Utilizing high throughput screening data for predictive toxicology models: protocols and application to MLSCN assays

2008 ◽  
Vol 22 (6-7) ◽  
pp. 367-384 ◽  
Author(s):  
Rajarshi Guha ◽  
Stephan C. Schürer
2020 ◽  
Vol 39 (5) ◽  
pp. 397-421
Author(s):  
Charlene Andraos ◽  
Il Je Yu ◽  
Mary Gulumian

Despite several studies addressing nanoparticle (NP) interference with conventional toxicity assay systems, it appears that researchers still rely heavily on these assays, particularly for high-throughput screening (HTS) applications in order to generate “big” data for predictive toxicity approaches. Moreover, researchers often overlook investigating the different types of interference mechanisms as the type is evidently dependent on the type of assay system implemented. The approaches implemented in the literature appear to be not adequate as it often addresses only one type of interference mechanism with the exclusion of others. For example, interference of NPs that have entered cells would require intracellular assessment of their interference with fluorescent dyes, which has so far been neglected. The present study investigated the mechanisms of interference of gold NPs and silver NPs in assay systems implemented in HTS including optical interference as well as adsorption or catalysis. The conventional assays selected cover all optical read-out systems, that is, absorbance (XTT toxicity assay), fluorescence (CytoTox-ONE Homogeneous membrane integrity assay), and luminescence (CellTiter Glo luminescent assay). Furthermore, this study demonstrated NP quenching of fluorescent dyes also used in HTS (2′,7′-dichlorofluorescein, propidium iodide, and 5,5′,6,6′-tetrachloro-1,1′,3,3′-tetraethyl-benzamidazolocarbocyanin iodide). To conclude, NP interference is, as such, not a novel concept, however, ignoring this aspect in HTS may jeopardize attempts in predictive toxicology. It should be mandatory to report the assessment of all mechanisms of interference within HTS, as well as to confirm results with label-free methodologies to ensure reliable big data generation for predictive toxicology.


Planta Medica ◽  
2012 ◽  
Vol 78 (11) ◽  
Author(s):  
L Hingorani ◽  
NP Seeram ◽  
B Ebersole

Planta Medica ◽  
2015 ◽  
Vol 81 (16) ◽  
Author(s):  
K Georgousaki ◽  
N DePedro ◽  
AM Chinchilla ◽  
N Aliagiannis ◽  
F Vicente ◽  
...  

Planta Medica ◽  
2016 ◽  
Vol 81 (S 01) ◽  
pp. S1-S381
Author(s):  
LS Espindola ◽  
RG Dusi ◽  
KR Gustafson ◽  
J McMahon ◽  
JA Beutler

2014 ◽  
Author(s):  
Clair Cochrane ◽  
Halil Ruso ◽  
Anthony Hope ◽  
Rosemary G Clarke ◽  
Christopher Barratt ◽  
...  

2020 ◽  
Author(s):  
Jia Shen Chew ◽  
Ken Chi Lik Lee ◽  
THI THANH NHA HO

<p>Lee and coworkers offers a kind of new concept to enzyme immobilization and explores its suitability in the context of miniaturisation and high-throughput screening. Here, polystyrene-immobilized ketoreductases are compared with its non-immobilized counterparts in terms of conversion and stereoselectivity (both determined by chiral HPLC), and the study indicates that the BioBeads perform similarly (sometimes slightly more selective) which may be useful whenever defined micro-scale amounts of biocatalysts were required in high-throughput experiment settings.</p>


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