High-Throughput Screening: today's biochemical and cell-based approaches

High-throughput screening unveils nitazoxanide as a potent PRRSV inhibitor by targeting NMRAL1

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) poses a major threat to the global swine industry, yet effective prevention and control measures remain elusive. This study unveils Nitazoxanide (NTZ) as a potent inhibitor of PRRSV both in vitro and in vivo. Through High-Throughput Screening techniques, 16 potential anti-PRRSV compounds are identified from a library comprising FDA-approved and pharmacopeial drugs. We show that NTZ displays strong efficacy in reducing PRRSV proliferation and transmission in a swine model, alleviating viremia and lung damage. Additionally, Tizoxanide (TIZ), the primary metabolite of NTZ, has been identified as a facilitator of NMRAL1 dimerization. This finding potentially sheds light on the underlying mechanism contributing to TIZ's role in augmenting the sensitivity of the IFN-β pathway. These results indicate the promising potential of NTZ as a repurposed therapeutic agent for Porcine Reproductive and Respiratory Syndrome (PRRS). Additionally, they provide valuable insights into the antiviral mechanisms underlying NTZ's effectiveness.

Unleashing the power of generative AI in drug discovery

Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development.

Creating a more strategic small molecule biophysical hit characterization workflow

To confirm target engagement of hits from our high-throughput screening efforts, we ran biophysical assays on several hundreds of hits from 15 different high-throughput screening campaigns. Analyzing the biophysical assay results from these screening campaigns led us to conclude that we could be more strategic in our biophysical analysis of hits by first confirming activity in a thermal shift assay (TSA) and then confirming activity in either a surface plasmon resonance (SPR) assay or a temperature-related intensity change (TRIC) assay. To understand how this new workflow shapes the quality of the final hits, we compared TSA/SPR or TSA/TRIC confirmed and unconfirmed hits to one another using four measures of compound quality: quantitative estimate of drug-likeness (QED), Pan-Assay Interference Compounds (PAINS), promiscuity, and aqueous solubility. In general, we found that the biophysically confirmed hits performed better in the compound quality metrics than the unconfirmed hits, demonstrating that our workflow not only confirmed target engagement of the hits but also enriched for higher quality hits.

Interfacing High-Throughput Electrosynthesis and Mass Spectrometric Analysis of Azines

Combinatorial electrochemistry has great promise for accelerated reaction screening, organic synthesis, and catalysis. Recently, we described a new high-throughput electrochemistry platform, colloquially named "Legion". Legion fits the footprint of a 96-well microtiter plate with simultaneous individual control over all 96 electrochemical cells. Here, we demonstrate the versatility of Legion when coupled with high-throughput mass spectrometry (MS) for electrosynthetic product screening and quantitation. Electrosynthesis of benzophenone azine was selected as a model reaction and was arrayed and optimized using a combination of Legion and nanoelectrospray ionization MS. The combination of high-throughput synthesis with Legion and analysis via MS proves a compelling strategy for accelerating reaction discovery and optimization in electro-organic synthesis.

Tuning single-molecule ClyA nanopore tweezers for real-time tracking of the conformational dynamics of West Nile viral NS2B/NS3 protease

The flaviviral NS2B/NS3 protease is a conserved enzyme required for flavivirus replication. Its highly dynamic conformation poses major challenges but also offers opportunities for antiviral inhibition. Here, we established a nanopore tweezers-based platform to monitor NS2B/NS3 conformational dynamics in real-time. Molecular simulations coupled with electrophysiology revealed that the protease could be captured in the middle of the ClyA nanopore lumen, stabilized mainly by dynamic electrostatic interactions. We designed a new Salmonella typhi ClyA nanopore with enhanced nanopore/protease interaction that can resolve the open and closed states at the single-molecule level for the first time. We demonstrated that the tailored ClyA could track the conformational transitions of the West Nile NS2B/NS3 protease and unravel the conformational energy landscape of various protease constructs through population and kinetic analysis. The new ClyA-protease platform paves a way to high-throughput screening strategies for discovering new allosteric inhibitors that target the NS2B and NS3 interface.

A Hybrid Approach Combining Shape-Based and Docking Methods to Identify Novel Potential P2X7 Antagonists from Natural Product Databases

P2X7 is an ATP-activated purinergic receptor implicated in pro-inflammatory responses. It is associated with the development of several diseases, including inflammatory and neurodegenerative conditions. Although several P2X7 receptor antagonists have recently been reported in the literature, none of them is approved for clinical use. However, the structure of the known antagonists can serve as a scaffold for discovering effective compounds in clinical therapy. This study aimed to propose an improved virtual screening methodology for the identification of novel potential P2X7 receptor antagonists from natural products through the combination of shape-based and docking approaches. First, a shape-based screening was performed based on the structure of JNJ-47965567, a P2X7 antagonist, using two natural product compound databases, MEGx (~5.8 × 103 compounds) and NATx (~32 × 103 compounds). Then, the compounds selected by the proposed shape-based model, with Shape-Tanimoto score values ranging between 0.624 and 0.799, were filtered for drug-like properties. Finally, the compounds that met the drug-like filter criteria were docked into the P2X7 allosteric binding site, using the docking programs GOLD and DockThor. The docking poses with the best score values were submitted to careful visual inspection of the P2X7 allosteric binding site. Based on our established visual inspection criteria, four compounds from the MEGx database and four from the NATx database were finally selected as potential P2X7 receptor antagonists. The selected compounds are structurally different from known P2X7 antagonists, have drug-like properties, and are predicted to interact with key P2X7 allosteric binding pocket residues, including F88, F92, F95, F103, M105, F108, Y295, Y298, and I310. Therefore, the combination of shape-based screening and docking approaches proposed in our study has proven useful in selecting potential novel P2X7 antagonist candidates from natural-product-derived compounds databases. This approach could also be useful for selecting potential inhibitors/antagonists of other receptors and/or biological targets.

Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery

Efficient prioritization of bioactive compounds from high throughput screening campaigns is a fundamental challenge for accelerating drug development efforts. In this study, we present the first data-driven approach to simultaneously detect assay interferents and prioritize true bioactive compounds. By analyzing the learning dynamics during training of a gradient boosting model on noisy high throughput screening data using a novel formulation of sample influence, we are able to distinguish between compounds exhibiting the desired biological response and those producing assay artifacts. Therefore, our method enables false positive and true positive detection without relying on prior screens or assay interference mechanisms, making it applicable to any high throughput screening campaign. We demonstrate that our approach consistently excludes assay interferents with different mechanisms and prioritizes biologically relevant compounds more efficiently than all tested baselines, including a retrospective case study simulating its use in a real drug discovery campaign. Finally, our tool is extremely computationally efficient, requiring less than 30 s per assay on low-resource hardware. As such, our findings show that our method is an ideal addition to existing false positive detection tools and can be used to guide further pharmacological optimization after high throughput screening campaigns.

Fragment-based drug discovery for disorders of the central nervous system: designing better drugs piece by piece

Fragment-based drug discovery (FBDD) has emerged as a powerful strategy to confront the challenges faced by conventional drug development approaches, particularly in the context of central nervous system (CNS) disorders. FBDD involves the screening of libraries that comprise thousands of small molecular fragments, each no greater than 300 Da in size. Unlike the generally larger molecules from high-throughput screening that limit customisation, fragments offer a more strategic starting point. These fragments are inherently compact, providing a strong foundation with good binding affinity for the development of drug candidates. The minimal elaboration required to transition the hit into a drug-like molecule is not only accelerated, but also it allows for precise modifications to enhance both their activity and pharmacokinetic properties. This shift towards a fragment-centric approach has seen commercial success and holds considerable promise in the continued streamlining of the drug discovery and development process. In this review, we highlight how FBDD can be integrated into the CNS drug discovery process to enhance the exploration of a target. Furthermore, we provide recent examples where FBDD has been an integral component in CNS drug discovery programs, enabling the improvement of pharmacokinetic properties that have previously proven challenging. The FBDD optimisation process provides a systematic approach to explore this vast chemical space, facilitating the discovery and design of compounds piece by piece that are capable of modulating crucial CNS targets.

Advancing preclinical drug evaluation through automated 3D imaging for high-throughput screening with kidney organoids

High-throughput drug screening is crucial for advancing healthcare through drug discovery. However, a significant limitation arises from availablein vitromodels using conventional 2D cell culture, which lack the proper phenotypes and architectures observed in three-dimensional (3D) tissues. Recent advancements in stem cell biology have facilitated the generation of organoids-3D tissue constructs that mimic human organsin vitro. Kidney organoids, derived from human pluripotent stem cells, represent a significant breakthrough in disease representation. They encompass major kidney cell types organized within distinct nephron segments, surrounded by stroma and endothelial cells. This tissue allows for the assessment of structural alterations such as nephron loss, a characteristic of chronic kidney disease. Despite these advantages, the complexity of 3D structures has hindered the use of organoids for large-scale drug screening, and the drug screening pipelines utilizing these complexin vitromodels remain to be established for high-throughput screening. In this study, we address the technical limitations of kidney organoids through fully automated 3D imaging, aided by a machine-learning approach for automatic profiling of nephron segment-specific epithelial morphometry. Kidney organoids were exposed to the nephrotoxic agent cisplatin to model severe acute kidney injury. An U.S. Food and Drug Administration (FDA)-approved drug library was tested for therapeutic and nephrotoxicity screening. The fully automated pipeline of 3D image acquisition and analysis identified nephrotoxic or therapeutic drugs during cisplatin chemotherapy. The nephrotoxic potential of these drugs aligned with previousin vivoand human reports. Additionally, Imatinib, a tyrosine kinase inhibitor used in hematological malignancies, was identified as a potential preventive therapy for cisplatin-induced kidney injury. Our proof-of-concept report demonstrates that the automated screening process, using 3D morphometric assays with kidney organoids, enables high-throughput screening for nephrotoxicity and therapeutic assessment in 3D tissue constructs.

High-throughput nephelometry methodology for qualitative determination of aqueous solubility of chemical libraries

The purpose of the protocol reported in this work is the solubility profiling of large chemical libraries using nephelometry. This technique allows the qualitative classification of compounds as highly, moderately, or poorly water-soluble. The described methodology is not intended to yield quantitative solubility values of the studied compounds but can be used as a primary solubility assessment of large chemical libraries, to guide hit prioritization after High Throughput Screening (HTS) campaigns.

Discovery of RORγ Allosteric Fluorescent Probes and Their Application: Fluorescence Polarization, Screening, and Bioimaging

Retinoic acid receptor-related orphan receptor γ (RORγ) acts as a crucial transcription factor in Th17 cells and is involved in diverse autoimmune disorders. RORγ allosteric inhibitors have gained significant research focus as a novel strategy to inhibit RORγ transcriptional activity. Leveraging the high affinity and selectivity of RORγ allosteric inhibitor MRL-871 (1), this study presents the design, synthesis, and characterization of 11 allosteric fluorescent probes. Utilizing the preferred probe 12h, we established an efficient and cost-effective fluorescence polarization-based affinity assay for screening RORγ allosteric binders. By employing virtual screening in conjunction with this assay, 10 novel RORγ allosteric inhibitors were identified. The initial SAR studies focusing on the hit compound G381-0087 are also presented. The encouraging outcomes indicate that probe 12h possesses the potential to function as a powerful tool in facilitating the exploration of RORγ allosteric inhibitors and furthering understanding of RORγ function.

An in vivo screening platform identifies senolytic compounds that target p16INK4a+ fibroblasts in lung fibrosis

The appearance of senescent cells in age-related diseases has spurred the search for compounds that can target senescent cells in tissues, termed senolytics. However, a major caveat with current senolytic screens is the use of cell lines as targets where senescence is induced in vitro, which does not necessarily reflect the identity and function of pathogenic senescent cells in vivo. Here, we developed a new pipeline leveraging a fluorescent murine reporter that allows for isolation and quantification of p16Ink4a+ cells in diseased tissues. By high-throughput screening in vitro, precision-cut lung slice (PCLS) screening ex vivo, and phenotypic screening in vivo, we identified a HSP90 inhibitor, XL888, as a potent senolytic in tissue fibrosis. XL888 treatment eliminated pathogenic p16Ink4a+ fibroblasts in a murine model of lung fibrosis and reduced fibrotic burden. Finally, XL888 preferentially targeted p16INK4a-hi human lung fibroblasts isolated from patients with idiopathic pulmonary fibrosis (IPF), and reduced p16INK4a+ fibroblasts from IPF PCLS ex vivo. This study provides proof of concept for a platform where p16INK4a+ cells are directly isolated from diseased tissues to identify compounds with in vivo and ex vivo efficacy in mice and humans, respectively, and provides a senolytic screening platform for other age-related diseases.

