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

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.

Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening.

Integration of statistical learning methods with structure-based modeling approaches is a contemporary strategy to identify novel lead compounds in drug discovery. Hepatic organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are classical off-targets, and it is well recognized that their ability to interfere with a wide range of chemically unrelated drugs, environmental chemicals, or food additives can lead to unwanted adverse effects like liver toxicity and drug-drug or drug-food interactions. Therefore, the identification of novel (tool) compounds for hepatic OATPs by virtual screening approaches and subsequent experimental validation is a major asset for elucidating structure-function relationships of (related) transporters: they enhance our understanding about molecular determinants and structural aspects of hepatic OATPs driving ligand binding and selectivity. In the present study, we performed a consensus virtual screening approach by using different types of machine learning models (proteochemometric models, conformal prediction models, and XGBoost models for hepatic OATPs), followed by molecular docking of preselected hits using previously established structural models for hepatic OATPs. Screening the diverse REAL drug-like set (Enamine) shows a comparable hit rate for OATP1B1 (36% actives) and OATP1B3 (32% actives), while the hit rate for OATP2B1 was even higher (66% actives). Percentage inhibition values for 44 selected compounds were determined using dedicated in vitro assays and guided the prioritization of several highly potent novel hepatic OATP inhibitors: six (strong) OATP2B1 inhibitors (IC50 values ranging from 0.04 to 6 µM), three OATP1B1 inhibitors (2.69 to 10 µM), and five OATP1B3 inhibitors (1.53 to 10 µM) were identified. Strikingly, two novel OATP2B1 inhibitors were uncovered (C7 and H5) which show high affinity (IC50 values: 40 nM and 390 nM) comparable to the recently described estrone-based inhibitor (IC50 = 41 nM). A molecularly detailed explanation for the observed differences in ligand binding to the three transporters is given by means of structural comparison of the detected binding sites and docking poses.

Associations between clinical signs and pathological findings in toxicity testing

Animal testing for toxicity assessment of chemicals and pharmaceuticals must take the 3R principles into consideration. During toxicity testing in vivo, clinical signs are used to monitor animal welfare and to inform about potential toxicity. This study investigated possible associations between clinical signs, body weight change and histopathological findings observed after necropsy. We hypothesized that clinical signs and body weight loss observed during experiments could be used as early markers of organ toxicity. This represents a potential for refinement in terms of improved study man­agement and decreasing of pain and distress experienced during animal experiments. Data from three sequential toxicity studies of an anti-cancer drug candidate in rats were analyzed using the multivariate partial least squares (PLS) regression method. Associations with a predictive value over 80% were found between the occurrence of mild to severe clinical signs and histopathological findings in the thymus, testes, epididymides and bone marrow. Piloerection, eyes half shut and slightly decreased motor activity were most strongly associated with the pathological findings. A 5% body weight loss was found to be a strong empirical predictor of pathological findings but could also be predicted accurately by clinical signs. Thus, we suggest using mild clinical signs and a 5% body weight loss as toxicity markers and as a non-invasive surveillance tool to monitor research animal welfare in toxicity testing. These clinical signs may also enable reduction of animal use due to their informative potential to support scientific decisions regarding drug candidate selection, dose setting, study design, and toxicity assessment.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

Quantification of Intracellular Accumulation and Retention of Lysosomotropic Macrocyclic Compounds by High-Throughput Imaging of Lysosomal Changes.

