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The growth hormone secretagogue receptor-1a (GHSR1a) is the cognate G protein–coupled receptor (GPCR) for the peptide hormone ghrelin. GHSR1a is a promising therapeutic target for a wide range of metabolic, age-related, and central nervous system (CNS)–based conditions. In addition, growing evidence supports that GHSR1a is a modulator of dopamine (DA) homeostasis and is neuroprotective within brain DA circuits. GHSR1a signaling originates from pharmacologically separable G protein– and β-arrestin (βarr)–dependent pathways, and consequently, GHSR1a-mediated physiological responses depend upon their distinctive signaling contributions. Thus, when treating disorders of disrupted DA homeostasis, a pharmacological strategy that modulates biased GHSR1a signaling may uncouple desired therapeutic outcomes from unwanted side effects. Here, we report the discovery of a small molecule GHSR1a agonist, N8279 (NCATS-SM8864), functionally selective for G protein signaling. Comprehensive pharmacological characterization reveals that N8279 elicits potent Gαq activity at the apo- and ghrelin-bound GHSR1a. Further biochemical analysis and molecular modeling demonstrate that N8279 signaling requires the extracellular domain of GHSR1a, especially extra-cellular loop 2. Collectively, these findings suggest that N8279 possesses an extended binding mode into the extracellular vestibule of the GHSR1a that preferentially favors Gαq signaling over alternative G proteins and βarr2-dependent cellular responses. Critically, N8279 is brain-penetrant in mice, exhibits CNS stability, and attenuates dysfunctional DA-mediated behaviors in both genetic and pharmacological mouse models of hyperdopaminergia. Our findings provide insight into the mechanisms governing GPCR functional selectivity and emphasize how biased ligand drug development can produce novel GHSR1a pharmacotherapeutics to treat pathological disruptions of brain DA homeostasis. [ABSTRACT FROM AUTHOR]

We recently applied a Wertheim integral equation theory (IET) and a thermodynamic perturbation theory (TPT) to the Mercedes–Benz (MB) model of pure water. These analytical theories offer the advantage of being computationally less intensive than the Monte Carlo simulations by orders of magnitudes. The long-term goal of this work is to develop analytical theories of water that can handle orientation-dependent interactions and the MB model serves as a simple workbench for this development. Here we apply the IET and TPT to the hydrophobic effect, the transfer of a nonpopular solute into MB water. As before, we find that the theories reproduce the Monte Carlo results quite accurately at higher temperatures, while they predict the qualitative trends in cold water. © 2002 American Institute of Physics. [ABSTRACT FROM AUTHOR]

Context: Thyroid-stimulating hormone (or thyrotropin) receptor (TSHR) could be a selective target for small molecule ligands to treat thyroid cancer (TC)., Objective: We report a novel, orally efficacious ligand for TSHR that exhibits proliferation inhibitory activity against human TC in vitro and in vivo, and inhibition of metastasis in vivo., Methods: A35 (NCATS-SM4420; NCGC00241808) was selected from a sublibrary of >200 TSHR ligands. Cell proliferation assays including BrdU incorporation and WST-1, along with molecular docking studies were done. In vivo activity of A35 was assessed in TC cell-derived xenograft (CDX) models with immunocompromised (NSG) mice. Formalin-fixed, paraffin-embedded sections of tumor and lung tissues were observed for the extent of cell death and metastasis., Results: A35 was shown to stimulate cAMP production in some cell types by activating TSHR but not in TC cells, MDA-T32, and MDA-T85. A35 inhibited proliferation of MDA-T32 and MDA-T85 in vitro and in vivo, and pulmonary metastasis of MDA-T85F1 in mice. In vitro, A35 inhibition of proliferation was reduced by a selective TSHR antagonist. Inhibition of CDX tumor growth without decreases in mouse weights and liver function showed A35 to be efficacious without apparent toxicity. Lastly, A35 reduced levels of Ki67 in the tumors and metastatic markers in lung tissues., Conclusion: We conclude that A35 is a TSHR-selective inhibitor of TC cell proliferation and metastasis, and suggest that A35 may be a promising lead drug candidate for the treatment of differentiated TC in humans., (Published by Oxford University Press on behalf of the Endocrine Society 2024.)

