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• W2062384549 abstract "Abstract Background: The European Committee for the Validation of Alternative Methods (ECVAM) supported the development of a linear discriminant embryotoxicity prediction model founded on rat whole embryo culture (Piersma et al. (2004). Altern Lab Anim 32:275–307). Our goals were to (1) assess the accuracy of this model with pharmaceuticals, and (2) to use the data to develop a more accurate prediction model. Methods: Sixty‐one chemicals of known in vivo activity were tested. They were part of the ECVAM validation set ( N = 13), commercially available pharmaceuticals ( N = 31), and Pfizer chemicals that did not reach the market, but for which developmental toxicity data were available ( N = 17). They were tested according to the ECVAM procedures. Fifty‐seven of these chemicals were used for Random Forest modeling to develop an alternate model with the goal of using surrogate endpoints for simplified assessments and to improve the predictivity of the model. Results: Using part of the ECVAM chemical test set, the ECVAM prediction model was 77% accurate. This approximated what was reported in the validation study (80%; Piersma et al. (2004). Altern Lab Anim 32:275–307). However, when confronted with novel chemicals, the accuracy of the linear discriminant model dropped to 56%. In an attempt to improve this performance, we used a Random Forest model that provided rankings and confidence estimates. Although the model used simpler endpoints, its performance was no better than the ECVAM linear discriminant model. Conclusions: This study confirms previous concerns about the applicability of the ECVAM prediction model to a more diverse chemical set, and underscores the challenges associated with developing embryotoxicity prediction models. Birth Defects Res (Part B) 92:111–121, 2011. © 2011 Wiley‐Liss, Inc." @default.
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• W2062384549 date "2011-03-02" @default.
• W2062384549 modified "2023-10-17" @default.
• W2062384549 title "Not a walk in the park: the ECVAM whole embryo culture model challenged with pharmaceuticals and attempted improvements with random forest design" @default.
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• W2062384549 doi "https://doi.org/10.1002/bdrb.20289" @default.
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