Advances in Predictive Toxicology for Discovery Safety through High

Review pubs.acs.org/crt

Advances in Predictive Toxicology for Discovery Safety through High Content Screening Mikael Persson and Jorrit J. Hornberg* Drug Safety and Metabolism, Innovative Medicines and Early Development, AstraZeneca R&D Gothenburg, Pepparedsleden 1, 431 83 Mölndal, Sweden ABSTRACT: High content screening enables parallel acquisition of multiple molecular and cellular readouts. In particular the predictive toxicology field has progressed from the advances in high content screening, as more refined end points that report on cellular health can be studied in combination, at the single cell level, and in relatively high throughput. Here, we discuss how high content screening has become an essential tool for Discovery Safety, the discipline that integrates safety and toxicology in the drug discovery process to identify and mitigate safety concerns with the aim to design drug candidates with a superior safety profile. In addition to customized mechanistic assays to evaluate target safety, routine screening assays can be applied to identify risk factors for frequently occurring organ toxicities. We discuss the current state of high content screening assays for hepatotoxicity, cardiotoxicity, neurotoxicity, nephrotoxicity, and genotoxicity, including recent developments and current advances.

■

CONTENTS

1. Basics of High Content Screening 2. High Content Screening As a Tool in Discovery Safety 3. Organ Toxicity 3.1. Hepatotoxicity Screening 3.2. Cardiotoxicity Screening 3.3. Neurotoxicity Screening 3.4. Nephrotoxicity Screening 4. Genetic Toxicity 5. Current Advances 5.1. 3D Models, Microphysiological Systems, and Whole Organisms 5.2. Future Outlook Author Information Corresponding Author Notes Biographies Acknowledgments Abbreviations References

populations or whole organisms. The ability to acquire multiparametric information at the individual cell level aids in gaining mechanistic insight into the actions of compounds. As more informed decisions can be made based on increased biological understanding, HCS has developed into an important tool for predictive toxicology and safety assessment during early stages of drug discovery.1−3 Since HCS is based on automated imaging of cells or tissues, it typically makes use of fluorescent dyes or fluorescently labeled antibodies to identify and quantify various biological processes, molecules, organelles, etc. In addition, one can make use of engineered reporter cell lines. This allows the user to customize and multiplex end points. Limitations to that are the need for good spectral separation in the fluorescent channels as well as the specificity of the probes or biosensors, and the ability of the image analysis software to accurately detect and quantify the relevant biological phenotypes. Independent of whether the assays are performed using wide field or confocal imaging, on living cells or fixed cells, the key to successful assays is the image quality. Unlike the human mind, the HCS equipment is unable to make any inference beyond what is depicted, and thus the images must be of sufficient quality such that the biological phenotypes or processes of interest are accurately displayed. In addition to image quality, a crucial component of successful application of high content analysis is the image analysis algorithm. It has to produce robust data and accurately

A B C C D D E E F F F F F F F G G G

1. BASICS OF HIGH CONTENT SCREENING High content screening (HCS) combines automated microscopy with image analysis to assess biological end points ranging from simple cell-based readouts to complex multiparametric phenotypes. It has emerged as a powerful tool to assess spatial, temporal, and complex biological changes induced by small molecules, biologics, or other modalities, which are typically hard to assess using traditional biochemical assays. HCS enables analysis at the single cell level but also on cell © XXXX American Chemical Society

Special Issue: Mass Spectrometry and Emerging Technologies for Biomarker Discovery in the Assessment of Human Health and Disease Received: July 15, 2016

A

DOI: 10.1021/acs.chemrestox.6b00248 Chem. Res. Toxicol. XXXX, XXX, XXX−XXX

Chemical Research in Toxicology

Review

Figure 1. HCS as a tool in Discovery Safety. Differently colored boxes are schematic representations of isolated 2D, isolated 3D, or connected systems for different organs.

secondary pharmacology, cytotoxicity, etc.); (iii) the optimization of lead chemistry to steer away from potential “chemtox” concerns; and (iv) broad investigational toxicology and safety pharmacology studies and mechanistic evaluation of observed effects to select the most promising candidates for progression into the development pipeline. High content screening has proven to be a particularly useful technology to Discovery Safety through numerous applications, of which we discuss a subset in this review (Figure 1). The acquisition of multiparametric data at the single cell level allows for the simultaneous assessment of target expression, target activity, or target engagement, downstream signaling or gene expression, subcellular structural or functional features, and cytotoxicity. In this way, molecular or cellular toxicology and target biology can be studied simultaneously. As in vitro high content screening can be performed in most (preferably adherent) cell types, perturbations with tool compounds or knockdown of specific gene products with RNA interference can yield relevant information on the safety of novel drug targets as early as druggability is assessed. Furthermore, mechanistic understanding about a particular observed toxicity can be obtained by studying subcellular structures such as mitochondria, lysosomes, endoplasmatic reticulum, DNA content, nucleus structure, tight junctions, actin cytoskeleton, etc. or by simultaneously assessing specific pathways or molecular markers that could be involved in the affected process. Most in vitro high content screening assays can be run in high-throughput fashion, by miniaturization of the assay to 96, 384, or even 1536-well microtiter plate-based format and, if needed, by coupling the imager to robotic plate handling equipment. This enables testing of a large number of compounds over a wide concentration range, using only small amounts of compound material, which is required for the assessment of potentially many hits and the generation of structure−activity relationships for optimization of lead series. As the image analysis software produces numerical output values on the various parameters and a large number of cells can be analyzed from several images per test condition, one can

describe the biological phenomena or phenotypes that are the subject of study. Several high content imaging equipment providers offer basic software for image analysis, dedicated software suites are available from independent providers, and specific algorithms can be developed as stand-alone applications in existing software packages. Free open source software such as the CellProfiler is also readily available.4−6 Machine based learning methods for image analysis, in the form of support vector machines or artificial neuronal networks, have emerged as a powerful tools to detect even very small phenotypical changes in single cells.7,8 This has been shown to improve both the robustness and precision of high content screens.9,10 As HCS can produce a wealth of quantitative data, it is very amenable for bioinformatics, systems biology, and machine based learning approaches, in order to reveal patterns, trends, and associations, identify complex phenotypes, and classify compounds according to pharmacological or toxicological mode of action.11−13

2. HIGH CONTENT SCREENING AS A TOOL IN DISCOVERY SAFETY Safety and toxicology are still major causes of late-stage drug development failures.14−16 Safety assessment and predictive toxicology studies have therefore been implemented in earlier stages of the pharmaceutical R&D value chain. We and others have argued that, in addition to the early deselection of candidates with an unfavorable safety profile (fail early), predictive toxicology needs to be an integrated part of the drug discovery process to facilitate the design of candidates with a superior safety profile (increase success).17−19 Active optimization of safety features, alongside other drug properties, ensures that safety issues are not only identified early but also mitigated in a timely fashion. Discovery Safety encompasses a range of activities, including (i) the generation of a target safety assessment to address potential safety concerns associated with novel drug targets (i.e., mechanism-based toxicities); (ii) the evaluation of early hit series to prioritize chemistry without inherent overtly toxic properties (such as obvious structural alerts, promiscuous B



