. are co-inventors on multiple patent applications describing novel nanoformulation systems and interactions between excipients and drugs. D.R. acts as a mentor for the German Accelerator Life Sciences and acts as a scientific consultant for pharmaceutical and biotechnology companies.

Additional information

Peer review information Nature Nanotechnology thanks Michael Keiser, Jie Zheng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–20, Methods, Tables 1–13 and Notes 1–7.

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Reker, D., Rybakova, Y., Kirtane, A.R. et al. Computationally guided high-throughput design of self-assembling drug nanoparticles. Nat. Nanotechnol. 16, 725–733 (2021). https://doi.org/10.1038/s41565-021-00870-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41565-021-00870-y

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research