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  • Review Article
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A framework for designing delivery systems

Nature Nanotechnology volume 15, pages 819–829 (2020)Cite this article

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Abstract

The delivery of medical agents to a specific diseased tissue or cell is critical for diagnosing and treating patients. Nanomaterials are promising vehicles to transport agents that include drugs, contrast agents, immunotherapies and gene editors. They can be engineered to have different physical and chemical properties that influence their interactions with their biological environments and delivery destinations. In this Review Article, we discuss nanoparticle delivery systems and how the biology of disease should inform their design. We propose developing a framework for building optimal delivery systems that uses nanoparticle–biological interaction data and computational analyses to guide future nanomaterial designs and delivery strategies.

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Fig. 1: Biological levels of nanoparticle barriers.
Fig. 2: A systematic view of nanoparticle delivery barriers.
Fig. 3: A computational framework for analysing nanoparticle–biological interaction datasets.
Fig. 4: A rational strategy for designing and testing nanoformulations for delivery.

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References

  1. Walkey, C. D. et al. Protein corona fingerprinting predicts the cellular interaction of gold and silver nanoparticles. ACS Nano 8, 2439–2455 (2014).

    CAS  Google Scholar 

  2. Tenzer, S. et al. Rapid formation of plasma protein corona critically affects nanoparticle pathophysiology. Nat. Nanotechnol. 8, 772–781 (2013).

    CAS  Google Scholar 

  3. Monopoli, M. P. et al. Physical−chemical aspects of protein corona: relevance to in vitro and in vivo biological impacts of nanoparticles. J. Am. Chem. Soc. 133, 2525–2534 (2011).

    CAS  Google Scholar 

  4. Walczyk, D., Bombelli, F. B., Monopoli, M. P., Lynch, I. & Dawson, K. A. What the cell ‘sees’ in bionanoscience. J. Am. Chem. Soc. 132, 5761–5768 (2010).

    CAS  Google Scholar 

  5. Monopoli, M. P., Åberg, C., Salvati, A. & Dawson, K. A. Biomolecular coronas provide the biological identity of nanosized materials. Nat. Nanotechnol. 7, 779–786 (2012).

    CAS  Google Scholar 

  6. Varnamkhasti, B. S. et al. Protein corona hampers targeting potential of MUC1 aptamer functionalized SN-38 core–shell nanoparticles. Int. J. Pharm. 494, 430–444 (2015).

    CAS  Google Scholar 

  7. Salvati, A. et al. Transferrin-functionalized nanoparticles lose their targeting capabilities when a biomolecule corona adsorbs on the surface. Nat. Nanotechnol. 8, 137–143 (2013).

    CAS  Google Scholar 

  8. Schipper, M. L. et al. Particle size, surface coating, and PEGylation influence the biodistribution of quantum dots in living mice. Small 5, 126–134 (2009).

    CAS  Google Scholar 

  9. Fonge, H., Huang, H., Scollard, D., Reilly, R. M. & Allen, C. Influence of formulation variables on the biodistribution of multifunctional block copolymer micelles. J. Control. Release 157, 366–374 (2012).

    CAS  Google Scholar 

  10. De Jong, W. H. et al. Particle size-dependent organ distribution of gold nanoparticles after intravenous administration. Biomaterials 29, 1912–1919 (2008).

    Google Scholar 

  11. Lee, J. S. et al. Circulation kinetics and biodistribution of dual-labeled polymersomes with modulated surface charge in tumor-bearing mice: comparison with stealth liposomes. J. Control. Release 155, 282–288 (2011).

    CAS  Google Scholar 

  12. Shimada, K. et al. Biodistribution of liposomes containing synthetic galactose-terminated diacylglyceryl-poly(ethyleneglycol)s. Biochim. Biophys. Acta 1326, 329–341 (1997).

    CAS  Google Scholar 

  13. Sadauskas, E. et al. Protracted elimination of gold nanoparticles from mouse liver. Nanomedicine 5, 162–169 (2009).

    CAS  Google Scholar 

  14. Tsoi, K. M. et al. Mechanism of hard-nanomaterial clearance by the liver. Nat. Mater. 15, 1212–1221 (2016).

    CAS  Google Scholar 

  15. Liliemark, E. et al. Targeting of teniposide to the mononuclear phagocytic system (MPS) by incorporation in liposomes and submicron lipid particles; an autoradiographic study in mice. Leuk. Lymphoma 18, 113–118 (1995).

