A Quantitative Systems Pharmacology Platform Reveals NAFLD Pathophysiological States and Targeting Strategies.

Non-alcoholic fatty liver disease (NAFLD) has a high global prevalence with a heterogeneous and complex pathophysiology that presents barriers to traditional targeted therapeutic approaches. We describe an integrated quantitative systems pharmacology (QSP) platform that comprehensively and unbiasedly defines disease states, in contrast to just individual genes or pathways, that promote NAFLD progression. The QSP platform can be used to predict drugs that normalize these disease states and experimentally test predictions in a human liver acinus microphysiology system (LAMPS) that recapitulates key aspects of NAFLD. Analysis of a 182 patient-derived hepatic RNA-sequencing dataset generated 12 gene signatures mirroring these states. Screening against the LINCS L1000 database led to the identification of drugs predicted to revert these signatures and corresponding disease states. A proof-of-concept study in LAMPS demonstrated mitigation of steatosis, inflammation, and fibrosis, especially with drug combinations. Mechanistically, several structurally diverse drugs were predicted to interact with a subnetwork of nuclear receptors, including pregnane X receptor (PXR; NR1I2), that has evolved to respond to both xenobiotic and endogenous ligands and is intrinsic to NAFLD-associated transcription dysregulation. In conjunction with iPSC-derived cells, this platform has the potential for developing personalized NAFLD therapeutic strategies, informing disease mechanisms, and defining optimal cohorts of patients for clinical trials.

Quantifying the progression of non-alcoholic fatty liver disease in human biomimetic liver microphysiology systems with fluorescent protein biosensors.

Metabolic syndrome is a complex disease that involves multiple organ systems including a critical role for the liver. Non-alcoholic fatty liver disease (NAFLD) is a key component of the metabolic syndrome and fatty liver is linked to a range of metabolic dysfunctions that occur in approximately 25% of the population. A panel of experts recently agreed that the acronym, NAFLD, did not properly characterize this heterogeneous disease given the associated metabolic abnormalities such as type 2 diabetes mellitus (T2D), obesity, and hypertension. Therefore, metabolic dysfunction-associated fatty liver disease (MAFLD) has been proposed as the new term to cover the heterogeneity identified in the NAFLD patient population. Although many rodent models of NAFLD/NASH have been developed, they do not recapitulate the full disease spectrum in patients. Therefore, a platform has evolved initially focused on human biomimetic liver microphysiology systems that integrates fluorescent protein biosensors along with other key metrics, the microphysiology systems database, and quantitative systems pharmacology. Quantitative systems pharmacology is being applied to investigate the mechanisms of NAFLD/MAFLD progression to select molecular targets for fluorescent protein biosensors, to integrate computational and experimental methods to predict drugs for repurposing, and to facilitate novel drug development. Fluorescent protein biosensors are critical components of the platform since they enable monitoring of the pathophysiology of disease progression by defining and quantifying the temporal and spatial dynamics of protein functions in the biosensor cells, and serve as minimally invasive biomarkers of the physiological state of the microphysiology system experimental disease models. Here, we summarize the progress in developing human microphysiology system disease models of NAFLD/MAFLD from several laboratories, developing fluorescent protein biosensors to monitor and to measure NAFLD/MAFLD disease progression and implementation of quantitative systems pharmacology with the goal of repurposing drugs and guiding the creation of novel therapeutics.

Development and Optimization of a High-Content Analysis Platform to Identify Suppressors of Lamin B1 Overexpression as a Therapeutic Strategy for Autosomal Dominant Leukodystrophy.