Review

Figure 2. Typical images from an HCS assay of liver cells that were untreated (A−D) or treated with a hepatotoxic compound (E−H), in which stains for (A,E) nuclei, and (B,F) the mitochondrial membrane potential have been used. C and G show the merged image, and D and H show how the algorithm quantifies the image with cell outlines in magenta and active mitochondria in green. Nuclei counts, nuclei morphology, and mitochondrial membrane potential are among the most common parameters to be assessed in HCS assays for hepatotoxicity predictions and can be multiplexed with lysosomal activity, calcium ion homeostasis, apoptosis/necrosis markers, and other stains of interest.

derived cell lines or primary hepatocytes and apply multiparametric imaging of cytotoxicity and mitochondrial toxicityrelated end points after test article incubation (Figure 2).1,3 Predictivity for human clinical hepatotoxicity has been established based on validation studies with large compound sets. HCS hepatotoxicity assays typically show 40−60% sensitivity, depending on the end points assessed, and >90% specificity (i.e., < 10% false positives) on average, independent of cell type.26−33 Pioneering work by O’Brien and colleagues in HepG2 cells and Xu and colleagues in primary hepatocytes showed the utility of HCS to predict hepatotoxicity and general organ toxicity.27,28 We subsequently developed a similar assay that was tailored for use in Discovery Safety and obtained similar predictivity values, i.e., ∼50% sensitivity with 0−10% false positives, depending on analysis model.26 Though the assay does not identify all hepatotoxic compounds (due to, for example the lack of full hepatic metabolic activity, the absence of immune reactions and the fact that some hepatotoxic compounds have therapeutic plasma exposures exceeding the top test concentration in the assay), the relatively high throughput and low cost make it very fit for purpose as an early screening assay. The high specificity ensures that promising compounds are not unnecessarily discarded. On the basis of data on similar assays from various pharmaceutical companies, gathered as part of the Innovative Medicines Initiative’s Mechanism-Based Integrated Systems for Prediction of Drug-Induced Liver Injury (IMI MIP-DILI) consortium, confirmed these predictivity values.34 Not surprisingly, it has been shown that taking the human therapeutic maximum concentration (Cmax) into account greatly reduces the amount of false positives.26 Although the Cmax is seldom known in early drug discovery, the safety margins from the validations can often be used to estimate what exposures would carry low risk for hepatotoxicity. Safety margins of 30−100× between human therapeutic Cmax and the in vitro toxicity is generally recommended depending on the assay.26−28 Without accounting for Cmax as a generic guide for human exposures, larger validations in HepG2 typically show similar sensitivity but with more false positives.26,33 Similar assays have been extended to induced pluripotent stem cell derived hepatocytes, micropatterned cocultures of hepatocytes and supporting fibroblasts, as well as specialized applications such as analysis of

readily obtain reliable quantitative data that are suitable for decision making, influencing chemical design, or establishing safety margins.

3. ORGAN TOXICITY The safety testing strategy for a typical discovery project, in terms of employed assays and models, consists of projectspecific components, i.e., assays and models that are related to safety concerns around the primary target or to specific characteristics of the intended patient population, as well as generic components, i.e., assays and models that are related to safety concerns that occur frequently and/or have a high impact on project progression and that should therefore be assessed for all projects.20 Various reports have shown that, although impactful adverse events and toxicities occur in many different organ systems, there is a clear trend that safety concerns around the liver, the cardiovascular system, the CNS, and to a lesser extent the kidney are the most frequent causes behind safetyrelated discontinuation of candidate drugs or withdrawal of a marketed drug.16,17,21−24 Testing strategies to identify and mitigate risk factors for particularly those organ toxicities have been developed and are refined continuously. In recent years, many high content screening assays using 2D cell systems have been reported that are well-suited for Discovery Safety as they are compatible with the demands of discovery projects (e.g., throughput, quantitative results), and as some of these assays are highly predictive for these organ toxicities. In some cases (e.g., hepatotoxicity), the assays are more sensitive to identify the propensity of compounds to cause clinical toxicity than later stage in vivo toxicology studies. In other cases, the validation in terms of in vitro/in vivo correlations and animal-to-human extrapolations still needs to be established. Such validation studies are essential for the adoption of new assays as routine screens, much like the qualification of new biomarkers for toxicology studies.25 3.1. Hepatotoxicity Screening. HCS for the prediction of drug-induced hepatotoxicity in early drug discovery has evolved into a standard tool in the pharmaceutical industry because the commonly used end points are relatively sensitive, they can be assessed simultaneously, and in sufficient throughput to be amenable to the large amounts of compounds coming out of primary efficacy screens. Typical approaches make use of liverC



Review

Figure 3. Typical images from an HCS assay in stem cell derived cardiomyocytes where stains for (A) nuclei, (B) cardiac troponin, and (C) alpha smooth muscle actin have been used. D shows the merged image.

The risk for structural cardiotoxicity, i.e., loss of cardiomyocyte viability and structural damage, can also be assessed using recently developed high content screening assays (Figure 3). The principle of identifying different mechanisms of structural cardiotoxicity was shown using an assay that monitors apoptosis (caspase activity), mitochondrial function, cell membrane integrity, and nuclear morphology in stem cellderived cardiomyocytes.50 Upon validation using a set of more than 60 compounds, Pointon and colleagues reported a high content screening assay that has a predictivity of 74% for in vivo structural cardiotoxicity and also provides insight into the affected subcellular mechanisms, often mitochondrial function or calcium mobilization.51 Initial studies have been reported on the combination of high content screening end points with cardiomyocyte beating and electrophysiology.52,53 Ultimately, an integrative approach in which both structural and functional effects are assayed in conjunction should benefit the overall cardiotoxicity risk assessment during drug discovery. 3.3. Neurotoxicity Screening. In vitro assessment of neurotoxicity is typically performed using neurite outgrowth measurements in neuronal cells.54 More advanced assay end points include synaptic markers and dendritic morphology and density, reactivity of glial cells, and in vitro myelinization, but these assays have typically been used more for pharmacology than routine toxicity testing.55,56 As high content screening enables integrated data acquisition of additional end points as well as automated image analysis for a large number of cells and conditions, a variety of neurite end points, such as the neurite area, number of neurites per neuron, neurite length, and branching can be studied in combination with cell health parameters (Figure 4).57 Several groups have reported HCS neurite outgrowth assays in recent years and shown that, by establishing the ratio between cell viability and neurite outgrowth, developmental neurotoxic compounds can be