Many marketed pharmaceuticals reach extremely high tissue concentrations due to accumulation in lysosomes (lysosomotropism). Quantitative prediction of intracellular concentrations of accumulating drugs is challenging, especially for macrocyclic compounds that mainly do not fit in current in silico models. We tested a unique library of 47 compounds (containing 39 macrocycles) specifically designed to cover the entire range of accumulation intensities observed with pharmaceuticals so far. For the first time, we show that intracellular concentration of compounds measured by liquid chromatography with tandem mass spectrometry correlates with the induction of phospholipidosis and inhibition of autophagy, but the highest correlation was observed with the increase of lysosomal volume (R = 0.95), all measured by high-throughput imaging assays. Based only on imaging data, we developed a 5-class in vitro model for the prediction of compound accumulation with the accuracy of 81%. The measured change of total lysosomal volume can thus be used in high-throughput screening for determination of the actual intensity of intracellular accumulation of new macrocyclic compounds. The models are largely based on macrocycles, greatly improving the screening and prediction of intracellular accumulation of this challenging class. However, all tested nonmacrocyclic compounds fitted well in the models, indicating potential use of the models in broader chemical space.

Fluoxetine Affects Differentiation of Midbrain Dopaminergic Neurons In Vitro.

Recent meta-analyses found an association between prenatal exposure to the antidepressant fluoxetine (FLX) and an increased risk of autism in children. This developmental disorder has been related to dysfunctions in the brains' rewards circuitry, which, in turn, has been linked to dysfunctions in dopaminergic (DA) signaling. The present study investigated if FLX affects processes involved in dopaminergic neuronal differentiation. Mouse neuronal precursors were differentiated into midbrain dopaminergic precursor cells (mDPCs) and concomitantly exposed to clinically relevant doses of FLX. Subsequently, dopaminergic precursors were evaluated for expression of differentiation and stemness markers using quantitative polymerase chain reaction. FLX treatment led to increases in early regional specification markers orthodenticle homeobox 2 (Otx2) and homeobox engrailed-1 and -2 (En1 and En2). On the other hand, two transcription factors essential for midbrain dopaminergic (mDA) neurogenesis, LIM homeobox transcription factor 1 α (Lmx1a) and paired-like homeodomain transcription factor 3 (Pitx3) were downregulated by FLX treatment. The stemness marker nestin (Nes) was increased, whereas the neuronal differentiation marker ß3-tubulin (Tubb3) decreased. Additionally, we observed that FLX modulates the expression of several genes associated with autism spectrum disorder and downregulates the estrogen receptors (ERs) α and ß Further investigations using ERß knockout (BERKO) mDPCs showed that FLX had no or even opposite effects on several of the genes analyzed. These findings suggest that FLX affects differentiation of the dopaminergic system by increasing production of dopaminergic precursors, yet decreasing their maturation, partly via interference with the estrogen system.

Cellular accumulation and lipid binding of perfluorinated alkylated substances (PFASs) - A comparison with lysosomotropic drugs.

Many chemicals accumulate in organisms through a variety of different mechanisms. Cationic amphiphilic drugs (CADs) accumulate in lysosomes and bind to membranes causing phospholipidosis, whereas many lipophilic chemicals target adipose tissue. Perfluoroalkyl substances (PFASs) are widely used as surfactants, but many of them are highly bioaccumulating and persistent in the environment, making them notorious environmental toxicants. Understanding the mechanisms of their bioaccumulation is, therefore, important for their regulation and substitution with new, less harmful chemicals. We compared the highly bioaccumulative perfluorooctanesulfonic acid PFOS to its three less bioaccumulative alternatives perfluorooctanoic acid (PFOA), perfluorohexanoic acid (PFHxA) and perfluorobutane sulfonic acid (PFBS), in their ability to accumulate and remain in lung epithelial cells (NCI-H292) and adipocytes (3T3-L1K) in vitro. As a reference point we tested a set of cationic amphiphilic drugs (CADs), known to highly accumulate in cells and strongly bind to phospholipids, together with their respective non-CAD controls. Finally, all compounds were examined for their ability to bind to neutral lipids and phospholipids in cell-free systems. Cellular accumulation and retention of the test compounds were highly correlated between the lung epithelial cells and adipocytes. Interestingly, although an anion itself, intensities of PFOS accumulation and retention in cells were comparable to those of CAD compounds, but PFOS failed to induce phospholipidosis or alter lysosomal volume. Compared to other lipophilicity measures, phospholipophilicity shows the highest correlation (Rˆ2 = 0.75) to cellular accumulation data in both cell types and best distinguishes between high and low accumulating compounds. This indicates that binding to phospholipids may be the most important component in driving high cellular accumulation in lung epithelial cells, as well as in adipocytes, and for both CADs and bioaccumulating PFASs. Obtained continuous PLS models based on compound's affinity for phospholipids and neutral lipids can be used as good prediction models of cellular accumulation and retention of PFASs and CADs.