Autosomal dominant leukodystrophy (ADLD) is a fatal, progressive adult-onset disease characterized by widespread central nervous system (CNS) demyelination and significant morbidity. The late age of onset together with the relatively slow disease progression provides a large therapeutic window for the disorder. However, no treatment exists for ADLD, representing an urgent and unmet clinical need. We have previously shown that ADLD is caused by duplications of the lamin B1 gene causing increased expression of the lamin B1 protein, a major constituent of the nuclear lamina, and demonstrated that transgenic mice with oligodendrocyte-specific overexpression of lamin B1 exhibit temporal and histopathological features reminiscent of the human disease. As increased levels of lamin B1 are the causative event triggering ADLD, approaches aimed at reducing lamin B1 levels and associated functional consequences represent a promising strategy for discovery of small-molecule ADLD therapeutics. To this end, we have created an inducible cell culture model of lamin B1 overexpression and developed high-content analysis in connection with multivariate analysis to define, analyze, and quantify lamin B1 expression and its associated abnormal nuclear phenotype in mouse embryonic fibroblasts (MEFs). The assay has been optimized to meet high-throughput screening (HTS) criteria in multiday variability studies. To control for batch-to-batch variation in the primary MEFs, we have implemented a screening strategy that employs sentinel cells to avoid costly losses during HTS. We posit the assay will identify bona fide suppressors of lamin B1 pathophysiology as candidates for development into potential therapies for ADLD.

Applications of the microphysiology systems database for experimental ADME-Tox and disease models.

To accelerate the development and application of Microphysiological Systems (MPS) in biomedical research and drug discovery/development, a centralized resource is required to provide the detailed design, application, and performance data that enables industry and research scientists to select, optimize, and/or develop new MPS solutions, as well as to harness data from MPS models. We have previously implemented an open source Microphysiology Systems Database (MPS-Db), with a simple icon driven interface, as a resource for MPS researchers and drug discovery/development scientists (https://mps.csb.pitt.edu). The MPS-Db captures and aggregates data from MPS, ranging from static microplate models to integrated, multi-organ microfluidic models, and associates those data with reference data from chemical, biochemical, pre-clinical, clinical and post-marketing sources to support the design, development, validation, application and interpretation of the models. The MPS-Db enables users to manage their multifactor, multichip studies, then upload, analyze, review, computationally model and share data. Here we discuss how the sharing of MPS study data in the MS-Db is under user control and can be kept private to the individual user, shared with a select group of collaborators, or be made accessible to the general scientific community. We also present a test case using our liver acinus MPS model (LAMPS) as an example and discuss the use of the MPS-Db in managing, designing, and analyzing MPS study data, assessing the reproducibility of MPS models, and evaluating the concordance of MPS model results with clinical findings. We introduce the Disease Portal module with links to resources for the design of MPS disease models and studies and discuss the integration of computational models for the prediction of PK/PD and disease pathways using data generated from MPS models.

A High-Throughput Assay for DNA Replication Inhibitors Based upon Multivariate Analysis of Yeast Growth Kinetics.

Mcm2-7 is the molecular motor of eukaryotic replicative helicase, and the regulation of this complex is a major focus of cellular S-phase regulation. Despite its cellular importance, few small-molecule inhibitors of this complex are known. Based upon our genetic analysis of synthetic growth defects between mcm alleles and a range of other alleles, we have developed a high-throughput screening (HTS) assay using a well-characterized mcm mutant (containing the mcm2DENQ allele) to identify small molecules that replicate such synthetic growth defects. During assay development, we found that aphidicolin (inhibitor of DNA polymerase alpha) and XL413 (inhibitor of the DNA replication-dependent kinase CDC7) preferentially inhibited growth of the mcm2DENQ strain relative to the wild-type parental strain. However, as both strains demonstrated some degree of growth inhibition with these compounds, small and variable assay windows can result. To increase assay sensitivity and reproducibility, we developed a strategy combining the analysis of cell growth kinetics with linear discriminant analysis (LDA). We found that LDA greatly improved assay performance and captured a greater range of synthetic growth inhibition phenotypes, yielding a versatile analysis platform conforming to HTS requirements.

Integrating Analysis of Cellular Heterogeneity in High-Content Dose-Response Studies.

