Kidney toxicity is a major problem both in drug development and clinical settings. It is difficult to predict nephrotoxicity in part because of the lack of appropriate in vitro cell models, limited endpoints, and the observation that the activity of membrane transporters which plays important roles in nephrotoxicity by affecting the pharmacokinetic profile of drugs is often not taken into account. We developed a new cell model using pseudo-immortalized human primary renal proximal tubule epithelial cells. This cell line (SA7K) was characterized by the presence of proximal tubule cell markers as well as several functional properties, including transporter activity and response to a few well-characterized nephrotoxicants. We subsequently evaluated a group of potential nephrotoxic compounds in SA7K cells and compared them to a commonly used human immortalized kidney cell line (HK-2). Cells were treated with test compounds and three endpoints were analyzed, including cell viability, apoptosis and mitochondrial membrane potential. The results showed that most of the known nephrotoxic compounds could be detected in one or more of these endpoints. There were sensitivity differences in response to several of the chemicals between HK-2 and SA7K cells, which may relate to differences in expressions of key transporters or other components of nephrotoxicity pathways. Our data suggest that SA7K cells appear as promising for the early detection of renal toxicants.

Drug-induced liver injury (DILI) is complex in mechanism. Different drugs could undergo different mechanisms but result in the same DILI type, while the same drug could lead to different DILI types via different mechanisms. Therefore, predicting a drug's potential for DILI should take its underlying mechanisms into consideration. To achieve that, we constructed a novel approach by incorporating the drug's Mode of Action (MOA) into Quantitative Structure-Activity Relationship (QSAR) modeling. This MOA-DILI approach was examined using a data set of 333 drugs. The drugs were first grouped according to their MOA profiles (positive or negative in each MOA) based on the Tox21 qHTS assays. QSAR models for individual MOA assays were developed and subsequently combined to obtain the MOA-DILI model. A hold-out testing strategy (222 drugs for training and 111 drugs as a test set) was employed, which yielded a predictive accuracy of 0.711. The MOA-DILI model was directly compared with the standard QSAR approach using the same hold-out strategy, and the QSAR model yielded an accuracy of 0.662. To minimize the random chance in splitting training/test sets, the hold-out testing process was repeated 1000 times, and the observed difference in prediction accuracy between MOA-DILI and QSARs was statistically significant (P value <0.0001). Out of 17 MOAs used, four assays (i.e., antioxidant response elements, PPAR-gamma, estrogen receptor, and thyroid receptor assays) contributed most to the improved prediction of the MOA-DILI model over QSARs. In conclusion, the MOA-DILI approach has the potential to significantly improve predictive outcomes and to reveal complex relationships between MOAs and DILI, all of which would be helpful in developing DILI predictive models in drug screening and for risk assessment of industrial chemicals.

Estrogen-related receptor alpha (ERRα), the first orphan nuclear receptor discovered, is crucial for the control of cellular energy metabolism. ERRα and its coactivator, peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α), are required for rapid energy production in response to environmental challenges. They have been implicated in the etiology of metabolic disorders such as type 2 diabetes and metabolic syndrome. ERRα also plays a role in the pathogenesis of breast cancer. Identification of compounds that modulate ERRα signaling may elucidate environmental factors associated with these diseases. Therefore, we developed stable cell lines containing an intact ERRα signaling pathway, with and without the coactivator PGC-1α, to use as high-throughput screening tools to detect ERRα modulators. The lentiviral PGC-1α expression constructs and ERRα multiple hormone response element (MHRE) reporters were introduced into HEK293T cells that express endogenous ERRα. A cell line expressing the reporter alone was designated "ERR." A second cell line expressing both reporter and PGC-1α was named "PGC/ERR." Initial screenings of the Library of Pharmacologically Active Compounds (LOPAC) identified 33 ERR and 22 PGC/ERR agonists, and 54 ERR and 15 PGC/ERR antagonists. Several potent ERRα agonists were dietary plant compounds (e.g., genistein). In conclusion, these cell lines are suitable for high-throughput screens to identify environmental chemicals affecting metabolic pathways and breast cancer progression.

