Tong Ying Shun

Automated High-Content Live Animal Drug Screening Using C. elegans Expressing the Aggregation Prone Serpin α1-antitrypsin Z

The development of preclinical models amenable to live animal bioactive compound screening is an attractive approach to discovering effective pharmacological therapies for disorders caused by misfolded and aggregation-prone proteins. In general, however, live animal drug screening is labor and resource intensive, and has been hampered by the lack of robust assay designs and high throughput work-flows. Based on their small size, tissue transparency and ease of cultivation, the use of C. elegans should obviate many of the technical impediments associated with live animal drug screening. Moreover, their genetic tractability and accomplished record for providing insights into the molecular and cellular basis of human disease, should make C. elegans an ideal model system for in vivo drug discovery campaigns. The goal of this study was to determine whether C. elegans could be adapted to high-throughput and high-content drug screening strategies analogous to those developed for cell-based systems. Using transgenic animals expressing fluorescently-tagged proteins, we first developed a high-quality, high-throughput work-flow utilizing an automated fluorescence microscopy platform with integrated image acquisition and data analysis modules to qualitatively assess different biological processes including, growth, tissue development, cell viability and autophagy. We next adapted this technology to conduct a small molecule screen and identified compounds that altered the intracellular accumulation of the human aggregation prone mutant that causes liver disease in α1-antitrypsin deficiency. This study provides powerful validation for advancement in preclinical drug discovery campaigns by screening live C. elegans modeling α1-antitrypsin deficiency and other complex disease phenotypes on high-content imaging platforms.

HTS Identifies Novel and Specific Uncompetitive Inhibitors of the Two-Component NS2B-NS3 Proteinase of West Nile Virus

West Nile virus (WNV), a member of the Flavividae family, is a mosquito-borne, emerging pathogen. In addition to WNV, the family includes dengue, yellow fever, and Japanese encephalitis viruses, which affect millions of individuals worldwide. Because countermeasures are currently unavailable, flaviviral therapy is urgently required. The flaviviral two-component nonstructural NS2B-NS3 proteinase (protease [pro]) is essential for viral life cycle and, consequently, is a promising drug target. We report here the results of the miniaturization of an NS2B-NS3pro activity assay, followed by high-throughput screening of the National Institutes of Healths 65,000 compound library and identification of novel, uncompetitive inhibitors of WNV NS2B-NS3pro that appear to interfere with the productive interactions of the NS2B cofactor with the NS3pro domain. We anticipate that following structure optimization, the identified probes could form the foundation for the design of novel and specific therapeutics for WNV infection. We also provide the structural basis for additional species-selective allosteric inhibitors of flaviviruses.

Development of a 384-Well Colorimetric Assay to Quantify Hydrogen Peroxide Generated by the Redox Cycling of Compounds in the Presence of Reducing Agents

We report here the development and optimization of a simple 384-well colorimetric assay to measure H(2)O(2) generated by the redox cycling of compounds incubated with reducing agents in high-throughput screening (HTS) assay buffers. The phenol red-horseradish peroxidase (HRP) assay readily detected H(2)O(2) either added exogenously or generated by the redox cycling of compounds in dithiothreitol (DTT). The generation of H(2)O(2) was dependent on the concentration of both the compound and DTT and was abolished by catalase. Although both DTT and tris(2-carboxyethyl) phosphine sustain the redox cycling generation of H(2)O(2) by a model quinolinedione, 6-chloro-7-(2-morpholin-4-yl-ethylamino)-quinoline-5,8-dione (NSC 663284; DA3003-1), other reducing agents such as beta-mercaptoethanol, glutathione, and cysteine do not. The assay is compatible with HTS. Once terminated, the assay signal was stable for at least 5 h, allowing for a reasonable throughput. The assay tolerated up to 20% dimethyl sulfoxide, allowing a wide range of compound concentrations to be tested. The assay signal window was robust and reproducible with average Z-factors of > or =0.8, and the redox cycling generation of H(2)O(2) by DA3003-1 in DTT exhibited an average 50% effective concentration of 0.830 +/- 0.068 microM. Five of the mitogen-activated protein kinase phosphatase (MKP) 1 inhibitors identified in an HTS were shown to generate H(2)O(2) in the presence of DTT, and their inhibition of MKP-1 activity was shown to be time dependent and was abolished or significantly reduced by either 100 U of catalase or by higher DTT levels. A cross-target query of the PubChem database with three structurally related pyrimidotriazinediones revealed active flags in 36-39% of the primary screening assays. Activity was confirmed against a number of targets containing active site cysteines, including protein tyrosine phosphatases, cathepsins, and caspases, as well as a number of cellular cytotoxicity assays. Rather than utilize resources to conduct a hit characterization effort involving several secondary assays, the phenol red-HRP assay provides a simple, rapid, sensitive, and inexpensive method to identify compounds that redox cycle in DTT or tris(2-carboxyethyl)phosphine to produce H(2)O(2) that may indirectly modulate target activity and represent promiscuous false-positives from a primary screen.

