SLAS Discovery最新文献

Coronaviruses (CoV) are one of the largest families of viruses that infect human beings causing mild common cold or severe diseases like Middle East Respiratory Syndrome (MERS-CoV), and Severe Acute Respiratory Syndrome (SARS-CoV). A new strain emerged known as novel coronavirus (nCoV) causing fatal respiratory failure disease. This virus was characterized by rapid spread from asymptomatic and symptomatic patients to healthy people. Thus, vaccine should be considered as one of the important protective measures to control the spread of this virus. One of the challenges to this vaccine is the high mutation rate of this virus and appearance of new strains. Therefore, vaccine should stimulate the immune system in order to overcome the emergence of new strain of this virus.

MicroRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation and have been implicated in various diseases, including cancers and lung disease. In recent years, Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing graph-structured data, making them well-suited for the analysis of molecular structures. In this work, we explore the application of GNNs in ligand-based drug screening for small molecules targeting miR-21. By representing a known dataset of small molecules targeting miR-21 as graphs, GNNs can learn complex relationships between their structures and activities, enabling the prediction of potential miRNA-targeting small molecules by capturing the structural features and similarity between known miRNA-targeting compounds. The use of GNNs in miRNA-targeting drug screening holds promise for the discovery of novel therapeutic agents and provides a computational framework for efficient screening of large chemical libraries.

PARP1/2 inhibitors (PARPi) are effective clinically used drugs for the treatment of cancers with BRCA deficiencies. PARPi have had limited success and applicability beyond BRCA deficient cancers, and their effect is diminished by resistance mechanisms. The recent discovery of Histone PARylation Factor (HPF1) and the role it plays in the PARylation reaction by forming a shared active site with PARP1 raises the possibility that novel inhibitors that target the PARP1–HPF1 complex can be identified. Herein we describe a simple and cost-effective high-throughput screening (HTS) method aimed at discovering inhibitors of the PARP1–HPF1 complex. Upon HTS validation, we first applied this method to screen a small PARP-focused library of compounds and then scale up our approach using robotic automation to conduct a pilot screen of 10,000 compounds and validating >100 hits. This work demonstrates for the first time the capacity to discover potent inhibitors of the PARP1-HPF1 complex, which may have utility as probes to better understand the DNA damage response and as therapeutics for cancer.

Ubiquitination is a reversible protein post-translational modification in which consequent enzymatic activity results in the covalent linking of ubiquitin to a target protein. Once ubiquitinated, a protein can undergo multiple rounds of ubiquitination on multiple sites or form poly-ubiquitin chains. Ubiquitination regulates various cellular processes, and dysregulation of ubiquitination has been associated with more than one type of cancer. Therefore, efforts have been carried out to identify modulators of the ubiquitination cascade. Herein, we present the development of a FRET-based assay that allows us to monitor ubiquitination activity of DTX3L, a RING-type E3 ubiquitin ligase. Our method shows a good signal window with a robust average Z’ factor of 0.76 on 384-well microplates, indicating a good assay for screening inhibitors in a high-throughput setting. From a validatory screening experiment, we have identified the first molecules that inhibit DTX3L with potencies in the low micromolar range. We also demonstrate that the method can be expanded to study deubiquitinases, such as USP28, that reduce FRET due to hydrolysis of fluorescent poly-ubiquitin chains.

Neurological disorders associated with inflammation and oxidative stress show reduced glutathione (GSH) levels in the human brain. Drug discovery efforts and pharmacological studies would benefit from tools (e.g. chemical probes) that detect changes to oxidative stress, from the perspective of physiologically-relevant reporters like cellular thiols, including GSH. To this end, we have developed a fluorescence visualization assay using iPSC-derived cortical glutamatergic neurons that were loaded with 25 μM of a novel thiol-detection fluorescent probe, SemKur-IM. This probe enables visualization of cellular thiol level changes in the neuronal somas and neurites, in response exposure to N-acetyl-cysteine (NAC). Cellular thiol redox state was observed to change, based on an increase in green fluorescence (485 nm excitation maximum; 525 nm emission maximum) due to changes in thiol levels, from 0 to 40 mM. Interestingly, prior to treatment with NAC, cells did not appear to have significant levels of reduced thiols. Our studies demonstrate the utility of SemKur-IM in the detection of thiol levels in live cells in response to chemical exposures, such as from drugs that return the cell to a healthier reduced state. An initial application to screening the effects of an Alzheimer's disease drug candidate, Posiphen, using fluorescence cell sorting is presented. Other potential applications include high throughput screening of central nervous system (CNS) drugs thought to work by affecting cellular redox state in neurons.

High-content imaging approaches, in combination with the use of perturbing agents such as small molecules or CRISPR-driven gene editing, have widely contributed to the identification of new therapeutic compounds. Thanks to recent advances in image-analysis methods, the use of high-content screens is increasingly gaining popularity and thus accelerating the discovery of new therapeutics. However, due to the lack of fully biocompatible fluorescent markers, large-scale high-content screens are mostly performed on fixed cells, which complicates the monitoring of changes in cell physiology over time.