bioactivation induced hepatotoxicty, showing the general applicability of the methods.30−32 Besides the most common HCS assays that assess cell health and mitochondrial parameters, there are also other phenotypical screens that can predict adverse effects in the liver. HCS has been used for predictions of cholestasis through inhibition of canalicular transporters, induction of steatosis and phospholipidosis, and adaptation and survival responses using reporter cell lines for stress signaling pathways such as Nrf2 and NFκB.1,35−37 More end points related to hepatotoxicity should likely increase sensitivity as it would cover more mechanisms; however, it is equally important to include end points that rule out false positives. One such example is assessment of cell cycle analysis as otherwise compounds that inhibit cell cycle progression may be mistaken for cytotoxic compounds.38,39 3.2. Cardiotoxicity Screening. Cardiovascular safety concerns are among the most frequent causes of safety-related discontinuation of candidate drugs as well as of withdrawal of marketed drugs. As mechanisms behind cardiac conduction issues and, more recently, structural cardiac toxicity have become clearer, preclinical cardiac safety testing strategies are evolving to include a broad range of in vitro assays that can be employed to identify and mitigate hazards in a timely manner.40 In addition to electrophysiological assessment of the hERG function to detect QT prolongation potential,41 other cardiac ion channel currents that can be involved in arrhythmia, such as Cav1.2, Nav1.5, Kv7.1, and others, are routinely screened. As the electrical activity is the result of the concerted action of cardiac ion channels, various models have emerged in recent years to look at an integrated response of drug effects, for example, using impedance or field potential measurements in stem-cell derived cardiomyocytes.20,40,42−46 High content imaging has been applied to develop an interesting technology referred to as kinetic imaging cytometry, in which a fluorescent dye is used to monitor changes in the intracellular calcium concentration over time at the single cell level in human induced pluripotent stem cell-derived cardiomyocytes.47 It was shown that perturbations of the transient calcium profile correlate well with known effects on action potential, for example, for drugs inducing QT prolongation, QT shortening, and sodium or calcium channel block.48 A further validation study driven by a consortium with multiple pharma companies involved 44 compounds that are linked to clinical QT prolongation and 46 compounds that are not linked to that and therefore established that the method has a sensitivity for clinical QT prolongation of 80% and a specificity of 98%.49 Taken together with the throughput (96 well format) and the possibility to include further end points in the assay (only two dyes are used) to look at cell health parameters, the low false positive rate makes this high content assay well suited to screen for conduction hazards in an integrated cell system during early drug discovery.

Figure 4. Typical images from an HCS assay in neurons where (A) stains have been used for nuclei (blue), the cytoskeleton (red), and synapses (green). B shows how the algorithm quantifies the image with the nuclei (magenta), the soma (cyan), the neurites (yellow), and the synaptic proteins (blue). This assay can provide details of the number of neurons, how extended and branched their neurites are, and provide measurements of synaptic density. D



Review

Figure 5. Typical images from an HCS assay in kidney cells where stains for (A) nuclei and (B) the actin cytoskeleton have been used. C shows the merged image, and D shows how the algorithm quantifies the image with cell outlines in magenta and strands of polymerized actin in green.

distinguished from general cytotoxic compounds.58−61 These studies range from demonstrations of the principle of the method using few compounds to validation studies using larger sets of environmental toxicants as well as pharmaceuticals. What remains to be shown is how the in vitro data from these assays can be used for the assessment of a neurotoxicity risk for humans. Validation studies using larger sets of compounds from the drug-like chemical space that place the data into a context of human drug exposure in relevant tissues such as the brain or uterus are needed. However, due to the nature of the assays, amenable to high throughput screening, they hold promise to be integrated into Discovery Safety screening cascades. 3.4. Nephrotoxicity Screening. Drug-induced kidney injury and dysfunction accounts for 5−10% of safety-related attrition of drug development programs.16,17,21,24 Moreover, it is commonly induced by a large number of marketed drugs.62 The consequent need to develop drugs with a more favorable kidney safety profile has led to the development of novel renal biomarkers that can be applied in preclinical safety testing and even in the clinic.63,64 However, high throughput in vitro screening approaches that allow for early detection of nephrotoxicity hazards and subsequent mitigation have not yet been established as part of routine safety testing during early drug discovery. This may be due to the fact that renal toxicity can depend on the interplay of multiple factors, such as expression and activity of specific renal transporters, compound accumulation, cell layer morphology, presence of various differentiated cell types, etc. Indeed, cytotoxicity assessment in simple cell systems does not predict kidney toxicity in an organ-specific fashion.65 However, recent progress demonstrates that in vitro systems that make use of primary proximal tubule cells, stem cell-derived proximal tubule-like cells, conditionally immortalized proximal tubule epithelial cells, or stem cell-derived kidney organoids are suitable tools for the identification of nephrotoxic compounds.66−71 Perhaps the most promising study, in terms of identifying an assay that combines predictivity toward clinical nephrotoxicity with the ability to apply it during early drug discovery, was recently reported by Daniele Zink and colleagues.67 In a comprehensive high content-imaging study using four stains (whole cell, DNA, actin, and RelA) in primary human proximal tubule cells, 129 phenotypic features were derived and analyzed using machine learning upon profiling 44 compounds, of which 24 are known to be toxic to proximal tubules in humans, and 20 are not. Hierarchical clustering of the data identified two main clusters, one cluster containing 18 compounds of which 15 were nephrotoxic (positive predictive value: 83%) and one containing 26 compounds of which 17 were non-nephrotoxic (negative predictive value: 65%). The method correctly identified 15 of the 24 proximal tubule toxicants (63% sensitivity) and 17 of the 20 compounds not toxic to proximal tubules (85% specificity). Such a hierarchical clustering

approach was previously applied to a high content-imaging assay for the prediction of drug induced liver injury with similar predictivity values.26 These data indicate that, despite the complexity of kidney physiology, it is possible to develop a predictive 2D cell system for nephrotoxicity using high content imaging (Figure 5).

4. GENETIC TOXICITY Since HCS is imaging based, it is very amenable to the genotoxicity assays that score visual readouts. The prototypical example is counting of micronuclei, but HCS has also successfully been applied to colony counting in Ames tests, and morphology assessment in the in vitro or in vivo Comet assay. The main advantages of using HCS for these classical genotoxicity assays are that it reduces the time and labor intensity needed to perform the assays and perhaps also enables more objective scoring. The micronucleus assay is used for assessing direct compound induced damages to the DNA or mis-incorporations of chromosomes during cell division.72,73 Both mechanisms generate small pieces of DNA not incorporated in the main nucleus, hence the term micronuclei. These are readily assessed by using nuclear stains, such as the Hoechst dyes and imaging algorithms; however, additional stains often need to be employed to rule out false positives due to nuclear collapse caused by cytotoxic mechanisms. The possibilities for multiplexing also open up for more mechanistic assessments. Besides morphology readouts, such as micronuclei size and texture, one can use markers for double strand breaks (e.g., pH2AX) or antibodies against kinetocore proteins such as centromere proteins (e.g., CENP-B) to elucidate whether micronuclei have been generated through an aneugenic or clastogenic mechanism of action.1 This is important during early drug discovery as compounds classified as an aneugen may have a threshold level below which exposure could be considered safe and may thus be progressed if the safety margin is large enough, whereas clastogenic compounds are less likely to progress. In its basic form, the micronucleus assay has been validated in different cell systems, such as CHO and HepG2 cells, and show very reasonable sensitivity (60−80%) and specificity (88%) for use in the pharmaceutical industry.74,75 Likewise, the in vitro pH2AX HCS assay for genotoxicity shows >80% sensitivity and >80% specificity.76 The HCS version of the micronucleus assay is therefore comparable to the high throughput flow cytometric micronucleus assay, which has gained a lot of interest over recent years.77−79 As with the flow cytometric method, the HCS assay provides the opportunity to multiplex end points to gain insight into the mechanism of action of genotoxicants (e.g., addition of a 8-hydroxydeoxyguanosine marker for oxidative DNA damage would indicate whether oxidative stress is part of the mechanism or not) and to filter out false positives. It may E