VIBRANT: spectral profiling for single-cell drug responses

High-content cell profiling has proven invaluable for single-cell phenotyping in response to chemical perturbations. However, methods with improved throughput, information content and affordability are still needed. We present a new high-content spectral profiling method named vibrational painting (VIBRANT), integrating mid-infrared vibrational imaging, multiplexed vibrational probes and an optimized data analysis pipeline for measuring single-cell drug responses. Three infrared-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. The resulting spectral profile is highly sensitive to phenotypic changes under drug perturbation. Using this property, we built a machine learning classifier to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with new mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic screening.

Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures

Major Depressive Disorder (MDD) is a complex mental disorder that involves alterations in signal transmission across multiple scales and structural abnormalities. The development of effective antidepressants (ADs) has been hindered by the dominance of monoamine hypothesis, resulting in slow progress. Traditional ADs have undesirable traits like delayed onset of action, limited efficacy, and severe side effects. Recently, two categories of fast-acting antidepressant compounds have surfaced, dissociative anesthetics S-ketamine and its metabolites, as well as psychedelics such as lysergic acid diethylamide (LSD). This has led to structural research and drug development of the receptors that they target. This review provides breakthroughs and achievements in the structure of depression-related receptors and novel ADs based on these. Cryo-electron microscopy (cryo-EM) has enabled researchers to identify the structures of membrane receptors, including the N-methyl-D-aspartate receptor (NMDAR) and the 5-hydroxytryptamine 2A (5-HT2A) receptor. These high-resolution structures can be used for the development of novel ADs using virtual drug screening (VDS). Moreover, the unique antidepressant effects of 5-HT1A receptors in various brain regions, and the pivotal roles of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) and tyrosine kinase receptor 2 (TrkB) in regulating synaptic plasticity, emphasize their potential as therapeutic targets. Using structural information, a series of highly selective ADs were designed based on the different role of receptors in MDD. These molecules have the favorable characteristics of rapid onset and low adverse drug reactions. This review offers researchers guidance and a methodological framework for the structure-based design of ADs.

Asymmetry of Dual-Display DNA-Encoded Chemical Libraries

While dual-display DNA-encoded chemical libraries (DELs) are increasingly employed for ligand discovery, some of their fundamental properties have not yet been studied in-depth. Aided with fluorescence polarization experiments, we demonstrate that dual-display DELs are intrinsically asymmetrical entities, and we deduce practical guidelines to perform better-informed on-DNA hit validation from these libraries.

Fourier Ptychographic Microscopy 10 Years on: A Review

Fourier ptychographic microscopy (FPM) emerged as a prominent imaging technique in 2013, attracting significant interest due to its remarkable features such as precise phase retrieval, expansive field of view (FOV), and superior resolution. Over the past decade, FPM has become an essential tool in microscopy, with applications in metrology, scientific research, biomedicine, and inspection. This achievement arises from its ability to effectively address the persistent challenge of achieving a trade-off between FOV and resolution in imaging systems. It has a wide range of applications, including label-free imaging, drug screening, and digital pathology. In this comprehensive review, we present a concise overview of the fundamental principles of FPM and compare it with similar imaging techniques. In addition, we present a study on achieving colorization of restored photographs and enhancing the speed of FPM. Subsequently, we showcase several FPM applications utilizing the previously described technologies, with a specific focus on digital pathology, drug screening, and three-dimensional imaging. We thoroughly examine the benefits and challenges associated with integrating deep learning and FPM. To summarize, we express our own viewpoints on the technological progress of FPM and explore prospective avenues for its future developments.

Perspectives on Nuclear Magnetic Resonance Spectroscopy in Drug Discovery Research

The drug discovery landscape has undergone a significant transformation over the past decade, owing to research endeavors in a wide range of areas leading to strategies for pursuing new drug targets and the emergence of novel drug modalities. NMR spectroscopy has been a technology of fundamental importance to these research pursuits and has seen its use expanded both within and outside of traditional medicinal chemistry applications. In this perspective, we will present advancement of NMR-derived methods that have facilitated the characterization of small molecules and novel drug modalities including macrocyclic peptides, cyclic dinucleotides, and ligands for protein degradation. We will discuss innovations in NMR spectroscopy at the chemistry and biology interface that have broadened NMR's utility from hit identification through lead optimization activities. We will also discuss the promise of emerging NMR approaches in bridging our understanding and addressing challenges in the pursuit of the therapeutic agents of the future.

Decoding Allosteric Control in Hypoxia-Inducible Factors

The mammalian family of basic helix-loop-helix-PER-ARNT-SIM (bHLH-PAS) transcription factors possess the ability to sense and respond to diverse environmental and physiological cues. These proteins all share a common structural framework, comprising a bHLH domain, two PAS domains, and transcriptional activation or repression domain. To function effectively as transcription factors, members of the family must form dimers, bringing together bHLH segments to create a functional unit that allows for DNA response element binding. The significance of bHLH-PAS family is underscored by their involvement in many major human diseases, offering potential avenues for therapeutic intervention. Notably, the clear identification of ligand-binding cavities within their PAS domains enables the development of targeted small molecules. Two examples are Belzutifan, targeting hypoxia-inducible factor (HIF)-2α, and Tapinarof, targeting the aryl hydrocarbon receptor (AHR), both of which have gained regulatory approval recently. Here, we focus on the HIF subfamily. The crystal structures of all three HIF-α proteins have been elucidated, revealing their bHLH and tandem PAS domains are used to engage their dimerization partner aryl hydrocarbon receptor nuclear translocator (ARNT, also called HIF-1β). A broad range of recent findings point to a shared allosteric modulation mechanism among these proteins, whereby small-molecules at the PAS-B domains exert direct influence over the HIF-α transcriptional functions. As our understanding of the architectural and allosteric mechanisms of bHLH-PAS proteins continues to advance, the possibility of discovering new therapeutic drugs becomes increasingly promising.

