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Development and Characterization of a 96-Well Exposure System for Safety Assessment of Nanomaterials.

Yvonne KohlMichelle MüllerMarielle FinkMarc MamierSiegfried FürtauerRoland DrexelChristine HerrmannStephan Dähnhardt-PfeifferRamona HornbergerMarius I ArzChristoph MetzgerSylvia WagnerSven SängerlaubHeiko BriesenFlorian MeierTobias Krebs
Published in: Small (Weinheim an der Bergstrasse, Germany) (2023)
In this study, a 96-well exposure system for safety assessment of nanomaterials is developed and characterized using an air-liquid interface lung epithelial model. This system is designed for sequential nebulization. Distribution studies verify the reproducible distribution over all 96 wells, with lower insert-to-insert variability compared to non-sequential application. With a first set of chemicals (TritonX), drugs (Bortezomib), and nanomaterials (silver nanoparticles and (non-)fluorescent crystalline nanocellulose), sequential exposure studies are performed with human lung epithelial cells followed by quantification of the deposited mass and of cell viability. The developed exposure system offers for the first time the possibility of exposing an air-liquid interface model in a 96-well format, resulting in high-throughput rates, combined with the feature for sequential dosing. This exposure system allows the possibility of creating dose-response curves resulting in the generation of more reliable cell-based assay data for many types of applications, such as safety analysis. In addition to chemicals and drugs, nanomaterials with spherical shapes, but also morphologically more complex nanostructures can be exposed sequentially with high efficiency. This allows new perspectives on in vivo-like and animal-free approaches for chemical and pharmaceutical safety assessment, in line with the 3R principle of replacing and reducing animal experiments.
Keyphrases
  • high throughput
  • silver nanoparticles
  • high efficiency
  • machine learning
  • deep learning
  • bone marrow
  • room temperature
  • case control
  • single molecule
  • neural network