Review

5.2. Future Outlook. As advanced biological model systems, imaging equipment, and analysis algorithms continuously develop, more physiologically relevant and more contextualized data can be acquired. This should lead to increased ability to identify, quantify, and mitigate safety hazards, as well as to understand the origin and translation of such concerns to inform decisions during drug discovery. High content imaging has the clear advantage of monitoring multiple parameters simultaneously in both a temporal manner and a spatial manner, which makes it amenable to customization and also suitable to study biological systems of varying complexity, ranging from 2D cell layers, to 3D structures, microphysiological systems, and even whole organisms. We expect the 2D assays will continue to develop and progress predictive toxicology methodology, but the next step change is likely going to occur in the more physiologically relevant systems. Comprehensive validation studies with using larger compound sets that go beyond studying a prototypical tool compound, as have been performed on 2D systems, are required to demonstrate the added value of HCS in advanced physiological systems. Ultimately, we anticipate that there will be room for both 2D and 3D HCS predictive toxicology models in the early drug discovery space, as compound numbers, throughput, costs, and turn-around time of data to project teams, require a tiered approach for early safety screening (Figure 1). Therefore, it is exciting to monitor how advances in HCS assay capabilities will progress predictive toxicology and thereby enable Discovery Safety to impact the discovery of drug candidates with a superior safety profile.

even be attractive to integrate genotoxicity assessments in early screens for cytotoxicity, hepatotoxicity, etc. The Comet assay is not considered a high throughput assay for genotoxicity as it is typically slide based instead of using a multiwell plate format. Since the primary read out is DNA damage in the form of the “Comet’s tail”, it is very amenable to imaging and image analysis. Algorithms can give objective measurements on tail length, head size, Olive tail moment, and other aspects that are relevant for assessing the genotoxicity. The principle works for both in vitro and in vivo samples, and although the Comet assay is classically restricted to slides because of the electrophoresis step in sample preparation, there has been several advances in 96-well plate format to increase throughput.80,81

5. CURRENT ADVANCES 5.1. 3D Models, Microphysiological Systems, and Whole Organisms. In vitro models continue to evolve to more closely mimic true physiological systems that should deliver more insightful and relevant information. Besides the general aspect that one can use human material in vitro, the main advantages include multiple cell types interacting in a physiologically relevant tissue structure, and the possibility to dose compounds chronically for several days or even weeks. Several 3D organotypic models have been developed in recent years, ranging from one cell type self-assembling spheroids to more advanced architected multiple cell type organoids and full microphysiological systems with fluidics.82 However, the development of imaging systems and capabilities have not kept the same pace in all instances. From a safety perspective, simple ATP measurements are still used for assessing toxicity, and given the effort it takes to generate the 3D cultures, it almost seems wasteful to use a single and destructive measurement instead of a combination of more refined end points.83 Whereas HCS is relatively straightforward in 2D, it is more complex and technically challenging in 3D, and one is usually restricted to using confocal systems to properly deconvolute the image planes. Experimental challenges include suboptimal diffusion of antibodies or dyes into the biological system, lower level of light penetration to obtain sufficient imaging depth, limitations in microscope z-axis travel to assess the entire organoid, and the need for automated target/ organoid finding algorithms. Still, the potential for increased predictivity due to the higher physiological relevance is driving the advancement of HCS in 3D systems, as exemplified by the development of 3D HCS assays for cell health, hepatotoxicity, neurotoxicity, and cardiac microphysiological systems with improved sensitivity for model compounds.59,84−87 Validations with large amounts of compounds, similar to what has been done in 2D, are needed to prove the added value of HCS in 3D and microfluidic systems. When discussing advanced 3D systems, it is noteworthy that HCS is very well-suited for studying full, albeit small, organisms such as zebrafish or C. elegans. HCS screens in zebrafish are applied to assess developmental toxicity but can also be used to study nanoparticle toxicity, organism activity, and cardiovascular structure and function.88−92 HCS assays in C. elegans have been used to study neuronal activity and behavior, tissue development and growth, as well as germ cell fate.93−96 These examples highlight the use of HCS in whole organisms and that imaging and algorithms can be tailored to assess very complex phenotypes.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest. Biographies

Mikael Persson holds an M.Sc. in Neurochemistry from Stockholm University and a Ph.D. in Medicine from the Institute of Neuroscience and Physiology at Sahlgrenska, Gothenburg University. He has previously been at Abbott and Lundbeck and currently holds a position as Principal Scientist in Discovery Safety at AstraZeneca. Dr. Persson has developed numerous high content analysis assays on various organ systems to support drug discovery projects, from target identification to predictive toxicology. He leads a multi-institutional high content screening project to identify optimal screening approaches for drug-induced liver injury within the IMI MIP-DILI project. F



