Developing predictive assays: the phenotypic screening "rule of 3"

Innovating cancer drug discovery with refined phenotypic screens

Before molecular pathways in cancer were known to a depth that could predict targets, drug development relied on phenotypic screening, where the effectiveness of candidate chemicals is judged from functional readouts without considering the mechanisms of action. The unraveling of tumor-specific pathways has brought targets for molecularly driven drug discovery, but precedents in the field have shown that awareness of pathways does not necessarily predict therapeutic efficacy, and many cancers still lack druggable targets. Phenotypic screening therefore retains a niche in drug development where a targeted approach is not informative. We analyze the unique advantages of phenotypic screens, and how technological advances have improved their discovery power. Notable advances include the use of larger biological panels and refined protocols that address the disease-relevance and increase data content with imaging and omic approaches.

Scalable Hypothalamic Arcuate Neuron Differentiation from Human Pluripotent Stem Cells Suitable for Modeling Metabolic and Reproductive Disorders

The hypothalamus, composed of several nuclei, is essential for maintaining our body's homeostasis. The arcuate nucleus (ARC), located in the mediobasal hypothalamus, contains neuronal populations with eminent roles in energy and glucose homeostasis as well as reproduction. These neuronal populations are of great interest for translational research. To fulfill this promise, we used a robotic cell culture platform to provide a scalable and chemically defined approach for differentiating human pluripotent stem cells (hPSCs) into pro-opiomelanocortin (POMC), somatostatin (SST), tyrosine hydroxylase (TH) and gonadotropin-releasing hormone (GnRH) neuronal subpopulations with an ARC-like signature. This robust approach is reproducible across several distinct hPSC lines and exhibits a stepwise induction of key ventral diencephalon and ARC markers in transcriptomic profiling experiments. This is further corroborated by direct comparison to human fetal hypothalamus, and the enriched expression of genes implicated in obesity and type 2 diabetes (T2D). Genome-wide chromatin accessibility profiling by ATAC-seq identified accessible regulatory regions that can be utilized to predict candidate enhancers related to metabolic disorders and hypothalamic development. In depth molecular, cellular, and functional experiments unveiled the responsiveness of the hPSC-derived hypothalamic neurons to hormonal stimuli, such as insulin, neuropeptides including kisspeptin, and incretin mimetic drugs such as Exendin-4, highlighting their potential utility as physiologically relevant cellular models for disease studies. In addition, differential glucose and insulin treatments uncovered adaptability within the generated ARC neurons in the dynamic regulation of POMC and insulin receptors. In summary, the establishment of this model represents a novel, chemically defined, and scalable platform for manufacturing large numbers of hypothalamic arcuate neurons and serves as a valuable resource for modeling metabolic and reproductive disorders.

Modeling inherited retinal diseases using human induced pluripotent stem cell derived photoreceptor cells and retinal pigment epithelial cells

Since the discovery of induced pluripotent stem cell (iPSC) technology, there have been many attempts to create cellular models of inherited retinal diseases (IRDs) for investigation of pathogenic processes to facilitate target discovery and validation activities. Consistency remains key in determining the utility of these findings. Despite the importance of consistency, quality control metrics are still not widely used. In this review, a toolkit for harnessing iPSC technology to generate photoreceptor, retinal pigment epithelial cell, and organoid disease models is provided. Considerations while developing iPSC-derived IRD models such as iPSC origin, reprogramming methods, quality control metrics, control strategies, and differentiation protocols are discussed. Various iPSC IRD models are dissected and the scientific hurdles of iPSC-based disease modeling are discussed to provide an overview of current methods and future directions in this field.

Illuminating Dark Chemical Matter Using the Cell Painting Assay

Screening for small-molecule modulators of disease-relevant targets and phenotypes is the first step on the way to new drugs. Large compound libraries have been synthesized by academia and, particularly, pharmaceutical companies to meet the need for novel chemical entities that are as diverse as possible. Screening of these compound libraries revealed a portion of small molecules that is inactive in more than 100 different assays and was therefore termed "dark chemical matter" (DCM). Deorphanization of DCM promises to yield very selective compounds as they are expected to have less off-target effects. We employed morphological profiling using the Cell Painting assay to detect bioactive DCM. Within the DCM collection, we identified bioactive compounds and confirmed several modulators of microtubules, DNA synthesis, and pyrimidine biosynthesis. Profiling approaches are, therefore, powerful tools to probe compound collections for bioactivity in an unbiased manner and are particularly suitable for deorphanization of DCM.

Naïve T-cell decline is a significant contributor to expression changes in ageing blood

No clear consensus has emerged from the literature on the gene expression changes that occur in human whole blood with age. In this study we compared whole blood ageing genes from the published literature with data on gene specificity for leukocyte subtypes. Surprisingly we found that highly ranked ageing genes were predominantly expressed by naïve T cells, with limited expression from more common cell types. Highly ranked ageing genes were also more likely to have decreased expression with age. Taken together, it is plausible that much of the observed gene expression changes in whole blood is reflecting the decline in abundance of naïve T cells known to occur with age, rather than changes in transcription rates in common cell types. Correct attribution of the gene expression changes that occur with age is essential for understanding the underlying mechanisms.

Exploiting Cell-Based Assays to Accelerate Drug Development for G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are relevant targets for health and disease as they regulate various aspects of metabolism, proliferation, differentiation, and immune pathways. They are implicated in several disease areas, including cancer, diabetes, cardiovascular diseases, and mental disorders. It is worth noting that about a third of all marketed drugs target GPCRs, making them prime pharmacological targets for drug discovery. Numerous functional assays have been developed to assess GPCR activity and GPCR signaling in living cells. Here, we review the current literature of genetically encoded cell-based assays to measure GPCR activation and downstream signaling at different hierarchical levels of signaling, from the receptor to transcription, via transducers, effectors, and second messengers. Singleplex assay formats provide one data point per experimental condition. Typical examples are bioluminescence resonance energy transfer (BRET) assays and protease cleavage assays (e.g., Tango or split TEV). By contrast, multiplex assay formats allow for the parallel measurement of multiple receptors and pathways and typically use molecular barcodes as transcriptional reporters in barcoded assays. This enables the efficient identification of desired on-target and on-pathway effects as well as detrimental off-target and off-pathway effects. Multiplex assays are anticipated to accelerate drug discovery for GPCRs as they provide a comprehensive and broad identification of compound effects.