In Vitro Platforms for Evaluating Liver Toxicity

The liver is a heterogeneous organ with many vital functions, including metabolism of pharmaceutical drugs and is highly susceptible to injury from these substances. The etiology of drug-induced liver disease is still debated although generally regarded as a continuum between an activated immune response and hepatocyte metabolic dysfunction, most often resulting from an intermediate reactive metabolite. This debate stems from the fact that current animal and in vitro models provide limited physiologically relevant information, and their shortcomings have resulted in “silent” hepatotoxic drugs being introduced into clinical trials, garnering huge financial losses for drug companies through withdrawals and late stage clinical failures. As we advance our understanding into the molecular processes leading to liver injury, it is increasingly clear that (a) the pathologic lesion is not only due to liver parenchyma but is also due to the interactions between the hepatocytes and the resident liver immune cells, stellate cells, and endothelial cells; and (b) animal models do not reflect the human cell interactions. Therefore, a predictive human, in vitro model must address the interactions between the major human liver cell types and measure key determinants of injury such as the dosage and metabolism of the drug, the stress response, cholestatic effect, and the immune and fibrotic response. In this mini-review, we first discuss the current state of macro-scale in vitro liver culture systems with examples that have been commercialized. We then introduce the paradigm of microfluidic culture systems that aim to mimic the liver with physiologically relevant dimensions, cellular structure, perfusion, and mass transport by taking advantage of micro and nanofabrication technologies. We review the most prominent liver-on-a-chip platforms in terms of their physiological relevance and drug response. We conclude with a commentary on other critical advances such as the deployment of fluorescence-based biosensors to identify relevant toxicity pathways, as well as computational models to create a predictive tool.

A human liver microphysiology platform for investigating physiology, drug safety, and disease models

This paper describes the development and characterization of a microphysiology platform for drug safety and efficacy in liver models of disease that includes a human, 3D, microfluidic, four-cell, sequentially layered, self-assembly liver model (SQL-SAL); fluorescent protein biosensors for mechanistic readouts; as well as a microphysiology system database (MPS-Db) to manage, analyze, and model data. The goal of our approach is to create the simplest design in terms of cells, matrix materials, and microfluidic device parameters that will support a physiologically relevant liver model that is robust and reproducible for at least 28 days for stand-alone liver studies and microfluidic integration with other organs-on-chips. The current SQL-SAL uses primary human hepatocytes along with human endothelial (EA.hy926), immune (U937) and stellate (LX-2) cells in physiological ratios and is viable for at least 28 days under continuous flow. Approximately, 20% of primary hepatocytes and/or stellate cells contain fluorescent protein biosensors (called sentinel cells) to measure apoptosis, reactive oxygen species (ROS) and/or cell location by high content analysis (HCA). In addition, drugs, drug metabolites, albumin, urea and lactate dehydrogenase (LDH) are monitored in the efflux media. Exposure to 180 μM troglitazone or 210 μM nimesulide produced acute toxicity within 2–4 days, whereas 28 μM troglitazone produced a gradual and much delayed toxic response over 21 days, concordant with known mechanisms of toxicity, while 600 µM caffeine had no effect. Immune-mediated toxicity was demonstrated with trovafloxacin with lipopolysaccharide (LPS), but not levofloxacin with LPS. The SQL-SAL exhibited early fibrotic activation in response to 30 nM methotrexate, indicated by increased stellate cell migration, expression of alpha-smooth muscle actin and collagen, type 1, alpha 2. Data collected from the in vitro model can be integrated into a database with access to related chemical, bioactivity, preclinical and clinical information uploaded from external databases for constructing predictive models.

