Abstract
The Aryl hydrocarbon receptor (AhR) plays important roles in many normal and pathological physiological processes, including endocrine homeostasis, foetal development, cell cycle regulation, cellular oxidation/antioxidation, immune regulation, metabolism of endogenous and exogenous substances, and carcinogenesis. An experimental data set for human in vitro AhR activation comprising 324,858 substances, of which 1,982 were confirmed actives, was used to test an in-house-developed approach to rationally select Quantitative Structure-Activity Relationship (QSAR) training set substances from an unbalanced data set. In the first iteration, active and inactive substances were selected by random to make QSAR models. Then, more inactive substances were added to the training set in two further iterations based on incorrect or out-of-domain predictions to produce larger models. The resulting ‘rational’ model, comprising 832 actives and four times as many inactives, i.e. 3,328, was compared to a model with a training set of same size and proportion of inactives chosen entirely by random. Both models underwent robust cross-validation and external validation showing good statistical performance, with the rational model having external validation sensitivity of 85.1% and specificity of 97.1%, compared to the random model with sensitivity 89.1% and specificity 91.3%. Furthermore, we integrated the training sets for both models with the 93 external validation test set actives and 372 randomly selected inactives to make two final models. They also underwent external validations for specificity and cross-validations, which confirmed that good predictivity was maintained. All developed models were applied to predict 80,086 EU REACH substances. The rational and random final models had 63.1% and 56.9% coverage of the REACH set, respectively, and predicted 1,256 and 3,214 substances as actives. The final models as well as predictions for AhR activation for 650,000 substances will be published in the Danish (Q)SAR Database and can, for example, be used for priority setting, in read-across predictions and in weight-of-evidence assessments of chemicals.
Highlights
The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that regulates the expression of genes, whose products are involved in multiple biological processes such as metabolism of endogenous and exogenous small molecules, as well as regulation of organ development and the immune system [1,2,3]
Results from the cross-validations showed that both sensitivities and specificities were lower for the rational models than for the QSAR4:1-R random model, performance and robustness decreased by each consecutive iteration of the rational selection approach
Since more robust results for specificity performance can be achieved with external validation applying such huge test set, combined with the presented interpretation of the poor cross-validation results for the rational models as a kind of artefact to the rational selection process, more weight is given to the external validation result
Summary
The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that regulates the expression of genes, whose products are involved in multiple biological processes such as metabolism of endogenous and exogenous small molecules, as well as regulation of organ development and the immune system [1,2,3]. A number of high-throughput screening (HTS) in vitro assays for AhR interaction have been developed and applied to screen thousands of small molecules [17,18]. Such data have previously been used in the development of quantitative structure-activity relationship (QSAR) models for AhR activation, e.g. QSAR models based on Tox HTS data under the Tox challenge in 2014 [17]
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