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Incorporating Biological, Chemical and Toxicological Knowledge into Predictive Models of Toxicity: Letter to the Editor
Citation:
DIX, D. J., K. A. HOUCK, R. JUDSON, N. KLEINSTREUER, T. B. KNUDSEN, M. T. MARTIN, D. REIF, A. M. RICHARD, I. A. SHAH, N. SIPES, AND R. J. KAVLOCK. Incorporating Biological, Chemical and Toxicological Knowledge into Predictive Models of Toxicity: Letter to the Editor. TOXICOLOGICAL SCIENCES. Oxford University Press, Cary, NC, 130(2):440-441, (2012).
Impact/Purpose:
Letter to the Editor in Toxicological Sciences
Description:
Thomas et al. (2012) recently published an evaluation of statistical models for classifying in vivo toxicity endpoints from ToxRefDB (Knudsen et al. 2009; Martin et al. 2009a and 2009b) using ToxCast in vitro bioactivity data (Judson et al. 2010) and chemical structure descriptors. We commend the authors for a thorough assessment of statistical tools for uncovering patterns of associations among thousands of covariate features derived from in vitro measurements, chemical structure, and toxicity endpoints from animal studies. They were largely unsuccessful in accurately classifying toxicities based on in vitro bioactivity or chemical structure. However, their conclusion that the current ToxCast Phase I assays and chemicals have limited applicability for predicting in vivo chemical hazards using statistical classification methods is misleading and warrants clarification.
