Novel Trypanocidal Inhibitors that Block Glycosome Biogenesis by Targeting PEX3–PEX19 Interaction

Human pathogenic trypanosomatid parasites harbor a unique form of peroxisomes termed glycosomes that are essential for parasite viability. We and others previously identified and characterized the essential Trypanosoma brucei ortholog TbPEX3, which is the membrane-docking factor for the cytosolic receptor PEX19 bound to the glycosomal membrane proteins. Knockdown of TbPEX3 expression leads to mislocalization of glycosomal membrane and matrix proteins, and subsequent cell death. As an early step in glycosome biogenesis, the PEX3–PEX19 interaction is an attractive drug target. We established a high-throughput assay for TbPEX3–TbPEX19 interaction and screened a compound library for small-molecule inhibitors. Hits from the screen were further validated using an in vitro ELISA assay. We identified three compounds, which exhibit significant trypanocidal activity but show no apparent toxicity to human cells. Furthermore, we show that these compounds lead to mislocalization of glycosomal proteins, which is toxic to the trypanosomes. Moreover, NMR-based experiments indicate that the inhibitors bind to PEX3. The inhibitors interfering with glycosomal biogenesis by targeting the TbPEX3–TbPEX19 interaction serve as starting points for further optimization and anti-trypanosomal drug development.

DANCR Mediates the Rescuing Effects of Sesamin on Postmenopausal Osteoporosis Treatment via Orchestrating Osteogenesis and Osteoclastogenesis

As one of the leading causes of bone fracture in postmenopausal women and in older men, osteoporosis worldwide is attracting more attention in recent decades. Osteoporosis is a common disease mainly resulting from an imbalance of bone formation and bone resorption. Pharmaceutically active compounds that both activate osteogenesis, while repressing osteoclastogenesis hold the potential of being therapeutic medications for osteoporosis treatment. In the present study, sesamin, a bioactive ingredient derived from the seed of Sesamum Indicum, was screened out from a bioactive compound library and shown to exhibit dual-regulating functions on these two processes. Sesamin was demonstrated to promote osteogenesis by upregulating Wnt/β-catenin, while repressing osteoclastogenesis via downregulating NF-κB signaling . Furthermore, DANCR was found to be the key regulator in sesamin-mediated bone formation and resorption . In an ovariectomy (OVX)-induced osteoporotic mouse model, sesamin could rescue OVX-induced bone loss and impairment. The increased serum level of DANCR caused by OVX was also downregulated upon sesamin treatment. In conclusion, our results demonstrate that sesamin plays a dual-functional role in both osteogenesis activation and osteoclastogenesis de-activation in a DANCR-dependent manner, suggesting that it may be a possible medication candidate for osteoporotic patients with elevated DNACR expression levels.

Virtual Screening with Gnina 1.0

Virtual screening—predicting which compounds within a specified compound library bind to a target molecule, typically a protein—is a fundamental task in the field of drug discovery. Doing virtual screening well provides tangible practical benefits, including reduced drug development costs, faster time to therapeutic viability, and fewer unforeseen side effects. As with most applied computational tasks, the algorithms currently used to perform virtual screening feature inherent tradeoffs between speed and accuracy. Furthermore, even theoretically rigorous, computationally intensive methods may fail to account for important effects relevant to whether a given compound will ultimately be usable as a drug. Here we investigate the virtual screening performance of the recently released Gnina molecular docking software, which uses deep convolutional networks to score protein-ligand structures. We find, on average, that Gnina outperforms conventional empirical scoring. The default scoring in Gnina outperforms the empirical AutoDock Vina scoring function on 89 of the 117 targets of the DUD-E and LIT-PCBA virtual screening benchmarks with a median 1% early enrichment factor that is more than twice that of Vina. However, we also find that issues of bias linger in these sets, even when not used directly to train models, and this bias obfuscates to what extent machine learning models are achieving their performance through a sophisticated interpretation of molecular interactions versus fitting to non-informative simplistic property distributions.

Drug repurposing screening identified tropifexor as a SARS-CoV-2 papain-like protease inhibitor

The global COVID-19 pandemic underscores the dire need of effective antivirals. Encouraging progress has been made in developing small molecule inhibitors targeting the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) and main protease (Mpro). However, the development of papain-like protease (PLpro) inhibitors faces several obstacles. Nevertheless, PLpro represents a high-profile drug target given its multifaceted roles in viral replication. PLpro is involved in not only the cleavage of viral polyprotein but also modulation of host immune response. In this study, we conducted a drug-repurposing screening of PLpro against the MedChemExpress bioactive compound library and identified three hits, EACC, KY-226, and tropifexor, as potent PLpro inhibitors with IC50 values ranging from 3.39 to 8.28 µM. The three hits showed dose-dependent binding to PLpro in the thermal shift assay. In addition, tropifexor inhibited the cellular PLpro activity in the FlipGFP assay with an IC50 of 10.6 µM. Gratifyingly, tropifexor showed antiviral activity against SARS-CoV-2 in Calu-3 cells with an EC50 of 4.03 µM, a 7.8-fold increase compared to GRL0617 (EC50 = 31.4 µM). Overall, tropifexor represents a novel PLpro inhibitor that can be further developed as SARS-CoV-2 antivirals.