    CAS  Google Scholar 

  16. Sun, X. et al. Improved tumor uptake by optimizing liposome based RES blockade strategy. Theranostics 7, 319–328 (2017).

    CAS  Google Scholar 

  17. Klibanov, A. L., Maruyama, K., Torchilin, V. P. & Huang, L. Amphipathic polyethyleneglycols effectively prolong the circulation time of liposomes. FEBS Lett. 268, 235–237 (1990).

    CAS  Google Scholar 

  18. Choi, H. S. et al. Renal clearance of quantum dots. Nat. Biotechnol. 25, 1165–1170 (2007).

    CAS  Google Scholar 

  19. Kwon, Y. J., James, E., Shastri, N. & Frechet, J. M. J. In vivo targeting of dendritic cells for activation of cellular immunity using vaccine carriers based on pH-responsive microparticles. Proc. Natl Acad. Sci. 102, 18264–18268 (2005).

    CAS  Google Scholar 

  20. Poon, W. et al. Elimination pathways of nanoparticles. ACS Nano 13, 5785–5798 (2019).

    CAS  Google Scholar 

  21. Lawrence, M. G. et al. Permeation of macromolecules into the renal glomerular basement membrane and capture by the tubules. Proc. Natl Acad. Sci. USA 114, 2958–2963 (2017).

    CAS  Google Scholar 

  22. Choi, H. S. et al. Renal clearance of quantum dots. Nat. Biotechnol. 25, 1165–1170 (2007).

    CAS  Google Scholar 

  23. Satchell, S. C. & Braet, F. Glomerular endothelial cell fenestrations: an integral component of the glomerular filtration barrier. Am. J. Physiol. Ren. Physiol. 296, F947–56 (2009).

    CAS  Google Scholar 

  24. Satchell, S. C. The glomerular endothelium emerges as a key player in diabetic nephropathy. Kidney Int. 82, 949–951 (2012).

    Google Scholar 

  25. Du, B. et al. Glomerular barrier behaves as an atomically precise bandpass filter in a sub-nanometre regime. Nat. Nanotechnol. 12, 1096–1102 (2017).

    CAS  Google Scholar 

  26. Balogh, L. et al. Significant effect of size on the in vivo biodistribution of gold composite nanodevices in mouse tumor models. Nanomedicine 3, 281–296 (2007).

    CAS  Google Scholar 

  27. Du, B., Yu, M. & Zheng, J. Transport and interactions of nanoparticles in the kidneys. Nat. Rev. Mater. 3, 358–374 (2018).

    Google Scholar 

  28. Ruggiero, A. et al. Paradoxical glomerular filtration of carbon nanotubes. Proc. Natl Acad. Sci. USA 107, 12369–12374 (2010).

    CAS  Google Scholar 

  29. Jasim, D. A. et al. The effects of extensive glomerular filtration of thin graphene oxide sheets on kidney physiology. ACS Nano 10, 10753–10767 (2016).

    CAS  Google Scholar 

  30. Saraiva, C. et al. Nanoparticle-mediated brain drug delivery: Overcoming blood–brain barrier to treat neurodegenerative diseases. J. Control. Release 235, 34–47 (2016).

    CAS  Google Scholar 

  31. Sindhwani, S. et al. The entry of nanoparticles into solid tumours. Nat. Mater. https://doi.org/10.1038/s41563-019-0566-2 (2020).

  32. Wiley, D. T., Webster, P., Gale, A. & Davis, M. E. Transcytosis and brain uptake of transferrin-containing nanoparticles by tuning avidity to transferrin receptor. Proc. Natl Acad. Sci. USA. 110, 8662–8667 (2013).

    CAS  Google Scholar 

  33. Bonnans, C., Chou, J. & Werb, Z. Remodelling the extracellular matrix in development and disease. Nat. Rev. Mol. Cell Biol. 15, 786–801 (2014).

    CAS  Google Scholar 

  34. Karsdal, M. A. et al. Novel insights into the function and dynamics of extracellular matrix in liver fibrosis. Am. J. Physiol. Gastrointest. Liver Physiol. 308, G807–30 (2015).