Heterogeneity is a complex property of cellular systems and therefore presents challenges to the reliable identification and characterization. Large-scale biology projects may span many months, requiring a systematic approach to quality control to track reproducibility and correct for instrumental variation and assay drift that could mask biological heterogeneity and preclude comparisons of heterogeneity between runs or even between plates. However, presently there is no standard approach to the tracking and analysis of heterogeneity. Previously, we demonstrated the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in a screen and described the use of three heterogeneity indices as a means to characterize, filter, and browse cellular heterogeneity in big data sets (Gough et al., Methods 96:12-26, 2016). In this chapter, we present a detailed method for integrating the analysis of cellular heterogeneity in assay development, validation, screening, and post screen. Importantly, we provide a detailed method for quality control, to normalize cellular data, track heterogeneity over time, and analyze heterogeneity in big data sets, along with software tools to assist in that process. The example screen for this method is from an HCS project, but the approach applies equally to other experimental methods that measure populations of cells.

Clinically Observed Estrogen Receptor Alpha Mutations within the Ligand-Binding Domain Confer Distinguishable Phenotypes.

OBJECTIVE: Twenty to fifty percent of estrogen receptor-positive (ER+) metastatic breast cancers express mutations within the ER ligand-binding domain. While most studies focused on the constitutive ER signaling activity commonly engendered by these mutations selected during estrogen deprivation therapy, our study was aimed at investigating distinctive phenotypes conferred by different mutations within this class. METHODS: We examined the two most prevalent mutations, D538G and Y537S, employing corroborative genome-edited and lentiviral-transduced ER+ T47D cell models. We used a luciferase-based reporter and endogenous phospho-ER immunoblot analysis to characterize the estrogen response of ER mutants and determined their resistance to known ER antagonists. RESULTS: Consistent with their selection during estrogen deprivation therapy, these mutants conferred constitutive ER activity. While Y537S mutants showed no estrogen dependence, D538G mutants demonstrated an enhanced estrogen-dependent response. Both mutations conferred resistance to ER antagonists that was overcome at higher doses acting specifically through their ER target. CONCLUSIONS: These observations provide a tenable hypothesis for how D538G ESR1-expressing clones can contribute to shorter progression-free survival observed in the exemestane arm of the BOLERO-2 study. Thus, in those patients with dominant D538G-expressing clones, longitudinal analysis for this mutation in circulating free DNA may prove beneficial for informing more optimal therapeutic regimens.

High Content Imaging Assays for IL-6-Induced STAT3 Pathway Activation in Head and Neck Cancer Cell Lines.

In the canonical STAT3 signaling pathway, IL-6 receptor engagement leads to the recruitment of latent STAT3 to the activated IL-6 complex and the associated Janus kinase (JAK) phosphorylates STAT3 at Y705. pSTAT3-Y705 dimers traffic into the nucleus and bind to specific DNA response elements in the promoters of target genes to regulate their transcription. However, IL-6 receptor activation induces the phosphorylation of both the Y705 and S727 residues of STAT3, and S727 phosphorylation is required to achieve maximal STAT3 transcriptional activity. STAT3 continuously shuttles between the nucleus and cytoplasm and maintains a prominent nuclear presence that is independent of Y705 phosphorylation. The constitutive nuclear entry of un-phosphorylated STAT3 (U-STAT3) drives expression of a second round of genes by a mechanism distinct from that used by pSTAT3-Y705 dimers. The abnormally elevated levels of U-STAT3 produced by the constitutive activation of pSTAT3-Y705 observed in many tumors drive the expression of an additional set of pSTAT3-independent genes that contribute to tumorigenesis. In this chapter, we describe the HCS assay methods to measure IL-6-induced STAT3 signaling pathway activation in head and neck tumor cell lines as revealed by the expression and subcellular distribution of pSTAT3-Y705, pSTAT3-S727, and U-STAT3. Only the larger dynamic range provided by the pSTAT3-Y705 antibody would be robust and reproducible enough for screening.

Biologically Relevant Heterogeneity: Metrics and Practical Insights.

Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.

