Phenotypic screening of the ReFRAME drug repurposing library to discover new drugs for treating sickle cell disease.

Stem cell transplantation and genetic therapies offer potential cures for patients with sickle cell disease (SCD), but these options require advanced medical facilities and are expensive. Consequently, these treatments will not be available for many years to the majority of patients suffering from this disease. What is urgently needed now is an inexpensive oral drug in addition to hydroxyurea, the only drug approved by the FDA that inhibits sickle-hemoglobin polymerization. Here, we report the results of the first phase of our phenotypic screen of the 12,657 compounds of the Scripps ReFRAME drug repurposing library using a recently developed high-throughput assay to measure sickling times following deoxygenation to 0% oxygen of red cells from sickle trait individuals. The ReFRAME library is a very important collection because the compounds are either FDA-approved drugs or have been tested in clinical trials. From dose-response measurements, 106 of the 12,657 compounds exhibit statistically significant antisickling at concentrations ranging from 31 nM to 10 µM. Compounds that inhibit sickling of trait cells are also effective with SCD cells. As many as 21 of the 106 antisickling compounds emerge as potential drugs. This estimate is based on a comparison of inhibitory concentrations with free concentrations of oral drugs in human serum. Moreover, the expected therapeutic potential for each level of inhibition can be predicted from measurements of sickling times for cells from individuals with sickle syndromes of varying severity. Our results should motivate others to develop one or more of these 106 compounds into drugs for treating SCD.

Multipathway regulation induced by 4-(phenylsulfonyl)morpholine derivatives against triple-negative breast cancer.

Phenotypic drug discovery (PDD) is an effective drug discovery approach by observation of therapeutic effects on disease phenotypes, especially in complex disease systems. Triple-negative breast cancer (TNBC) is composed of several complex disease features, including high tumor heterogeneity, high invasive and metastatic potential, and a lack of effective therapeutic targets. Therefore, identifying effective and novel agents through PDD is a current trend in TNBC drug development. In this study, 23 novel small molecules were synthesized using 4-(phenylsulfonyl)morpholine as a pharmacophore. Among these derivatives, GL24 (4m) exhibited the lowest half-maximal inhibitory concentration value (0.90 µM) in MDA-MB-231 cells. To investigate the tumor-suppressive mechanisms of GL24, transcriptomic analyses were used to detect the perturbation for gene expression upon GL24 treatment. Followed by gene ontology (GO) analysis, gene set enrichment analysis (GSEA), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, multiple ER stress-dependent tumor suppressive signals were identified, such as unfolded protein response (UPR), p53 pathway, G2/M checkpoint, and E2F targets. Most of the identified pathways triggered by GL24 eventually led to cell-cycle arrest and then to apoptosis. In summary, we developed a novel 4-(phenylsulfonyl)morpholine derivative GL24 with a strong potential for inhibiting TNBC cell growth through ER stress-dependent tumor suppressive signals.

[Phenotypic drug discovery promotes research and development of innovative drugs based on traditional Chinese medicine].

Traditional Chinese medicine with rich resources in China and definite therapeutic effects on complex diseases demonstrates great development potential. However, the complex composition, the unclear pharmacodynamic substances and mechanisms of action, and the lack of reasonable methods for evaluating clinical safety and efficacy have limited the research and development of innovative drugs based on traditional Chinese medicine. The progress in cutting-edge disciplines such as artificial intelligence and biomimetics, especially the emergence of cell painting and organ-on-a-chip, helps to identify and characterize the active ingredients in traditional Chinese medicine based on the changes in model characteristics, thus providing more accurate guidance for the development and application of traditional Chinese medicine. The application of phenotypic drug discovery in the research and development of innovative drugs based on traditional Chinese medicine is gaining increasing attention. In recent years, the technology for phenotypic drug discovery keeps advancing, which improves the early discovery rate of new drugs and the success rate of drug research and development. Accordingly, phenotypic drug discovery gradually becomes a key tool for the research on new drugs. This paper discusses the enormous potential of traditional Chinese medicine in the discovery and development of innovative drugs and illustrates how the application of phenotypic drug discovery, supported by cutting-edge technologies such as cell painting, deep learning, and organ-on-a-chip, propels traditional Chinese medicine into a new stage of development.