Exploiting urine-derived induced pluripotent stem cells for advancing precision medicine in cell therapy, disease modeling, and drug testing

The field of regenerative medicine has witnessed remarkable advancements with the emergence of induced pluripotent stem cells (iPSCs) derived from a variety of sources. Among these, urine-derived induced pluripotent stem cells (u-iPSCs) have garnered substantial attention due to their non-invasive and patient-friendly acquisition method. This review manuscript delves into the potential and application of u-iPSCs in advancing precision medicine, particularly in the realms of drug testing, disease modeling, and cell therapy. U-iPSCs are generated through the reprogramming of somatic cells found in urine samples, offering a unique and renewable source of patient-specific pluripotent cells. Their utility in drug testing has revolutionized the pharmaceutical industry by providing personalized platforms for drug screening, toxicity assessment, and efficacy evaluation. The availability of u-iPSCs with diverse genetic backgrounds facilitates the development of tailored therapeutic approaches, minimizing adverse effects and optimizing treatment outcomes. Furthermore, u-iPSCs have demonstrated remarkable efficacy in disease modeling, allowing researchers to recapitulate patient-specific pathologies in vitro. This not only enhances our understanding of disease mechanisms but also serves as a valuable tool for drug discovery and development. In addition, u-iPSC-based disease models offer a platform for studying rare and genetically complex diseases, often underserved by traditional research methods. The versatility of u-iPSCs extends to cell therapy applications, where they hold immense promise for regenerative medicine. Their potential to differentiate into various cell types, including neurons, cardiomyocytes, and hepatocytes, enables the development of patient-specific cell replacement therapies. This personalized approach can revolutionize the treatment of degenerative diseases, organ failure, and tissue damage by minimizing immune rejection and optimizing therapeutic outcomes. However, several challenges and considerations, such as standardization of reprogramming protocols, genomic stability, and scalability, must be addressed to fully exploit u-iPSCs' potential in precision medicine. In conclusion, this review underscores the transformative impact of u-iPSCs on advancing precision medicine and highlights the future prospects and challenges in harnessing this innovative technology for improved healthcare outcomes.

Discovery of the sEH Inhibitor Epoxykynin as a Potent Kynurenine Pathway Modulator

Disease-related phenotypic assays enable unbiased discovery of novel bioactive small molecules and may provide novel insights into physiological systems and unprecedented molecular modes of action (MMOA). Herein, we report the identification and characterization of epoxykynin, a potent inhibitor of the soluble epoxide hydrolase (sEH). Epoxykynin was discovered by means of a cellular assay monitoring modulation of kynurenine (Kyn) levels in BxPC-3 cells upon stimulation with the cytokine interferon-γ (IFN-γ) and subsequent target identification employing affinity-based chemical proteomics. Increased Kyn levels are associated with immune suppression in the tumor microenvironment and, thus, the Kyn pathway and its key player indoleamine 2,3-dioxygenase 1 (IDO1) are appealing targets in immuno-oncology. However, targeting IDO1 directly has led to limited success in clinical investigations, demonstrating that alternative approaches to reduce Kyn levels are in high demand. We uncover a cross-talk between sEH and the Kyn pathway that may provide new opportunities to revert cancer-induced immune tolerance.

Perspectives on phenotypic screening-Screen Design and Assay Technology Special Interest Group

Here we offer perspectives on phenotypic screening based on a wide-ranging discussion entitled "Phenotypic screening, target ID, and multi-omics: enabling more disease relevance in early discovery?" at the Screen Design and Assay Technology Special Interest Group Meeting at the 2023 SLAS Conference. During the session, the authors shared their own experience from within their respective organizations, followed by an open discussion with the audience. It was recognized that while substantial progress has been made towards translating disease-relevant phenotypic early discovery into clinical success, there remain significant operational and scientific challenges to implementing phenotypic screening efforts, and improving translation of screening hits comes with substantial resource demands and organizational commitment. This Perspective assesses progress, highlights pitfalls, and offers possible solutions to help unlock the therapeutic potential of phenotypic drug discovery. Areas explored comprise screening and hit validation strategy, choice of cellular model, moving beyond 2D cell culture into three dimensions, and leveraging high-dimensional data sets downstream of phenotypic screens.

High-content screening identifies a small molecule that restores AP-4-dependent protein trafficking in neuronal models of AP-4-associated hereditary spastic paraplegia

Unbiased phenotypic screens in patient-relevant disease models offer the potential to detect therapeutic targets for rare diseases. In this study, we developed a high-throughput screening assay to identify molecules that correct aberrant protein trafficking in adapter protein complex 4 (AP-4) deficiency, a rare but prototypical form of childhood-onset hereditary spastic paraplegia characterized by mislocalization of the autophagy protein ATG9A. Using high-content microscopy and an automated image analysis pipeline, we screened a diversity library of 28,864 small molecules and identified a lead compound, BCH-HSP-C01, that restored ATG9A pathology in multiple disease models, including patient-derived fibroblasts and induced pluripotent stem cell-derived neurons. We used multiparametric orthogonal strategies and integrated transcriptomic and proteomic approaches to delineate potential mechanisms of action of BCH-HSP-C01. Our results define molecular regulators of intracellular ATG9A trafficking and characterize a lead compound for the treatment of AP-4 deficiency, providing important proof-of-concept data for future studies.

Particulate Matter Induced Adverse Effects on Eye Development in Zebrafish (Danio rerio) Embryos

Particulate matter (PM) can cause human diseases, particularly respiratory diseases. Since eyes are directly exposed to the air, they might be directly adversely affected by PM. Therefore, we determined the toxicity caused to eye development by PM using zebrafish (Danio rerio) embryos. The PM-induced embryo toxicity was dependent on dose and time and caused significant morphological defects, reducing the total body length and the total eye area. Reactive oxygen species (ROS) overproduction was confirmed in the PM treatment group, and antioxidant genes (cat and sod2), photoreceptor cell development, pigmentation genes (atoh8, vsx1, and rho), eye-embryogenesis genes (pax6a and pax6b), and eye-lens-development genes (cryaa) were downregulated, while eye-development genes (crybb1) were upregulated. In conclusion, PM had a direct adverse effect on the eyes, and zebrafish embryos can be used as a model to evaluate PM-induced eye toxicity in vivo.