Virtual Screening with Gnina 1.0

Virtual screening - predicting which compounds within a specified compound library bind to a target molecule, typically a protein - is a fundamental task in the field of drug discovery. Doing virtual screening well provides tangible practical benefits, including reduced drug development costs, faster time to therapeutic viability, and fewer unforeseen side effects. As with most applied computational tasks, the algorithms currently used to perform virtual screening feature inherent tradeoffs between speed and accuracy. Furthermore, even theoretically rigorous, computationally intensive methods may fail to account for important effects relevant to whether a given compound will ultimately be usable as a drug. Here we investigate the virtual screening performance of the recently released Gnina molecular docking software, which uses deep convolutional networks to score protein-ligand structures. We find, on average, that Gnina outperforms conventional empirical scoring. The default scoring in Gnina outperforms the empirical AutoDock Vina scoring function on 89 of the 117 targets of the DUD-E and LIT-PCBA virtual screening benchmarks with a median 1% early enrichment factor that is more than twice that of Vina. However, we also find that issues of bias linger in these sets, even when not used directly to train models, and this bias obfuscates to what extent machine learning models are achieving their performance through a sophisticated interpretation of molecular interactions versus fitting to non-informative simplistic property distributions.

Screening of a Small Molecule Compound Library Identifies Toosendanin as an Inhibitor Against Bunyavirus and SARS-CoV-2

Severe fever with thrombocytopenia syndrome virus (SFTSV) is an emerging tick-borne virus causing serious infectious disease with a high case-fatality of up to 50% in severe cases. Currently, no effective drug has been approved for the treatment of SFTSV infection. Here, we performed a high-throughput screening of a natural extracts library for compounds with activities against SFTSV infection. Three hit compounds, notoginsenoside Ft1, punicalin, and toosendanin were identified for displaying high anti-SFTSV efficacy, in which, toosendanin showed the highest inhibition potency. Mechanistic investigation indicated that toosendanin inhibited SFTSV infection at the step of virus internalization. The anti-viral effect of toosendanin against SFTSV was further verified in mouse infection models, and the treatment with toosendanin significantly reduced viral load and histopathological changes in vivo. The antiviral activity of toosendanin was further expanded to another bunyavirus and the emerging SARS-CoV-2. This study revealed a broad anti-viral effect of toosendanin and indicated its potential to be developed as an anti-viral drug for clinical use.

New Series of Pyrazoles and Imidazo-Pyrazoles Targeting Different Cancer and Inflammation Pathways

(1) Background: different previously synthesized pyrazoles and imidazo-pyrazoles showed interesting anti-angiogenic action, being able to interfere with ERK1/2, AKT and p38MAPK phosphorylation in different manners and with different potency; (2) Methods: here, a new small compound library, endowed with the same differently decorated chemical scaffolds, has been synthetized to obtain new agents able to inhibit different pathways involved in inflammation, cancer and human platelet aggregation. (3) Results: most of the new synthesized derivatives resulted able to block ROS production, platelet aggregation and p38MAPK phosphorylation both in platelets and Human Umbilical Vein Endothelial cells (HUVEC). This paves the way for the development of new agents with anti-angiogenic activity.

Natural Compound Library Screening Identifies Sanguinarine Chloride For The Treatment of SCLC By Upregulating CDKN1A

Background: Small cell lung cancer (SCLC) is notorious for aggressive malignancy without effective treatment, and most patients eventually develop tumor progression with a poor prognosis. There is an urgent need for discovering novel antitumor agents or therapeutic strategies for SCLC. Drug discovery from natural compounds has been proved to be an effective and innovative approach. Here, we performed a screening method with a natural compound library to identify the potential SCLC inhibitors. Methods: In this study, we performed a screening method based on CCK-8 assay to screen 640 natural compounds for SCLC. The effects of Sanguinarine chloride on SCLC cell proliferation, colony formation, cell cycle, apoptosis, migration and invasion were determined. RNA-seq and bioinformatics analysis was performed to investigate the anti-SCLC mechanism of Sanguinarine chloride. Publicly available datasets and samples were analyzed to investigate the expression level of CDKN1A and its clinical significance. Loss of functional cancer cell models were constructed by shRNA-mediated silencing. Quantitative RT-PCR and Western blot were used to measure gene and protein expression. Immunohistochemistry staining was performed to detect the expression of CDKN1A, Ki67, and Cleaved caspase 3 in xenograft tissues. Results: We identified Sanguinarine chloride as a potential inhibitor of SCLC, which inhibited cell proliferation, colony formation, cell cycle, cell migration and invasion, and promoted apoptosis of SCLC cells. Sanguinarine chloride played an important role in anti-SCLC by upregulating the expression of CDKN1A. Furthermore, Sanguinarine chloride in combination with panobinostat, or THZ1, or gemcitabine, or (+)-JQ-1 increased the anti-SCLC effect compared with either agent alone treatment. Conclusions: Our findings identified Sanguinarine chloride as a potential inhibitor of SCLC by upregulating the expression of CDKN1A. Sanguinarine chloride in combination with chemotherapy compounds exhibited strong synergism anti-SCLC properties, which could be further clinically explored for the treatment of SCLC.