Review

Enhanced CellClassifier: a multi-class classification tool for microscopy images. BMC Bioinf. 11, 30. (8) Ramo, P., Sacher, R., Snijder, B., Begemann, B., and Pelkmans, L. (2009) CellClassifier: supervised learning of cellular phenotypes. Bioinformatics 25, 3028−3030. (9) Horvath, P., Wild, T., Kutay, U., and Csucs, G. (2011) Machine learning improves the precision and robustness of high-content screens: using nonlinear multiparametric methods to analyze screening results. J. Biomol. Screening 16, 1059−1067. (10) Banerjee, I., Yamauchi, Y., Helenius, A., and Horvath, P. (2013) High-content analysis of sequential events during the early phase of influenza A virus infection. PLoS One 8, e68450. (11) Hartung, T. (2016) Making big sense from big data in toxicology by read-across. Altex 33, 83−93. (12) De Abrew, K. N., Kainkaryam, R. M., Shan, Y. K., Overmann, G. J., Settivari, R. S., Wang, X., Xu, J., Adams, R. L., Tiesman, J. P., Carney, E. W., Naciff, J. M., and Daston, G. P. (2016) Grouping 34 Chemicals Based on Mode of Action Using Connectivity Mapping. Toxicol. Sci. 151, 447−461. (13) Knudsen, T. B., Keller, D. A., Sander, M., Carney, E. W., Doerrer, N. G., Eaton, D. L., Fitzpatrick, S. C., Hastings, K. L., Mendrick, D. L., Tice, R. R., Watkins, P. B., and Whelan, M. (2015) FutureTox II: in vitro data and in silico models for predictive toxicology. Toxicol. Sci. 143, 256−267. (14) Leeson, P. D. (2016) Molecular inflation, attrition and the rule of five. Adv. Drug Delivery Rev. 101, 22−33. (15) Arrowsmith, J., and Miller, P. (2013) Trial watch: phase II and phase III attrition rates 2011−2012. Nat. Rev. Drug Discovery 12, 569. (16) Cook, D., Brown, D., Alexander, R., March, R., Morgan, P., Satterthwaite, G., and Pangalos, M. N. (2014) Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat. Rev. Drug Discovery 13, 419−431. (17) Hornberg, J. J., Laursen, M., Brenden, N., Persson, M., Thougaard, A. V., Toft, D. B., and Mow, T. (2014) Exploratory toxicology as an integrated part of drug discovery. Part I: Why and how. Drug Discovery Today 19, 1131−1136. (18) Blomme, E. A., and Will, Y. (2016) Toxicology Strategies for Drug Discovery: Present and Future. Chem. Res. Toxicol. 29, 473−504. (19) Hornberg, J. J., and Mow, T. (2014) How can we discover safer drugs? Future Med. Chem. 6, 481−483. (20) Hornberg, J. J., Laursen, M., Brenden, N., Persson, M., Thougaard, A. V., Toft, D. B., and Mow, T. (2014) Exploratory toxicology as an integrated part of drug discovery. Part II: Screening strategies. Drug Discovery Today 19, 1137−1144. (21) Olson, H., Betton, G., Robinson, D., Thomas, K., Monro, A., Kolaja, G., Lilly, P., Sanders, J., Sipes, G., Bracken, W., Dorato, M., Van Deun, K., Smith, P., Berger, B., and Heller, A. (2000) Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul. Toxicol. Pharmacol. 32, 56−67. (22) Fung, M., Thornton, A., Mybeck, K., Hsiao-Hui, J., Hornbuckle, K., and Muniz, E. (2001) Evaluation of the characteristics of safety withdrawals of prescription drugs from worldwide pharmaceutical markets-1960−199. Drug Information Journal 35, 293−317. (23) Stevens, J. L., and Baker, T. K. (2009) The future of drug safety testing: expanding the view and narrowing the focus. Drug Discovery Today 14, 162−167. (24) Gad, S. C., Ed. (2014) Pharmaceutical Sciences Encyclopedia: Drug Discovery, Development, and Manufacturing, John Wiley and Sons, New York. (25) Sistare, F. D., and DeGeorge, J. J. (2011) Promise of new translational safety biomarkers in drug development and challenges to regulatory qualification. Biomarkers Med. 5, 497−514. (26) Persson, M., Loye, A. F., Mow, T., and Hornberg, J. J. (2013) A high content screening assay to predict human drug-induced liver injury during drug discovery. J. Pharmacol. Toxicol. Methods 68, 302− 313. (27) O’Brien, P. J., Irwin, W., Diaz, D., Howard-Cofield, E., Krejsa, C. M., Slaughter, M. R., Gao, B., Kaludercic, N., Angeline, A., Bernardi, P., Brain, P., and Hougham, C. (2006) High concordance of drug-induced

Jorrit J. Hornberg holds an M.Sc. in Molecular Cell Biology and a Ph.D. in Systems Biology from VU University in Amsterdam. He has a broad drug discovery expertise ranging from target validation and molecular pharmacology to exploratory toxicology and has worked on multiple disease indications in immunology, oncology, neuroscience, respiratory, cardiovascular, and metabolic diseases. Dr. Hornberg has held positions at Organon (later Schering-Plough), Bayer, Lundbeck, and AstraZeneca. He is currently Director Discovery Safety at AstraZeneca, where he drives the delivery of science-based safety strategies through drug-hunting partnerships, to discover safe innovative medicines for patients.

■

ACKNOWLEDGMENTS We thank Anna-Karin Sjö gren, Malin Forsgard, Lauren Drowley, and Amy Pointon for kindly providing high content screening images.

■

ABBREVIATIONS HCS, high content screening; IMI MIP-DILI, innovative medicines initiative mechanism-based integrated systems for prediction of drug-induced liver injury

■

REFERENCES

(1) Persson, M., Loye, A. F., Jacquet, M., Mow, N. S., Thougaard, A. V., Mow, T., and Hornberg, J. J. (2014) High-content analysis/ screening for predictive toxicology: application to hepatotoxicity and genotoxicity. Basic Clin. Pharmacol. Toxicol. 115, 18−23. (2) van Vliet, E., Daneshian, M., Beilmann, M., Davies, A., Fava, E., Fleck, R., Jule, Y., Kansy, M., Kustermann, S., Macko, P., Mundy, W. R., Roth, A., Shah, I., Uteng, M., van de Water, B., Hartung, T., and Leist, M. (2014) Current approaches and future role of high content imaging in safety sciences and drug discovery. Altex 31, 479−493. (3) O’Brien, P. J. (2014) High-content analysis in toxicology: screening substances for human toxicity potential, elucidating subcellular mechanisms and in vivo use as translational safety biomarkers. Basic Clin. Pharmacol. Toxicol. 115, 4−17. (4) Carpenter, A. E., Jones, T. R., Lamprecht, M. R., Clarke, C., Kang, I. H., Friman, O., Guertin, D. A., Chang, J. H., Lindquist, R. A., Moffat, J., Golland, P., and Sabatini, D. M. (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome.Biol. 7, R100. (5) Jones, T. R., Kang, I. H., Wheeler, D. B., Lindquist, R. A., Papallo, A., Sabatini, D. M., Golland, P., and Carpenter, A. E. (2008) CellProfiler Analyst: data exploration and analysis software for complex image-based screens. BMC Bioinf. 9, 482. (6) Logan, D. J., and Carpenter, A. E. (2010) Screening cellular feature measurements for image-based assay development. J. Biomol. Screening 15, 840−846. (7) Misselwitz, B., Strittmatter, G., Periaswamy, B., Schlumberger, M. C., Rout, S., Horvath, P., Kozak, K., and Hardt, W. D. (2010) G