The molecular mechanisms underlying seizure generation remain elusive, yet they are crucial for developing effective treatments for epilepsy. The current study shows that inhibiting c-Abl tyrosine kinase prevents apoptosis, reduces dendritic spine loss, and maintains N-methyl-d-aspartate (NMDA) receptor subunit 2B (NR2B) phosphorylated in in vitro models of excitotoxicity. Pilocarpine-induced status epilepticus (SE) in mice promotes c-Abl phosphorylation, and disrupting c-Abl activity leads to fewer seizures, increases latency toward SE, and improved animal survival. Currently, clinically used c-Abl inhibitors are non-selective and have poor brain penetration. The allosteric c-Abl inhibitor, neurotinib, used here has favorable potency, selectivity, pharmacokinetics, and vastly improved brain penetration. Neurotinib-administered mice have fewer seizures and improved survival following pilocarpine-SE induction. Our findings reveal c-Abl kinase activation as a key factor in ictogenesis and highlight the impact of its inhibition in preventing the insurgence of epileptic-like seizures in rodents and humans., Competing Interests: Declaration of interests Neurotinib is under patent WO2019/173761 A1., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Knowledge graphs are well-suited for modeling complex, unstructured, and multi-source data and facilitating their analysis. During the COVID-19 pandemic, adverse event data were integrated into a knowledge graph to support vaccine safety surveillance and nimbly respond to urgent health authority questions. Here, we provide details of this post-marketing safety system using public data sources. In addition to challenges with varied data representations, adverse event reporting on the COVID-19 vaccines generated an unprecedented volume of data; an order of magnitude larger than adverse events for all previous vaccines. The Patient Safety Knowledge Graph (PSKG) is a robust data store to accommodate the volume of adverse event data and harmonize primary surveillance data sources., Methods: We designed a semantic model to represent key safety concepts. We built an extract-transform-load (ETL) data pipeline to parse and import primary public data sources; align key elements such as vaccine names; integrated the Medical Dictionary for Regulatory Activities (MedDRA); and applied quality metrics. PSKG is deployed in a Neo4J graph database, and made available via a web interface and Application Programming Interfaces (APIs)., Results: We import and align adverse event data and vaccine exposure data from 250 countries on a weekly basis, producing a graph with 4,340,980 nodes and 30,544,475 edges as of July 1, 2022. PSKG is used for ad-hoc analyses and periodic reporting for several widely available COVID-19 vaccines. Analysis code using the knowledge graph is 80% shorter than an equivalent implementation written entirely in Python, and runs over 200 times faster., Conclusions: Organizing safety data into a concise model of nodes, properties, and edge relationships has greatly simplified analysis code by removing complex parsing and transformation algorithms from individual analyses and instead managing these centrally. The adoption of the knowledge graph transformed how the team answers key scientific and medical questions. Whereas previously an analysis would involve aggregating and transforming primary datasets from scratch to answer a specific question, the team can now iterate easily and respond as quickly as requests evolve (e.g., "Produce vaccine-X safety profile for adverse event-Y by country instead of age-range")., (© 2023. The Author(s).)

