Login / Signup

A new approach for the assessment of the toxicity of polyphenol-rich compounds with the use of high content screening analysis.

Magdalena BonclerJacek GolanskiMagdalena LukasiakMalgorzata RedzyniaJaroslaw DastychCezary Watala
Published in: PloS one (2017)
The toxicity of in vitro tested compounds is usually evaluated based on AC50 values calculated from dose-response curves. However, there is a large group of compounds for which a standard four-parametric sigmoid curve fitting may be inappropriate for estimating AC50. In the present study, 22 polyphenol-rich compounds were prioritized from the least to the most toxic based on the total area under and over the dose-response curves (AUOC) in relation to baselines. The studied compounds were ranked across three key cell indicators (mitochondrial membrane potential, cell membrane integrity and nuclear size) in a panel of five cell lines (HepG2, Caco-2, A549, HMEC-1, and 3T3), using a high-content screening (HCS) assay. Regarding AUOC score values, naringin (negative control) was the least toxic phenolic compound. Aronox, spent hop extract and kale leaf extract had very low cytotoxicity with regard to mitochondrial membrane potential and cell membrane integrity, as well as nuclear morphology (nuclear area). Kaempferol (positive control) exerted strong cytotoxic effects on the mitochondrial and nuclear compartments. Extracts from buckthorn bark, walnut husk and hollyhock flower were highly cytotoxic with regard to the mitochondrion and cell membrane, but not the nucleus. We propose an alternative algorithm for the screening of a large number of agents and for identifying those with adverse cellular effects at an early stage of drug discovery, using high content screening analysis. This approach should be recommended for series of compounds producing a non-sigmoidal cell response, and for agents with unknown toxicity or mechanisms of action.
Keyphrases
  • oxidative stress
  • early stage
  • drug discovery
  • single cell
  • cell therapy
  • stem cells
  • machine learning
  • high throughput
  • deep learning
  • neoadjuvant chemotherapy
  • oxide nanoparticles