Review

cardiomyocytes as models for cardiac arrhythmias. Front. Physiol. 3, 346. (44) Kramer, J., Obejero-Paz, C. A., Myatt, G., Kuryshev, Y. A., Bruening-Wright, A., Verducci, J. S., and Brown, A. M. (2013) MICE models: superior to the HERG model in predicting Torsade de Pointes. Sci. Rep. 3, 2100. (45) Guo, L., Abrams, R. M., Babiarz, J. E., Cohen, J. D., Kameoka, S., Sanders, M. J., Chiao, E., and Kolaja, K. L. (2011) Estimating the risk of drug-induced proarrhythmia using human induced pluripotent stem cell-derived cardiomyocytes. Toxicol. Sci. 123, 281−289. (46) Caspi, O., Itzhaki, I., Kehat, I., Gepstein, A., Arbel, G., Huber, I., Satin, J., and Gepstein, L. (2009) In vitro electrophysiological drug testing using human embryonic stem cell derived cardiomyocytes. Stem Cells Dev. 18, 161−172. (47) Cerignoli, F., Charlot, D., Whittaker, R., Ingermanson, R., Gehalot, P., Savchenko, A., Gallacher, D. J., Towart, R., Price, J. H., McDonough, P. M., and Mercola, M. (2012) High throughput measurement of Ca(2)(+) dynamics for drug risk assessment in human stem cell-derived cardiomyocytes by kinetic image cytometry. J. Pharmacol. Toxicol. Methods 66, 246−256. (48) Lu, H. R., Whittaker, R., Price, J. H., Vega, R., Pfeiffer, E. R., Cerignoli, F., Towart, R., and Gallacher, D. J. (2015) High Throughput Measurement of Ca++ Dynamics in Human Stem CellDerived Cardiomyocytes by Kinetic Image Cytometery: A Cardiac Risk Assessment Characterization Using a Large Panel of Cardioactive and Inactive Compounds. Toxicol. Sci. 148, 503−516. (49) Pfeiffer, E. R., Vega, R., McDonough, P. M., Price, J. H., and Whittaker, R. (2016) Specific prediction of clinical QT prolongation by kinetic image cytometry in human stem cell derived cardiomyocytes. J. Pharmacol. Toxicol. Methods 81, 263. (50) Mioulane, M., Foldes, G., Ali, N. N., Schneider, M. D., and Harding, S. E. (2012) Development of high content imaging methods for cell death detection in human pluripotent stem cell-derived cardiomyocytes. Journal of cardiovascular translational research 5, 593− 604. (51) Pointon, A., Abi-Gerges, N., Cross, M. J., and Sidaway, J. E. (2013) Phenotypic profiling of structural cardiotoxins in vitro reveals dependency on multiple mechanisms of toxicity. Toxicol. Sci. 132, 317−326. (52) Doherty, K. R., Talbert, D. R., Trusk, P. B., Moran, D. M., Shell, S. A., and Bacus, S. (2015) Structural and functional screening in human induced-pluripotent stem cell-derived cardiomyocytes accurately identifies cardiotoxicity of multiple drug types. Toxicol. Appl. Pharmacol. 285, 51−60. (53) Clements, M., Millar, V., Williams, A. S., and Kalinka, S. (2015) Bridging Functional and Structural Cardiotoxicity Assays Using Human Embryonic Stem Cell-Derived Cardiomyocytes for a More Comprehensive Risk Assessment. Toxicol. Sci. 148, 241−260. (54) Radio, N. M., and Mundy, W. R. (2008) Developmental neurotoxicity testing in vitro: models for assessing chemical effects on neurite outgrowth. NeuroToxicology 29, 361−376. (55) Anderl, J. L., Redpath, S., and Ball, A. J. (2009) A neuronal and astrocyte co-culture assay for high content analysis of neurotoxicity. J. Visualized Exp., DOI: 10.3791/1173. (56) Pedraza, C. E., Taylor, C., Pereira, A., Seng, M., Tham, C. S., Izrael, M., and Webb, M. (2014) Induction of oligodendrocyte differentiation and in vitro myelination by inhibition of rho-associated kinase. ASN Neuro, 6, DOI: 10.1177/1759091414538134. (57) Wilson, M. S., Graham, J. R., and Ball, A. J. (2014) Multiparametric High Content Analysis for assessment of neurotoxicity in differentiated neuronal cell lines and human embryonic stem cell-derived neurons. NeuroToxicology 42, 33−48. (58) Stiegler, N. V., Krug, A. K., Matt, F., and Leist, M. (2011) Assessment of chemical-induced impairment of human neurite outgrowth by multiparametric live cell imaging in high-density cultures. Toxicol. Sci. 121, 73−87. (59) Krug, A. K., Balmer, N. V., Matt, F., Schonenberger, F., Merhof, D., and Leist, M. (2013) Evaluation of a human neurite growth assay

human hepatotoxicity with in vitro cytotoxicity measured in a novel cell-based model using high content screening. Arch. Toxicol. 80, 580− 604. (28) Xu, J. J., Henstock, P. V., Dunn, M. C., Smith, A. R., Chabot, J. R., and de Graaf, D. (2008) Cellular imaging predictions of clinical drug-induced liver injury. Toxicol. Sci. 105, 97−105. (29) Tolosa, L., Pinto, S., Donato, M. T., Lahoz, A., Castell, J. V., O’Connor, J. E., and Gomez-Lechon, M. J. (2012) Development of a multiparametric cell-based protocol to screen and classify the hepatotoxicity potential of drugs. Toxicol. Sci. 127, 187−198. (30) Peyre, L., de Sousa, G., Barcellini-Couget, S., Luzy, A. P., Zucchini-Pascal, N., and Rahmani, R. (2015) High-content screening imaging and real-time cellular impedance monitoring for the assessment of chemical’s bio-activation with regards hepatotoxicity. Toxicol. In Vitro 29, 1916−1931. (31) Trask, O. J., Jr., Moore, A., and LeCluyse, E. L. (2014) A micropatterned hepatocyte coculture model for assessment of liver toxicity using high-content imaging analysis. Assay Drug Dev. Technol. 12, 16−27. (32) Sirenko, O., Hesley, J., Rusyn, I., and Cromwell, E. F. (2014) High-content assays for hepatotoxicity using induced pluripotent stem cell-derived cells. Assay Drug Dev. Technol. 12, 43−54. (33) Garside, H., Marcoe, K. F., Chesnut-Speelman, J., Foster, A. J., Muthas, D., Kenna, J. G., Warrior, U., Bowes, J., and Baumgartner, J. (2014) Evaluation of the use of imaging parameters for the detection of compound-induced hepatotoxicity in 384-well cultures of HepG2 cells and cryopreserved primary human hepatocytes. Toxicol. In Vitro 28, 171−181. (34) Persson, M., Aerts, H., Edsbagge, J., Gerets, H., Hewitt, P., Hornberg, J., Juhila, S., Karjalainen, M., Lowatt, C., Mesens, N., Newham, P., Richert, L., Thelin, A., and Weaver, R. (2016) High Content Analysis for Prediction of Human Drug Induced Liver Injury across Several Pharmaceutical Companies within the Innovative Medicines Initiative MIP-DILI Consortium, in 2016 Annual Meeting Supplement, Society of Toxicology, 2016, Abstract no. 3666. (35) Barber, J. A., Stahl, S. H., Summers, C., Barrett, G., Park, B. K., Foster, J. R., and Kenna, J. G. (2015) Quantification of Drug-Induced Inhibition of Canalicular Cholyl-l-Lysyl-Fluorescein Excretion From Hepatocytes by High Content Cell Imaging. Toxicol. Sci. 148, 48−59. (36) Pradip, A., Steel, D., Jacobsson, S., Holmgren, G., IngelmanSundberg, M., Sartipy, P., Bjorquist, P., Johansson, I., and Edsbagge, J. (2016) High Content Analysis of Human Pluripotent Stem Cell Derived Hepatocytes Reveals Drug Induced Steatosis and Phospholipidosis. Stem Cells Int. 2016, 1. (37) Herpers, B., Wink, S., Fredriksson, L., Di, Z., Hendriks, G., Vrieling, H., de Bont, H., and van de Water, B. (2016) Activation of the Nrf2 response by intrinsic hepatotoxic drugs correlates with suppression of NF-kappaB activation and sensitizes toward TNFalphainduced cytotoxicity. Arch. Toxicol. 90, 1163−1179. (38) Schorpp, K., Rothenaigner, I., Maier, J., Traenkle, B., Rothbauer, U., and Hadian, K. (2016) A Multiplexed High-Content Screening Approach Using the Chromobody Technology to Identify Cell Cycle Modulators in Living Cells. J. Biomol. Screening 21, 965. (39) Massey, A. J. (2015) Multiparametric Cell Cycle Analysis Using the Operetta High-Content Imager and Harmony Software with PhenoLOGIC. PLoS One 10, e0134306. (40) Gintant, G., Sager, P. T., and Stockbridge, N. (2016) Evolution of strategies to improve preclinical cardiac safety testing. Nat. Rev. Drug Discovery 15, 457. (41) Pollard, C. E., Abi Gerges, N., Bridgland-Taylor, M. H., Easter, A., Hammond, T. G., and Valentin, J. P. (2010) An introduction to QT interval prolongation and non-clinical approaches to assessing and reducing risk. British journal of pharmacology 159, 12−21. (42) Harmer, A. R., Valentin, J. P., and Pollard, C. E. (2011) On the relationship between block of the cardiac Na(+) channel and druginduced prolongation of the QRS complex. British journal of pharmacology 164, 260−273. (43) Hoekstra, M., Mummery, C. L., Wilde, A. A., Bezzina, C. R., and Verkerk, A. O. (2012) Induced pluripotent stem cell derived H