A growth-based assay using fluorescent protein emission to screen for S-adenosylmethionine synthetase inhibitors

The use of cell growth-based assays to identify inhibitory compounds is straightforward and inexpensive, but is also inherently insensitive and somewhat nonspecific. To overcome these limitations and develop a sensitive, specific cell-based assay, two different approaches were combined. To address the sensitivity limitation, different fluorescent proteins have been introduced into a bacterial expression system to serve as growth reporters. To overcome the lack of specificity, these protein reporters have been incorporated into a plasmid in which they are paired with different orthologs of an essential target enzyme, in this case l-methionine S-adenosyltransferase (MAT, AdoMet synthetase). Screening compounds that serve as specific inhibitors will reduce the growth of only a subset of strains, because these strains are identical, except for which target ortholog they carry. Screening several such strains in parallel not only reveals potential inhibitors but the strains also serve as specificity controls for one another. The present study makes use of an existing Escherichia coli strain that carries a deletion of metK, the gene for MAT. Transformation with these plasmids leads to a complemented strain that no longer requires externally supplied S-adenosylmethionine for growth, but its growth is now dependent on the activity of the introduced MAT ortholog. The resulting fluorescent strains provide a platform to screen chemical compound libraries and identify species-selective inhibitors of AdoMet synthetases. A pilot study of several chemical libraries using this platform identified new lead compounds that are ortholog-selective inhibitors of this enzyme family, some of which target the protozoal human pathogen Cryptosporidium parvum.

Exploring the Potential of Bioactive Peptides: From Natural Sources to Therapeutics

Bioactive peptides, specific protein fragments with positive health effects, are gaining traction in drug development for advantages like enhanced penetration, low toxicity, and rapid clearance. This comprehensive review navigates the intricate landscape of peptide science, covering discovery to functional characterization. Beginning with a peptidomic exploration of natural sources, the review emphasizes the search for novel peptides. Extraction approaches, including enzymatic hydrolysis, microbial fermentation, and specialized methods for disulfide-linked peptides, are extensively covered. Mass spectrometric analysis techniques for data acquisition and identification, such as liquid chromatography, capillary electrophoresis, untargeted peptide analysis, and bioinformatics, are thoroughly outlined. The exploration of peptide bioactivity incorporates various methodologies, from in vitro assays to in silico techniques, including advanced approaches like phage display and cell-based assays. The review also discusses the structure-activity relationship in the context of antimicrobial peptides (AMPs), ACE-inhibitory peptides (ACEs), and antioxidative peptides (AOPs). Concluding with key findings and future research directions, this interdisciplinary review serves as a comprehensive reference, offering a holistic understanding of peptides and their potential therapeutic applications.

Target-based drug discovery: Applications of fluorescence techniques in high throughput and fragment-based screening

Target-based discovery of first-in-class therapeutics demands an in-depth understanding of the molecular mechanisms underlying human diseases. Precise measurements of cellular and biochemical activities are critical to gain mechanistic knowledge of biomolecules and their altered function in disease conditions. Such measurements enable the development of intervention strategies for preventing or treating diseases by modulation of desired molecular processes. Fluorescence-based techniques are routinely employed for accurate and robust measurements of in-vitro activity of molecular targets and for discovering novel chemical molecules that modulate the activity of molecular targets. In the current review, the authors focus on the applications of fluorescence-based high throughput screening (HTS) and fragment-based ligand discovery (FBLD) techniques such as fluorescence polarization (FP), Förster resonance energy transfer (FRET), fluorescence thermal shift assay (FTSA) and microscale thermophoresis (MST) for the discovery of chemical probe to exploring target's role in disease biology and ultimately, serve as a foundation for drug discovery. Some recent advancements in these techniques for compound library screening against important classes of drug targets, such as G-protein-coupled receptors (GPCRs) and GTPases, as well as phosphorylation- and acetylation-mediated protein-protein interactions, are discussed. Overall, this review presents a landscape of how these techniques paved the way for the discovery of small-molecule modulators and biologics against these targets for therapeutic benefits.

The application of nanoparticles based on ferroptosis in cancer therapy

Ferroptosis is a new form of non-apoptotic programmed cell death. Due to its effectiveness in cancer treatment, there are increasing studies on the application of nanoparticles based on ferroptosis in cancer therapy. In this paper, we present a summary of the latest progress in nanoparticles based on ferroptosis for effective tumor therapy. We also describe the combined treatment of ferroptosis with other therapies, including chemotherapy, radiotherapy, phototherapy, immunotherapy, and gene therapy. This summary of drug delivery systems based on ferroptosis aims to provide a basis and inspire opinions for researchers concentrating on exploring this field. Finally, we present some prospects and challenges for the application of nanotherapies to clinical treatment by promoting ferroptosis in cancer cells.

Virtual plates: Getting the best out of high content screens

High content screening (HCS) is becoming widely adopted as a high throughput screening modality, using hundred-of-thousands compounds library. The use of machine learning and artificial intelligence in image analysis is amplifying this trend. Another factor is the recognition that diverse cell phenotypes can be associated with changes in biological pathways relevant to disease processes. There are numerous challenges in HCS campaigns. These include limited ability to support replicates, low availability of precious and unique cells or reagents, high number of experimental batches, lengthy preparation of cells for imaging, image acquisition time (45-60 min per plate) and image processing time, deterioration of image quality with time post cell fixation and variability within wells and batches. To take advantage of the data in HCS, cell population based rather than well-based analyses are required. Historically, statistical analysis and hypothesis testing played only a limited role in non-high content high throughput campaigns. Thus, only a limited number of standard statistical criteria for hit selection in HCS have been developed so far. In addition to complex biological content in HCS campaigns, additional variability is impacted by cell and reagent handling and by instruments which may malfunction or perform unevenly. Together these can cause a significant number of wells or plates to fail. Here we describe an automated approach for hit analysis and detection in HCS. Our approach automates HCS hit detection using a methodology that is based on a documented statistical framework. We introduce the Virtual Plate concept in which selected wells from different plates are collated into a new, virtual plate. This allows the rescue and analysis of compound wells which have failed due to technical issues as well as to collect hit wells into one plate, allowing the user easier access to the hit data.