Chemical space, diversity and activity landscape analysis of estrogen receptor binders.

Understanding the structure-activity relationships (SAR) of endocrine-disrupting chemicals has a major importance in toxicology. Despite the fact that classifiers and predictive models have been developed for estrogens for the past 20 years, to the best of our knowledge, there are no studies of their activity landscape or the identification of activity cliffs. Herein, we report the first SAR of a public dataset of 121 chemicals with reported estrogen receptor binding affinities using activity landscape modeling. To this end, we conducted a systematic quantitative and visual analysis of the chemical space of the 121 chemicals. The global diversity of the dataset was characterized by means of Consensus Diversity Plot, a recently developed method. Adding pairwise activity difference information to the chemical space gave rise to the activity landscape of the data set uncovering a heterogeneous SAR, in particular for some structural classes. At least eight compounds were identified with high propensity to form activity cliffs. The findings of this work further expand the current knowledge of the underlying SAR of estrogenic compounds and can be the starting point to develop novel and potentially improved predictive models.

Conformal Prediction Classification of a Large Data Set of Environmental Chemicals from ToxCast and Tox21 Estrogen Receptor Assays.

Quantitative structure-activity relationships (QSAR) are critical to exploitation of the chemical information in toxicology databases. Exploitation can be extraction of chemical knowledge from the data but also making predictions of new chemicals based on quantitative analysis of past findings. In this study, we analyzed the ToxCast and Tox21 estrogen receptor data sets using Conformal Prediction to enhance the full exploitation of the information in these data sets. We applied aggregated conformal prediction (ACP) to the ToxCast and Tox21 estrogen receptor data sets using support vector machine classifiers to compare overall performance of the models but, more importantly, to explore the performance of ACP on data sets that are significantly enriched in one class without employing sampling strategies of the training set. ACP was also used to investigate the problem of applicability domain using both data sets. Comparison of ACP to previous results obtained on the same data sets using traditional QSAR approaches indicated similar overall balanced performance to methods in which careful training set selections were made, e.g., sensitivity and specificity for the external Tox21 data set of 70-75% and far superior results to those obtained using traditional methods without training set sampling where the corresponding results showed a clear imbalance of 50 and 96%, respectively. Application of conformal prediction to imbalanced data sets facilitates an unambiguous analysis of all data, allows accurate predictive models to be built which display similar accuracy in external validation to external validation, and, most importantly, allows an unambiguous treatment of the applicability domain.

Evaluation of 309 molecules as inducers of CYP3A4, CYP2B6, CYP1A2, OATP1B1, OCT1, MDR1, MRP2, MRP3 and BCRP in cryopreserved human hepatocytes in sandwich culture.