    Google Scholar 

  35. Cox, T. R. & Erler, J. T. Remodeling and homeostasis of the extracellular matrix: implications for fibrotic diseases and cancer. Dis. Model. Mech. 4, 165–178 (2011).

    CAS  Google Scholar 

  36. Sykes, E. A. et al. Tailoring nanoparticle designs to target cancer based on tumor pathophysiology. Proc. Natl Acad. Sci. USA 113, E1142–E1151 (2016).

    CAS  Google Scholar 

  37. Netti, P. A., Berk, D. A., Swartz, M. A., Grodzinsky, A. J. & Jain, R. K. Role of extracellular matrix assembly in interstitial transport in solid tumors. Cancer Res. 60, 2497–2503 (2000).

    CAS  Google Scholar 

  38. Dai, Q. et al. Quantifying the ligand-coated nanoparticle delivery to cancer cells in solid tumors. ACS Nano 12, 8423–8435 (2018).

    CAS  Google Scholar 

  39. Miller, M. A. et al. Tumour-associated macrophages act as a slow-release reservoir of nano-therapeutic Pt(IV) pro-drug. Nat. Commun. 6, 8692 (2015).

    CAS  Google Scholar 

  40. Korangath, P. et al. Nanoparticle interactions with immune cells dominate tumor retention and induce T cell-mediated tumor suppression in models of breast cancer. Sci. Adv. 6, eaay1601 (2020).

    Google Scholar 

  41. Miller, M. A. et al. Predicting therapeutic nanomedicine efficacy using a companion magnetic resonance imaging nanoparticle. Sci. Transl. Med. 7, 314ra183 (2015).

    Google Scholar 

  42. Cuccarese, M. F. et al. Heterogeneity of macrophage infiltration and therapeutic response in lung carcinoma revealed by 3D organ imaging. Nat. Commun. 8, 14293 (2017).

    CAS  Google Scholar 

  43. Kim, H.-Y. et al. Quantitative imaging of tumor-associated macrophages and their response to therapy using 64Cu-Labeled Macrin. ACS Nano 12, 12015–12029 (2018).

    CAS  Google Scholar 

  44. Düzgüneş, N. & Nir, S. Mechanisms and kinetics of liposome–cell interactions. Adv. Drug Deliv. Rev. 40, 3–18 (1999).

    Google Scholar 

  45. Sahay, G., Kim, J. O., Kabanov, A. V. & Bronich, T. K. The exploitation of differential endocytic pathways in normal and tumor cells in the selective targeting of nanoparticulate chemotherapeutic agents. Biomaterials 31, 923–933 (2010).

    CAS  Google Scholar 

  46. Harush-Frenkel, O., Debotton, N., Benita, S. & Altschuler, Y. Targeting of nanoparticles to the clathrin-mediated endocytic pathway. Biochem. Biophys. Res. Commun. 353, 26–32 (2007).

    CAS  Google Scholar 

  47. Meng, H. et al. Aspect ratio determines the quantity of mesoporous silica nanoparticle uptake by a small GTPase-dependent macropinocytosis mechanism. ACS Nano 5, 4434–4447 (2011).

    CAS  Google Scholar 

  48. Lunov, O. et al. Differential uptake of functionalized polystyrene nanoparticles by human macrophages and a monocytic cell line. ACS Nano 5, 1657–1669 (2011).

    CAS  Google Scholar 

  49. van de Water, B. & van de Water, B. Quantitative assessment of mitochondrial toxicity and downstream cellular perturbations in adverse outcome pathways. Toxicol. Lett. 295, S32 (2018).

    Google Scholar 

  50. dos Santos, T., Varela, J., Lynch, I., Salvati, A. & Dawson, K. A. Effects of transport inhibitors on the cellular uptake of carboxylated polystyrene nanoparticles in different cell lines. PLoS One 6, e24438 (2011).

    Google Scholar 

  51. Hafez, I. M., Maurer, N. & Cullis, P. R. On the mechanism whereby cationic lipids promote intracellular delivery of polynucleic acids. Gene Ther. 8, 1188–1196 (2001).

    CAS  Google Scholar 

  52. Akinc, A., Thomas, M., Klibanov, A. M. & Langer, R. Exploring polyethylenimine-mediated DNA transfection and the proton sponge hypothesis. J. Gene Med. 7, 657–663 (2005).

