Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability

The free fraction of a xenobiotic in plasma (<i>F</i>_ub) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data are scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict <i>F</i>_ub for environmentally relevant chemicals via machine learning techniques. Quantitative structure–activity relationship (QSAR) models were constructed with <i>k</i> nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals. The models were then evaluated with independent test sets of pharmaceuticals (200 compounds) and environmentally relevant ToxCast chemicals (406 total, in two groups of 238 and 168 compounds). The selection of a minimal feature set of 10–15 2D molecular descriptors allowed for both informative feature interpretation and practical applicability domain assessment via a bounded box of descriptor ranges and principal component analysis. The diverse pharmaceutical and environmental chemical sets exhibit similarities in terms of chemical space (99–82% overlap), as well as comparable bias and variance in constructed learning curves. All the models exhibit significant predictability with mean absolute errors (MAE) in the range of 0.10–0.18<i>F</i>_ub. The models performed best for highly bound chemicals (MAE 0.07–0.12), neutrals (MAE 0.11–0.14), and acids (MAE 0.14–0.17). A consensus model had the highest accuracy across both pharmaceuticals (MAE 0.151–0.155) and environmentally relevant chemicals (MAE 0.110–0.131). The inclusion of the majority of the ToxCast test sets within the AD of the consensus model, coupled with high prediction accuracy for these chemicals, indicates the model provides a QSAR for <i>F</i>_ub that is broadly applicable to both pharmaceuticals and environmentally relevant chemicals

Predicting the Bioconcentration of Fragrance Ingredients by Rainbow Trout Using Measured Rates of <i>in Vitro</i> Intrinsic Clearance

Bioaccumulation in aquatic species is a critical end point in the regulatory assessment of chemicals. Few measured fish bioconcentration factors (BCFs) are available for fragrance ingredients. Thus, predictive models are often used to estimate their BCFs. Because biotransformation can reduce chemical accumulation in fish, models using QSAR-estimated biotransformation rates have been developed. Alternatively, biotransformation can be measured by <i>in vitro</i> methods. In this study, biotransformation rates for nine fragrance ingredients were measured using trout liver S9 fractions and used as inputs to a recently refined <i>in vitro-in vivo</i> extrapolation (IVIVE) model. BCFs predicted by the model were then compared to (i) <i>in vivo</i> BCFs, (ii) BCFs predicted using QSAR-derived biotransformation rates, (iii) BCFs predicted without biotransformation, and (iv) BCFs predicted by a well-known regression model. For fragrance ingredients with relatively low (<4.7) log <i>K</i>_OW values, all models predicted BCFs below a bioaccumulation threshold of 1000. For chemicals with higher (4.7–5.8) log <i>K</i>_OW values, the model incorporating measured <i>in vitro</i> biotransformation rates and assuming no correction for potential binding effects on hepatic clearance provided the most accurate predictions of measured BCFs. This study demonstrates the value of integrating measured biotransformation rates for prediction of chemical bioaccumulation in fish

Intra- and Interlaboratory Reliability of a Cryopreserved Trout Hepatocyte Assay for the Prediction of Chemical Bioaccumulation Potential

Measured rates of intrinsic clearance determined using cryopreserved trout hepatocytes can be extrapolated to the whole animal as a means of improving modeled bioaccumulation predictions for fish. To date, however, the intra- and interlaboratory reliability of this procedure has not been determined. In the present study, three laboratories determined in vitro intrinsic clearance of six reference compounds (benzo­[<i>a</i>]­pyrene, 4-nonylphenol, di-<i>tert</i>-butyl phenol, fenthion, methoxychlor and <i>o</i>-terphenyl) by conducting substrate depletion experiments with cryopreserved trout hepatocytes from a single source. <i>O</i>-terphenyl was excluded from the final analysis due to nonfirst-order depletion kinetics and significant loss from denatured controls. For the other five compounds, intralaboratory variability (% CV) in measured in vitro intrinsic clearance values ranged from 4.1 to 30%, while interlaboratory variability ranged from 27 to 61%. Predicted bioconcentration factors based on in vitro clearance values exhibited a reduced level of interlaboratory variability (5.3–38% CV). The results of this study demonstrate that cryopreserved trout hepatocytes can be used to reliably obtain in vitro intrinsic clearance of xenobiotics, which provides support for the application of this in vitro method in a weight-of-evidence approach to chemical bioaccumulation assessment