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@ARTICLE{Calderazzo:144413,
      author       = {S. Calderazzo$^*$ and D. Tavel and M.-G. Zurich and A.
                      Kopp-Schneider$^*$},
      title        = {{M}odel-based estimation of lowest observed effect
                      concentration from replicate experiments to identify
                      potential biomarkers of in vitro neurotoxicity.},
      journal      = {Archives of toxicology},
      volume       = {93},
      number       = {9},
      issn         = {1432-0738},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DKFZ-2019-01866},
      pages        = {2635-2644},
      year         = {2019},
      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.},
      cin          = {C060},
      ddc          = {610},
      cid          = {I:(DE-He78)C060-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31324950},
      doi          = {10.1007/s00204-019-02520-8},
      url          = {https://inrepo02.dkfz.de/record/144413},
}