Target Prediction by Multiple Virtual Screenings: Analyzing the SARS-CoV-2 Phenotypic Screening by the Docking Simulations Submitted to the MEDIATE Initiative

Phenotypic screenings are usually combined with deconvolution techniques to characterize the mechanism of action for the retrieved hits. These studies can be supported by various computational analyses, although docking simulations are rarely employed. The present study aims to assess if multiple docking calculations can prove successful in target prediction. In detail, the docking simulations submitted to the MEDIATE initiative are utilized to predict the viral targets involved in the hits retrieved by a recently published cytopathic screening. Multiple docking results are combined by the EFO approach to develop target-specific consensus models. The combination of multiple docking simulations enhances the performances of the developed consensus models (average increases in EF1% value of 40% and 25% when combining three and two docking runs, respectively). These models are able to propose reliable targets for about half of the retrieved hits (31 out of 59). Thus, the study emphasizes that docking simulations might be effective in target identification and provide a convincing validation for the collaborative strategies that inspire the MEDIATE initiative. Disappointingly, cross-target and cross-program correlations suggest that common scoring functions are not specific enough for the simulated target.

Discovery of a Small-Molecule Modulator of the Autophagy-Lysosome Pathway That Targets Lamin A/C and LAMP1, Induces Autophagic Flux, and Affects Lysosome Positioning in Neurons

Autophagy is a major catabolic degradation and recycling process that maintains homeostasis in cells and is especially important in postmitotic neurons. We implemented a high-content phenotypic assay to discover small molecules that promote autophagic flux and completed target identification and validation studies to identify protein targets that modulate the autophagy pathway and promote neuronal health and survival. Efficient syntheses of the prioritized compounds were developed to readily access analogues of the initial hits, enabling initial structure-activity relationship studies to improve potency and preparation of a biotin-tagged pulldown probe that retains activity. This probe facilitated target identification and validation studies through pulldown and competition experiments using both an unbiased proteomics approach and western blotting to reveal Lamin A/C and LAMP1 as the protein targets of compound RH1115. Evaluation of RH1115 in neurons revealed that this compound induces changes to LAMP1 vesicle properties and alters lysosome positioning. Dysfunction of the autophagy-lysosome pathway has been implicated in a variety of neurodegenerative diseases, including Alzheimer's disease, highlighting the value of new strategies for therapeutic modulation and the importance of small-molecule probes to facilitate the study of autophagy regulation in cultured neurons and in vivo.

Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows

Synthetic polymers are highly customizable with tailored structures and functionality, yet this versatility generates challenges in the design of advanced materials due to the size and complexity of the design space. Thus, exploration and optimization of polymer properties using combinatorial libraries has become increasingly common, which requires careful selection of synthetic strategies, characterization techniques, and rapid processing workflows to obtain fundamental principles from these large data sets. Herein, we provide guidelines for strategic design of macromolecule libraries and workflows to efficiently navigate these high-dimensional design spaces. We describe synthetic methods for multiple library sizes and structures as well as characterization methods to rapidly generate data sets, including tools that can be adapted from biological workflows. We further highlight relevant insights from statistics and machine learning to aid in data featurization, representation, and analysis. This Perspective acts as a "user guide" for researchers interested in leveraging high-throughput screening toward the design of multifunctional polymers and predictive modeling of structure-property relationships in soft materials.

Cellular Assays for Dynamic Quantification of Deubiquitinase Activity and Inhibition

Deubiquitinases (DUBs) are proteolytic enzymes that catalyze the removal of ubiquitin from protein substrates. The critical role of DUBs in regulating protein ubiquitination makes them attractive drug targets in oncology, neurodegenerative disease, and antiviral development. Biochemical assays for quantifying DUB activity have enabled characterization of substrate preferences and discovery of small molecule inhibitors. However, assessing the efficacy of these inhibitors in cellular contexts to support clinical drug development has been limited by a lack of tractable cell-based assays. To address this gap, we developed a two-color flow cytometry-based assay that allows for sensitive quantification of DUB activity and inhibition in living cells. The utility of this system was demonstrated by quantifying the potency of GRL0617 against the viral DUB SARS-CoV-2 PLpro, identifying potential GRL0617 resistance mutations, and performing structure-function analysis of the vOTU domain from the recently emerged Yezo virus. In addition, the system was optimized for cellular DUBs by modifying a GFP-targeting nanobody to recruit USP7 and USP28 to benchmark a panel of reported inhibitors and assess inhibition kinetics. Together, these results demonstrate the utility of these assays for studying DUB biology in a cellular context with potential to aid in inhibitor discovery and development.

From intuition to AI: evolution of small molecule representations in drug discovery

Within drug discovery, the goal of AI scientists and cheminformaticians is to help identify molecular starting points that will develop into safe and efficacious drugs while reducing costs, time and failure rates. To achieve this goal, it is crucial to represent molecules in a digital format that makes them machine-readable and facilitates the accurate prediction of properties that drive decision-making. Over the years, molecular representations have evolved from intuitive and human-readable formats to bespoke numerical descriptors and fingerprints, and now to learned representations that capture patterns and salient features across vast chemical spaces. Among these, sequence-based and graph-based representations of small molecules have become highly popular. However, each approach has strengths and weaknesses across dimensions such as generality, computational cost, inversibility for generative applications and interpretability, which can be critical in informing practitioners' decisions. As the drug discovery landscape evolves, opportunities for innovation continue to emerge. These include the creation of molecular representations for high-value, low-data regimes, the distillation of broader biological and chemical knowledge into novel learned representations and the modeling of up-and-coming therapeutic modalities.

FormulationAI: a novel web-based platform for drug formulation design driven by artificial intelligence

Today, pharmaceutical industry faces great pressure to employ more efficient and systematic ways in drug discovery and development process. However, conventional formulation studies still strongly rely on personal experiences by trial-and-error experiments, resulting in a labor-consuming, tedious and costly pipeline. Thus, it is highly required to develop intelligent and efficient methods for formulation development to keep pace with the progress of the pharmaceutical industry. Here, we developed a comprehensive web-based platform (FormulationAI) for in silico formulation design. First, the most comprehensive datasets of six widely used drug formulation systems in the pharmaceutical industry were collected over 10 years, including cyclodextrin formulation, solid dispersion, phospholipid complex, nanocrystals, self-emulsifying and liposome systems. Then, intelligent prediction and evaluation of 16 important properties from the six systems were investigated and implemented by systematic study and comparison of different AI algorithms and molecular representations. Finally, an efficient prediction platform was established and validated, which enables the formulation design just by inputting basic information of drugs and excipients. FormulationAI is the first freely available comprehensive web-based platform, which provides a powerful solution to assist the formulation design in pharmaceutical industry. It is available at https://formulationai.computpharm.org/.