Novel small molecules inhibit proteotoxicity and inflammation: Mechanistic and therapeutic implications for Alzheimer's Disease, healthspan and lifespan- Aging as a consequence of glycolysis

Inflammation drives many age-related, especially neurological, diseases, and likely mediates age-related proteotoxicity. For example, dementia due to Alzheimer's Disease (AD), cerebral vascular disease, many other neurodegenerative conditions is increasingly among the most devastating burdens on the American (and world) health system and threatens to bankrupt the American health system as the population ages unless effective treatments are developed. Dementia due to either AD or cerebral vascular disease, and plausibly many other neurodegenerative and even psychiatric conditions, is driven by increased age-related inflammation, which in turn appears to mediate Abeta and related proteotoxic processes. The functional significance of inflammation during aging is also supported by the fact that Humira, which is simply an antibody to the pro-inflammatory cytokine TNF-a, is the best-selling drug in the world by revenue. These observations led us to develop parallel high-throughput screens to discover small molecules which inhibit age-related Abeta proteotoxicity in a C. elegans model of AD AND LPS-induced microglial TNF-a. In the initial screen of 2560 compounds (Microsource Spectrum library) to delay Abeta proteotoxicity, the most protective compounds were, in order, phenylbutyrate, methicillin, and quetiapine, which belong to drug classes (HDAC inhibitors, beta lactam antibiotics, and tricyclic antipsychotics, respectably) already robustly implicated as promising to protect in neurodegenerative diseases, especially AD. RNAi and chemical screens indicated that the protective effects of HDAC inhibitors to reduce Abeta proteotoxicity are mediated by inhibition of HDAC2, also implicated in human AD, dependent on the HAT Creb binding protein (Cbp), which is also required for the protective effects of both dietary restriction and the daf-2 mutation (inactivation of IGF-1 signaling) during aging. In addition to methicillin, several other beta lactam antibiotics also delayed Abeta proteotoxicity and reduced microglial TNF-a. In addition to quetiapine, several other tricyclic antipsychotic drugs also delayed age-related Abeta proteotoxicity and increased microglial TNF-a, leading to the synthesis of a novel congener, GM310, which delays Abeta as well as Huntingtin proteotoxicity, inhibits LPS-induced mouse and human microglial and monocyte TNF-a, is highly concentrated in brain after oral delivery with no apparent toxicity, increases lifespan, and produces molecular responses highly similar to those produced by dietary restriction, including induction of Cbp inhibition of inhibitors of Cbp, and genes promoting a shift away from glycolysis and toward metabolism of alternate (e.g., lipid) substrates. GM310, as well as FDA-approved tricyclic congeners, prevented functional impairments and associated increase in TNF-a in a mouse model of stroke. Robust reduction of glycolysis by GM310 was functionally corroborated by flux analysis, and the glycolytic inhibitor 2-DG inhibited microglial TNF-a and other markers of inflammation, delayed Abeta proteotoxicity, and increased lifespan. These results support the value of phenotypic screens to discover drugs to treat age-related, especially neurological and even psychiatric diseases, including AD and stroke, and to clarify novel mechanisms driving neurodegeneration (e.g., increased microglial glycolysis drives neuroinflammation and subsequent neurotoxicity) suggesting novel treatments (selective inhibitors of microglial glycolysis).

High-Content Small Molecule Screen Identifies a Novel Compound That Restores AP-4-Dependent Protein Trafficking in Neuronal Models of AP-4-Associated Hereditary Spastic Paraplegia

Unbiased phenotypic screens in patient-relevant disease models offer the potential to detect novel therapeutic targets for rare diseases. In this study, we developed a high-throughput screening assay to identify molecules that correct aberrant protein trafficking in adaptor protein complex 4 (AP-4) deficiency, a rare but prototypical form of childhood-onset hereditary spastic paraplegia, characterized by mislocalization of the autophagy protein ATG9A. Using high-content microscopy and an automated image analysis pipeline, we screened a diversity library of 28,864 small molecules and identified a lead compound, C-01, that restored ATG9A pathology in multiple disease models, including patient-derived fibroblasts and induced pluripotent stem cell-derived neurons. We used multiparametric orthogonal strategies and integrated transcriptomic and proteomic approaches to delineate putative molecular targets of C-01 and potential mechanisms of action. Our results define molecular regulators of intracellular ATG9A trafficking and characterize a lead compound for the treatment of AP-4 deficiency, providing important proof-of-concept data for future Investigational New Drug (IND)-enabling studies.

Drug cytotoxicity screening using human intestinal organoids propagated with extensive cost-reduction strategies

Organoids are regarded as physiologically relevant cell models and useful for compound screening for drug development; however, their applications are currently limited because of the high cost of their culture. We previously succeeded in reducing the cost of human intestinal organoid culture using conditioned medium (CM) of L cells co-expressing Wnt3a, R-spondin1, and Noggin. Here, we further reduced the cost by replacing recombinant hepatocyte growth factor with CM. Moreover, we showed that embedding organoids in collagen gel, a more inexpensive matrix than Matrigel, maintains organoid proliferation and marker gene expression similarly when using Matrigel. The combination of these replacements also enabled the organoid-oriented monolayer cell culture. Furthermore, screening thousands of compounds using organoids expanded with the refined method identified several compounds with more selective cytotoxicity against organoid-derived cells than Caco-2 cells. The mechanism of action of one of these compounds, YC-1, was further elucidated. We showed that YC-1 induces apoptosis through the mitogen-activated protein kinase/extracellular signal-regulated kinase pathway, the mechanism of which was distinct from cell death caused by other hit compounds. Our cost-cutting methodology enables large-scale intestinal organoid culture and subsequent compound screening, which could expand the application of intestinal organoids in various research fields.

