Development of a multiparametric cell-based protocol to screen and classify the hepatotoxicity potential of drugs.

Fecha de publicación:

Autores de IIS La Fe

Participantes ajenos a IIS La Fe

  • Pinto S

Grupos

Abstract

Hepatotoxicity is a major reason for drug nonapprovals and withdrawals. The multiparametric analysis of xenobiotic toxicity at the single cells level using flow cytometry and cellular imaging-based approaches, such as high-content screening (HCS) technology, could play a key role in the detection of toxicity and the classification of compounds based on patterns of cellular injury. This study aimed to develop and validate a practical, reproducible, in vitro multiparametric cell-based protocol to assess those drugs that are potentially hepatotoxic to humans and to suggest their mechanisms of action. The assay was applied to HepG2 human cell line cultured in 96-well plates and exposed to 78 different compounds for 3 and 24 h at a range of concentrations (1-1000µM). After treatments, cells were simultaneously loaded with five fluorescent dyes showing optical compatibility and were then analyzed with the High-Content Screening Station Scan^R (Olympus). By using the new technology of HCS cell parameters associated with nuclear morphology, plasma membrane integrity, mitochondrial function, intracellular calcium concentration, and oxidative stress, indicative of prelethal cytotoxic effects and representative of different mechanisms of toxicity, were measured at the single cells level, which allows high-throughput screening. This strategy appears to identify early and late events in the hepatotoxic process and also suggests the mechanism(s) implicated in the toxicity of compounds to thereby classify them according to their degree of injury (no injury, low, moderate, and high injury).

Datos de la publicación

ISSN/ISSNe:
1096-6080, 1096-0929

TOXICOLOGICAL SCIENCES  OXFORD UNIV PRESS

Tipo:
Article
Páginas:
187-198
PubMed:
22331495
Factor de Impacto:
1,812 SCImago
Cuartil:
Q1 SCImago

Citas Recibidas en Web of Science: 106

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