scholarly journals Data Mining in the U.S. National Toxicology Program (NTP) Database Reveals a Potential Bias Regarding Liver Tumors in Rodents Irrespective of the Test Agent

PLoS ONE ◽  
2015 ◽  
Vol 10 (2) ◽  
pp. e0116488 ◽  
Author(s):  
Matthias Ring ◽  
Bjoern M. Eskofier
2018 ◽  
Vol 2 ◽  
pp. 239784731877112
Author(s):  
Carr J Smith ◽  
Thomas A Perfetti

The degree of correlation between tumors predicted by OncoLogic™ (Oncologic) and the actual formation of tumors as observed in the National Toxicology Program (NTP) 2-year rodent studies is lower for “justification reports” that incorporate historical data than for “data reports” that do not. The correlation between the ordinal ranking of the observed carcinogenicity of parent NTP chemicals and the predicted “level of carcinogenicity concern” from the justification reports obtained from Oncologic is poor ( r = 0.56). Similarly, the correlation between the ordinal ranking of the carcinogenicity of metabolites from parent NTP chemicals and the predicted “level of carcinogenicity concern” from the justification report obtained from Oncologic is also poor ( r = 0.43). In contrast, the correlation between the ordinal ranking of the observed carcinogenicity of parent NTP chemicals and the predicted level of carcinogenicity concern from the data reports obtained from Oncologic is comparatively better ( r = 0.75). The correlation between the ordinal ranking of the carcinogenicity of metabolites from parent NTP chemicals and the predicted “level of carcinogenicity concern” from the data reports generated from Oncologic is also comparatively good ( r = 0.68). The level of correlation between the ordinal tumorigenicity ranks of parent chemicals and between the ordinal tumorigenicity ranks of chemicals reported to induce liver tumors in the National Center for Toxicological Research liver cancer database was also investigated. There was a higher degree of correlation seen for Oncologic “data reports” as compared with Oncologic “justification reports.” Incorporation of additional information via “justification reports” weakens the predictive power of Oncologic.


10.1289/ehp21 ◽  
2017 ◽  
Vol 125 (2) ◽  
pp. 181-188 ◽  
Author(s):  
Yun Xie ◽  
Stephanie Holmgren ◽  
Danica M. K. Andrews ◽  
Mary S. Wolfe

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