If there is one thing the human body does very well, it is to attack and eliminate anything inside it which is perceived by the immune system as ‘non-self.’ As blood circulates through the human body, it is continuously cleansed by a series of natural clearance systems primarily the kidneys, spleen, and liver. Although this process is necessary to clean the blood of metabolic waste and toxins, it complicates drug-delivery as therapeutic agents and nanoparticle-based delivery systems are also cleared out of the blood stream often before they can achieve their desired therapeutic effect. Recently, researchers at University of Toronto and The Peter Gilgan Centre for Research & Learning (Canada) used mPEG-PLGA (AK037) and PLA-NH2 (AI032) from PolySciTech (www.polyscitech.com) to generate cyanine-5 (fluorescent dye) traceable nanoparticles for investigating particle clearance in-vivo. This research holds promise to improve the efficacy of nanoparticle-delivered medicines. Read more: Lazarovits, James, Shrey Sindhwani, Anthony James Tavares, Yuwei Zhang, Fayi Song, Julie Audet, Jonathan Robert Krieger, Abdullah Muhammad Syed, Benjamin Stordy, and Warren CW Chan. "Supervised Learning And Mass Spectrometry Predicts The In Vivo Fate Of Nanomaterials." ACS Nano (2019). https://pubs.acs.org/doi/abs/10.1021/acsnano.9b02774
“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 in vivo. 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 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 upwards 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 behaviour, and also to engineer surface chemistries that are specifically designed by the body. Keywords: nanoparticles, protein corona, mass spectrometry, neural networks, machine learning, artificial intelligence, predictive biology”
Biotech, Pharma, Cancer, Research (BPCR) is a free, 1-day scientific networking conference hosted by Akina, Inc. on Aug 28, 2019. See more and register to attend at http://bpcrconference.com
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