Review

as specific screen for developmental neurotoxicants. Arch. Toxicol. 87, 2215−2231. (60) Harrill, J. A., Robinette, B. L., Freudenrich, T., and Mundy, W. R. (2013) Use of high content image analyses to detect chemicalmediated effects on neurite sub-populations in primary rat cortical neurons. NeuroToxicology 34, 61−73. (61) Ryan, K. R., Sirenko, O., Parham, F., Hsieh, J. H., Cromwell, E. F., Tice, R. R., and Behl, M. (2016) Neurite outgrowth in human induced pluripotent stem cell-derived neurons as a high-throughput screen for developmental neurotoxicity or neurotoxicity. NeuroToxicology 53, 271−281. (62) Choudhury, D., and Ahmed, Z. (2006) Drug-associated renal dysfunction and injury. Nat. Clin. Pract. Nephrol. 2, 80−91. (63) Ennulat, D., and Adler, S. (2015) Recent successes in the identification, development, and qualification of translational biomarkers: the next generation of kidney injury biomarkers. Toxicol. Pathol. 43, 62−69. (64) Fuchs, T. C., and Hewitt, P. (2011) Biomarkers for druginduced renal damage and nephrotoxicity-an overview for applied toxicology. AAPS J. 13, 615−631. (65) Lin, Z., and Will, Y. (2012) Evaluation of drugs with specific organ toxicities in organ-specific cell lines. Toxicol. Sci. 126, 114−127. (66) Li, Y., Kandasamy, K., Chuah, J. K., Lam, Y. N., Toh, W. S., Oo, Z. Y., and Zink, D. (2014) Identification of nephrotoxic compounds with embryonic stem-cell-derived human renal proximal tubular-like cells. Mol. Pharmaceutics 11, 1982−1990. (67) Su, R., Xiong, S., Zink, D., and Loo, L. H. (2015) Highthroughput imaging-based nephrotoxicity prediction for xenobiotics with diverse chemical structures. Arch. Toxicol., DOI: 10.1007/s00204015-1638-y. (68) Kandasamy, K., Chuah, J. K., Su, R., Huang, P., Eng, K. G., Xiong, S., Li, Y., Chia, C. S., Loo, L. H., and Zink, D. (2015) Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods. Sci. Rep. 5, 12337. (69) Adler, M., Ramm, S., Hafner, M., Muhlich, J. L., Gottwald, E. M., Weber, E., Jaklic, A., Ajay, A. K., Svoboda, D., Auerbach, S., Kelly, E. J., Himmelfarb, J., and Vaidya, V. S. (2016) A Quantitative Approach to Screen for Nephrotoxic Compounds In Vitro. J. Am. Soc. Nephrol. 27, 1015−1028. (70) Nieskens, T. T., Peters, J. G., Schreurs, M. J., Smits, N., Woestenenk, R., Jansen, K., van der Made, T. K., Roring, M., Hilgendorf, C., Wilmer, M. J., and Masereeuw, R. (2016) A Human Renal Proximal Tubule Cell Line with Stable Organic Anion Transporter 1 and 3 Expression Predictive for Antiviral-Induced Toxicity. AAPS J. 18, 465−475. (71) Takasato, M., Er, P. X., Chiu, H. S., Maier, B., Baillie, G. J., Ferguson, C., Parton, R. G., Wolvetang, E. J., Roost, M. S., Chuva de Sousa Lopes, S. M., and Little, M. H. (2015) Kidney organoids from human iPS cells contain multiple lineages and model human nephrogenesis. Nature 526, 564−568. (72) Kirsch-Volders, M., Plas, G., Elhajouji, A., Lukamowicz, M., Gonzalez, L., Vande Loock, K., and Decordier, I. (2011) The in vitro MN assay in 2011: origin and fate, biological significance, protocols, high throughput methodologies and toxicological relevance. Arch. Toxicol. 85, 873−899. (73) Doherty, A. T. (2012) The in vitro micronucleus assay. Methods Mol. Biol. 817, 121−141. (74) Mondal, M. S., Gabriels, J., McGinnis, C., Magnifico, M., Marsilje, T. H., Urban, L., Collis, A., Bojanic, D., Biller, S. A., Frieauff, W., Martus, H. J., Suter, W., and Bentley, P. (2010) High-content micronucleus assay in genotoxicity profiling: initial-stage development and some applications in the investigative/lead-finding studies in drug discovery. Toxicol. Sci. 118, 71−85. (75) Westerink, W. M., Schirris, T. J., Horbach, G. J., and Schoonen, W. G. (2011) Development and validation of a high-content screening in vitro micronucleus assay in CHO-k1 and HepG2 cells. Mutat. Res., Genet. Toxicol. Environ. Mutagen. 724, 7−21.