A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens.

Heterogeneity is well recognized as a common property of cellular systems that impacts biomedical research and the development of therapeutics and diagnostics. Several studies have shown that analysis of heterogeneity: gives insight into mechanisms of action of perturbagens; can be used to predict optimal combination therapies; and can be applied to tumors where heterogeneity is believed to be associated with adaptation and resistance. Cytometry methods including high content screening (HCS), high throughput microscopy, flow cytometry, mass spec imaging and digital pathology capture cell level data for populations of cells. However it is often assumed that the population response is normally distributed and therefore that the average adequately describes the results. A deeper understanding of the results of the measurements and more effective comparison of perturbagen effects requires analysis that takes into account the distribution of the measurements, i.e. the heterogeneity. However, the reproducibility of heterogeneous data collected on different days, and in different plates/slides has not previously been evaluated. Here we show that conventional assay quality metrics alone are not adequate for quality control of the heterogeneity in the data. To address this need, we demonstrate the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in an SAR screen, describe a workflow for quality control in heterogeneity analysis. One major challenge in high throughput biology is the evaluation and interpretation of heterogeneity in thousands of samples, such as compounds in a cell-based screen. In this study we also demonstrate that three heterogeneity indices previously reported, capture the shapes of the distributions and provide a means to filter and browse big data sets of cellular distributions in order to compare and identify distributions of interest. These metrics and methods are presented as a workflow for analysis of heterogeneity in large scale biology projects.

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.

The Microphysiology Systems Database for Analyzing and Modeling Compound Interactions with Human and Animal Organ Models.

Microfluidic human organ models, microphysiology systems (MPS), are currently being developed as predictive models of drug safety and efficacy in humans. To design and validate MPS as predictive of human safety liabilities requires safety data for a reference set of compounds, combined with in vitro data from the human organ models. To address this need, we have developed an internet database, the MPS database (MPS-Db), as a powerful platform for experimental design, data management, and analysis, and to combine experimental data with reference data, to enable computational modeling. The present study demonstrates the capability of the MPS-Db in early safety testing using a human liver MPS to relate the effects of tolcapone and entacapone in the in vitro model to human in vivo effects. These two compounds were chosen to be evaluated as a representative pair of marketed drugs because they are structurally similar, have the same target, and were found safe or had an acceptable risk in preclinical and clinical trials, yet tolcapone induced unacceptable levels of hepatotoxicity while entacapone was found to be safe. Results demonstrate the utility of the MPS-Db as an essential resource for relating in vitro organ model data to the multiple biochemical, preclinical, and clinical data sources on in vivo drug effects.

HCS campaign to identify selective inhibitors of IL-6-induced STAT3 pathway activation in head and neck cancer cell lines.

Signal transducer and activator of transcription factor 3 (STAT3) is hyperactivated in head and neck squamous cell carcinomas (HNSCC). Cumulative evidence indicates that IL-6 production by HNSCC cells and/or stromal cells in the tumor microenvironment activates STAT3 and contributes to tumor progression and drug resistance. A library of 94,491 compounds from the Molecular Library Screening Center Network (MLSCN) was screened for the ability to inhibit interleukin-6 (IL-6)-induced pSTAT3 activation. For contractual reasons, the primary high-content screening (HCS) campaign was conducted over several months in 3 distinct phases; 1,068 (1.1%) primary HCS actives remained after cytotoxic or fluorescent outliers were eliminated. One thousand one hundred eighty-seven compounds were cherry-picked for confirmation; actives identified in the primary HCS and compounds selected by a structural similarity search of the remaining MLSCN library using hits identified in phases I and II of the screen. Actives were confirmed in pSTAT3 IC50 assays, and an IFNγ-induced pSTAT1 activation assay was used to prioritize selective inhibitors of STAT3 activation that would not inhibit STAT1 tumor suppressor functions. Two hundred three concentration-dependent inhibitors of IL-6-induced pSTAT3 activation were identified and 89 of these also produced IC50s against IFN-γ-induced pSTAT1 activation. Forty-nine compounds met our hit criteria: they reproducibly inhibited IL-6-induced pSTAT3 activation by ≥70% at 20 µM; their pSTAT3 activation IC50s were ≤25 µM; they were ≥2-fold selective for pSTAT3 inhibition over pSTAT1 inhibition; a cross target query of PubChem indicated that they were not biologically promiscuous; and they were ≥90% pure. Twenty-six chemically tractable hits that passed filters for nuisance compounds and had acceptable drug-like and ADME-Tox properties by computational evaluation were purchased for characterization. The hit structures were distributed among 5 clusters and 8 singletons. Twenty-four compounds inhibited IL-6-induced pSTAT3 activation with IC50s ≤20 µM and 13 were ≥3-fold selective versus inhibition of pSTAT1 activation. Eighteen hits inhibited the growth of HNSCC cell lines with average IC50s ≤ 20 µM. Four chemical series were progressed into lead optimization: the guanidinoquinazolines, the triazolothiadiazines, the amino alcohols, and an oxazole-piperazine singleton.