    CAS  Google Scholar 

  53. Pack, D. W., Putnam, D. & Langer, R. Design of imidazole-containing endosomolytic biopolymers for gene delivery. Biotechnol. Bioeng. 67, 217–223 (2000).

    CAS  Google Scholar 

  54. Hu, Y. et al. Cytosolic delivery of membrane-impermeable molecules in dendritic cells using pH-responsive core−shell nanoparticles. Nano Lett. 7, 3056–3064 (2007).

    CAS  Google Scholar 

  55. Pan, L. et al. Nuclear-targeted drug delivery of TAT peptide-conjugated monodisperse mesoporous silica nanoparticles. J. Am. Chem. Soc. 134, 5722–5725 (2012).

    CAS  Google Scholar 

  56. Nakielny, S. & Dreyfuss, G. Transport of Proteins and RNAs in and out of the Nucleus. Cell 99, 677–690 (1999).

    CAS  Google Scholar 

  57. Garbuzenko, O. B. et al. Inhibition of lung tumor growth by complex pulmonary delivery of drugs with oligonucleotides as suppressors of cellular resistance. Proc. Natl Acad. Sci. USA 107, 10737–10742 (2010).

    CAS  Google Scholar 

  58. Griffin, J. I. et al. Revealing dynamics of accumulation of systemically injected liposomes in the skin by intravital microscopy. ACS Nano 11, 11584–11593 (2017).

    CAS  Google Scholar 

  59. Moghimi, S. M., Hunter, A. C. & Murray, J. C. Long-circulating and target-specific nanoparticles: theory to practice. Pharmacol. Rev. 53, 283–318 (2001).

    CAS  Google Scholar 

  60. Lotem, M. et al. Skin toxic effects of polyethylene glycol-coated liposomal doxorubicin. Arch. Dermatol. 136, 1475–1480 (2000).

    CAS  Google Scholar 

  61. Lu, M., Cohen, M. H., Rieves, D. & Pazdur, R. FDA report: Ferumoxytol for intravenous iron therapy in adult patients with chronic kidney disease. Am. J. Hematol. 85, 315–319 (2010).

    CAS  Google Scholar 

  62. Lu, F., Wu, S.-H., Hung, Y. & Mou, C.-Y. Size effect on cell uptake in well-suspended, uniform mesoporous silica nanoparticles. Small 5, 1408–1413 (2009).

    CAS  Google Scholar 

  63. Jin, H., Heller, D. A., Sharma, R. & Strano, M. S. Size-dependent cellular uptake and expulsion of single-walled carbon nanotubes: single particle tracking and a generic uptake model for nanoparticles. ACS Nano 3, 149–158 (2009).

    CAS  Google Scholar 

  64. Agarwal, R. et al. Mammalian cells preferentially internalize hydrogel nanodiscs over nanorods and use shape-specific uptake mechanisms. Proc. Natl Acad. Sci. USA 110, 17247–17252 (2013).

    CAS  Google Scholar 

  65. Huang, X., Teng, X., Chen, D., Tang, F. & He, J. The effect of the shape of mesoporous silica nanoparticles on cellular uptake and cell function. Biomaterials 31, 438–448 (2010).

    CAS  Google Scholar 

  66. Wang, Z., Zhang, J., Ekman, J. M., Kenis, P. J. A. & Lu, Y. DNA-mediated control of metal nanoparticle shape: one-pot synthesis and cellular uptake of highly stable and functional gold nanoflowers. Nano Lett. 10, 1886–1891 (2010).

    CAS  Google Scholar 

  67. Elias, D. R., Poloukhtine, A., Popik, V. & Tsourkas, A. Effect of ligand density, receptor density, and nanoparticle size on cell targeting. Nanomedicine 9, 194–201 (2013).

    CAS  Google Scholar 

  68. Giljohann, D. A. et al. Oligonucleotide loading determines cellular uptake of DNA-modified gold nanoparticles. Nano Lett. 7, 3818–3821 (2007).

    CAS  Google Scholar 

  69. Bai, X. et al. Regulation of cell uptake and cytotoxicity by nanoparticle core under the controlled shape, size, and surface chemistries. ACS Nano 14, 289–302 (2020).

