High-content analysis to leverage a robust phenotypic profiling approach to vascular modulation

Morphological landscape of endothelial cell networks reveals a functional role of glutamate receptors in angiogenesis

Angiogenesis plays a key role in several diseases including cancer, ischemic vascular disease, and Alzheimer's disease. Chemical genetic screening of endothelial tube formation provides a robust approach for identifying signalling components that impact microvascular network morphology as well as endothelial cell biology. However, the analysis of the resulting imaging datasets has been limited to a few phenotypic features such as the total tube length or the number of branching points. Here we developed a high content analysis framework for detailed quantification of various aspects of network morphology including network complexity, symmetry and topology. By applying our approach to a high content screen of 1,280 characterised drugs, we found that drugs that result in a similar phenotype share the same mechanism of action or common downstream signalling pathways. Our multiparametric analysis revealed that a group of glutamate receptor antagonists enhances branching and network connectivity. Using an integrative meta-analysis approach, we validated the link between these receptors and angiogenesis. We further found that the expression of these genes is associated with the prognosis of Alzheimer's patients. In conclusion, our work shows that detailed image analysis of complex endothelial phenotypes can reveal new insights into biological mechanisms modulating the morphogenesis of endothelial networks and identify potential therapeutics for angiogenesis-related diseases.

Integrated Multiparametric High-Content Profiling of Endothelial Cells

Endothelial cells (ECs) are widely heterogeneous at the cell level and serve different functions at the vessel and tissue levels. EC-forming colonies derived from induced pluripotent stem cells (iPSC-ECFCs) alongside models such as primary human umbilical vein ECs (HUVECs) are slowly becoming available for research with future applications in cell therapies, disease modeling, and drug discovery. We and others previously described high-content analysis approaches capturing unbiased morphology-based measurements coupled with immunofluorescence and used these for multidimensional reduction and population analysis. Here, we report a tailored workflow to characterize ECs. We acquire images at high resolution with high-magnification water-immersion objectives with Hoechst, vascular endothelial cadherin (VEC), and activated NOTCH staining. We hypothesize that via these key markers alone we would be able to distinguish and assess different EC populations. We used cell population software analysis to phenotype HUVECs and iPSC-ECFCs in the absence or presence of vascular endothelial growth factor (VEGF). To our knowledge, this study presents the first parallel quantitative high-content multiparametric profiling of EC models. Importantly, it highlights a simple strategy to benchmark ECs in different conditions and develop new approaches for biological research and translational applications for regenerative medicine.

The inadequacy of the reductionist approach in discovering new therapeutic agents against complex diseases

For more than 20 years, drug discovery has relied on two assumptions, i.e. (i) a therapeutic response can be triggered by modulating the activity of a single gene product, and (ii) a compound uncovered by its activity on a recombinant protein in vitro can perform its activity in vivo. Drug discovery operates accordingly by using the concepts of targets and pipelines. The target, such as a gene product, is the intended point of therapeutic intervention, and compounds that modulate its activity in vitro follow a series of downstream developments. This reductionist approach has developed due to advances in combinatorial chemistry, robotics, molecular biology, and genomics. The expectation of this approach is that the frequency of drug discovery will dramatically increase, while its associated cost would decrease. However, the frequency of new drug discovery has decreased, while the associated costs have surged. We performed a retrospective study that examined how successful development programs have led to marketed drugs for all indications except anti-infective and anti-neoplastic agents. We concluded that the target and pipeline paradigms are limited and are actually causing the drug development industry to collectively fail to meet the critical medical needs. Impact statement The initial scope of this investigation was to build the set of human genes that are presumed to be the therapeutic intervention points of US FDA-approved drugs, in all therapeutics areas but oncology. The prerequisite for this study was the establishment of the non-redundant set of all active pharmaceutical ingredients for these disease areas. Pertaining to complex diseases, the main observation was that there is not a single instance in the history of drug discovery, where a compound, initially selected by means of a biochemical assay, achieved a significant therapeutic response. The whole field of Drug R&D faces an unacceptable lack of new treatments to address unmet medical needs. The conclusion is that complex biological assays have to be designed for the primary selection of candidate therapeutics.