Early Pharmacological Profiling of Antiproliferative Compounds by Live Cell Imaging.

Natural products represent an excellent source of unprecedented anticancer compounds. However, the identification of the mechanism of action remains a major challenge. Several techniques and methodologies have been considered, but with limited success. In this work, we explored the combination of live cell imaging and machine learning techniques as a promising tool to depict in a fast and affordable test the mode of action of natural compounds with antiproliferative activity. To develop the model, we selected the non-small cell lung cancer cell line SW1573, which was exposed to the known antimitotic drugs paclitaxel, colchicine and vinblastine. The novelty of our methodology focuses on two main features with the highest relevance, (a) meaningful phenotypic metrics, and (b) fast Fourier transform (FFT) of the time series of the phenotypic parameters into their corresponding amplitudes and phases. The resulting algorithm was able to cluster the microtubule disruptors, and meanwhile showed a negative correlation between paclitaxel and the other treatments. The FFT approach was able to group the samples as efficiently as checking by eye. This methodology could easily scale to group a large amount of data without visual supervision.

Mass spectrometry-based Cellular Thermal Shift Assay (CETSA®) for target deconvolution in phenotypic drug discovery.

The recent renewed interest in phenotypic drug discovery has concomitantly put a focus on target deconvolution in order to achieve drug-target identification. Even though there are prescribed therapies whose mode of action is not fully understood, knowledge of the primary target will inevitably facilitate the discovery and translation of efficacy from bench to bedside. Elucidating targets and subsequent pathways engaged will also facilitate safety studies and overall development of novel drug candidates. Today, there are several techniques available for identifying the primary target, many of which rely on mass spectrometry (MS) to identify compound - target protein interactions. The Cellular Thermal Shift Assay (CETSA®) is well suited for identifying target engagement between ligands and their protein targets. Several studies have shown that CETSA combined with MS is a powerful technique that allows unlabeled target deconvolution in complex samples such as intact cells and tissues in addition to cell lysates and other protein suspensions. The applicability of CETSA MS for target deconvolution purposes will be discussed and exemplified in this mini review.

A phenotypic drug discovery study on thienodiazepine derivatives as inhibitors of T cell proliferation induced by CD28 co-stimulation leads to the discovery of a first bromodomain inhibitor.

A phenotypic screening of thienodiazepines derived from a hit compound found through a binding assay targeting co-stimulatory molecules on T cells and antigen presenting cells successfully led to the discovery of a thienotriazolodiazepine compound (7f) possessing potent immunosuppressive activity. A chemical biology approach has succeeded in revealing that 7f is a first inhibitor of epigenetic bromodomain-containing proteins. 7f is expected to become an anti-cancer agent as well as an immunosuppressive agent.

Identification of 4-amino-2-Pyridones as new potent PCSK9 inhibitors: From phenotypic hit discovery to in vivo tolerability.

Among the strategies to overcome the underperformance of statins in cardiovascular diseases (CVDs), the development of drugs targeting the Proprotein Convertase Subtilisin-like Kexin type 9 (PCSK9) is considered one of the most promising. However, only anti-PCSK9 biological drugs have been approved to date, and orally available small-molecules for the treatment of hypercholesterolemic conditions are still missing on the market. In the present work, we describe the application of a phenotypic approach to the identification and optimization of 4-amino-2-pyridone derivatives as a new chemotype with anti-PCSK9 activity. Starting from an in-house collection of compounds, functional assays on HepG2 cells followed by a chemistry-driven hit optimization campaign, led to the potent anti-PCSK9 candidate 5c. This compound, at 5 µM, totally blocked PCSK9 secretion from HepG2 cells, significantly increased LDL receptor (LDLR) expression, and acted cooperatively with simvastatin by reducing its induction of PCSK9 expression. Finally, compound 5c also proved to be well tolerated in C57BL/6J mice at the tested concentration (40 mg/kg) with no sign of toxicity or behavior modifications.

AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery.

Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.

Phenotypic drug discovery: a case for thymosin alpha-1.

Phenotypic drug discovery (PDD) involves screening compounds for their effects on cells, tissues, or whole organisms without necessarily understanding the underlying molecular targets. PDD differs from target-based strategies as it does not require knowledge of a specific drug target or its role in the disease. This approach can lead to the discovery of drugs with unexpected therapeutic effects or applications and allows for the identification of drugs based on their functional effects, rather than through a predefined target-based approach. Ultimately, disease definitions are mostly symptom-based rather than mechanism-based, and the therapeutics should be likewise. In recent years, there has been a renewed interest in PDD due to its potential to address the complexity of human diseases, including the holistic picture of multiple metabolites engaging with multiple targets constituting the central hub of the metabolic host-microbe interactions. Although PDD presents challenges such as hit validation and target deconvolution, significant achievements have been reached in the era of big data. This article explores the experiences of researchers testing the effect of a thymic peptide hormone, thymosin alpha-1, in preclinical and clinical settings and discuss how its therapeutic utility in the precision medicine era can be accommodated within the PDD framework.

Molecular Mechanisms of MmpL3 Function and Inhibition.

Mycobacteria species include a large number of pathogenic organisms such as Mycobacterium tuberculosis, Mycobacterium leprae, and various non-tuberculous mycobacteria. Mycobacterial membrane protein large 3 (MmpL3) is an essential mycolic acid and lipid transporter required for growth and cell viability. In the last decade, numerous studies have characterized MmpL3 with respect to protein function, localization, regulation, and substrate/inhibitor interactions. This review summarizes new findings in the field and seeks to assess future areas of research in our rapidly expanding understanding of MmpL3 as a drug target. An atlas of known MmpL3 mutations that provide resistance to inhibitors is presented, which maps amino acid substitutions to specific structural domains of MmpL3. In addition, chemical features of distinct classes of Mmpl3 inhibitors are compared to provide insights into shared and unique features of varied MmpL3 inhibitors.

A Robust Phenotypic Screening Assay Utilizing Human Podocytes to Identify Agents that Modulate Lipid Droplets.

Lipid droplets (LDs), initially thought to be mere lipid storage structures, are highly dynamic organelles with complex functions that control cell fate and behavior. In recent years, their relevance as therapeutic targets for a wide array of human diseases has been well established. Consequently, efforts to develop tools to study them have intensified, including assays that can accurately track LD levels in clinically relevant cell-based models. We previously reported that LD accumulation destines podocytes for lipotoxicity and cell death in renal diseases of metabolic and nonmetabolic origin. We also showed that LD accumulation in those cells serves as both a marker for disease progression and as a therapeutic target. Here, we describe a robust phenotypic screening method, using differentiated human podocytes, for identifying small-molecule compounds that rescue podocytes from LD accumulation and lipotoxicity under cellular stress. Major assay advances include 1) the use of a solvatochromic dye to improve LD staining, reduce background noise, and improve detection accuracy, 2) use of confocal imaging to reduce vertical overlap of LDs and enable accurate counting, 3) combining membrane and cytoskeleton stains to improve cell segmentation in confocal mode, and 4) use of an optimized spot detection algorithm that requires minimal configuration per individual run. The assay is robust and yields a Z-factor that is consistently >0.5.

Deep learning in image-based phenotypic drug discovery.