Reference compounds for characterizing cellular injury in high-content cellular morphology assays

Robust, generalizable approaches to identify compounds efficiently with undesirable mechanisms of action in complex cellular assays remain elusive. Such a process would be useful for hit triage during high-throughput screening and, ultimately, predictive toxicology during drug development. Here we generate cell painting and cellular health profiles for 218 prototypical cytotoxic and nuisance compounds in U-2 OS cells in a concentration-response format. A diversity of compounds that cause cellular damage produces bioactive cell painting morphologies, including cytoskeletal poisons, genotoxins, nonspecific electrophiles, and redox-active compounds. Further, we show that lower quality lysine acetyltransferase inhibitors and nonspecific electrophiles can be distinguished from more selective counterparts. We propose that the purposeful inclusion of cytotoxic and nuisance reference compounds such as those profiled in this resource will help with assay optimization and compound prioritization in complex cellular assays like cell painting.

The Highly Potent AhR Agonist Picoberin Modulates Hh-Dependent Osteoblast Differentiation

Identification and analysis of small molecule bioactivity in target-agnostic cellular assays and monitoring changes in phenotype followed by identification of the biological target are a powerful approach for the identification of novel bioactive chemical matter in particular when the monitored phenotype is disease-related and physiologically relevant. Profiling methods that enable the unbiased analysis of compound-perturbed states can suggest mechanisms of action or even targets for bioactive small molecules and may yield novel insights into biology. Here we report the enantioselective synthesis of natural-product-inspired 8-oxotetrahydroprotoberberines and the identification of Picoberin, a low picomolar inhibitor of Hedgehog (Hh)-induced osteoblast differentiation. Global transcriptome and proteome profiling revealed the aryl hydrocarbon receptor (AhR) as the molecular target of this compound and identified a cross talk between Hh and AhR signaling during osteoblast differentiation.

Phenotypic screening of 1,953 FDA-approved drugs reveals 26 hits with potential for repurposing for Peyronie's disease

Drug repurposing has been shown to bring safe medications to new patient populations, as recently evidenced by the COVID-19 pandemic. We investigated whether we could use phenotypic screening to repurpose drugs for the treatment of Peyronie's disease (PD). PD is a fibrotic disease characterised by continued myofibroblast presence and activity leading to formation of a plaque in the penile tunica albuginea (TA) that can cause pain during erection, erectile dysfunction, and penile deformity. PD affects 3-9% of men with treatment options limited to surgery or injection of collagenase which can only be utilised at late stages after the plaque is formed. Currently there are no approved medications that can be offered to patients presenting with early disease before the formation of the plaque. Drug repurposing may therefore be the ideal strategy to identify medical treatments to address this unmet medical need in early PD. We used primary human fibroblasts from PD patients in a phenotypic screening assay that measures TGF-β1-induced myofibroblast transformation which is the main cellular phenotype that drives the pathology in early PD. A library of FDA-approved 1,953 drugs was screened in duplicate wells at a single concentration (10 μM) in presence of TGF-β1. The myofibroblast marker α-SMA was quantified after 72h incubation. A positive control of SB-505124 (TGF-β1 receptor antagonist) was included on each plate. Hits were defined as showing >80% inhibition, whilst retaining >80% cell viability. 26 hits (1.3%) were identified which were divided into the following main groups: anti-cancer drugs, anti-inflammation, neurology, endocrinology, and imaging agents. Five of the top-ten drugs that increase myofibroblast-transformation appear to act on VEGFR. This is the first phenotypic screening of FDA-approved drugs for PD and our results suggest that it is a viable method to predict drugs with potential for repurposing to treat early PD.

Recent Advances in Cancer Drug Discovery Through the Use of Phenotypic Reporter Systems, Connectivity Mapping, and Pooled CRISPR Screening

Multi-omic approaches offer an unprecedented overview of the development, plasticity, and resistance of cancer. However, the translation from anti-cancer compounds identified in vitro to clinically active drugs have a notoriously low success rate. Here, we review how technical advances in cell culture, robotics, computational biology, and development of reporter systems have transformed drug discovery, enabling screening approaches tailored to clinically relevant functional readouts (e.g., bypassing drug resistance). Illustrating with selected examples of “success stories,” we describe the process of phenotype-based high-throughput drug screening to target malignant cells or the immune system. Second, we describe computational approaches that link transcriptomic profiling of cancers with existing pharmaceutical compounds to accelerate drug repurposing. Finally, we review how CRISPR-based screening can be applied for the discovery of mechanisms of drug resistance and sensitization. Overall, we explore how the complementary strengths of each of these approaches allow them to transform the paradigm of pre-clinical drug development.

Cellular and Engineered Organoids for Cardiovascular Models

An ensemble of in vitro cardiac tissue models has been developed over the past several decades to aid our understanding of complex cardiovascular disorders using a reductionist approach. These approaches often rely on recapitulating single or multiple clinically relevant end points in a dish indicative of the cardiac pathophysiology. The possibility to generate disease-relevant and patient-specific human induced pluripotent stem cells has further leveraged the utility of the cardiac models as screening tools at a large scale. To elucidate biological mechanisms in the cardiac models, it is critical to integrate physiological cues in form of biochemical, biophysical, and electromechanical stimuli to achieve desired tissue-like maturity for a robust phenotyping. Here, we review the latest advances in the directed stem cell differentiation approaches to derive a wide gamut of cardiovascular cell types, to allow customization in cardiac model systems, and to study diseased states in multiple cell types. We also highlight the recent progress in the development of several cardiovascular models, such as cardiac organoids, microtissues, engineered heart tissues, and microphysiological systems. We further expand our discussion on defining the context of use for the selection of currently available cardiac tissue models. Last, we discuss the limitations and challenges with the current state-of-the-art cardiac models and highlight future directions.

