SLAS Discovery最新文献

The beta-glucocerebrosidase (GBA1) gene encodes the lysosomal beta-glucocerebrosidase (GCase) that metabolizes the lipids glucosylceramide (GlcCer) and glucosylsphingosine (GlcSph). Biallelic loss-of-function mutations in GBA1 such as L444P cause Gaucher disease (GD), which is the most prevalent lysosomal storage disease and is histopathologically characterized by abnormal accumulation of the GCase substrates GlcCer and GlcSph. GD with neurological symptoms is associated with severe mutations in the GBA1 gene, most of which cause impairment in the process of GCase trafficking to lysosomes. Given that recombinant GCase protein cannot cross the blood-brain barrier due to its high molecular weight, it is invaluable to develop a brain-penetrant small-molecule pharmacological chaperone as a viable therapeutic strategy to boost GCase activity in the central nervous system.

Despite considerable efforts to screen potent GCase activators/chaperones, cell-free assays using recombinant GCase protein have yielded compounds with only marginal efficacy and micromolar EC₅₀ that would not have sufficient clinical efficacy or an acceptable safety margin. Therefore, we utilized a fluorescence-labeled GCase suicide inhibitor, MDW933, to directly monitor lysosomal GCase activity and performed a cell-based screening in fibroblasts from a GD patient with homozygotic L444P mutations. Here, we identified novel compounds that increase the fluorescence signal from labeled GCase with L444P mutations in a dose-dependent manner. Secondary assays using an artificial cell-permeable lysosomal GCase substrate also demonstrated that the identified compounds augment lysosomal GCase L444P in the fibroblast. Moreover, those compounds increased the total GCase L444P protein levels, suggesting the pharmacological chaperone-like mechanism of action. To further elucidate the effect of the compounds on the endogenous GCase substrate GlcSph, we generated iPSC-derived dopaminergic neurons with a GBA1 L444P mutation that exhibit GlcSph accumulation in vitro. Importantly, the identified compounds reduce GlcSph in iPSC-derived dopaminergic neurons with a GBA1 L444P mutation, indicating that the increase in lysosomal GCase resulting from application of the compounds leads to the clearance of pathologically-accumulated GlcSph. Together, our findings pave the way for developing potent and efficacious GCase chaperone compounds as a potential therapeutic approach for neurological GD.

The increasing use of automation in cellular assays and cell culture presents significant opportunities to enhance the scale and throughput of imaging assays, but to do so, reliable data quality and consistency are critical. Realizing the full potential of automation will thus require the design of robust analysis pipelines that span the entire workflow in question. Here we present FocA, a deep learning tool that, in near real-time, identifies in-focus and out-of-focus images generated on a fully automated cell biology research platform, the NYSCF Global Stem Cell Array®. The tool is trained on small patches of downsampled images to maximize computational efficiency without compromising accuracy, and optimized to make sure no sub-quality images are stored and used in downstream analyses. The tool automatically generates balanced and maximally diverse training sets to avoid bias. The resulting model correctly identifies 100% of out-of-focus and 98% of in-focus images in under 4 s per 96-well plate, and achieves this result even in heavily downsampled data (∼30 times smaller than native resolution). Integrating the tool into automated workflows minimizes the need for human verification as well as the collection and usage of low-quality data. FocA thus offers a solution to ensure reliable image data hygiene and improve the efficiency of automated imaging workflows using minimal computational resources.

