Machine learning and image-based profiling in drug discovery.

A recent decline in the discovery of novel medications challenges the widespread use of 2D monolayer cell assays in the drug discovery process. As a result, the need for more appropriate cellular models of human physiology and disease has renewed the interest in spheroid 3D culture as a pertinent model for drug screening. However, despite technological progress that has significantly simplified spheroid production and analysis, the seeming complexity of the 3D approach has delayed its adoption in many laboratories. The present report demonstrates that the use of a spheroid model may be straightforward and can provide information that is not directly available with a standard 2D approach. We describe a cost-efficient method that allows for the production of an array of uniform spheroids, their staining with vital dyes, real-time monitoring of drug effects, and an ATP-endpoint assay, all in the same 96-well U-bottom plate. To demonstrate the method performance, we analyzed the effect of the preclinical anticancer drug MLN4924 on spheroids formed by VCaP and LNCaP prostate cancer cells. The drug has different outcomes in these cell lines, varying from cell cycle arrest and protective dormancy to senescence and apoptosis. We demonstrate that by using high-content analysis of spheroid arrays, the effect of the drug can be described as a series of EC50 values that clearly dissect the cytostatic and cytotoxic drug actions. The method was further evaluated using four standard cancer chemotherapeutics with different mechanisms of action, and the effect of each drug is described as a unique multi-EC50 diagram. Once fully validated in a wider range of conditions, this method could be particularly valuable for phenotype-based drug discovery.

/pdf/high-content-monitoring-of-drug-effects-in-a-3d-spheroid-5cmg6ezvwj.pdf

High-Content Monitoring of Drug Effects in a 3D Spheroid Model.

The liver is one of the largest organs in the body and is responsible for a diverse repertoire of metabolic processes. Such processes include the secretion of serum proteins, carbohydrate and lipid metabolism, bile acid and urea synthesis, detoxification of drugs and metabolic waste products, and vitamin and carbohydrate storage. Currently, liver disease is one of the most prevalent causes of mortality in the USA with congenital liver defects contributing to a significant proportion of these deaths. Historically the study of liver disease has been hampered by a shortage of organ donors, the subsequent scarcity of healthy tissue, and the failure of animal models to fully recapitulate human liver function. In vitro culture of hepatocytes has also proven difficult because primary hepatocytes rapidly de-differentiate in culture. Recent advances in stem cell technology have facilitated the generation of induced pluripotent stem cells (iPSCs) from various somatic cell types from patients. Such cells can be differentiated to a liver cell fate, essentially providing a limitless supply of cells with hepatocyte characteristics that can mimic the pathophysiology of liver disease. Furthermore, development of the CRISPR-Cas9 system, as well as advancement of miniaturized differentiation platforms has facilitated the development of high throughput models for the investigation of hepatocyte differentiation and drug discovery. In this review, we will explore the latest advances in iPSC-based disease modeling and drug screening platforms and examine how this technology is being used to identify new pharmacological interventions, and to advance our understanding of liver development and mechanisms of disease. We will cover how iPSC technology is being used to develop predictive models for rare diseases and how information gained from large in vitro screening experiments can be used to directly inform clinical investigation.

/pdf/ipsc-derived-hepatocytes-as-a-platform-for-disease-modeling-4k1ybbfq3l.pdf

iPSC-Derived Hepatocytes as a Platform for Disease Modeling and Drug Discovery.

Individual Control and Quantification of 3D Spheroids in a High-Density Microfluidic Droplet Array.

The poor predictability of human liver toxicity is still causing high attrition rates of drug candidates in the pharmaceutical industry at the non-clinical, clinical, and post-marketing authorization stages. This is in part caused by animal models that fail to predict various human adverse drug reactions (ADRs), resulting in undetected hepatotoxicity at the non-clinical phase of drug development. In an effort to increase the prediction of human hepatotoxicity, different approaches to enhance the physiological relevance of hepatic in vitro systems are being pursued. Three-dimensional (3D) or microfluidic technologies allow to better recapitulate hepatocyte organization and cell-matrix contacts, to include additional cell types, to incorporate fluid flow and to create gradients of oxygen and nutrients, which have led to improved differentiated cell phenotype and functionality. This comprehensive review addresses the drug-induced hepatotoxicity mechanisms and the currently available 3D liver in vitro models, their characteristics, as well as their advantages and limitations for human hepatotoxicity assessment. In addition, since toxic responses are greatly dependent on the culture model, a comparative analysis of the toxicity studies performed using two-dimensional (2D) and 3D in vitro strategies with recognized hepatotoxic compounds, such as paracetamol, diclofenac, and troglitazone is performed, further highlighting the need for harmonization of the respective characterization methods. Finally, taking a step forward, we propose a roadmap for the assessment of drugs hepatotoxicity based on fully characterized fit-for-purpose in vitro models, taking advantage of the best of each model, which will ultimately contribute to more informed decision-making in the drug development and risk assessment fields.