High-Throughput Liquid Chromatographic Analysis Using a Segmented Flow Injector with a 1 s Cycle Time

High-throughput screening (HTS) workflows are revolutionizing many fields, including drug discovery, reaction discovery and optimization, diagnostics, sensing, and enzyme engineering. Liquid chromatography (LC) is commonly deployed during HTS to reduce matrix effects, distinguish isomers, and preconcentrate prior to detection, but LC separation time often limits throughput. Although subsecond LC separations have been demonstrated, they are rarely utilized during HTS due to limitations associated with the speed of common autosamplers. In this work, these limits are overcome by utilizing droplet microfluidics for sample introduction. In the method, a train of samples segmented by air are continuously pumped into the inlet of an LC injection valve that is actuated once each sample fills the sample loop. Coupled with 2.1 mm diameter × 5 mm long columns packed with 2.7 μm superficially porous C18 particles operated at 5 mL/min, the injector enabled separation of 3 components at 1 s/sample and analysis of a 96-well plate in 1.6 min with <2% peak area relative standard deviation. Analyte-dependent carryover was minimized by including wash droplets composed of organic solvent in between sample droplets. High-throughput LC coupled with mass spectrometric detection using the segmented flow injector was applied to a screen of inhibitors of a cytochrome P450-catalyzed hydroxylation reaction. Measurements of the reaction substrate and product concentrations made using fast LC with the segmented flow injector correlated well with measurements made using a more conventional, 3 min LC method. These results demonstrate the potential for droplet microfluidics to be used for sample introduction during high-throughput LC analysis.

Legion: An Instrument for High-Throughput Electrochemistry

Electrochemical arrays promise utility for accelerated hypothesis testing and breakthrough discoveries. Herein, we report a new high-throughput electrochemistry platform, colloquially called "Legion," for applications in electroanalysis and electrosynthesis. Legion consists of 96 electrochemical cells dimensioned to match common 96-well plates that are independently controlled with a field-programmable gate array. We demonstrate the utility of Legion by measuring model electrochemical probes, pH-dependent electron transfers, and electrocatalytic dehalogenation reactions. We consider advantages and disadvantages of this new instrumentation, with the hope of expanding the electrochemical toolbox.

High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery

Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.

Solvents and detergents compatible with enzyme kinetic studies of cholinesterases

Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) are enzymes that serve a wide range of physiological functions including the hydrolysis of the neurotransmitter acetylcholine and several other xenobiotics. The development of inhibitors for these enzymes has been the focus for the treatment of several conditions, such as Alzheimer's disease. Novel chemical entities are evaluated as potential inhibitors of AChE and BChE using enzyme kinetics. A common issue encountered in these studies is low aqueous solubility of the possible inhibitor. Additives such as cosolvents or detergents can be included in these studies improve the aqueous solubility. Typical cosolvents include acetonitrile or dimethyl sulfoxide while typical detergents include Polysorbate 20 (Tween 20) or 3-((3-cholamidopropyl) dimethylammonio)-1-propanesulfonate (CHAPS). When solubility is not improved, these molecules are often not evaluated further. To address this issue eleven cosolvents and six detergents that could facilitate aqueous solubility were evaluated to understand how they would affect cholinesterase enzymes using Ellman's assay. These studies show that propylene glycol, acetonitrile, methanol, Tween 20, Polysorbate 80 (Tween 80), polyoxyethylene 23 lauryl ether (Brij 35) and polyoxyethylene 10 oleoyl ether (Brij 96v) have the least inhibitory effects towards cholinesterase activity. It is concluded that these cosolvents and detergents should be considered as solubilizing agents for evaluation of potential cholinesterase inhibitors with low aqueous solubility.

Special Issue "Drug Candidates for the Treatment of Infectious Diseases"

Infectious diseases encompass a range of conditions stemming from parasites [...].

Navigating drug repurposing for Chagas disease: advances, challenges, and opportunities

Chagas disease is a vector-borne illness caused by the protozoan parasite Trypanosoma cruzi (T. cruzi). It poses a significant public health burden, particularly in the poorest regions of Latin America. Currently, there is no available vaccine, and chemotherapy has been the traditional treatment for Chagas disease. However, the treatment options are limited to just two outdated medicines, nifurtimox and benznidazole, which have serious side effects and low efficacy, especially during the chronic phase of the disease. Collectively, this has led the World Health Organization to classify it as a neglected disease. To address this problem, new drug regimens are urgently needed. Drug repurposing, which involves the use of existing drugs already approved for the treatment of other diseases, represents an increasingly important option. This approach offers potential cost reduction in new drug discovery processes and can address pharmaceutical bottlenecks in the development of drugs for Chagas disease. In this review, we discuss the state-of-the-art of drug repurposing approaches, including combination therapy with existing drugs, to overcome the formidable challenges associated with treating Chagas disease. Organized by original therapeutic area, we describe significant recent advances, as well as the challenges in this field. In particular, we identify candidates that exhibit potential for heightened efficacy and reduced toxicity profiles with the ultimate objective of accelerating the development of new, safe, and effective treatments for Chagas disease.

Deep Learning Approach for the Discovery of Tumor-Targeting Small Organic Ligands from DNA-Encoded Chemical Libraries

DNA-Encoded Chemical Libraries (DELs) have emerged as efficient and cost-effective ligand discovery tools, which enable the generation of protein-ligand interaction data of unprecedented size. In this article, we present an approach that combines DEL screening and instance-level deep learning modeling to identify tumor-targeting ligands against carbonic anhydrase IX (CAIX), a clinically validated marker of hypoxia and clear cell renal cell carcinoma. We present a new ligand identification and hit-to-lead strategy driven by machine learning models trained on DELs, which expand the scope of DEL-derived chemical motifs. CAIX-screening datasets obtained from three different DELs were used to train machine learning models for generating novel hits, dissimilar to elements present in the original DELs. Out of the 152 novel potential hits that were identified with our approach and screened in an in vitro enzymatic inhibition assay, 70% displayed submicromolar activities (IC₅₀ < 1 μM). To generate lead compounds that are functionalized with anticancer payloads, analogues of top hits were prioritized for synthesis based on the predicted CAIX affinity and synthetic feasibility. Three lead candidates showed accumulation on the surface of CAIX-expressing tumor cells in cellular binding assays. The best compound displayed an in vitro K_D of 5.7 nM and selectively targeted tumors in mice bearing human renal cell carcinoma lesions. Our results demonstrate the synergy between DEL and machine learning for the identification of novel hits and for the successful translation of lead candidates for in vivo targeting applications.

