Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells

Abstract

Oxidative stress is the consequence of an abnormal increase of reactive oxygen species (ROS). ROS are generated mainly during the metabolism in both normal and pathological conditions as well as from exposure to xenobiotics. Xenobiotics can, on the one hand, disrupt molecular machinery involved in redox processes and, on the other hand, reduce the effectiveness of the antioxidant activity. Such dysregulation may lead to oxidative damage when combined with oxidative stress overpassing the cell capacity to detoxify ROS. In this work, a green fluorescent protein (GFP)-tagged nuclear factor erythroid 2-related factor 2 (NRF2)-regulated sulfiredoxin reporter (Srxn1-GFP) was used to measure the antioxidant response of HepG2 cells to a large series of drug and drug-like compounds (2230 compounds). These compounds were then classified as positive or negative depending on cellular response and distributed among different modeling groups to establish structure-activity relationship (SAR) models. A selection of models was used to prospectively predict oxidative stress induced by a new set of compounds subsequently experimentally tested to validate the model predictions. Altogether, this exercise exemplifies the different challenges of developing SAR models of a phenotypic cellular readout, model combination, chemical space selection, and results interpretation.

Figure 1

Distribution of the number of erroneous predictions made by models reported in Table 3 in the PCA space of the Spectrum holdout test set.

Figure 2

Reference clustered chemical space (A) with projections of the Spectrum Library (B) and Enamine HTS collection (C). Clusters and centers are represented in color, and projected datasets are represented in black. Clusters 0–5 are represented in purple, red, green, cyan, indigo, and yellow, respectively.

Figure 3

Schematic representation of the prospective candidate selection. First, Enamine compounds were classified by Tanimoto similarity (Tc) with respect to the Spectrum training set into two groups; similarity equal to or greater than 0.7 as the similar group, and similarity equal to or lower than 0.3. Compounds were further projected in the reference chemical space and classified upon the cluster they fell into. Finally, compounds with the top probabilities of being active and inactive in each subgroup were considered for experimental validation.