High-content positional biosensor screening assay for compounds to prevent or disrupt androgen receptor and transcriptional intermediary factor 2 protein-protein interactions.

The androgen receptor-transcriptional intermediary factor 2 (AR-TIF2) positional protein-protein interaction (PPI) biosensor assay described herein combines physiologically relevant cell-based assays with the specificity of binding assays by incorporating structural information of AR and TIF2 functional domains along with intracellular targeting sequences and fluorescent reporters. Expression of the AR-red fluorescent protein (RFP) "prey" and TIF2-green fluorescent protein (GFP) "bait" components of the biosensor was directed by recombinant adenovirus constructs that expressed the ligand binding and activation function 2 surface domains of AR fused to RFP with nuclear localization and nuclear export sequences, and three α-helical LXXLL motifs from TIF2 fused to GFP and an HIV Rev nucleolar targeting sequence. In unstimulated cells, AR-RFP was localized predominantly to the cytoplasm and TIF2-GFP was localized to nucleoli. Dihydrotestosterone (DHT) treatment induced AR-RFP translocation into the nucleus where the PPIs between AR and TIF2 resulted in the colocalization of both biosensors within the nucleolus. We adapted the translocation enhanced image analysis module to quantify the colocalization of the AR-RFP and TIF2-GFP biosensors in images acquired on the ImageXpress platform. DHT induced a concentration-dependent AR-TIF2 colocalization and produced a characteristic condensed punctate AR-RFP PPI nucleolar distribution pattern. The heat-shock protein 90 inhibitor 17-N-allylamino-17-demethoxygeldanamycin (17-AAG) and antiandrogens flutamide and bicalutamide inhibited DHT-induced AR-TIF2 PPI formation with 50% inhibition concentrations (IC50s) of 88.5±12.5 nM, 7.6±2.4 µM, and 1.6±0.4 µM, respectively. Images of the AR-RFP distribution phenotype allowed us to distinguish between 17-AAG and flutamide, which prevented AR translocation, and bicalutamide, which blocked AR-TIF2 PPIs. We screened the Library of Pharmacologically Active Compounds (LOPAC) set for compounds that inhibited AR-TIF2 PPI formation or disrupted preexisting complexes. Eleven modulators of steroid family nuclear receptors (NRs) and 6 non-NR ligands inhibited AR-TIF2 PPI formation, and 10 disrupted preexisting complexes. The hits appear to be either AR antagonists or nonspecific inhibitors of NR activation and trafficking. Given that the LOPAC set represents such a small and restricted biological and chemical diversity, it is anticipated that screening a much larger and more diverse compound library will be required to find AR-TIF2 PPI inhibitors/disruptors. The AR-TIF2 protein-protein interaction biosensor (PPIB) approach offers significant promise for identifying molecules with potential to modulate AR transcriptional activity in a cell-specific manner that is distinct from the existing antiandrogen drugs that target AR binding or production. Small molecules that disrupt AR signaling at the level of AR-TIF2 PPIs may also overcome the development of resistance and progression to castration-resistant prostate cancer.