(76) Garcia-Canton, C., Anadon, A., and Meredith, C. (2013) Assessment of the in vitro gammaH2AX assay by High Content Screening as a novel genotoxicity test. Mutat. Res., Genet. Toxicol. Environ. Mutagen. 757, 158−166. (77) Bryce, S. M., Avlasevich, S. L., Bemis, J. C., Lukamowicz, M., Elhajouji, A., Van Goethem, F., De Boeck, M., Beerens, D., Aerts, H., Van Gompel, J., Collins, J. E., Ellis, P. C., White, A. T., Lynch, A. M., and Dertinger, S. D. (2008) Interlaboratory evaluation of a flow cytometric, high content in vitro micronucleus assay. Mutat. Res., Genet. Toxicol. Environ. Mutagen. 650, 181−195. (78) Bryce, S. M., Bernacki, D. T., Bemis, J. C., and Dertinger, S. D. (2016) Genotoxic mode of action predictions from a multiplexed flow cytometric assay and a machine learning approach. Environ. Mol. Mutagen 57, 171−189. (79) Thougaard, A. V., Christiansen, J., Mow, T., and Hornberg, J. J. (2014) Validation of a high throughput flow cytometric in vitro micronucleus assay including assessment of metabolic activation in TK6 cells. Environ. Mol. Mutagen 55, 704−718. (80) Azqueta, A., Gutzkow, K. B., Priestley, C. C., Meier, S., Walker, J. S., Brunborg, G., and Collins, A. R. (2013) A comparative performance test of standard, medium- and high-throughput comet assays. Toxicol. In Vitro 27, 768−773. (81) Watson, C., Ge, J., Cohen, J., Pyrgiotakis, G., Engelward, B. P., and Demokritou, P. (2014) High-throughput screening platform for engineered nanoparticle-mediated genotoxicity using CometChip technology. ACS Nano 8, 2118−2133. (82) Marx, U., Andersson, T. B., Bahinski, A., Beilmann, M., Beken, S., Cassee, F. R., Cirit, M., Daneshian, M., Fitzpatrick, S., Frey, O., Gaertner, C., Giese, C., Griffith, L., Hartung, T., Heringa, M. B., Hoeng, J., de Jong, W. H., Kojima, H., Kuehnl, J., Leist, M., Luch, A., Maschmeyer, I., Sakharov, D., Sips, A. J., Steger-Hartmann, T., Tagle, D. A., Tonevitsky, A., Tralau, T., Tsyb, S., van de Stolpe, A., Vandebriel, R., Vulto, P., Wang, J., Wiest, J., Rodenburg, M., and Roth, A. (2016) Biology-inspired microphysiological system approaches to solve the prediction dilemma of substance testing. Altex., DOI: 10.14573/altex.1603161. (83) Kijanska, M., and Kelm, J. (2004) In vitro 3D Spheroids and Microtissues: ATP-based Cell Viability and Toxicity Assays, in Assay Guidance Manual (Sittampalam, G. S., Coussens, N. P., Nelson, H., Arkin, M., Auld, D., Austin, C., Bejcek, B., Glicksman, M., Inglese, J., Iversen, P. W., Li, Z., McGee, J., McManus, O., Minor, L., Napper, A., Peltier, J. M., Riss, T., Trask, O. J., Jr., and Weidner, J., Eds.) Eli Lilly & Company and the National Center for Advancing Translational Sciences, Bethesda, MD. (84) Mathur, A., Loskill, P., Shao, K., Huebsch, N., Hong, S., Marcus, S. G., Marks, N., Mandegar, M., Conklin, B. R., Lee, L. P., and Healy, K. E. (2015) Human iPSC-based cardiac microphysiological system for drug screening applications. Sci. Rep. 5, 8883. (85) Sirenko, O., Mitlo, T., Hesley, J., Luke, S., Owens, W., and Cromwell, E. F. (2015) High-content assays for characterizing the viability and morphology of 3D cancer spheroid cultures. Assay Drug Dev. Technol. 13, 402−414. (86) Kim, M. J., Lee, S. C., Pal, S., Han, E., and Song, J. M. (2011) High-content screening of drug-induced cardiotoxicity using quantitative single cell imaging cytometry on microfluidic device. Lab Chip 11, 104−114. (87) Anguissola, S., Garry, D., Salvati, A., O’Brien, P. J., and Dawson, K. A. (2014) High content analysis provides mechanistic insights on the pathways of toxicity induced by amine-modified polystyrene nanoparticles. PLoS One 9, e108025. (88) Lantz-McPeak, S., Guo, X., Cuevas, E., Dumas, M., Newport, G. D., Ali, S. F., Paule, M. G., and Kanungo, J. (2015) Developmental toxicity assay using high content screening of zebrafish embryos. J. Appl. Toxicol. 35, 261−272. (89) Lin, S., Zhao, Y., Xia, T., Meng, H., Ji, Z., Liu, R., George, S., Xiong, S., Wang, X., Zhang, H., Pokhrel, S., Madler, L., Damoiseaux, R., Lin, S., and Nel, A. E. (2011) High content screening in zebrafish speeds up hazard ranking of transition metal oxide nanoparticles. ACS Nano 5, 7284−7295. I



Review

(90) Raftery, T. D., Isales, G. M., Yozzo, K. L., and Volz, D. C. (2014) High-content screening assay for identification of chemicals impacting spontaneous activity in zebrafish embryos. Environ. Sci. Technol. 48, 804−810. (91) Yozzo, K. L., Isales, G. M., Raftery, T. D., and Volz, D. C. (2013) High-content screening assay for identification of chemicals impacting cardiovascular function in zebrafish embryos. Environ. Sci. Technol. 47, 11302−11310. (92) Spomer, W., Pfriem, A., Alshut, R., Just, S., and Pylatiuk, C. (2012) High-throughput screening of zebrafish embryos using automated heart detection and imaging. Journal of laboratory automation 17, 435−442. (93) Albrecht, D. R., and Bargmann, C. I. (2011) High-content behavioral analysis of Caenorhabditis elegans in precise spatiotemporal chemical environments. Nat. Methods 8, 599−605. (94) Larsch, J., Ventimiglia, D., Bargmann, C. I., and Albrecht, D. R. (2013) High-throughput imaging of neuronal activity in Caenorhabditis elegans. Proc. Natl. Acad. Sci. U. S. A. 110, E4266−4273. (95) Gosai, S. J., Kwak, J. H., Luke, C. J., Long, O. S., King, D. E., Kovatch, K. J., Johnston, P. A., Shun, T. Y., Lazo, J. S., Perlmutter, D. H., Silverman, G. A., and Pak, S. C. (2010) Automated high-content live animal drug screening using C. elegans expressing the aggregation prone serpin alpha1-antitrypsin Z. PLoS One 5, e15460. (96) Benson, J. A., Cummings, E. E., O’Reilly, L. P., Lee, M. H., and Pak, S. C. (2014) A high-content assay for identifying small molecules that reprogram C. elegans germ cell fate. Methods 68, 529−535.

J


Advances in Predictive Toxicology for Discovery Safety through High

Recommend Documents