The activation of pregnane X receptor (PXR), a member of the nuclear receptor superfamily, can mediate potential drug-drug interactions by regulating the expression of several drug- mediated enzymes and transporters, resulting in reduced therapeutic efficacy or increased toxicity by producing reactive metabolites. Therefore, in the early stage of drug development, it is important to predict these risks using an in silico approach. We constructed a human PXR (hPXR) pharmacophore model based on known structural information of compounds that activate PXR. We evaluated the prediction accuracy of the model using datasets generated on 68 original synthetic compounds from the Mitsubishi Tanabe Pharma Corporation (MTPC) and over 2500 drugs from the National Institutes of Health Chemical Genomics Center Pharmaceutical Collection (NPC) for their ability to activate hPXR. The prediction accuracies of the PXR pharmacophore model were 0.78 and 0.86 for the MTPC and NPC, respectively. The compounds resulting in the smallest root-mean square deviation hit by pharmacophore search were the well-known PXR inducers such as Bosentan. These results suggest that using the in silico approach developed in this study is useful to identify potential hPXR activators and modify the drug design during the early stage of drug development.

Acetylcholinesterase (AChE) is an enzyme responsible for metabolism of acetylcholine, a neurotransmitter associated with muscle movement, cognition, and other neurobiological processes. Inhibition of AChE activity can serve as a therapeutic mechanism, but also cause adverse health effects and neurotoxicity. In order to efficiently identify AChE inhibitors from large compound libraries, homogenous cell-based assays in high-throughput screening platforms are needed. In this study, a fluorescent method using Amplex Red (10-acetyl-3,7-dihydroxyphenoxazine) and the Ellman absorbance method were both developed in a homogenous format using a human neuroblastoma cell line (SH-SY5Y). An enzyme-based assay using Amplex Red was also optimized and used to confirm the potential inhibitors. These three assays were used to screen 1368 compounds, which included a library of pharmacologically active compounds (LOPAC) and 88 additional compounds from the Tox21 program, at multiple concentrations in a quantitative high-throughput screening (qHTS) format. All three assays exhibited exceptional performance characteristics including assay signal quality, precision, and reproducibility. A group of inhibitors were identified from this study, including known (e.g., physostigmine and neostigmine bromide) and novel AChE inhibitors (e.g., chelerythrine chloride and cilostazol). These results demonstrate that this platform is a promising means to profile large numbers of chemicals that inhibit AChE activity.

Drug-induced QT prolongation leads to life-threatening cardiotoxicity, mostly through blockage of the human ether-à-go-go-related gene (hERG) encoded potassium ion (K(+) ) channels. The hERG channel is one of the most important antitargets to be addressed in the early stage of drug discovery process, in order to avoid more costly failures in the development phase. Using a thallium flux assay, 4,323 molecules were screened for hERG channel inhibition in a quantitative high throughput screening (qHTS) format. Here, we present support vector classification (SVC) models of hERG channel inhibition with the averaged area under the receiver operator characteristics curve (AUC-ROC) of 0.93 for the tested compounds. Both Jackknifing and bootstrapping have been employed to rebalance the heavily biased training datasets, and the impact of these two under-sampling rebalance methods on the performance of the predictive models is discussed. Our results indicated that the rebalancing techniques did not enhance the predictive power of the resulting models; instead, adoption of optimal cutoffs could restore the desirable balance of sensitivity and specificity of the binary classifiers. In an external validation set of 66 drug molecules, the SVC model exhibited an AUC-ROC of 0.86, further demonstrating the utility of this modeling approach to predict hERG liabilities.

Testing thousands of chemicals to identify potential androgen receptor (AR) agonists or antagonists would cost millions of dollars and take decades to complete using current validated methods. High-throughput in vitro screening (HTS) and computational toxicology approaches can more rapidly and inexpensively identify potential androgen-active chemicals. We integrated 11 HTS ToxCast/Tox21 in vitro assays into a computational network model to distinguish true AR pathway activity from technology-specific assay interference. The in vitro HTS assays probed perturbations of the AR pathway at multiple points (receptor binding, coregulator recruitment, gene transcription, and protein production) and multiple cell types. Confirmatory in vitro antagonist assay data and cytotoxicity information were used as additional flags for potential nonspecific activity. Validating such alternative testing strategies requires high-quality reference data. We compiled 158 putative androgen-active and -inactive chemicals from a combination of international test method validation efforts and semiautomated systematic literature reviews. Detailed in vitro assay information and results were compiled into a single database using a standardized ontology. Reference chemical concentrations that activated or inhibited AR pathway activity were identified to establish a range of potencies with reproducible reference chemical results. Comparison with existing Tier 1 AR binding data from the U.S. EPA Endocrine Disruptor Screening Program revealed that the model identified binders at relevant test concentrations (<100 μM) and was more sensitive to antagonist activity. The AR pathway model based on the ToxCast/Tox21 assays had balanced accuracies of 95.2% for agonist (n = 29) and 97.5% for antagonist (n = 28) reference chemicals. Out of 1855 chemicals screened in the AR pathway model, 220 chemicals demonstrated AR agonist or antagonist activity and an additional 174 chemicals were predicted to have potential weak AR pathway activity.

