1. Machine Learning-Assisted Identification and Quantification of Hydroxylated Metabolites of Polychlorinated Biphenyls in Animal Samples
- Author
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Chun-Yun Zhang, Xueshu Li, Kimberly P. Keil Stietz, Sunjay Sethi, Weizhu Yang, Rachel F. Marek, Xinxin Ding, Pamela J. Lein, Keri C. Hornbuckle, and Hans-Joachim Lehmler
- Subjects
model prediction ,GC-MS/MS method ,General Chemistry ,relative response factor ,Hydroxylation ,Polychlorinated Biphenyls ,Gas Chromatography-Mass Spectrometry ,Machine Learning ,Mice ,Tandem Mass Spectrometry ,OH-PCBs ,Environmental Chemistry ,Animals ,Environmental Sciences ,relative retention time - Abstract
Laboratory studies of the disposition and toxicity of hydroxylated polychlorinated biphenyl (OH-PCB) metabolites are challenging because authentic analytical standards for most unknown OH-PCBs are not available. To assist with the characterization of these OH-PCBs (as methylated derivatives), we developed machine learning-based models with multiple linear regression (MLR) or random forest regression (RFR) to predict the relative retention times (RRT) and MS/MS responses of methoxylated (MeO-)PCBs on a gas chromatograph-tandem mass spectrometry system. The final MLR model estimated the retention times of MeO-PCBs with a mean absolute error of 0.55 min (n = 121). The similarity coefficients cos θ between the predicted (by RFR model) and experimental MS/MS data of MeO-PCBs were >0.95 for 92% of observations (n = 96). The levels of MeO-PCBs quantified with the predicted MS/MS response factors approximated the experimental values within a 2-fold difference for 85% of observations and 3-fold differences for all observations (n = 89). Subsequently, these model predictions were used to assist with the identification of OH-PCB 95 or OH-PCB 28 metabolites in mouse feces or liver by suggesting candidate ranking information for identifying the metabolite isomers. Thus, predicted retention and MS/MS response data can assist in identifying unknown OH-PCBs.
- Published
- 2023