Modern drug discovery approaches often use high-content imaging to systematically study the effect on cells of large libraries of chemical compounds. By automatically screening thousands or millions of images to identify specific drug-induced cellular phenotypes, for example, altered cellular morphology, these approaches can reveal 'hit' compounds offering therapeutic promise. In the past few years, artificial intelligence (AI) methods based on deep learning (DL) [a family of machine learning (ML) techniques] have disrupted virtually all image analysis tasks, from image classification to segmentation. These powerful methods also promise to impact drug discovery by accelerating the identification of effective drugs and their modes of action. In this review, we highlight applications and adaptations of ML, especially DL methods for cell-based phenotypic drug discovery (PDD).

C. elegans as an in vivo model system for the phenotypic drug discovery for treating paraquat poisoning.

BACKGROUND: Paraquat (PQ) is an effective and widely used herbicide and causes numerous fatalities by accidental or voluntary ingestion. However, neither the final cytotoxic mechanism nor effective treatments for PQ poisoning have been discovered. Phenotypic drug discovery (PDD), which does not rely on the molecular mechanism of the diseases, is having a renaissance in recent years owing to its potential to address the incompletely understood complexity of diseases. Herein, the C. elegans PDD model was established to pave the way for the future phenotypic discovery of potential agents for treating PQ poisoning. METHODS: C. elegans were treated with PQ-containing solid medium followed by statistical analysis of worm survival, pharyngeal pumping, and movement ability. Furthermore, coenzyme Q10 (CoQ10) was used to test the C. elegans model of PQ poisoning by measuring the levels of reactive oxygen species (ROS) and malondialdehyde (MDA), mitochondrial morphology, and worm survival rate. Additionally, we used the classic mice model of PQ intoxication to evaluate the validity of the C. elegans model of PQ poisoning by measuring the effect of CoQ10 as a potential antidote for PQ poisoning. RESULTS: In the C. elegans model of PQ poisoning, 5 mg/mL PQ increased the levels of ROS, MDA content, mitochondrial fragments, which significantly shortened the lifespan, while CoQ10 alleviated these phenotypes. In the mice model of PQ poisoning, CoQ10 increased the chance of survival in PQ poisoned mice while reducing ROS, MDA content in lung tissue and inhibiting PQ-induced lung edema. Moreover, CoQ10 alleviated the lung morphopathological changes induced by PQ. CONCLUSION: Here we established a C. elegans model of PQ poisoning, whose validity was confirmed by the classic mice model of PQ intoxication.

Evidence for anti-inflammatory effects and modulation of neurotransmitter metabolism by Salvia officinalis L.

BACKGROUND: Cognitive health is of great interest to society, with neuroinflammation and systemic inflammation age-related risk factors that are linked to declines in cognitive performance. Several botanical ingredients have been suggested to have benefits in this area including Salvia officinalis (sage), which has shown anti-inflammatory effects and exhibited promising cognitive improvements in multiple human studies. The current study demonstrates anti-inflammatory effects for S. officinalis across a broad set of in vitro models in human cells, and adds further evidence to support modulation of acetylcholine and monoamine neurostransmitter levels as mechanisms that contribute towards the benefits of the herb on cognitive health. METHODS: The effect of S. officinalis extract on release of multiple cytokines and chemokines was measured in human primary intestinal epithelial cells treated with or without LPS stimulation, and Blood Brain Barrier (BBB) cells in presence or absence of recombinant IL-17A and/or Human IL-17RA/IL-17R Antibody. Antioxidant effects were also assessed in BBB cells incubated with the extract and H2O2. The anti-inflammatory effects of S. officinalis extract were further assessed based on clinically-relevant biomarker readouts across 12 human primary cell-based disease models of the BioMAP Diversity PLUS panel. RESULTS: S. officinalis showed significant attenuation of the release of most cytokines/chemokines into apical media in LPS-stimulated intestinal cells, but small increases in the release of markers including IL-6, IL-8 in basolateral media; where TNF-α was the only marker to be significantly reduced. S. officinalis attenuated the release of CRP and VCAM-1 from BBB cells under IL-17A induced conditions, and also decreased H2O2 induced ROS overproduction in these cells. Phenotypic profiling with the BioMAP Diversity PLUS Panel identified additional anti-inflammatory mediators, and based on a similarity search analysis suggested potential mechanistic similarity to caffeic acid and drugs known to inhibit COMT and MAO activity to modulate monoamine metabolism. Subsequent in vitro assessment showed that S. officinalis was able to inhibit the activity of these same enzymes. CONCLUSIONS: S. officinalis extract showed anti-inflammatory effects across multiple human cell lines, which could potentially reduce peripheral inflammation and support cognitive health. S. officinalis extract also showed the ability to inhibit enzymes related to the metabolism of monoamine neurotransmitters, suggesting possible dopaminergic and serotonergic effects acting alongside proposed cholinergic effects to mediate acute cognitive performance benefits previously demonstrated for the extract.