1. Regulation of hepatic metabolism or transport may lead to increase in drug clearance and compromise efficacy or safety. In this study, cryopreserved human hepatocytes were used to assess the effect of 309 compounds on the activity and mRNA expression (using qPCR techniques) of CYP1A2, CYP2B6 and CYP3A4, as well as mRNA expression of six hepatic transport proteins: OATP1B1 (SCLO1B1), OCT1 (SLC22A1), MDR1 (ABCB1), MRP2 (ABCC2), MRP3 (ABCC3) and BCRP (ABCG2). 2. The results showed that 6% of compounds induced CYP1A2 activity (1.5-fold increase); 30% induced CYP2B6 while 23% induced CYP3A4. qPCR data identified 16, 33 or 32% inducers of CYP1A2, CYP2B6 or CYP3A4, respectively. MRP2 was induced by 27 compounds followed by MDR1 (16)>BCRP (9)>OCT1 (8)>OATP1B1 (5)>MRP3 (2). 3. CYP3A4 appeared to be down-regulated (≥2-fold decrease in mRNA expression) by 53 compounds, 10 for CYP2B6, 6 for OCT1, 4 for BCRP, 2 for CYP1A2 and OATP1B1 and 1 for MDR1 and MRP2. 4. Structure-activity relationship analysis showed that CYP2B6 and CYP3A4 inducers are bulky lipophilic molecules with a higher number of heavy atoms and a lower number of hydrogen bond donors. Finally, a strategy for testing CYP inducers in drug discovery is proposed.

Classification of inhibitors of hepatic organic anion transporting polypeptides (OATPs): influence of protein expression on drug-drug interactions.

The hepatic organic anion transporting polypeptides (OATPs) influence the pharmacokinetics of several drug classes and are involved in many clinical drug-drug interactions. Predicting potential interactions with OATPs is, therefore, of value. Here, we developed in vitro and in silico models for identification and prediction of specific and general inhibitors of OATP1B1, OATP1B3, and OATP2B1. The maximal transport activity (MTA) of each OATP in human liver was predicted from transport kinetics and protein quantification. We then used MTA to predict the effects of a subset of inhibitors on atorvastatin uptake in vivo. Using a data set of 225 drug-like compounds, 91 OATP inhibitors were identified. In silico models indicated that lipophilicity and polar surface area are key molecular features of OATP inhibition. MTA predictions identified OATP1B1 and OATP1B3 as major determinants of atorvastatin uptake in vivo. The relative contributions to overall hepatic uptake varied with isoform specificities of the inhibitors.

Identification of novel specific and general inhibitors of the three major human ATP-binding cassette transporters P-gp, BCRP and MRP2 among registered drugs.

PURPOSE: To study the inhibition patterns of the three major human ABC transporters P-gp (ABCB1), BCRP (ABCG2) and MRP2 (ABCC2), using a dataset of 122 structurally diverse drugs. METHODS: Inhibition was investigated in cellular and vesicular systems over-expressing single transporters. Computational models discriminating either single or general inhibitors from non-inhibitors were developed using multivariate statistics. RESULTS: Specific (n = 23) and overlapping (n = 19) inhibitors of the three ABC transporters were identified. GF120918 and Ko143 were verified to specifically inhibit P-gp/BCRP and BCRP in defined concentration intervals, whereas the MRP inhibitor MK571 was revealed to inhibit all three transporters within one log unit of concentration. Virtual docking experiments showed that MK571 binds to the ATP catalytic site, which could contribute to its multi-specific inhibition profile. A computational model predicting general ABC inhibition correctly classified 80% of both ABC transporter inhibitors and non-inhibitors in an external test set. CONCLUSIONS: The inhibitor specificities of P-gp, BCRP and MRP2 were shown to be highly overlapping. General ABC inhibitors were more lipophilic and aromatic than specific inhibitors and non-inhibitors. The identified specific inhibitors can be used to delineate transport processes in complex experimental systems, whereas the multi-specific inhibitors are useful in primary ABC transporter screening in drug discovery settings.

Structural requirements for drug inhibition of the liver specific human organic cation transport protein 1.

The liver-specific organic cation transport protein (OCT1; SLC22A1) transports several cationic drugs including the antidiabetic drug metformin and the anticancer agents oxaliplatin and imatinib. In this study, we explored the chemical space of registered oral drugs with the aim of studying the inhibition pattern of OCT1 and of developing predictive computational models of OCT1 inhibition. In total, 191 structurally diverse compounds were examined in HEK293-OCT1 cells. The assay identified 47 novel inhibitors and confirmed 15 previously known inhibitors. The enrichment of OCT1 inhibitors was seen in several drug classes including antidepressants. High lipophilicity and a positive net charge were found to be the key physicochemical properties for OCT1 inhibition, whereas a high molecular dipole moment and many hydrogen bonds were negatively correlated to OCT1 inhibition. The data were used to generate OPLS-DA models for OCT1 inhibitors; the final model correctly predicted 82% of the inhibitors and 88% of the noninhibitors of the test set.