Identifying and quantifying heterogeneity in high content analysis: application of heterogeneity indices to drug discovery.

One of the greatest challenges in biomedical research, drug discovery and diagnostics is understanding how seemingly identical cells can respond differently to perturbagens including drugs for disease treatment. Although heterogeneity has become an accepted characteristic of a population of cells, in drug discovery it is not routinely evaluated or reported. The standard practice for cell-based, high content assays has been to assume a normal distribution and to report a well-to-well average value with a standard deviation. To address this important issue we sought to define a method that could be readily implemented to identify, quantify and characterize heterogeneity in cellular and small organism assays to guide decisions during drug discovery and experimental cell/tissue profiling. Our study revealed that heterogeneity can be effectively identified and quantified with three indices that indicate diversity, non-normality and percent outliers. The indices were evaluated using the induction and inhibition of STAT3 activation in five cell lines where the systems response including sample preparation and instrument performance were well characterized and controlled. These heterogeneity indices provide a standardized method that can easily be integrated into small and large scale screening or profiling projects to guide interpretation of the biology, as well as the development of therapeutics and diagnostics. Understanding the heterogeneity in the response to perturbagens will become a critical factor in designing strategies for the development of therapeutics including targeted polypharmacology.

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.

High-content pSTAT3/1 imaging assays to screen for selective inhibitors of STAT3 pathway activation in head and neck cancer cell lines.

The oncogenic transcription factor signal transducer and activator of transcription 3 (STAT3) is hyperactivated in most cancers and represents a plausible therapeutic target. In the absence of STAT3-selective small-molecule inhibitors, we sought to develop pSTAT3/1 high-content imaging (HCS) assays to screen for selective inhibitors of STAT3 pathway activation in head and neck squamous cell carcinomas (HNSCC) tumor cell lines. Based on the expression of the interleukin-6 (IL-6)Rα and gp130 subunits of the IL-6 receptor complex and STAT3, we selected the Cal33 HNSCC cell line as our model. After developing image acquisition and analysis procedures, we rigorously investigated the cytokine activation responses to optimize the dynamic ranges of both assays and demonstrated that the pan-Janus kinase inhibitor pyridone 6 nonselectively inhibited pSTAT3 and pSTAT1 activation with 50% inhibition concentrations of 7.19 ± 4.08 and 16.38 ± 8.45 nM, respectively. The optimized pSTAT3 HCS assay performed very well in a pilot screen of 1,726 compounds from the Library of Pharmacologically Active Compounds and the National Institutes of Health clinical collection sets, and we identified 51 inhibitors of IL-6-induced pSTAT3 activation. However, only three of the primary HCS actives selectively inhibited STAT3 compared with STAT1. Our follow-up studies indicated that the nonselective inhibition of cytokine induced pSTAT3 and pSTAT1 activation by G-alpha stimulatory subunit-coupled G-protein-coupled receptor agonists, and forskolin was likely due to cyclic adenosine monophosphate-mediated up-regulation of suppressors of cytokine signaling 3. Azelastine, an H1 receptor antagonist approved for the treatment of seasonal allergic rhinitis, nonallergic vasomotor rhinitis, and ocular conjunctivitis, was subsequently confirmed as a selective inhibitor of IL-6-induced pSTAT3 activation that also reduced the growth of HNSCC cell lines. These data illustrate the power of a chemical biology approach to lead generation that utilizes fully developed and optimized HCS assays as phenotypic screens to interrogate specific signaling pathways.

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.

Alkylation sensitivity screens reveal a conserved cross-species functionome.