Crystallographic fragment screening in academic cancer drug discovery

Fragment-based drug discovery (FBDD) has brought several drugs to the clinic, notably to target proteins once considered to be challenging, or even undruggable. Screening in FBDD relies upon observing and/or measuring weak (millimolar-scale) binding events using biophysical techniques or crystallographic fragment screening. This latter structural approach provides no information about binding affinity but can reveal binding mode and atomic detail on protein-fragment interactions to accelerate hit-to-lead development. In recent years, high-throughput platforms have been developed at synchrotron facilities to screen thousands of fragment-soaked crystals. However, using accessible manual techniques it is possible to run informative, smaller-scale screens within an academic lab setting. This chapter describes general protocols for home laboratory-scale fragment screening, from fragment soaking through to structure solution and, where appropriate, signposts to background, protocols or alternatives elsewhere.

YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images

Cell counting in fluorescence microscopy is an essential task in biomedical research for analyzing cellular dynamics and studying disease progression. Traditional methods for cell counting involve manual counting or threshold-based segmentation, which are time-consuming and prone to human error. Recently, deep learning-based object detection methods have shown promising results in automating cell counting tasks. However, the existing methods mainly focus on segmentation-based techniques that require a large amount of labeled data and extensive computational resources. In this paper, we propose a novel approach to detect and count multiple-size cells in a fluorescence image slide using You Only Look Once version 5 (YOLOv5) with a feature pyramid network (FPN). Our proposed method can efficiently detect multiple cells with different sizes in a single image, eliminating the need for pixel-level segmentation. We show that our method outperforms state-of-the-art segmentation-based approaches in terms of accuracy and computational efficiency. The experimental results on publicly available datasets demonstrate that our proposed approach achieves an average precision of 0.8 and a processing time of 43.9 ms per image. Our approach addresses the research gap in the literature by providing a more efficient and accurate method for cell counting in fluorescence microscopy that requires less computational resources and labeled data.

HTS driven by fluorescence lifetime detection of FRET identifies activators and inhibitors of cardiac myosin

Small molecules that bind to allosteric sites on target proteins to alter protein function are highly sought in drug discovery. High-throughput screening (HTS) assays are needed to facilitate the direct discovery of allosterically active compounds. We have developed technology for high-throughput time-resolved fluorescence lifetime detection of fluorescence resonance energy transfer (FRET), which enables the detection of allosteric modulators by monitoring changes in protein structure. We tested this approach at the industrial scale by adapting an allosteric FRET sensor of cardiac myosin to high-throughput screening (HTS), based on technology provided by Photonic Pharma and the University of Minnesota, and then used the sensor to screen 1.6 million compounds in the HTS facility at Bristol Myers Squibb. The results identified allosteric activators and inhibitors of cardiac myosin that do not compete with ATP binding, demonstrating high potential for FLT-based drug discovery.

Tissue-Engineered 3D In Vitro Disease Models for High-Throughput Drug Screening

During high-throughput drug screening, in vitro models are fabricated and the effects of therapeutics on the models evaluated in high throughput-for example, with automated liquid handling systems and microplate reader-based high-throughput screening (HTS) assays. The most frequently-used model systems for HTS, 2D models, do not adequately model the in vivo 3D microenvironment-an important aspect of which is the extracellular matrix-and therefore, 2D models may not be appropriate for drug screening. Instead, tissue-engineered 3D models with extracellular matrix-mimicking components are destined to become the preferred in vitro systems for HTS. However, for 3D models, such as 3D cell-laden hydrogels and scaffolds, cell sheets, and spheroids as well as 3D microfluidic and organ-on-a-chip systems, to replace 2D models in HTS, they must be compatible with high-throughput fabrication schemes and evaluation methods. In this review, we summarize HTS in 2D models and discuss recent studies that have successfully demonstrated HTS-compatible 3D models of high-impact diseases, such as cancers or cardiovascular diseases.

Inhibition of Replication Fork Formation and Progression: Targeting the Replication Initiation and Primosomal Proteins

Over 1.2 million deaths are attributed to multi-drug-resistant (MDR) bacteria each year. Persistence of MDR bacteria is primarily due to the molecular mechanisms that permit fast replication and rapid evolution. As many pathogens continue to build resistance genes, current antibiotic treatments are being rendered useless and the pool of reliable treatments for many MDR-associated diseases is thus shrinking at an alarming rate. In the development of novel antibiotics, DNA replication is still a largely underexplored target. This review summarises critical literature and synthesises our current understanding of DNA replication initiation in bacteria with a particular focus on the utility and applicability of essential initiation proteins as emerging drug targets. A critical evaluation of the specific methods available to examine and screen the most promising replication initiation proteins is provided.

Detection speed optimization of the OI-RD microscope for ultra-high throughput screening

The oblique-incidence reflectivity difference (OI-RD) microscope is a label-free detection system for microarrays that has many successful applications in high throughput drug screening. The increase and optimization of the detection speed of the OI-RD microscope will enable it to be a potential ultra-high throughput screening tool. This work presents a series of optimization methods that can significantly reduce the time to scan an OI-RD image. The wait time for the lock-in amplifier was decreased by the proper selection of the time constant and development of a new electronic amplifier. In addition, the time for the software to acquire data and for translation stage movement was also minimized. As a result, the detection speed of the OI-RD microscope is 10 times faster than before, making the OI-RD microscope suitable for ultra-high throughput screening applications.

Computational approaches streamlining drug discovery

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.