Next-generation phenotypic screening

Phenotypic drug discovery (PDD) strategies are defined by screening and selection of hit or lead compounds based on quantifiable phenotypic endpoints without prior knowledge of the drug target. We outline the challenges associated with traditional phenotypic screening strategies and propose solutions and new opportunities to be gained by adopting modern PDD technologies. We highlight both historical and recent examples of approved drugs and new drug candidates discovered by modern phenotypic screening. Finally, we offer a prospective view of a new era of PDD underpinned by a wealth of technology advances in the areas of in vitro model development, high-content imaging and image informatics, mechanism-of-action profiling and target deconvolution.

Flow Cytometry: Impact on Early Drug Discovery

Modern flow cytometers can make optical measurements of 10 or more parameters per cell at tens of thousands of cells per second and more than five orders of magnitude dynamic range. Although flow cytometry is used in most drug discovery stages, "sip-and-spit" sampling technology has restricted it to low-sample-throughput applications. The advent of HyperCyt sampling technology has recently made possible primary screening applications in which tens of thousands of compounds are analyzed per day. Target-multiplexing methodologies in combination with extended multiparameter analyses enable profiling of lead candidates early in the discovery process, when the greatest numbers of candidates are available for evaluation. The ability to sample small volumes with negligible waste reduces reagent costs, compound usage, and consumption of cells. Improved compound library formatting strategies can further extend primary screening opportunities when samples are scarce. Dozens of targets have been screened in 384- and 1536-well assay formats, predominantly in academic screening lab settings. In concert with commercial platform evolution and trending drug discovery strategies, HyperCyt-based systems are now finding their way into mainstream screening labs. Recent advances in flow-based imaging, mass spectrometry, and parallel sample processing promise dramatically expanded single-cell profiling capabilities to bolster systems-level approaches to drug discovery.

Consideration of the cellular microenvironment: physiologically relevant co-culture systems in drug discovery

There is renewed interest in phenotypic approaches to drug discovery, using cell-based assays to select new drugs, with the goal of improving pharmaceutical success. Assays that are more predictive of human biology can help researchers achieve this goal. Primary cells are more physiologically relevant to human biology and advances are being made in methods to expand the available cell types and improve the potential clinical translation of these assays through the use of co-cultures or three-dimensional (3D) technologies. Of particular interest are assays that may be suitable for industrial scale drug discovery. Here we review the use of primary human cells and co-cultures in drug discovery and describe the characteristics of co-culture models for inflammation biology (BioMAP systems), neo-vascularization and tumor microenvironments. Finally we briefly describe technical trends that may enable and impact the development of physiologically relevant co-culture assays in the near future.

Neoclassic drug discovery: the case for lead generation using phenotypic and functional approaches

Innovation and new molecular entity production by the pharmaceutical industry has been below expectations. Surprisingly, more first-in-class small-molecule drugs approved by the U.S. Food and Drug Administration (FDA) between 1999 and 2008 were identified by functional phenotypic lead generation strategies reminiscent of pre-genomics pharmacology than contemporary molecular targeted strategies that encompass the vast majority of lead generation efforts. This observation, in conjunction with the difficulty in validating molecular targets for drug discovery, has diminished the impact of the "genomics revolution" and has led to a growing grassroots movement and now broader trend in pharma to reconsider the use of modern physiology-based or phenotypic drug discovery (PDD) strategies. This "From the Guest Editors" column provides an introduction and overview of the two-part special issues of Journal of Biomolecular Screening on PDD. Terminology and the business case for use of PDD are defined. Key issues such as assay performance, chemical optimization, target identification, and challenges to the organization and implementation of PDD are discussed. Possible solutions for these challenges and a new neoclassic vision for PDD that combines phenotypic and functional approaches with technology innovations resulting from the genomics-driven era of target-based drug discovery (TDD) are also described. Finally, an overview of the manuscripts in this special edition is provided.