Kuo D.T.FDi Toro D.M.TA FU DAVE KUO2022-11-112022-11-11201307307268https://www.scopus.com/inward/record.uri?eid=2-s2.0-84881027036&doi=10.1002%2fetc.2283&partnerID=40&md5=aed4a7deeb4486a2bdf4bd195da5e704https://scholars.lib.ntu.edu.tw/handle/123456789/624994The bioconcentration factor (BCF) of neutral and weakly polar organic chemicals in fish is modeled using independently calibrated models of chemical partitioning (freely dissolved fraction of chemical in the aqueous phase [φsys] and wet-weight fish-water partition coefficient [KFW]), respiratory exchange (respiratory update rate constant [k1], and respiratory elimination rate constant [k2=k1/KFW]), and biotransformation (whole-body biotransformation rate constant [kM]) as BCF=φsysKFW/(1+kM/k2). Existing k1 models tend to overestimate for chemicals with log KOW<3.5, which constituted 30% to 50% of the examined chemicals. A revised k1 model covering a wider log KOW range (0-8.5) is presented k1=(5.46×10-6 MW+0.261/KOW)-1, where MW is the molecular weight. The biotransformation rate constant kM is modeled using biota internal partitioning and Abraham parameters as reactivity descriptors. The reductionist model was tested using 3 different BCF data sets (US Environmental Protection Agency's Estimation Programs Interface [EPI], n=548; Hertfordshire, n=210; Arnot-Gobas, n=1855) and compared with the following 3 state-of-the-art models: 1) the EPI Suite BCFBAF module, 2) the European Commision's Computer Assisted Evaluation of industrial chemical Substances According to Regulations (CAESAR), and 3) the EPI/Arnot mechanistic kinetic model. The reductionist model performed comparably with the alternative models (root mean square errors [RMSEs]=0.72-0.77), with only 5 fitting parameters and no training against experimental BCFs. Respiratory elimination and biotransformation dominate the total depuration (i.e., [k2+kM]/kT≥0.8) for approximately 98% of the data entries, thus validating the reductionist approximation. Mechanistic models provide greater insights into bioaccumulation and are more sensitive to biological variation. All three BCF data sets and relevant properties and checkpoint values necessary for reproducing predictions of the reductionist model have been documented. The present study shows that a streamlined mechanistic model of BCF is possible for assessment purposes. © 2013 SETAC.Bioconcentration; Biotransformation; Mechanistic model; Risk assessment; Screening[SDGs]SDG14Bioconcentration; Bioconcentration factor; Biotransformation; Mechanistic kinetic models; Mechanistic models; Polar organic chemicals; Polar organic compounds; US Environmental Protection Agency; Bioaccumulation; Biochemistry; Environmental Protection Agency; Fish; Industrial chemicals; Mean square error; Organic chemicals; Rate constants; Risk assessment; Screening; Bioconversion; industrial chemical; organic compound; bioaccumulation; biomarker; biotransformation; calibration; concentration (composition); data set; fish; organic compound; risk assessment; article; bioaccumulation; biotransformation; body weight; fish; hydrogen bond; lung gas exchange; molecular weight; nonhuman; priority journal; Bioconcentration; Biotransformation; Mechanistic model; Risk assessment; Screening; Animals; Biotransformation; Databases, Factual; Fishes; Kinetics; Models, Biological; Models, Chemical; Organic Chemicals; Regression Analysisjournal article10.1002/etc.2283237038652-s2.0-84881027036