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

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.

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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.

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.

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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.

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Authors and Affiliations

Authors

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.

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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.

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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.

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

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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

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