Development of FRET and Stress Granule Dual-Based System to Screen for Viral 3C Protease Inhibitors

3C proteases (3Cpros) of picornaviruses and 3C-like proteases (3CLpros) of coronaviruses and caliciviruses represent a group of structurally and functionally related viral proteases that play pleiotropic roles in supporting the viral life cycle and subverting host antiviral responses. The design and screening for 3C/3CLpro inhibitors may contribute to the development broad-spectrum antiviral therapeutics against viral diseases related to these three families. However, current screening strategies cannot simultaneously assess a compound’s cytotoxicity and its impact on enzymatic activity and protease-mediated physiological processes. The viral induction of stress granules (SGs) in host cells acts as an important antiviral stress response by blocking viral translation and stimulating the host immune response. Most of these viruses have evolved 3C/3CLpro-mediated cleavage of SG core protein G3BP1 to counteract SG formation and disrupt the host defense. Yet, there are no SG-based strategies screening for 3C/3CLpro inhibitors. Here, we developed a fluorescence resonance energy transfer (FRET) and SG dual-based system to screen for 3C/3CLpro inhibitors in living cells. We took advantage of FRET to evaluate the protease activity of poliovirus (PV) 3Cpro and live-monitor cellular SG dynamics to cross-verify its effect on the host antiviral response. Our drug screen uncovered a novel role of Telaprevir and Trifluridine as inhibitors of PV 3Cpro. Moreover, Telaprevir and Trifluridine also modulated 3Cpro-mediated physiological processes, including the cleavage of host proteins, inhibition of the innate immune response, and consequent facilitation of viral replication. Taken together, the FRET and SG dual-based system exhibits a promising potential in the screening for inhibitors of viral proteases that cleave G3BP1.

Cuproptosis correlates with immunosuppressive tumor microenvironment based on pan-cancer multiomics and single-cell sequencing analysis

Recent studies suggest that cuproptosis, a novel mode of cell death, may be associated with the development of cancer. However, no studies are showing its role in tumorigenesis, progression, and prognosis. In the present study, we comprehensively analyzed the expression difference, gene variation and methylation modification of cuproptosis-related genes (CRGs) in pan-cancer. Then, Single sample gene set enrichment analysis (ssGSEA) was used to calculate individual cuproptosis scores (CS). The association of CS with copy number variation, clinical features, immune-related genes, TMB, MSI, and tumor immune dysfunction and exclusion (TIDE) was comprehensively assessed. Single-cell transcriptome sequencing (scRNA-seq) to analyze the activation of cuproptosis in the tumor microenvironment. Immunohistochemistry (IHC) were used to validate the expression of cuproptosis hub-gene. Our study shows that CRGs were significantly expressed in a variety of tumors, and CDKN2A had the highest mutation frequency (49%) in all tumors. A significant increase in the CS was observed in most cancers and were associated with poor prognosis in the majority of tumors. CS was significantly negatively correlated with tumor microenvironment scores in more than 10 tumors and positively correlated with PD-L1 in 11 tumors, suggesting involvement in tumor immune escape. scRNA-seq suggests that CRG scores significantly increased in the cancer cells. This study opens avenues for further research on the role of cuproptosis in the occurrence and development of cancer and the development of targeted therapies based on cuproptosis.

Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade

Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.

Dendrophthoe falcata (L.f.) Ettingsh. and Dendrophthoe pentandra (L.) Miq.: A review of traditional medical uses, phytochemistry, pharmacology, toxicity, and applications

Dendrophthoe falcata (L.f.) Ettingsh. (DF) and Dendrophthoe pentandra (L.) Miq. (DP) have been traditionally used for the treatment of various ailments, such as cancer, ulcers, asthma, paralysis, skin diseases, tuberculosis, and menstrual troubles, in the ethnomedicinal systems of India and Indonesia. Currently, the chemical structures of 46 compounds have been elucidated from DF and DP, including flavonoids, triterpenes, tannins, steroids, open-chain aliphatics, benzyl derivates, and cyclic chain derivatives. In vitro assays have revealed their anti-tumor and anti-microbial activities. In vivo studies have unraveled their pharmacological properties against tumors, depression, fertility disorders, inflammatory responses, and so on. Additionally, their weak toxicity to rats and brine shrimp, as well as their promising applications for pharmaceutical preparations and combined medication, were also revealed. Herein, we not only recapitulated traditional medical uses, phytochemistry, pharmacology, toxicity, and applications of DF and DP but also discussed current research limitations and future perspectives, which are instructive for those interested in them and are committed to advancing parasitic plants to the Frontier of phytomedicine. We highlighted that DF and DP will become promising medical plants rather than being discarded as notorious pests, provided that more and deeper research is undertaken.

High throughput screening of 0.5 Million compounds against CRAF using Alpha CETSAⓇ

The cellular thermal shift assay (CETSA®) has increasingly been used in early drug discovery to provide a measure of cellular target engagement. Traditionally, CETSA has been employed for bespoke questions with small to medium throughput and has predominantly been applied during hit validation rather than in hit identification. Using a CETSA screen versus the kinase CRAF, we assessed 3 key questions: (1) technical feasibility - could the CETSA methodology technically be applied at truly high throughput scale? (2) relevance - could hits suitable for further optimisation be identified? (3) reliability - would the approach identify known chemical equity. Here, we describe the first large scale AlphaLISA SureFire based CETSA (Alpha CETSA) approach allowing us to screen a large library of almost 0.5 million compounds. We discuss the issues overcome in automating and executing the screen and describe the resulting screen output.

The Application of High-Throughput Approaches in Identifying Novel Therapeutic Targets and Agents to Treat Diabetes

During the past decades, unprecedented progress in technologies has revolutionized traditional research methodologies. Among these, advances in high-throughput drug screening approaches have permitted the rapid identification of potential therapeutic agents from drug libraries that contain thousands or millions of molecules. Moreover, high-throughput-based therapeutic target discovery strategies can comprehensively interrogate relationships between biomolecules (e.g., gene, RNA, and protein) and diseases and significantly increase the authors' knowledge of disease mechanisms. Diabetes is a chronic disease primarily characterized by the incapacity of the body to maintain normoglycemia. The prevalence of diabetes in modern society has become a severe public health issue that threatens the well-being of millions of patients. Although a number of pharmacological treatments are available, there is no permanent cure for diabetes, and discovering novel therapeutic targets and agents continues to be an urgent need. The present review discusses the technical details of high-throughput screening approaches in drug discovery, followed by introducing the applications of such approaches to diabetes research. This review aims to provide an example of the applicability of high-throughput technologies in facilitating different aspects of disease research.

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).