Human induced pluripotent stem cell (iPSC)-derived neurons are being increasingly used for high content imaging and screening. However, iPSC-derived neuronal differentiation and maturation is time-intensive, often requiring >8 weeks. Unfortunately, the differentiating and maturing iPSC-derived neuronal cultures also tend to migrate and coalesce into ganglion-like clusters making single-cell analysis challenging, especially in miniaturized formats. Using our defined extracellular matrix and low oxygen culturing conditions for the differentiation and maturation of human cortical neurons, we further modified neuronal progenitor cell seeding densities and feeder layer-free culturing conditions in miniaturized formats (i.e., 96 well) to decrease neuronal clustering, enhance single-cell identification and reduce edge effects usually observed after extended neuronal cell culture. Subsequent algorithm development refined capabilities to distinguish and identify single mature neurons, as identified by NeuN expression, from large cellular aggregates, which were excluded from image analysis. Incorporation of astrocyte conditioned medium during differentiation and maturation periods significantly increased the percentage (i.e., ∼10% to ∼30%) of mature neurons (i.e., NeuN+) detected at 4-weeks post-differentiation. Pilot, proof of concept studies using this optimized assay system yielded negligible edge effects and robust Z-factors in population-based as well as image-based neurotoxicity assay formats. Moreover, moxidectin, an FDA-approved drug with documented neurotoxic adverse effects, was identified as a hit using both screening formats. This miniaturized, feeder layer-free format and image analysis algorithm provides a foundational imaging and screening platform, which enables quantitative single-cell analysis of differentiated human neurons.

The genesis of most older medicinal agents has generally been empirical. During the past one and a half centuries, at least in the Western countries, discovering and developing drugs has been primarily the domain of pharmaceutical companies largely built upon concepts emerging from organic chemistry. Public sector funding for the discovery of new therapeutics has more recently stimulated local, national, and international groups to band together and focus on new human disease targets and novel treatment approaches. This Perspective describes one contemporary example of a newly formed collaboration that was simulated by a regional drug discovery consortium. University of Virginia, Old Dominion University, and a university spinout company, KeViRx, Inc., partnered under a NIH Small Business Innovation Research grant, to produce potential therapeutics for acute respiratory distress syndrome resulting from the ongoing COVID-19 pandemic.

The Department of Medicinal Chemistry, together with the Institute for Structural Biology, Drug Discovery and Development, at Virginia Commonwealth University (VCU) has evolved, organically with quite a bit of bootstrapping, into a unique drug discovery ecosystem in response to the environment and culture of the university and the wider research enterprise. Each faculty member that joined the department and/or institute added a layer of expertise, technology and most importantly, innovation, that fertilized numerous collaborations within the University and with outside partners. Despite moderate institutional support with respect to a typical drug discovery enterprise, the VCU drug discovery ecosystem has built and maintained an impressive array of facilities and instrumentation for drug synthesis, drug characterization, biomolecular structural analysis and biophysical analysis, and pharmacological studies. Altogether, this ecosystem has had major impacts on numerous therapeutic areas, such as neurology, psychiatry, drugs of abuse, cancer, sickle cell disease, coagulopathy, inflammation, aging disorders and others. Novel tools and strategies for drug discovery, design and development have been developed at VCU in the last five decades; e.g., fundamental rational structure-activity relationship (SAR)-based drug design, structure-based drug design, orthosteric and allosteric drug design, design of multi-functional agents towards polypharmacy outcomes, principles on designing glycosaminoglycans as drugs, and computational tools and algorithms for quantitative SAR (QSAR) and understanding the roles of water and the hydrophobic effect.

The sphingosine-1-phosphate (S1P) pathway remains an active area of research for drug discovery because S1P modulators are effective medicine for autoimmune diseases such as multiple sclerosis and ulcerative colitis. As such, other nodes in the pathway can be probed for alternative therapeutic candidates. As S1P elicits its function in an ‘outside-in’ fashion, targeting the transporter, Spns2, which is upstream of the receptors, is of great interest. To support our medicinal chemistry campaign to inhibit S1P transport, we developed a mammalian cell-based assay. In this protocol, Spns2 inhibition is assessed by treating HeLa, U-937, and THP-1 cells with inhibitors and S1P exported in the extracellular milieu is quantified by LC-MS/MS. Our studies demonstrated that the amount of S1P in the media in inversely proportional to inhibitor concentration. The details of our investigations are described herein.

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.