Here we present a novel fluorescent nontoxic dye that displays intensity and staining pattern changes in response to different physiological states. With multiparametric image analysis, these unique properties allow not only for the detection of distinct phenotypic fingerprints, but also for the quantification of more traditional disease-relevant phenotypes such as apoptosis, autophagy, ER stress and more. Since the dye only gets fluorescent when incorporated into cellular membranes, it is typically used without washing steps, therefore making it ideal to include in automation workflows. In this work, we present ​​relevant data on its biocompatibility and its potential to quantitatively assess subtle cellular phenotypes. Applications such as live kinetic imaging, and live image-based morphological profiling are also discussed. The rich information this fluorescent probe provides facilitates unbiased quantitative phenotypic analysis at larger scale, and ultimately paves the way for more discoveries of new therapeutic agents.

The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990′s. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging:

• Evolution and impact of high content imaging: An academic perspective

• Evolution and impact of high content imaging: An industry perspective

• Evolution of high content image analysis

• Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications

• The role of data integration and multiomics

• The role and evolution of image data repositories and sharing standards

• Future perspective of high content imaging hardware and software

Diabetes poses a global health crisis affecting individuals across age groups and backgrounds, with a prevalence estimate of 700 million people worldwide by 2045. Current therapeutic strategies primarily rely on insulin therapy or hypoglycemic agents, which fail to address the root cause of the disease - the loss of pancreatic insulin-producing beta-cells. Therefore, bioassays that recapitulate intact islets are needed to enable drug discovery for beta-cell replenishment, protection from beta-cell loss, and islet-cell interactions. Standard cancer insulinoma beta-cell lines MIN6 and INS-1 have been used to interrogate beta-cell metabolic pathways and function but are not suitable for studying proliferative effects. Screening using primary human/rodent intact islets offers a higher level of physiological relevance to enhance diabetes drug discovery and development. However, the 3-dimensionality of intact islets have presented challenges in developing robust, high-throughput assays to detect beta-cell proliferative effects. Established methods rely on either dissociated islet cells plated in 2D monolayer cultures for imaging or reconstituted pseudo-islets formed in round bottom plates to achieve homogeneity. These approaches have significant limitations due to the islet cell dispersion process. To address these limitations, we have developed a robust, intact ex vivo pancreatic islet bioassay in 384-well format that is capable of detecting diabetes-relevant endpoints including beta-cell proliferation, chemoprotection, and islet spatial morphometrics.

Three series of compounds were prioritized from a high content screening campaign that identified molecules that blocked dihydrotestosterone (DHT) induced formation of Androgen Receptor (AR) protein-protein interactions (PPIs) with the Transcriptional Intermediary Factor 2 (TIF2) coactivator and also disrupted preformed AR-TIF2 PPI complexes; the hydrobenzo-oxazepins (S1), thiadiazol-5-piperidine-carboxamides (S2), and phenyl-methyl-indoles (S3). Compounds from these series inhibited AR PPIs with TIF2 and SRC-1, another p160 coactivator, in mammalian 2-hybrid assays and blocked transcriptional activation in reporter assays driven by full length AR or AR-V7 splice variants. Compounds inhibited the growth of five prostate cancer cell lines, with many exhibiting differential cytotoxicity towards AR positive cell lines. Representative compounds from the 3 series substantially reduced both endogenous and DHT-enhanced expression and secretion of the prostate specific antigen (PSA) cancer biomarker in the C4–2 castration resistant prostate cancer (CRPC) cell line. The comparatively weak activities of series compounds in the H³-DHT and/or TIF2 box 3 LXXLL-peptide binding assays to the recombinant ligand binding domain of AR suggest that direct antagonism at the orthosteric ligand binding site or AF-2 surface respectively are unlikely mechanisms of action. Cellular enhanced thermal stability assays (CETSA) indicated that compounds engaged AR and reduced the maximum efficacy and right shifted the EC₅₀ of DHT-enhanced AR thermal stabilization consistent with the effects of negative allosteric modulators. Molecular docking of potent representative hits from each series to AR structures suggest that S1–1 and S2–6 engage a novel binding pocket (BP-1) adjacent to the orthosteric ligand binding site, while S3–11 occupies the AR binding function 3 (BF-3) allosteric pocket. Hit binding poses indicate spaces and residues adjacent to the BP-1 and BF-3 pockets that will be exploited in future medicinal chemistry optimization studies. Small molecule allosteric modulators that prevent/disrupt AR PPIs with coactivators like TIF2 to alter transcriptional activation in the presence of orthosteric agonists might evade the resistance mechanisms to existing prostate cancer drugs and provide novel starting points for medicinal chemistry lead optimization and future development into therapies for metastatic CRPC.

Mesenchymal stromal cells (MSCs) contribute to the microenvironment regulating normal and malignant hematopoiesis, and thus may support subpopulations of cancer cells to escape therapeutic pressure. Here, we engineered bone marrow MSCs to express a synthetic CD19-sensor receptor to detect and display interacting primary CD19+ leukemia cells in coculture. This implementation provides a versatile platform facilitating ex vivo drug response profiling of primary CD19+ leukemia cells in coculture with high-sensitivity and scalability.