    CAS  Google Scholar 

  70. Clift, M. J. D. et al. The impact of different nanoparticle surface chemistry and size on uptake and toxicity in a murine macrophage cell line. Toxicol. Appl. Pharmacol. 232, 418–427 (2008).

    CAS  Google Scholar 

  71. Oh, N. & Park, J.-H. Surface chemistry of gold nanoparticles mediates their exocytosis in macrophages. ACS Nano 8, 6232–6241 (2014).

    CAS  Google Scholar 

  72. Wang, J., Min, J., Eghtesadi, S. A., Kane, R. S. & Chilkoti, A. Quantitative study of the interaction of multivalent ligand-modified nanoparticles with breast cancer cells with tunable receptor density. ACS Nano 14, 372–383 (2020).

    CAS  Google Scholar 

  73. Ekdawi, S. N. et al. Spatial and temporal mapping of heterogeneity in liposome uptake and microvascular distribution in an orthotopic tumor xenograft model. J. Control. Release 207, 101–111 (2015).

    CAS  Google Scholar 

  74. Kingston, B. R., Syed, A. M., Ngai, J., Sindhwani, S. & Chan, W. C. W. Assessing micrometastases as a target for nanoparticles using 3D microscopy and machine learning. Proc. Natl Acad. Sci. USA 116, 14937–14946 (2019).

    CAS  Google Scholar 

  75. Stirland, D. L., Matsumoto, Y., Toh, K., Kataoka, K. & Bae, Y. H. Analyzing spatiotemporal distribution of uniquely fluorescent nanoparticles in xenograft tumors. J. Control. Release 227, 38–44 (2016).

    CAS  Google Scholar 

  76. Kai, M. P. et al. Tumor Presence Induces Global Immune Changes and Enhances Nanoparticle Clearance. ACS Nano 10, 861–870 (2016).

    CAS  Google Scholar 

  77. Wu, H. et al. Population pharmacokinetics of pegylated liposomal CKD-602 (S-CKD602) in patients with advanced malignancies. J. Clin. Pharmacol. 52, 180–194 (2012).

    CAS  Google Scholar 

  78. Lazarovits, J. et al. Supervised learning and mass spectrometry predicts the fate of nanomaterials. ACS Nano 13, 8023–8034 (2019).

    CAS  Google Scholar 

  79. Liu, R., Jiang, W., Walkey, C. D., Chan, W. C. W. & Cohen, Y. Prediction of nanoparticles-cell association based on corona proteins and physicochemical properties. Nanoscale 7, 9664–9675 (2015).

    CAS  Google Scholar 

  80. Ban, Z. et al. Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles. Proc. Natl Acad. Sci. USA 117, 10492–10499 (2020).

    CAS  Google Scholar 

  81. Fourches, D. et al. Quantitative nanostructure−activity relationship modeling. ACS Nano 4, 5703–5712 (2010).

    CAS  Google Scholar 

  82. Puzyn, T. et al. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nat. Nanotechnol. 6, 175–178 (2011).

    CAS  Google Scholar 

  83. Paunovska, K., Loughrey, D., Sago, C. D., Langer, R. & Dahlman, J. E. Using Large Datasets to Understand Nanotechnology. Adv. Mater. 31, e1902798 (2019).

    Google Scholar 

  84. Yamankurt, G. et al. Exploration of the nanomedicine-design space with high-throughput screening and machine learning. Nat. Biomed. Eng. 3, 318–327 (2019).

    CAS  Google Scholar 

  85. Ng, T. S. C., Garlin, M. A., Weissleder, R. & Miller, M. A. Improving nanotherapy delivery and action through image-guided systems pharmacology. Theranostics 10, 968–997 (2020).

    CAS  Google Scholar 

  86. Lee, H. et al. Cu-MM-302 positron emission tomography quantifies variability of enhanced permeability and retention of nanoparticles in relation to treatment response in patients with metastatic breast cancer. Clin. Cancer Res. 23, 4190–4202 (2017).

    CAS  Google Scholar 

  87. Syed, A. M. et al. Liposome imaging in optically cleared tissues. Nano Lett. 20, 1362–1369 (2020).

    CAS  Google Scholar 

  88. Koo, D.-J. et al. Large-scale 3D optical mapping and quantitative analysis of nanoparticle distribution in tumor vascular microenvironment. Bioconjug. Chem. https://doi.org/10.1021/acs.bioconjchem.0c00263 (2020).