The Pseudo‐Natural Product Rhonin Targets RHOGDI

For the discovery of novel chemical matter generally endowed with bioactivity, strategies may be particularly efficient that combine previous insight about biological relevance, e.g., natural product (NP) structure, with methods that enable efficient coverage of chemical space, such as fragment‐based design. We describe the de novo combination of different 5‐membered NP‐derived N‐heteroatom fragments to structurally unprecedented “pseudo‐natural products” in an efficient complexity‐generating and enantioselective one‐pot synthesis sequence. The pseudo‐NPs inherit characteristic elements of NP structure but occupy areas of chemical space not covered by NP‐derived chemotypes, and may have novel biological targets. Investigation of the pseudo‐NPs in unbiased phenotypic assays and target identification led to the discovery of the first small‐molecule ligand of the RHO GDP‐dissociation inhibitor 1 (RHOGDI1), termed Rhonin. Rhonin inhibits the binding of the RHOGDI1 chaperone to GDP‐bound RHO GTPases and alters the subcellular localization of RHO GTPases.

Phenotypic drug screening in a human fibrosis model identified a novel class of antifibrotic therapeutics

Fibrogenic processes instigate fatal chronic diseases leading to organ failure and death. Underlying biological processes involve induced massive deposition of extracellular matrix (ECM) by aberrant fibroblasts. We subjected diseased primary human lung fibroblasts to an advanced three-dimensional phenotypic high-content assay and screened a repurposing drug library of small molecules for inhibiting ECM deposition. Fibrotic Pattern Detection by Artificial Intelligence identified tranilast as an effective inhibitor. Structure-activity relationship studies confirmed N-(2-butoxyphenyl)-3-(phenyl)acrylamides (N23Ps) as a novel and highly potent compound class. N23Ps suppressed myofibroblast transdifferentiation, ECM deposition, cellular contractility, and altered cell shapes, thus advocating a unique mode of action. Mechanistically, transcriptomics identified SMURF2 as a potential therapeutic target network. Antifibrotic activity of N23Ps was verified by proteomics in a human ex vivo tissue fibrosis disease model, suppressing profibrotic markers SERPINE1 and CXCL8. Conclusively, N23Ps are a novel class of highly potent compounds inhibiting organ fibrosis in patients.

Identification and Development of Therapeutics for COVID-19

After emerging in China in late 2019, the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread worldwide, and as of mid-2021, it remains a significant threat globally. Only a few coronaviruses are known to infect humans, and only two cause infections similar in severity to SARS-CoV-2: Severe acute respiratory syndrome-related coronavirus, a species closely related to SARS-CoV-2 that emerged in 2002, and Middle East respiratory syndrome-related coronavirus, which emerged in 2012. Unlike the current pandemic, previous epidemics were controlled rapidly through public health measures, but the body of research investigating severe acute respiratory syndrome and Middle East respiratory syndrome has proven valuable for identifying approaches to treating and preventing novel coronavirus disease 2019 (COVID-19). Building on this research, the medical and scientific communities have responded rapidly to the COVID-19 crisis and identified many candidate therapeutics. The approaches used to identify candidates fall into four main categories: adaptation of clinical approaches to diseases with related pathologies, adaptation based on virological properties, adaptation based on host response, and data-driven identification (ID) of candidates based on physical properties or on pharmacological compendia. To date, a small number of therapeutics have already been authorized by regulatory agencies such as the Food and Drug Administration (FDA), while most remain under investigation. The scale of the COVID-19 crisis offers a rare opportunity to collect data on the effects of candidate therapeutics. This information provides insight not only into the management of coronavirus diseases but also into the relative success of different approaches to identifying candidate therapeutics against an emerging disease. IMPORTANCE The COVID-19 pandemic is a rapidly evolving crisis. With the worldwide scientific community shifting focus onto the SARS-CoV-2 virus and COVID-19, a large number of possible pharmaceutical approaches for treatment and prevention have been proposed. What was known about each of these potential interventions evolved rapidly throughout 2020 and 2021. This fast-paced area of research provides important insight into how the ongoing pandemic can be managed and also demonstrates the power of interdisciplinary collaboration to rapidly understand a virus and match its characteristics with existing or novel pharmaceuticals. As illustrated by the continued threat of viral epidemics during the current millennium, a rapid and strategic response to emerging viral threats can save lives. In this review, we explore how different modes of identifying candidate therapeutics have borne out during COVID-19.

Designing small molecules for therapeutic success: A contemporary perspective

Successful small-molecule drug design requires a molecular target with inherent therapeutic potential and a molecule with the right properties to unlock its potential. Present-day drug design strategies have evolved to leave little room for improvement in drug-like properties. As a result, inadequate safety or efficacy associated with molecular targets now constitutes the primary cause of attrition in preclinical development through Phase II. This finding has led to a deeper focus on target selection. In this current reality, design tactics that enable rapid identification of risk-balanced clinical candidates, translation of clinical experience into meaningful differentiation strategies, and expansion of the druggable proteome represent significant levers by which drug designers can accelerate the discovery of the next generation of medicines.

Applying Machine Learning to Stem Cell Culture and Differentiation

Machine learning techniques are increasingly becoming incorporated into biological research workflows in a variety of disciplines, most notably cancer research and drug discovery. Efforts in stem cell research comparatively lag behind. We detail key paradigms in machine learning, with a focus on equipping stem cell biologists with the understanding necessary to begin conceptualizing and designing machine learning workflows within their own domain of expertise. Supervised approaches in both regression and classification as well as unsupervised clustering techniques are all covered, with examples from across the biological sciences. High-throughput, high-content, multiplex assays for data acquisition are also discussed in the form of single-cell RNA sequencing and image-based approaches. Lastly, potential applications in stem cell biology, including the development of novel cell types, and improving model maturation are also discussed. Machine learning approaches applied in stem cell biology show promise in accelerating progress in developmental biology, drug screening, disease modeling, and personalized medicine. © 2021 Wiley Periodicals LLC.

Synthetic gene circuits as tools for drug discovery

Within mammalian systems, there exists enormous opportunity to use synthetic gene circuits to enhance phenotype-based drug discovery, to map the molecular origins of disease, and to validate therapeutics in complex cellular systems. While drug discovery has relied on marker staining and high-content imaging in cell-based assays, synthetic gene circuits expand the potential for precision and speed. Here we present a vision of how circuits can improve the speed and accuracy of drug discovery by enhancing the efficiency of hit triage, capturing disease-relevant dynamics in cell-based assays, and simplifying validation and readouts from organoids and microphysiological systems (MPS). By tracking events and cellular states across multiple length and time scales, circuits will transform how we decipher the causal link between molecular events and phenotypes to improve the selectivity and sensitivity of cell-based assays.