Introduction: As part of routine safety surveillance, thousands of articles of potential interest are manually triaged for review by safety surveillance teams. This manual triage task is an interesting candidate for automation based on the abundance of process data available for training, the performance of natural language processing algorithms for this type of cognitive task, and the small number of safety signals that originate from literature review, resulting in its lower risk profile. However, deep learning algorithms introduce unique risks and the validation of such models for use in Good Pharmacovigilance Practice remains an open question., Objective: Qualifying an automated, deep learning approach to literature surveillance for use at AstraZeneca., Methods: The study is a prospective validation of a literature surveillance triage model, comparing its real-world performance with that of human surveillance teams working in parallel. The biggest risk in modifying this triage process is missing a safety signal (resulting in model false negatives) and hence model recall is the main evaluation metric considered., Results: The model demonstrates consistent global performance from training through testing, with recall rates comparable to that of existing surveillance teams. The model is accepted for use specifically for those products where non-inferiority to the manual process is rigorously demonstrated., Conclusion: Characterizing model performance prospectively, under real-world conditions, allows us to thoroughly examine model consistency and failure modes, qualifying it for use in our surveillance processes. We also identify potential future improvements and recognize the opportunity for the community to collaborate on this shared task., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The small GTPase Cdc42 is an integral component of the cytoskeleton, and its dysregulation leads to pathophysiological conditions, such as cancer. Binding of Cdc42 to the scaffold protein IQGAP1 stabilizes Cdc42 in its active form. The interaction between Cdc42 and IQGAP1 enhances migration and invasion of cancer cells. Disrupting this association could impair neoplastic progression and metastasis; however, no effective means to achieve this has been described. Here, we screened 78,500 compounds using a homogeneous time resolved fluorescence-based assay to identify small molecules that disrupt the binding of Cdc42 to IQGAP1. From the combined results of the validation assay and counter-screens, we selected 44 potent compounds for cell-based experiments. Immunoprecipitation and cell viability analysis rendered four lead compounds, namely NCGC00131308, NCGC00098561, MLS000332963 and NCGC00138812, three of which inhibited proliferation and migration of breast carcinoma cells. Microscale thermophoresis revealed that two compounds bind directly to Cdc42. One compound reduced the amount of active Cdc42 in cells and effectively impaired filopodia formation. Docking analysis provided plausible models of the compounds binding to the hydrophobic pocket adjacent to the GTP binding site of Cdc42. In conclusion, we identified small molecules that inhibit binding between Cdc42 and IQGAP1, which could potentially yield chemotherapeutic agents., (© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Determining a molecule's mechanism of action is paramount during chemical probe development and drug discovery. The cellular thermal shift assay (CETSA) is a valuable tool to confirm target engagement in cells for a small molecule that demonstrates a pharmacological effect. CETSA directly detects biophysical interactions between ligands and protein targets, which can alter a protein's unfolding and aggregation properties in response to thermal challenge. In traditional CETSA experiments, each temperature requires an individual sample, which restricts throughput and requires substantial optimization. To capture the full aggregation profile of a protein from a single sample, we developed a prototype real-time CETSA (RT-CETSA) platform by coupling a real-time PCR instrument with a CCD camera to detect luminescence. A thermally stable Nanoluciferase variant (ThermLuc) was bioengineered to withstand unfolding at temperatures greater than 90 °C and was compatible with monitoring target engagement events when fused to diverse targets. Utilizing well-characterized inhibitors of lactate dehydrogenase alpha, RT-CETSA showed significant correlation with enzymatic, biophysical, and other cell-based assays. A data analysis pipeline was developed to enhance the sensitivity of RT-CETSA to detect on-target binding. RT-CETSA technology advances capabilities of the CETSA method and facilitates the identification of ligand-target engagement in cells, a critical step in assessing the mechanism of action of a small molecule.

Numerous studies have established the involvement of lysosomal and mitochondrial dysfunction in the pathogenesis of neurodegenerative disorders such as Alzheimer's and Parkinson diseases. Building on our previous studies of the neurodegenerative lysosomal lipidosis Niemann-Pick C1 (NPC1), we have unexpectedly discovered that activation of the mitochondrial chaperone tumor necrosis factor receptor-associated protein 1 (TRAP1) leads to the correction of the lysosomal storage phenotype in patient cells from multiple lysosomal storage disorders including NPC1. Using small compound activators specific for TRAP1, we find that activation of this chaperone leads to a generalized restoration of lysosomal and mitochondrial health. Mechanistically, we show that this process includes inhibition of oxidative phosphorylation and reduction of oxidative stress, which results in activation of AMPK and ultimately stimulates lysosome recycling. Thus, TRAP1 participates in lysosomal-mitochondrial crosstalk to maintain cellular homeostasis and could represent a potential therapeutic target for multiple disorders., Competing Interests: F.W.C. and Y.A.I. at the Icahn School of Medicine at Mount Sinai and S.P., R.C., J.J.M., N.S., M.F., J.D., and P.D. at NCATS (NIH) are inventors on a patent (WO16/130,774) for the TRAP1 activating compounds described in these studies, which has been licensed to Amathus Therapeutics for clinical development. Y.A.I. and the Icahn School of Medicine at Mount Sinai are founders of Amathus Therapeutics., (© 2022 The Authors.)

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems., (© 2022 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)