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Model-based estimation of lowest observed effect concentration from replicate experiments to identify potential
Silvia Calderazzo1, Denise Tavel2,3, Marie-Gabrielle Zurich2,3
1Division of Biostatistics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany. s.calderazzo@dkfz.de.
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 [Formula: see text]; cyclosporine A: 0.25-2 [Formula: see text]; chlorpromazine: 0.625-10 [Formula: see text]; diazepam: 1-8 [Formula: see text]; carbamazepine: 6.25-50 [Formula: see text]) 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.
Insights
New toxicity testing strategies utilize in vitro and in silico models. Researchers identified Cort, Bdnf, and Nov genes as potential biomarkers for drug-induced toxicity in brain cell cultures.
Area of Science:
- Toxicology
- Biomarker Discovery
- Computational Biology
Background:
- The field of toxicology is shifting towards predictive models, emphasizing in vitro and in silico methods.
- The National Research Council's report, "Toxicity testing in the 21st Century," advocates for this paradigm shift.
- Identifying reliable toxicity biomarkers is crucial for advancing drug safety assessment.
Purpose of the Study:
- To evaluate gene responsiveness to various drugs using 3D aggregate brain cell cultures.
- To establish a robust method for quantifying and ranking gene responsiveness based on the Lowest Observed Effect Concentration (LOEC).
- To identify novel candidate genes for in vitro toxicity biomarkers.
Main Methods:
- Utilized 3D aggregate brain cell cultures exposed to amiodarone, cyclosporine A, chlorpromazine, diazepam, and carbamazepine.
- Quantified gene responsiveness using a model-based approach to estimate the Lowest Observed Effect Concentration (LOEC) via reverse regression and a log-logistic model.
- Employed the Box-Cox transform for data normalization and a meta-analytic approach for summarizing repeated experimental estimates.
Main Results:
- A model-based approach provided principled estimation of LOEC and its uncertainty, outperforming traditional methods.
- The Box-Cox transform and meta-analysis effectively handled data heteroscedasticity and variability from repeated experiments.
- Genes Cort, Bdnf, and Nov demonstrated significant responsiveness and were identified as promising candidates for in vitro toxicity biomarkers.
Conclusions:
- The study successfully identified Cort, Bdnf, and Nov as reliable in vitro biomarkers for drug toxicity.
- The developed statistical methodology offers a robust framework for LOEC estimation in toxicological studies.
- This research supports the integration of advanced in vitro models and computational approaches in modern toxicology.
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