Establishment of live-cell-based coupled assay system for identification of compounds to modulate metabolic activities of cells

In this study, we present a live-cell-based fluorometric coupled assay system to identify the compounds that can regulate the targeted metabolic pathways in live cells. The assay is established through targeting specific metabolic pathways and using "input" and "output" metabolite pairs. The changes in the extracellular output that are generated and released into the extracellular media from the input are assessed as the activity of the pathway. The screening for the glycolytic pathway and amino acid metabolism reveals the activities of the present drugs, 6-BIO and regorafenib, that regulate the metabolic fate of tumor cells.

Application of a High-Content Screening Assay Utilizing Primary Human Lung Fibroblasts to Identify Antifibrotic Drugs for Rapid Repurposing in COVID-19 Patients

Lung imaging and autopsy reports among COVID-19 patients show elevated lung scarring (fibrosis). Early data from COVID-19 patients as well as previous studies from severe acute respiratory syndrome, Middle East respiratory syndrome, and other respiratory disorders show that the extent of lung fibrosis is associated with a higher mortality, prolonged ventilator dependence, and poorer long-term health prognosis. Current treatments to halt or reverse lung fibrosis are limited; thus, the rapid development of effective antifibrotic therapies is a major global medical need that will continue far beyond the current COVID-19 pandemic. Reproducible fibrosis screening assays with high signal-to-noise ratios and disease-relevant readouts such as extracellular matrix (ECM) deposition (the hallmark of fibrosis) are integral to any antifibrotic therapeutic development. Therefore, we have established an automated high-throughput and high-content primary screening assay measuring transforming growth factor-β (TGFβ)-induced ECM deposition from primary human lung fibroblasts in a 384-well format. This assay combines longitudinal live cell imaging with multiparametric high-content analysis of ECM deposition. Using this assay, we have screened a library of 2743 small molecules representing approved drugs and late-stage clinical candidates. Confirmed hits were subsequently profiled through a suite of secondary lung fibroblast phenotypic screening assays quantifying cell differentiation, proliferation, migration, and apoptosis. In silico target prediction and pathway network analysis were applied to the confirmed hits. We anticipate this suite of assays and data analysis tools will aid the identification of new treatments to mitigate against lung fibrosis associated with COVID-19 and other fibrotic diseases.

Human amyotrophic lateral sclerosis excitability phenotype screen: Target discovery and validation

Drug development is hampered by poor target selection. Phenotypic screens using neurons differentiated from patient stem cells offer the possibility to validate known and discover novel disease targets in an unbiased fashion. To identify targets for managing hyperexcitability, a pathological feature of amyotrophic lateral sclerosis (ALS), we design a multi-step screening funnel using patient-derived motor neurons. High-content live cell imaging is used to evaluate neuronal excitability, and from a screen against a chemogenomic library of 2,899 target-annotated compounds, 67 reduce the hyperexcitability of ALS motor neurons carrying the SOD1(A4V) mutation, without cytotoxicity. Bioinformatic deconvolution identifies 13 targets that modulate motor neuron excitability, including two known ALS excitability modulators, AMPA receptors and Kv7.2/3 ion channels, constituting target validation. We also identify D2 dopamine receptors as modulators of ALS motor neuron excitability. This screen demonstrates the power of human disease cell-based phenotypic screens for identifying clinically relevant targets for neurological disorders.

Motor neuron hyperexcitability is observed in both ALS patients and their iPSC-derived neurons. Combining a high-content live imaging excitability phenotypic assay, high-throughput screening against a cross-annotated chemogenomic library, and bioinformatic enrichment analysis, Huang et al. identify targets modulating the hyperexcitability of ALS patient-derived motor neurons in an unbiased manner.

Human Induced Pluripotent Stem Cell-Based Modelling of Spinocerebellar Ataxias

Abstract

Dominant spinocerebellar ataxias (SCAs) constitute a large group of phenotypically and genetically heterogeneous disorders that mainly present with dysfunction of the cerebellum as their main hallmark. Although animal and cell models have been highly instrumental for our current insight into the underlying disease mechanisms of these neurodegenerative disorders, they do not offer the full human genetic and physiological context. The advent of human induced pluripotent stem cells (hiPSCs) and protocols to differentiate these into essentially every cell type allows us to closely model SCAs in a human context. In this review, we systematically summarize recent findings from studies using hiPSC-based modelling of SCAs, and discuss what knowledge has been gained from these studies. We conclude that hiPSC-based models are a powerful tool for modelling SCAs as they contributed to new mechanistic insights and have the potential to serve the development of genetic therapies. However, the use of standardized methods and multiple clones of isogenic lines are essential to increase validity and reproducibility of the insights gained.

Graphical Abstract

The European Federation for Medicinal Chemistry and Chemical Biology (EFMC) Best Practice Initiative: Phenotypic Drug Discovery

Phenotypic drug discovery has a long track record of delivering innovative drugs and has received renewed attention in the last few years. The promise of this approach, however, comes with several challenges that should be addressed to avoid wasting time and resources on drugs with undesired modes of action or, worse, false-positive hits. In this set of best practices, we go over the essential steps of phenotypic drug discovery and provide guidance on how to increase the chance of success in identifying validated and relevant chemical starting points for optimization: selecting the right assay, selecting the right compound screening library and developing appropriate hit validation assays. Then, we highlight the importance of initiating studies to determine the mode of action of the identified hits early and present the current state of the art.

The right tools for the job: the central role for next generation chemical probes and chemistry-based target deconvolution methods in phenotypic drug discovery

The reconnection of the scientific community with phenotypic drug discovery has created exciting new possibilities to develop therapies for diseases with highly complex biology. It promises to revolutionise fields such as neurodegenerative disease and regenerative medicine, where the development of new drugs has consistently proved elusive. Arguably, the greatest challenge in readopting the phenotypic drug discovery approach exists in establishing a crucial chain of translatability between phenotype and benefit to patients in the clinic. This remains a key stumbling block for the field which needs to be overcome in order to fully realise the potential of phenotypic drug discovery. Excellent quality chemical probes and chemistry-based target deconvolution techniques will be a crucial part of this process. In this review, we discuss the current capabilities of chemical probes and chemistry-based target deconvolution methods and evaluate the next advances necessary in order to fully support phenotypic screening approaches in drug discovery.