  89. Tavares, A. J. et al. Effect of removing Kupffer cells on nanoparticle tumor delivery. Proc. Natl Acad. Sci. USA 114, E10871–E10880 (2017).

    CAS  Google Scholar 

  90. Souhami, R. L., Patel, H. M. & Ryman, B. E. The effect of reticuloendothelial blockade on the blood clearance and tissue distribution of liposomes. Biochim. Biophys. Acta 674, 354–371 (1981).

    CAS  Google Scholar 

  91. Liu, D., Mori, A. & Huang, L. Role of liposome size and RES blockade in controlling biodistribution and tumor uptake of GM1-containing liposomes. Biochim. Biophys. Acta 1104, 95–101 (1992).

    CAS  Google Scholar 

  92. Proffitt, R. T. et al. Liposomal blockade of the reticuloendothelial system: improved tumor imaging with small unilamellar vesicles. Science 220, 502–505 (1983).

    CAS  Google Scholar 

  93. Ouyang, B. et al. The dose threshold for nanoparticle tumour delivery. Nat. Mater. https://doi.org/10.1038/s41563-020-0755-z (2020).

  94. Chauhan, V. P. et al. Normalization of tumour blood vessels improves the delivery of nanomedicines in a size-dependent manner. Nat. Nanotechnol. 7, 383–388 (2012).

    CAS  Google Scholar 

  95. Arjaans, M. et al. Bevacizumab-induced normalization of blood vessels in tumors hampers antibody uptake. Cancer Res. 73, 3347–3355 (2013).

    CAS  Google Scholar 

  96. Chen, Y. et al. Therapeutic remodeling of the tumor microenvironment enhances nanoparticle delivery. Adv. Sci. 6, 1802070 (2019).

    Google Scholar 

  97. Miller, M. A. et al. Radiation therapy primes tumors for nanotherapeutic delivery via macrophage-mediated vascular bursts. Sci. Transl. Med. 9, eaal0225 (2017).

    Google Scholar 

  98. Kunjachan, S. et al. Selective priming of tumor blood vessels by radiation therapy enhances nanodrug delivery. Sci. Rep. 9, 15844 (2019).

    Google Scholar 

  99. Herrera, J., Henke, C. A. & Bitterman, P. B. Extracellular matrix as a driver of progressive fibrosis. J. Clin. Invest. 128, 45–53 (2018).

    Google Scholar 

  100. McKee, T. D. et al. Degradation of fibrillar collagen in a human melanoma xenograft improves the efficacy of an oncolytic herpes simplex virus vector. Cancer Res. 66, 2509–2513 (2006).

    CAS  Google Scholar 

  101. Gong, H. et al. Hyaluronidase to enhance nanoparticle-based photodynamic tumor therapy. Nano Lett. 16, 2512–2521 (2016).

    CAS  Google Scholar 

  102. Li, X. et al. Parallel accumulation of tumor hyaluronan, collagen, and other drivers of tumor progression. Clin. Cancer Res. 24, 4798–4807 (2018).

    CAS  Google Scholar 

  103. Murty, S. et al. Nanoparticles functionalized with collagenase exhibit improved tumor accumulation in a murine xenograft model. Part. Part. Syst. Charact. 31, 1307–1312 (2014).

    CAS  Google Scholar 

  104. Eikenes, L., Tari, M., Tufto, I., Bruland, Ø. S. & de Lange Davies, C. Hyaluronidase induces a transcapillary pressure gradient and improves the distribution and uptake of liposomal doxorubicin (CaelyxTM) in human osteosarcoma xenografts. Br. J. Cancer 93, 81–88 (2005).

    CAS  Google Scholar 

  105. Enriquez-Navas, P. M. et al. Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer. Sci. Transl. Med. 8, 327ra24 (2016).

    Google Scholar 

  106. Zarrinpar, A. et al. Individualizing liver transplant immunosuppression using a phenotypic personalized medicine platform. Sci. Transl. Med. 8, 333ra49 (2016).

    Google Scholar 

  107. Pantuck, A. J. et al. Modulating BET bromodomain inhibitor ZEN-3694 and enzalutamide combination dosing in a metastatic prostate cancer patient using CURATE.AI, an artificial intelligence platform. Adv. Ther. 1, 1800104 (2018).