Profiling the NIH Small Molecule Repository for compounds that generate H2O2 by redox cycling in reducing environments.

We have screened the Library of Pharmacologically Active Compounds (LOPAC) and the National Institutes of Health (NIH) Small Molecule Repository (SMR) libraries in a horseradish peroxidase-phenol red (HRP-PR) H2O2 detection assay to identify redox cycling compounds (RCCs) capable of generating H2O2 in buffers containing dithiothreitol (DTT). Two RCCs were identified in the LOPAC set, the ortho-naphthoquinone beta-lapachone and the para-naphthoquinone NSC 95397. Thirty-seven (0.02%) concentration-dependent RCCs were identified from 195,826 compounds in the NIH SMR library; 3 singleton structures, 9 ortho-quinones, 2 para-quinones, 4 pyrimidotriazinediones, 15 arylsulfonamides, 2 nitrothiophene-2-carboxylates, and 2 tolyl hydrazides. Sixty percent of the ortho-quinones and 80% of the pyrimidotriazinediones in the library were confirmed as RCCs. In contrast, only 3.9% of the para-quinones were confirmed as RCCs. Fifteen of the 251 arylsulfonamides in the library were confirmed as RCCs, and since we screened 17,868 compounds with a sulfonamide functional group we conclude that the redox cycling activity of the arylsulfonamide RCCs is due to peripheral reactive enone, aromatic, or heterocyclic functions. Cross-target queries of the University of Pittsburgh Drug Discovery Institute (UPDDI) and PubChem databases revealed that the RCCs exhibited promiscuous bioactivity profiles and have populated both screening databases with significantly higher numbers of active flags than non-RCCs. RCCs were promiscuously active against protein targets known to be susceptible to oxidation, but were also active in cell growth inhibition assays, and against other targets thought to be insensitive to oxidation. Profiling compound libraries or the hits from screening campaigns in the HRP-PR H(2)O(2) detection assay significantly reduce the timelines and resources required to identify and eliminate promiscuous nuisance RCCs from the candidates for lead optimization.

Development and optimization of high-throughput in vitro protein phosphatase screening assays

We describe here detailed protocols to design, optimize and validate in vitro phosphatase assays that we have utilized to conduct high-throughput screens for inhibitors of dual-specificity phosphatases: CDC25B, mitogen-activated protein kinase phosphatase (MKP)-1 and MKP-3. We provide details of the critical steps that are needed to effectively miniaturize the assay into a 384-well, high-throughput format that is both reproducible and cost effective. In vitro phosphatase assays that are optimized according to these protocols should satisfy the assay performance criteria required for a robust high-throughput assay with Z-factors >0.5, and with low intra-plate, inter-plate and day-to-day variability (CV <20%). Assuming the availability of sufficient active phosphatase enzyme and access to appropriate liquid handling automation and detection instruments, a single investigator should be able to develop a 384-well format high-throughput assay in a period of 3–4 weeks.