Nuisance compounds in cellular assays

Compounds that exhibit assay interference or undesirable mechanisms of bioactivity ("nuisance compounds") are routinely encountered in cellular assays, including phenotypic and high-content screening assays. Much is known regarding compound-dependent assay interferences in cell-free assays. However, despite the essential role of cellular assays in chemical biology and drug discovery, there is considerably less known about nuisance compounds in more complex cell-based assays. In our view, a major obstacle to realizing the full potential of chemical biology will not just be difficult-to-drug targets or even the sheer number of targets, but rather nuisance compounds, due to their ability to waste significant resources and erode scientific trust. In this review, we summarize our collective academic, government, and industry experiences regarding cellular nuisance compounds. We describe assay design strategies to mitigate the impact of nuisance compounds and suggest best practices to efficiently address these compounds in complex biological settings.

Phenotypic screening with target identification and validation in the discovery and development of E3 ligase modulators

The use of phenotypic screening was central to the discovery and development of novel thalidomide analogs, the IMiDs (immunomodulatory drugs) agents. With the discovery that these agents bind the E3 ligase, CRL4^CRBN, and alter its substrate specificity, there has been a great deal of endeavor to discover other small molecules that can modulate alternative E3 ligases. Furthermore, the chemical properties necessary for drug discovery and the rules by which neo-substrates are selected for degradation are being defined in the context of phenotypic alterations in specific cellular systems. This review gives a detailed summary of these recent advances and the methodologies being exploited to understand the mechanism of action of emerging protein degradation therapies.

Chemoproteomic-enabled phenotypic screening

The ID of disease-modifying, chemically accessible targets remains a central priority of modern therapeutic discovery. The phenotypic screening of small-molecule libraries not only represents an attractive approach to identify compounds that may serve as drug leads but also serves as an opportunity to uncover compounds with novel mechanisms of action (MoAs). However, a major bottleneck of phenotypic screens continues to be the ID of pharmacologically relevant target(s) for compounds of interest. The field of chemoproteomics aims to map proteome-wide small-molecule interactions in complex, native systems, and has proved a key technology to unravel the protein targets of pharmacological modulators. In this review, we discuss the application of modern chemoproteomic methods to identify protein targets of phenotypic screening hits and investigate MoAs, with a specific focus on the development of chemoproteomic-enabled compound libraries to streamline target discovery.

The future of phenotypic drug discovery

Phenotypic drug discovery (PDD) uses biological systems directly for new drug screening. While PDD has proved effective in the discovery of drugs with novel mechanisms, for broader adoption, key challenges need resolution: progression of poorly qualified leads and overloaded pipelines due to lack of effective tools to process and prioritize hits; and advancement of leads with undesirable mechanisms that fail at more expensive stages of discovery. Here I discuss how human-based phenotypic platforms are being applied throughout the discovery process for hit triage and prioritization, for elimination of hits with unsuitable mechanisms, and for supporting clinical strategies through pathway-based decision frameworks. Harnessing the data generated in these platforms can also fuel a deeper understanding of drug efficacy and toxicity mechanisms. As these approaches increase in use, they will gain in power for driving better decisions, generating better leads faster and in turn promoting greater adoption of PDD.

Image-based profiling for drug discovery: due for a machine-learning upgrade?

Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug's activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.

Use of artificial intelligence to enhance phenotypic drug discovery

Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.

Selecting Approaches for Hit Identification and Increasing Options by Building the Efficient Discovery of Actionable Chemical Matter from DNA-Encoded Libraries

Over the past 20 years, the toolbox for discovering small-molecule therapeutic starting points has expanded considerably. Pharmaceutical researchers can now choose from technologies that, in addition to traditional high-throughput knowledge-based and diversity screening, now include the screening of fragment and fragment-like libraries, affinity selection mass spectrometry, and selection against DNA-encoded libraries (DELs). Each of these techniques has its own unique combination of advantages and limitations that makes them more, or less, suitable for different target classes or discovery objectives, such as desired mechanism of action. Layered on top of this are the constraints of the drug-hunters themselves, including budgets, timelines, and available platform capacity; each of these can play a part in dictating the hit identification strategy for a discovery program. In this article, we discuss some of the factors that we use to govern our building of a hit identification roadmap for a program and describe the increasing role that DELs are playing in our discovery strategy. Furthermore, we share our learning during our initial exploration of DEL and highlight the approaches we have evolved to maximize the value returned from DEL selections. Topics addressed include the optimization of library design and production, reagent validation, data analysis, and hit confirmation. We describe how our thinking in these areas has led us to build a DEL platform that has begun to deliver tractable matter to our global discovery portfolio.

Improving target assessment in biomedical research: the GOT-IT recommendations

Academic research plays a key role in identifying new drug targets, including understanding target biology and links between targets and disease states. To lead to new drugs, however, research must progress from purely academic exploration to the initiation of efforts to identify and test a drug candidate in clinical trials, which are typically conducted by the biopharma industry. This transition can be facilitated by a timely focus on target assessment aspects such as target-related safety issues, druggability and assayability, as well as the potential for target modulation to achieve differentiation from established therapies. Here, we present recommendations from the GOT-IT working group, which have been designed to support academic scientists and funders of translational research in identifying and prioritizing target assessment activities and in defining a critical path to reach scientific goals as well as goals related to licensing, partnering with industry or initiating clinical development programmes. Based on sets of guiding questions for different areas of target assessment, the GOT-IT framework is intended to stimulate academic scientists' awareness of factors that make translational research more robust and efficient, and to facilitate academia-industry collaboration.

Early preclinical screening using zebrafish (Danio rerio) reveals the safety of the candidate anti-inflammatory therapeutic agent TnP

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TnP has been indicated for chronic inflammatory diseases, multiple sclerosis.

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Zebrafish is an alternative animal model for preclinical drug development.