A High-Throughput Assay for the Pancreatic Islet Beta-Cell Potassium Channel: Use in the Pharmacological Characterization of Insulin Secretagogues Identified from Phenotypic Screening.

Phenotypic screening is a neoclassical approach for drug discovery. We conducted phenotypic screening for insulin secretion enhancing agents using INS-1E insulinoma cells as a model system for pancreatic beta-cells. A principal regulator of insulin secretion in beta-cells is the metabolically regulated potassium channel Kir6.2/SUR1 complex. To characterize hit compounds, we developed an assay to quantify endogenous potassium channel activity in INS-1E cells. We quantified ligand-regulated potassium channel activity in INS-1E cells using fluorescence imaging and thallium flux. Potassium channel activity was metabolically regulated and coupled to insulin secretion. The pharmacology of channel opening agents (diazoxide) and closing agents (sulfonylureas) was used to validate the applicability of the assay. A precise high-throughput assay was enabled, and phenotypic screening hits were triaged to enable a higher likelihood of discovering chemical matter with novel and useful mechanisms of action.

Development of a chemogenomics library for phenotypic screening.

With the development of advanced technologies in cell-based phenotypic screening, phenotypic drug discovery (PDD) strategies have re-emerged as promising approaches in the identification and development of novel and safe drugs. However, phenotypic screening does not rely on knowledge of specific drug targets and needs to be combined with chemical biology approaches to identify therapeutic targets and mechanisms of actions induced by drugs and associated with an observable phenotype. In this study, we developed a system pharmacology network integrating drug-target-pathway-disease relationships as well as morphological profile from an existing high content imaging-based high-throughput phenotypic profiling assay known as "Cell Painting". Furthermore, from this network, a chemogenomic library of 5000 small molecules that represent a large and diverse panel of drug targets involved in diverse biological effects and diseases has been developed. Such a platform and a chemogenomic library could assist in the target identification and mechanism deconvolution of some phenotypic assays. The usefulness of the platform is illustrated through examples.

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-ß (TGFß)-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.

Discovery and mechanism of action studies of 4,6-diphenylpyrimidine-2-carbohydrazides as utrophin modulators for the treatment of Duchenne muscular dystrophy.

Duchenne muscular dystrophy is a fatal disease with no cure, caused by lack of the cytoskeletal protein dystrophin. Upregulation of utrophin, a dystrophin paralogue, offers a potential therapy independent of mutation type. The failure of first-in-class utrophin modulator ezutromid/SMT C1100 in Phase II clinical trials necessitates development of compounds with better efficacy, physicochemical and ADME properties and/or complementary mechanisms. We have discovered and performed a preliminary optimisation of a novel class of utrophin modulators using an improved phenotypic screen, where reporter expression is derived from the full genomic context of the utrophin promoter. We further demonstrate through target deconvolution studies, including expression analysis and chemical proteomics, that this compound series operates via a novel mechanism of action, distinct from that of ezutromid.

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.

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.