BACKGROUND: All biological processes are inherently dynamic. Biological systems evolve transiently or sustainably according to sequential time points after perturbation by environment insults, drugs and chemicals. Investigating the temporal behavior of molecular events has been an important subject to understand the underlying mechanisms governing the biological system in response to, such as, drug treatment. The intrinsic complexity of time series data requires appropriate computational algorithms for data interpretation. In this study, we propose, for the first time, the application of dynamic topic models (DTM) for analyzing time-series gene expression data. RESULTS: A large time-series toxicogenomics dataset was studied. It contains over 3144 microarrays of gene expression data corresponding to rat livers treated with 131 compounds (most are drugs) at two doses (control and high dose) in a repeated schedule containing four separate time points (4-, 8-, 15- and 29-day). We analyzed, with DTM, the topics (consisting of a set of genes) and their biological interpretations over these four time points. We identified hidden patterns embedded in this time-series gene expression profiles. From the topic distribution for compound-time condition, a number of drugs were successfully clustered by their shared mode-of-action such as PPARɑ agonists and COX inhibitors. The biological meaning underlying each topic was interpreted using diverse sources of information such as functional analysis of the pathways and therapeutic uses of the drugs. Additionally, we found that sample clusters produced by DTM are much more coherent in terms of functional categories when compared to traditional clustering algorithms. CONCLUSIONS: We demonstrated that DTM, a text mining technique, can be a powerful computational approach for clustering time-series gene expression profiles with the probabilistic representation of their dynamic features along sequential time frames. The method offers an alternative way for uncovering hidden patterns embedded in time series gene expression profiles to gain enhanced understanding of dynamic behavior of gene regulation in the biological system.

Human neuronal cells differentiated from induced pluripotent cells have emerged as a new model system for the study of disease pathophysiology and evaluation of drug efficacy. Differentiated neuronal cells are more similar in genetics and biological content to human brain cells than other animal disease models. However, culture of neuronal cells in assay plates requires a labor-intensive procedure of plate precoating, hampering its applications in high-throughput screening (HTS). We developed a simplified method with one-step seeding of neural stem cells in assay plates by supplementing the medium with a recombinant human vitronectin (VTN), thus avoiding plate precoating. Robust results were obtained from cell viability, calcium response, and neurite outgrowth assays using this new method. Our data demonstrate that this approach greatly simplifies high-throughput assays using neuronal cells differentiated from human stem cells for translational research.

In response to the current global health emergency posed by the Zika virus (ZIKV) outbreak and its link to microcephaly and other neurological conditions, we performed a drug repurposing screen of ∼6,000 compounds that included approved drugs, clinical trial drug candidates and pharmacologically active compounds; we identified compounds that either inhibit ZIKV infection or suppress infection-induced caspase-3 activity in different neural cells. A pan-caspase inhibitor, emricasan, inhibited ZIKV-induced increases in caspase-3 activity and protected human cortical neural progenitors in both monolayer and three-dimensional organoid cultures. Ten structurally unrelated inhibitors of cyclin-dependent kinases inhibited ZIKV replication. Niclosamide, a category B anthelmintic drug approved by the US Food and Drug Administration, also inhibited ZIKV replication. Finally, combination treatments using one compound from each category (neuroprotective and antiviral) further increased protection of human neural progenitors and astrocytes from ZIKV-induced cell death. Our results demonstrate the efficacy of this screening strategy and identify lead compounds for anti-ZIKV drug development.