Supervised Learning and Mass Spectrometry Predicts the Fate of Nanomaterials.

Journal: ACS nano
Published Date: Jul 23, 2019

Abstract

The surface of nanoparticles changes immediately after intravenous injection because blood proteins adsorb on the surface. How this interface changes during circulation and its impact on nanoparticle distribution within the body is not understood. Here, we developed a workflow to show that the evolution of proteins on nanoparticle surfaces predicts the biological fate of nanoparticles . This workflow involves extracting nanoparticles at multiple time points from circulation, isolating the proteins off the surface and performing proteomic mass spectrometry. The mass spectrometry protein library served as inputs, while blood clearance and organ accumulation were used as outputs to train a supervised deep neural network that predicts nanoparticle biological fate. In a double-blinded study, we tested the network by predicting nanoparticle spleen and liver accumulation with upward of 94% accuracy. Our neural network discovered that the mechanism of liver and spleen uptake is due to patterns of a multitude of nanoparticle surface adsorbed proteins. There are too many combinations to change these proteins manually using chemical or biological inhibitors to alter clearance. Therefore, we developed a technique that uses the host to act as a bioreactor to prepare nanoparticles with predictable clearance patterns that reduce liver and spleen uptake by 50% and 70%, respectively. These techniques provide opportunities to both predict nanoparticle behavior and also to engineer surface chemistries that are specifically designed by the body.

Authors

  • James Lazarovits
    Institute of Biomaterials and Biomedical Engineering , University of Toronto , 164 College Street , Toronto , Ontario M5S 3G9 , Canada.
  • Shrey Sindhwani
    Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada.
  • Anthony J Tavares
    Institute of Biomaterials and Biomedical Engineering , University of Toronto , 164 College Street , Toronto , Ontario M5S 3G9 , Canada.
  • Yuwei Zhang
    Institute of Biomaterials and Biomedical Engineering , University of Toronto , 164 College Street , Toronto , Ontario M5S 3G9 , Canada.
  • Fayi Song
    Institute of Biomaterials and Biomedical Engineering , University of Toronto , 164 College Street , Toronto , Ontario M5S 3G9 , Canada.
  • Julie Audet
    Institute of Biomaterials and Biomedical Engineering , University of Toronto , 164 College Street , Toronto , Ontario M5S 3G9 , Canada.
  • Jonathan R Krieger
    SPARC BioCentre, The Hospital for Sick Children , The Peter Gilgan Centre for Research & Learning , 686 Bay Street, 21st Floor Toronto , Ontario M5G 0A4 , Canada.
  • Abdullah Muhammad Syed
    Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada.
  • Benjamin Stordy
    Institute of Biomaterials and Biomedical Engineering , University of Toronto , 164 College Street , Toronto , Ontario M5S 3G9 , Canada.
  • Warren C W Chan
    Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada; warren.chan@utoronto.ca.

Keywords

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