Identification of Survival Genes in Human Glioblastoma Cells by Small Interfering RNA Screening

Target identification and validation remain difficult steps in the drug discovery process, and uncovering the core genes and pathways that are fundamental for cancer cell survival may facilitate this process. Glioblastoma represents a challenging form of cancer for chemotherapy. Therefore, we assayed 16,560 short interfering RNA (siRNA) aimed at identifying which of the 5520 unique therapeutically targetable gene products were important for the survival of human glioblastoma. We analyzed the viability of T98G glioma cells 96 h after siRNA transfection with two orthogonal statistical methods and identified 55 survival genes that encoded proteases, kinases, and transferases. It is noteworthy that 22% (12/55) of the survival genes were constituents of the 20S and 26S proteasome subunits. An expression survey of a panel of glioma cell lines demonstrated expression of the proteasome component PSMB4, and the validity of the proteasome complex as a target for survival inhibition was confirmed in a series of glioma and nonglioma cell lines by pharmacological inhibition and RNA interference. Biological networks were built with the other survival genes using a protein-protein interaction network, which identified clusters of cellular processes, including protein ubiquitination, purine and pyrimidine metabolism, nucleotide excision repair, and NF-κB signaling. The results of this study should broaden our understanding of the core genes and pathways that regulate cell survival; through either small molecule inhibition or RNA interference, we highlight the potential significance of proteasome inhibition.

Identifying Actives from HTS Data Sets: Practical Approaches for the Selection of an Appropriate HTS Data-Processing Method and Quality Control Review

High-throughput screening (HTS) has achieved a dominant role in drug discovery over the past 2 decades. The goal of HTS is to identify active compounds (hits) by screening large numbers of diverse chemical compounds against selected targets and/or cellular phenotypes. The HTS process consists of multiple automated steps involving compound handling, liquid transfers, and assay signal capture, all of which unavoidably contribute to systematic variation in the screening data. The challenge is to distinguish biologically active compounds from assay variability. Traditional plate controls-based and non-controls-based statistical methods have been widely used for HTS data processing and active identification by both the pharmaceutical industry and academic sectors. More recently, improved robust statistical methods have been introduced, reducing the impact of systematic row/column effects in HTS data. To apply such robust methods effectively and properly, we need to understand their necessity and functionality. Data from 6 HTS case histories are presented to illustrate that robust statistical methods may sometimes be misleading and can result in more, rather than less, false positives or false negatives. In practice, no single method is the best hit detection method for every HTS data set. However, to aid the selection of the most appropriate HTS data-processing and active identification methods, the authors developed a 3-step statistical decision methodology. Step 1 is to determine the most appropriate HTS data-processing method and establish criteria for quality control review and active identification from 3-day assay signal window and DMSO validation tests. Step 2 is to perform a multilevel statistical and graphical review of the screening data to exclude data that fall outside the quality control criteria. Step 3 is to apply the established active criterion to the quality-assured data to identify the active compounds.

Towards a three-dimensional microfluidic liver platform for predicting drug efficacy and toxicity in humans

Although the process of drug development requires efficacy and toxicity testing in animals prior to human testing, animal models have limited ability to accurately predict human responses to xenobiotics and other insults. Societal pressures are also focusing on reduction of and, ultimately, replacement of animal testing. However, a variety of in vitro models, explored over the last decade, have not been powerful enough to replace animal models. New initiatives sponsored by several US federal agencies seek to address this problem by funding the development of physiologically relevant human organ models on microscopic chips. The eventual goal is to simulate a human-on-a-chip, by interconnecting the organ models, thereby replacing animal testing in drug discovery and development. As part of this initiative, we aim to build a three-dimensional human liver chip that mimics the acinus, the smallest functional unit of the liver, including its oxygen gradient. Our liver-on-a-chip platform will deliver a microfluidic three-dimensional co-culture environment with stable synthetic and enzymatic function for at least 4 weeks. Sentinel cells that contain fluorescent biosensors will be integrated into the chip to provide multiplexed, real-time readouts of key liver functions and pathology. We are also developing a database to manage experimental data and harness external information to interpret the multimodal data and create a predictive platform.