    Google Scholar 

  108. Lou, B. et al. An image-based deep learning framework for individualising radiotherapy dose: a retrospective analysis of outcome prediction. Lancet Digit. Health 1, e136–e147 (2019).

    Google Scholar 

  109. Tang, J. et al. Immune cell screening of a nanoparticle library improves atherosclerosis therapy. Proc. Natl Acad. Sci. USA 113, E6731–E6740 (2016).

    CAS  Google Scholar 

  110. Dahlman, J. E. et al. Barcoded nanoparticles for high throughput in vivo discovery of targeted therapeutics. Proc. Natl Acad. Sci. USA 114, 2060–2065 (2017).

    CAS  Google Scholar 

  111. Sago, C. D. et al. High-throughput in vivo screen of functional mRNA delivery identifies nanoparticles for endothelial cell gene editing. Proc. Natl Acad. Sci. USA 115, E9944–E9952 (2018).

    CAS  Google Scholar 

  112. Mu, Q. et al. Conjugate-SELEX: A high-throughput screening of thioaptamer-liposomal nanoparticle conjugates for targeted intracellular delivery of anticancer drugs. Mol. Ther. Nucleic Acids 5, e382 (2016).

    CAS  Google Scholar 

  113. Cheng, Q. et al. Selective organ targeting (SORT) nanoparticles for tissue-specific mRNA delivery and CRISPR-Cas gene editing. Nat. Nanotechnol. 15, 313–320 (2020).

    CAS  Google Scholar 

  114. Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).

    CAS  Google Scholar 

  115. Chamunyonga, C., Edwards, C., Caldwell, P., Rutledge, P. & Burbery, J. The impact of artificial intelligence and machine learning in radiation therapy: Considerations for future curriculum enhancement. J. Med. Imaging Radiat. Sci. 51, 214–220 (2020).

    Google Scholar 

  116. McNeil, S. E. Evaluation of nanomedicines: stick to the basics. Nat. Rev. Mater. 1, 16073 (2016).

    Google Scholar 

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Acknowledgements

W.C.W.C. acknowledges the Canadian Institute of Health Research (CIHR, FDN-159932; MOP-130143), Natural Sciences and Engineering Research Council of Canada (NSERC, 2015–06397), Canadian Research Chairs program (950–223824), Collaborative Health Research Program (CPG-146468) and Canadian Cancer Society (705185–1) for funding support. We also acknowledge CIHR (W.P., B.O.), Vanier Canada Graduate Scholarships (B.O.), Ontario Graduate Scholarship (W.P., B.O.), NSERC (B.R.K., W.N.), Barbara and Frank Milligan (W. P.), Wildcat Foundation (B.R.K., W.N.), Jennifer Dorrington Award (B.R.K.), Royal Bank of Canada and Borealis AI (B.R.K.), Frank Fletcher Memorial Fund (B.O.), John J. Ruffo (B.O.), Cecil Yip family (W.P., B.R.K., B.O., W.N.) and McLaughlin Centre for MD/PhD studentships (B.O.) for financial support. The authors thank S. Sindhwani, J. Ngai, J. L. Y. Wu, and Z. Sepahi for manuscript revisions.

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  1. These authors contributed equally: Wilson Poon, Benjamin R. Kingston.

Authors and Affiliations

  1. Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada

    Wilson Poon, Benjamin R. Kingston, Ben Ouyang, Wayne Ngo & Warren C. W. Chan

  2. Terrence Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, Ontario, Canada

    Wilson Poon, Benjamin R. Kingston, Ben Ouyang, Wayne Ngo & Warren C. W. Chan

  3. MD/PhD Program, University of Toronto, Toronto, Ontario, Canada

    Ben Ouyang

  4. Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, Ontario, Canada

    Warren C. W. Chan

  5. Department of Materials Science & Engineering, University of Toronto, Toronto, Ontaro, Canada

    Warren C. W. Chan

  6. Department of Chemistry, University of Toronto, Toronto, Ontario, Canada

    Warren C. W. Chan

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Poon, W., Kingston, B.R., Ouyang, B. et al. A framework for designing delivery systems. Nat. Nanotechnol. 15, 819–829 (2020). https://doi.org/10.1038/s41565-020-0759-5

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