Prediction and identification of drug interactions with the human ATP-binding cassette transporter multidrug-resistance associated protein 2 (MRP2; ABCC2).

The chemical space of registered oral drugs was explored for inhibitors of the human multidrug-resistance associated protein 2 (MRP2; ABCC2), using a data set of 191 structurally diverse drugs and drug-like compounds. The data set included a new reference set of 75 compounds, for studies of hepatic drug interactions with transport proteins, CYP enzymes, and compounds associated with liver toxicity. The inhibition of MRP2-mediated transport of estradiol-17beta-D-glucuronide was studied in inverted membrane vesicles from Sf9 cells overexpressing human MRP2. A total of 27 previously unknown MRP2 inhibitors were identified, and the results indicate an overlapping but narrower inhibitor space for MRP2 compared with the two other major ABC efflux transporters P-gp (ABCB1) and BCRP (ABCG2). In addition, 13 compounds were shown to stimulate the transport of estradiol-17beta-D-glucuronide. The experimental results were used to develop a computational model able to discriminate inhibitors from noninhibitors according to their molecular structure, resulting in a predictive power of 86% for the training set and 72% for the test set. The inhibitors were in general larger and more lipophilic and presented a higher aromaticity than the noninhibitors. The developed computational model is applicable in an early stage of the drug discovery process and is proposed as a tool for prediction of MRP2-mediated hepatic drug interactions and toxicity.

A global drug inhibition pattern for the human ATP-binding cassette transporter breast cancer resistance protein (ABCG2).

In this article, we explore the entire structural space of registered drugs to obtain a global model for the inhibition of the drug efflux transporter breast cancer resistance protein (BCRP; ABCG2). For this purpose, the inhibitory effect of 123 structurally diverse drugs and drug-like compounds on mitoxantrone efflux was studied in Saos-2 cells transfected with human wild-type (Arg482) BCRP. The search for BCRP inhibitors throughout the drug-like chemical space resulted in the identification of 29 previously unknown inhibitors. The frequency of BCRP inhibition was 3 times higher for compounds reported to interact with other ATP-binding cassette (ABC) transporters than for compounds without reported ABC transporter affinity. An easily interpreted computational model capable of discriminating inhibitors from noninhibitors using only two molecular descriptors, octanol-water partition coefficient at pH 7.4 and molecular polarizability, was constructed. The discriminating power of this two-descriptor model was 93% for the training set and 79% for the test set, respectively. The results were supported by a global pharmacophore model and are in agreement with a two-step mechanism for the inhibition of BCRP, where both the drug's capacity to insert into the cell membrane and to interact with the inhibitory binding site of the transporter are important.

Improving structure-based virtual screening by multivariate analysis of scoring data.

Three different multivariate statistical methods, PLS discriminant analysis, rule-based methods, and Bayesian classification, have been applied to multidimensional scoring data from four different target proteins: estrogen receptor alpha (ERalpha), matrix metalloprotease 3 (MMP3), factor Xa (fXa), and acetylcholine esterase (AChE). The purpose was to build classifiers able to discriminate between active and inactive compounds, given a structure-based virtual screen. Seven different scoring functions were used to generate the scoring matrices. The classifiers were compared to classical consensus scoring and single scoring functions. The classifiers show a superior performance, with rule-based methods being most effective. The precision of correctly predicting an active compound is about 90% for three of the targets and about 25% for acetylcholine esterase. On the basis of these results, a new two-stage approach is suggested for structure-based virtual screening where limited activity information is available.