Model-based estimation of lowest observed effect concentration from replicate experiments to identify potential biomarkers of in vitro neurotoxicity

Abstract

A paradigm shift is occurring in toxicology following the report of the National Research Council of the USA National Academies entitled “Toxicity testing in the 21st Century: a vision and strategy”. This new vision encourages the use of in vitro and in silico models for toxicity testing. In the goal to identify new reliable markers of toxicity, the responsiveness of different genes to various drugs (amiodarone: 0.312–2.5 ${\upmu }\hbox {M}$; cyclosporine A: 0.25–2 ${\upmu }\hbox {M}$; chlorpromazine: 0.625–10 ${\upmu }\hbox {M}$; diazepam: 1–8 ${\upmu }\hbox {M}$; carbamazepine: 6.25–50 ${\upmu }\hbox {M}$) is studied in 3D aggregate brain cell cultures. Genes’ responsiveness is quantified and ranked according to the Lowest Observed Effect Concentration (LOEC), which is estimated by reverse regression under a log-logistic model assumption. In contrast to approaches where LOEC is identified by the first observed concentration level at which the response is significantly different from a control, the model-based approach allows a principled estimation of the LOEC and of its uncertainty. The Box–Cox transform both sides approach is adopted to deal with heteroscedastic and/or non-normal residuals, while estimates from repeated experiments are summarized by a meta-analytic approach. Different inferential procedures to estimate the Box–Cox coefficient, and to obtain confidence intervals for the log-logistic curve parameters and the LOEC, are explored. A simulation study is performed to compare coverage properties and estimation errors for each approach. Application to the toxicological data identifies the genes Cort, Bdnf, and Nov as good candidates for in vitro biomarkers of toxicity.