The toxicokinetic (TK) parameters fraction of the chemical unbound to plasma proteins and metabolic clearance are critical for relating exposure and internal dose when building in vitro-based risk assessment models. However, experimental toxicokinetic studies have only been carried out on limited chemicals of environmental interest (~1000 chemicals with TK data relative to tens of thousands of chemicals of interest). This work evaluated the utility of chemical structure information to predict TK parameters in silico; development of cluster-based read-across and quantitative structure-activity relationship models of fraction unbound or fub (regression) and intrinsic clearance or Cl(int) (classification and regression) using a dataset of 1487 chemicals; utilization of predicted TK parameters to estimate uncertainty in steady-state plasma concentration (C(ss)); and subsequent in vitro-in vivo extrapolation analyses to derive bioactivity-exposure ratio (BER) plot to compare human oral equivalent doses and exposure predictions using androgen and estrogen receptor activity data for 233 chemicals as an example dataset. The results demonstrate that fub is structurally more predictable than Cl(int). The model with the highest observed performance for fub had an external test set RMSE/Ï=0.62 and R(2)=0.61, for Cl(int) classification had an external test set accuracy = 65.9%, and for intrinsic clearance regression had an external test set RMSE/Ï=0.90 and R(2)=0.20. This relatively low performance is in part due to the large uncertainty in the underlying Cl(int) data. We show that C(ss) is relatively insensitive to uncertainty in Cl(int). The models were benchmarked against the ADMET Predictor software. Finally, the BER analysis allowed identification of 14 out of 136 chemicals for further risk assessment demonstrating the utility of these models in aiding risk-based chemical prioritization.
Using Chemical Structure Information to Develop Predictive Models for In Vitro Toxicokinetic Parameters to Inform High-throughput Risk-assessment.
阅读:4
作者:Pradeep Prachi, Patlewicz Grace, Pearce Robert, Wambaugh John, Wetmore Barbara, Judson Richard
| 期刊: | Computational Toxicology | 影响因子: | 2.900 |
| 时间: | 2020 | 起止号: | 2020 Nov 1; 16:10 |
| doi: | 10.1016/j.comtox.2020.100136 | ||
特别声明
1、本文转载旨在传播信息,不代表本网站观点,亦不对其内容的真实性承担责任。
2、其他媒体、网站或个人若从本网站转载使用,必须保留本网站注明的“来源”,并自行承担包括版权在内的相关法律责任。
3、如作者不希望本文被转载,或需洽谈转载稿费等事宜,请及时与本网站联系。
4、此外,如需投稿,也可通过邮箱info@biocloudy.com与我们取得联系。