To identify genes that contribute to chemotherapy resistance in glioblastoma, we conducted a synthetic lethal screen in a chemotherapy-resistant glioblastoma-derived cell line with the clinical alkylator temozolomide (TMZ) and an siRNA library tailored toward "druggable" targets. Select DNA repair genes in the screen were validated independently, confirming the DNA glycosylases uracil-DNA glycosylase (UNG) and A/G-specific adenine DNA glycosylase (MYH) as well as methylpurine-DNA glycosylase (MPG) to be involved in the response to high dose TMZ. The involvement of UNG and MYH is likely the result of a TMZ-induced burst of reactive oxygen species. We then compared the human TMZ sensitizing genes identified in our screen with those previously identified from alkylator screens conducted in Escherichia coli and Saccharomyces cerevisiae. The conserved biologic processes across all three species compose an alkylation functionome that includes many novel proteins not previously thought to impact alkylator resistance. This high-throughput screen, validation and cross-species analysis was then followed by a mechanistic analysis of two essential nodes: base excision repair (BER) DNA glycosylases (UNG, human and mag1, S. cerevisiae) and protein modification systems, including UBE3B and ICMT in human cells or pby1, lip22, stp22 and aim22 in S. cerevisiae. The conserved processes of BER and protein modification were dual targeted and yielded additive sensitization to alkylators in S. cerevisiae. In contrast, dual targeting of BER and protein modification genes in human cells did not increase sensitivity, suggesting an epistatic relationship. Importantly, these studies provide potential new targets to overcome alkylating agent resistance.

Development and validation of a high-content screening assay to identify inhibitors of cytoplasmic dynein-mediated transport of glucocorticoid receptor to the nucleus.

Rapid ligand-induced trafficking of glucocorticoid nuclear hormone receptor (GR) from the cytoplasm to the nucleus is an extensively studied model for intracellular retrograde cargo transport employed in constructive morphogenesis and many other cellular functions. Unfortunately, potent and selective small-molecule disruptors of this process are lacking, which has restricted pharmacological investigations. We describe here the development and validation of a 384-well high-content screening (HCS) assay to identify inhibitors of the rapid ligand-induced retrograde translocation of cytoplasmic glucocorticoid nuclear hormone receptor green fluorescent fusion protein (GR-GFP) into the nuclei of 3617.4 mouse mammary adenocarcinoma cells. We selected 3617.4 cells, because they express GR-GFP under the control of a tetracycline (Tet)-repressible promoter and are exceptionally amenable to image acquisition and analysis procedures. Initially, we investigated the time-dependent expression of GR-GFP in 3617.4 cells under Tet-on and Tet-off control to determine the optimal conditions to measure dexamethasone (Dex)-induced GR-GFP nuclear translocation on the ArrayScan-VTI automated imaging platform. We then miniaturized the assay into a 384-well format and validated the performance of the GR-GFP nuclear translocation HCS assay in our 3-day assay signal window and dimethylsulfoxide validation tests. The molecular chaperone heat shock protein 90 (Hsp90) plays an essential role in the regulation of GR steroid binding affinity and ligand-induced retrograde trafficking to the nucleus. We verified that the GR-GFP HCS assay captured the concentration-dependent inhibition of GR-GFP nuclear translocation by 17-AAG, a benzoquinone ansamycin that selectively blocks the binding and hydrolysis of ATP by Hsp90. We screened the 1280 compound library of pharmacologically active compounds set in the Dex-induced GR-GFP nuclear translocation assay and used the multi-parameter HCS data to eliminate cytotoxic compounds and fluorescent outliers. We identified five qualified hits that inhibited the rapid retrograde trafficking of GR-GFP in a concentration-dependent manner: Bay 11-7085, 4-phenyl-3-furoxancarbonitrile, parthenolide, apomorphine, and 6-nitroso-1,2-benzopyrone. The data presented here demonstrate that the GR-GFP HCS assay provides an effective phenotypic screen and support the proposition that screening a larger library of diversity compounds will yield novel small-molecule probes that will enable the further exploration of intracellular retrograde transport of cargo along microtubules, a process which is essential to the morphogenesis and function of all cells.