Science Inventory

Evaluation of Quantitative Structure Property Relationship algorithms for predicting plasma protein binding in humans

Citation:

Yun, Y., R. Tornero-Velez, Tom Purucker, D. Chang, AND A. Edginton. Evaluation of Quantitative Structure Property Relationship algorithms for predicting plasma protein binding in humans. Computational Toxicology. Elsevier B.V., Amsterdam, Netherlands, 17:100142, (2021). https://doi.org/10.1016/j.comtox.2020.100142

Impact/Purpose:

The free fraction of chemical in human plasma (fup) is a critical parameter in EPA's tool for high throughput toxicokinetics (HTTK). When experimentally determined fup are not available, quantitative structure-property relationship (QSPR) models can be used for prediction. Because available QSPR models were developed based on training sets containing pharmaceutical-like compounds, we compared the prediction accuracy of the QSPR models for environmentally relevant and pharmaceutical compounds. Overall, the three QSPR models considered resulted in over-prediction of fup for highly binding compounds and under-prediction for low or moderately binding compounds. The positive polar surface area, the number of basic functional groups and lipophilicity were the most important chemical descriptors for predicting fup. Our findings suggest that model accuracy may be improved with the augmentation of environmentally relevant chemicals in the training data. This research will be of interest to risk assessors and program offices who consider applying QSPR derived parameters such as fup in HTTK to address environmental health problems.

Description:

The extent of plasma protein binding is an important compound-specific property that influences a compound's pharmacokinetic behavior and is a critical input parameter for predicting exposure in physiologically based pharmacokinetic (PBPK) modeling. When experimentally determined fraction unbound in plasma (fup) data are not available, quantitative structure-property relationship (QSPR) models can be used for prediction. Because available QSPR models were developed based on training sets containing pharmaceutical-like compounds, we compared their prediction accuracy for environmentally relevant and pharmaceutical compounds. Fup values were calculated using Ingle et al., Watanabe et al. and ADMET Predictor (Simulation Plus). The test set included 818 pharmaceutical and environmentally relevant compounds with fup values ranging from 0.01 to 1. Overall, the three QSPR models resulted in over-prediction of fup for highly binding compounds and under-prediction for low or moderately binding compounds. For highly binding compounds (0.01≤ fup ≤ 0.25), Watanabe et al. performed better with a lower mean absolute error (MAE) of 6.7% and a lower mean absolute relative prediction error (RPE) of 171.7 % than other methods.  For low to moderately binding compounds, both Ingle et al. and ADMET Predictor performed better than Watanabe et al. with superior MAE and RPE values. The positive polar surface area, the number of basic functional groups and lipophilicity were the most important chemical descriptors for predicting fup. This study demonstrated that the prediction of fup was the most uncertain for highly binding compounds. This suggested that QSPR-predicted fup values should be used with caution in PBPK modeling.

Record Details:

Record Type:DOCUMENT( JOURNAL/ PEER REVIEWED JOURNAL)
Product Published Date:02/01/2021
Record Last Revised:02/26/2021
OMB Category:Other
Record ID: 350913