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Preclinical toxicology studies have shown that TnP has a wide therapeutic index range from 1 nM to 10 μM.

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TnP did not induce cardiotoxic effect or cardiac dysfunction.

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TnP crossed the blood-brain barrier without causing neurotoxicity.

The patented anti-inflammatory peptide TnP had its effectiveness recently confirmed in vivo in a murine model of multiple sclerosis and asthma. In this work, the safety of the TnP was evaluated in investigative toxicology tests using zebrafish (Danio rerio) as a model. We conducted the OECD #236 test to investigate effects of the TnP on the survival, hatching performance, and morphological formation of zebrafish embryos. After determining these endpoints, morphometric analysis termination of locomotion eartbeat rate in zebrafish larvae were evaluated to identify adverse effects such as neurotoxicity and cardiotoxicity. The results highlight a wide therapeutic index for TnP with non-lethal and safe doses rom 1 nM to 10 μM, without causing neurotoxicity or cardiotoxic effect. The low frequencyf abnormalities by TnP was associated with high safety of the molecule and the developing embryo's ability to process and eliminate it. TnP crossed the blood-brain barrier without disturbing the normal architecture of forebrain, midbrain and hindbrain. Our data reinforce the importance of zebrafish as an accurate investigative toxicology model to assess acute toxicity as well as cardiotoxicity and neurotoxicity of molecules in the preclinical phase of development.

The Resurrection of Phenotypic Drug Discovery

Prior to genetic mapping, the majority of drug discovery efforts involved phenotypic screening, wherein compounds were screened in either in vitro or in vivo models thought to mimic the disease state of interest. While never completely abandoning phenotypic approaches, the labor intensive nature of such tests encouraged the pharmaceutical industry to move away from them in favor of target-based drug discovery, which facilitated throughput and allowed for the efficient screening of large numbers of compounds. However, a consequence of reliance on target-based screening was an increased number of failures in clinical trials due to poor correlation between novel mechanistic targets and the actual disease state. As a result, the field has seen a recent resurrection in phenotypic drug discovery approaches. In this work, we highlight some recent phenotypic projects from our industrial past and in our current academic drug discovery environment that have provided encouraging results.

Photoaffinity Labeling and Quantitative Chemical Proteomics Identify LXRβ as the Functional Target of Enhancers of Astrocytic apoE

Utilizing a phenotypic screen, we identified chemical matter that increased astrocytic apoE secretion in vitro. We designed a clickable photoaffinity probe based on a pyrrolidine lead compound and carried out probe-based quantitative chemical proteomics in human astrocytoma CCF-STTG1 cells to identify liver x receptor β (LXRβ) as the target. Binding of the small molecule ligand stabilized LXRβ, as shown by cellular thermal shift assay (CETSA). In addition, we identified a probe-modified peptide by mass spectrometry and proposed a model where the photoaffinity probe is bound in the ligand-binding pocket of LXRβ. Taken together, our findings demonstrated that the lead chemical matter bound directly to LXRβ, and our results highlight the power of chemical proteomic approaches to identify the target of a phenotypic screening hit. Additionally, the LXR photoaffinity probe and lead compound described herein may serve as valuable tools to further evaluate the LXR pathway.

Recent advances in phenotypic drug discovery

There is a great need for innovative new medicines to treat unmet medical needs. The discovery and development of innovative new medicines is extremely difficult, costly, and inefficient. In the last decade, phenotypic drug discovery (PDD) was reintroduced as a strategy to provide first-in-class medicines. PDD uses empirical, target-agnostic lead generation to identify pharmacologically active molecules and novel therapeutics which work through unprecedented drug mechanisms. The economic and scientific value of PDD is exemplified through game-changing medicines for hepatitis C virus, spinal muscular atrophy, and cystic fibrosis. In this short review, recent advances are noted for the implementation and de-risking of PDD (for compound library selection, biomarker development, mechanism identification, and safety studies) and the potential for artificial intelligence. A significant barrier in the decision to implement PDD is balancing the potential impact of a novel mechanism of drug action with an under-defined scientific path forward, with the desire to provide infrastructure and metrics to optimize return on investment, which a known mechanism provides. A means to address this knowledge gap in the future is to empower precompetitive research utilizing the empirical concepts of PDD to identify new mechanisms and pharmacologically active compounds.

Phenotypic Screening in C. elegans as a Tool for the Discovery of New Geroprotective Drugs

Population aging is one of the largest challenges of the 21st century. As more people live to advanced ages, the prevalence of age-related diseases and disabilities will increase placing an ever larger burden on our healthcare system. A potential solution to this conundrum is to develop treatments that prevent, delay or reduce the severity of age-related diseases by decreasing the rate of the aging process. This ambition has been accomplished in model organisms through dietary, genetic and pharmacological interventions. The pharmacological approaches hold the greatest opportunity for successful translation to the clinic. The discovery of such pharmacological interventions in aging requires high-throughput screening strategies. However, the majority of screens performed for geroprotective drugs in C. elegans so far are rather low throughput. Therefore, the development of high-throughput screening strategies is of utmost importance.

Endpoint in ovarian cancer xenograft model predicted by nighttime motion metrics

Despite several therapeutics showing promise in nonclinical studies, survival from ovarian cancer remains poor. New technologies are urgently needed to optimize the translation of nonclinical studies into clinical successes. While most nonclinical settings utilize subjective measures of physiological parameters, which can hamper the accuracy of the results, this study assessed the physical activity of mice in real time using an objective, non-invasive, cloud-based, digital vivarium monitoring platform. An initial range-finding study in which varying numbers of ovarian cancer cells were inoculated in mice was conducted to characterize disease progression using digital metrics such as motion and breathing rate. Data from the range-finding study were used to establish a motion threshold (MT) that might predict terminal endpoint. Using the MT, the efficacies of cisplatin and OS2966, an anti-CD29 antibody, were assessed. Results showed that MT predicted terminal endpoint significantly earlier than traditional parameters and correlated with therapeutic efficacy. Thus, continuous motion monitoring sensitively predicts terminal endpoint in nonclinical ovarian cancer models and could be applicable for drug efficacy testing.