/pdf/a-critical-perspective-on-3d-liver-models-for-drug-141p3qfw4n.pdf

A Critical Perspective on 3D Liver Models for Drug Metabolism and Toxicology Studies.

Cell models are becoming more complex to better mimic the in vivo environment and provide greater predictivity for compound efficacy and toxicity. There is an increasing interest in exploring the use of three-dimensional (3D) spheroids for modeling developmental and tissue biology with the goal of accelerating translational research in these areas. Accordingly, the development of high-throughput quantitative assays using 3D cultures is an active area of investigation. In this study, we have developed and optimized methods for the formation of 3D liver spheroids derived from human iPS cells and used those for toxicity assessment. We used confocal imaging and 3D image analysis to characterize cellular information from a 3D matrix to enable a multi-parametric comparison of different spheroid phenotypes. The assay enables characterization of compound toxicities by spheroid size (volume) and shape, cell number and spatial distribution, nuclear characterization, number and distribution of cells express...

/pdf/phenotypic-characterization-of-toxic-compound-effects-on-djr7x0qxxx.pdf

Phenotypic Characterization of Toxic Compound Effects on Liver Spheroids Derived from iPSC Using Confocal Imaging and Three-Dimensional Image Analysis.

Methods and systems for counting nuclei for cells in a cell-containing sample are disclosed. The methods comprise: receiving a raw image of the cell-containing sample; transforming the raw image into a segmented image comprising one or more nuclei clusters. The methods further comprise: for each of the one or more nuclei clusters, obtaining a convex hull of the nuclei cluster; locating any indentations on the nuclei cluster by comparing the nuclei cluster to the convex hull of the nuclei cluster; calculating a first nuclei count based on a tally of the indentations; and assigning the nuclei cluster to a cell among the plurality of cells. The methods further comprise: calculating a second nuclei count for each cell by totaling the first nuclei counts of its constituent nuclei clusters; and presenting a result based on the second nuclei count for at least one of the plurality of cells.

Multi-nucleated cell classification and micronuclei scoring

In a method and system for identifying objects in an image, an image and training data are received. The training data identifies a pixel associated with an object of a particular type in the image. A plurality of filtered versions of the image are developed. The training data and the plurality of filtered versions of the image are processed to develop a trained model for classifying pixels associated with objects of the particular type. The trained model is applied to the image to identify pixels associated a plurality of objects of the particular type in the image. Additional image processing steps are developed to further refine the identified pixels for better fitting of the contour of the objects with their edges.

Method and system for classifying and identifying individual cells in a microscopy image

A computer-implemented method of processing a selected image using multiple processing operations is provided. An image analysis sequence having multiple processing steps is constructed. The image analysis sequence is constructed in response to receipt of multiple processing operation selections. Individual processing steps in the image analysis sequence are associated with a processing operation that is indicated in a corresponding processing operation selection. The processing steps are arranged in response to receipt of arrangement information that relates to a selective arrangement of the processing steps. At least one of the processing steps in the image analysis sequence is configured such that the processing operation associated with the processing step processes a specified input image to generate an output image when the processing step is performed. A display signal is generated for display of the output image at a display device.

Image processing system providing selective arrangement and configuration for an image analysis sequence

A computer-implemented system and method of processing a microscopy image are provided. A microscopy image is received, and a configuration for an image section that includes a portion of the microscopy image is determined. Multiple image sections are respectively assigned to multiple processing units, and the processing units respectively process the image sections in parallel. One or more objects are determined to be respectively present in the image sections, and the objects present in the image sections are measured to obtain object data associated with the objects.

System and method for sectioning a microscopy image for parallel processing

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