Structural Analysis and Development of Notum Fragment Screening Hits

The Wnt signaling suppressor Notum is a promising target for osteoporosis, Alzheimer's disease, and colorectal cancers. To develop novel Notum inhibitors, we used an X-ray crystallographic fragment screen with the Diamond-SGC Poised Library (DSPL) and identified 59 fragment hits from the analysis of 768 data sets. Fifty-eight of the hits were found bound at the enzyme catalytic pocket with potencies ranging from 0.5 to >1000 μM. Analysis of the fragments' diverse binding modes, enzymatic inhibitory activities, and chemical properties led to the selection of six hits for optimization, and five of these resulted in improved Notum inhibitory potencies. One hit, 1-phenyl-1,2,3-triazole 7, and its related cluster members, have shown promising lead-like properties. These became the focus of our fragment development activities, resulting in compound 7d with IC50 0.0067 μM. The large number of Notum fragment structures and their initial optimization provided an important basis for further Notum inhibitor development

Assessment of portal hypertension severity using machine learning models in patients with compensated cirrhosis

Background & Aims: In individuals with compensated advanced chronic liver disease (cACLD), the severity of portal hypertension (PH) determines the risk of decompensation. Invasive measurement of the hepatic venous pressure gradient (HVPG) is the diagnostic gold standard for PH. We evaluated the utility of machine learning models (MLMs) based on standard laboratory parameters to predict the severity of PH in individuals with cACLD. Methods: A detailed laboratory workup of individuals with cACLD recruited from the Vienna cohort (NCT03267615) was utilised to predict clinically significant portal hypertension (CSPH, i.e., HVPG ≥10 mmHg) and severe PH (i.e., HVPG ≥16 mmHg). The MLMs were then evaluated in individual external datasets and optimised in the merged cohort. Results: Among 1,232 participants with cACLD, the prevalence of CSPH/severe PH was similar in the Vienna (n = 163, 67.4%/35.0%) and validation (n = 1,069, 70.3%/34.7%) cohorts. The MLMs were based on 3 (3P: platelet count, bilirubin, international normalised ratio) or 5 (5P: +cholinesterase, +gamma-glutamyl transferase, +activated partial thromboplastin time replacing international normalised ratio) laboratory parameters. The MLMs performed robustly in the Vienna cohort. 5P-MLM had the best AUCs for CSPH (0.813) and severe PH (0.887) and compared favourably to liver stiffness measurement (AUC: 0.808). Their performance in external validation datasets was heterogeneous (AUCs: 0.589-0.887). Training on the merged cohort optimised model performance for CSPH (AUCs for 3P and 5P: 0.775 and 0.789, respectively) and severe PH (0.737 and 0.828, respectively). Conclusions: Internally trained MLMs reliably predicted PH severity in the Vienna cACLD cohort but exhibited heterogeneous results on external validation. The proposed 3P/5P online tool can reliably identify individuals with CSPH or severe PH, who are thus at risk of hepatic decompensation. Impact and implications: We used machine learning models based on widely available laboratory parameters to develop a non-invasive model to predict the severity of portal hypertension in individuals with compensated cirrhosis, who currently require invasive measurement of hepatic venous pressure gradient. We validated our findings in a large multicentre cohort of individuals with advanced chronic liver disease (cACLD) of any cause. Finally, we provide a readily available online calculator, based on 3 (platelet count, bilirubin, international normalised ratio) or 5 (platelet count, bilirubin, activated partial thromboplastin time, gamma-glutamyltransferase, choline-esterase) widely available laboratory parameters, that clinicians can use to predict the likelihood of their patients with cACLD having clinically significant or severe portal hypertension

Design of a Potent, Selective, and Brain-Penetrant Inhibitor of Wnt-Deactivating Enzyme Notum by Optimization of a Crystallographic Fragment Hit

Notum is a carboxylesterase that suppresses Wnt signaling through deacylation of an essential palmitoleate group on Wnt proteins. There is a growing understanding of the role Notum plays in human diseases such as colorectal cancer and Alzheimer's disease, supporting the need to discover improved inhibitors, especially for use in models of neurodegeneration. Here, we have described the discovery and profile of 8l (ARUK3001185) as a potent, selective, and brain-penetrant inhibitor of Notum activity suitable for oral dosing in rodent models of disease. Crystallographic fragment screening of the Diamond-SGC Poised Library for binding to Notum, supported by a biochemical enzyme assay to rank inhibition activity, identified 6a and 6b as a pair of outstanding hits. Fragment development of 6 delivered 8l that restored Wnt signaling in the presence of Notum in a cell-based reporter assay. Assessment in pharmacology screens showed 8l to be selective against serine hydrolases, kinases, and drug targets

Role of Mutagenicity in Asbestos Fiber-Induced Carcinogenicity and Other Diseases

The cellular and molecular mechanisms of how asbestos fibers induce cancers and other diseases are not well understood. Both serpentine and amphibole asbestos fibers have been shown to induce oxidative stress, inflammatory responses, cellular toxicity and tissue injuries, genetic changes, and epigenetic alterations in target cells in vitro and tissues in vivo. Most of these mechanisms are believe to be shared by both fiber-induced cancers and noncancerous diseases. This article summarizes the findings from existing literature with a focus on genetic changes, specifically, mutagenicity of asbestos fibers. Thus far, experimental evidence suggesting the involvement of mutagenesis in asbestos carcinogenicity is more convincing than asbestos-induced fibrotic diseases. The potential contributions of mutagenicity to asbestos-induced diseases, with an emphasis on carcinogenicity, are reviewed from five aspects: (1) whether there is a mutagenic mode of action (MOA) in fiber-induced carcinogenesis; (2) mutagenicity/carcinogenicity at low dose; (3) biological activities that contribute to mutagenicity and impact of target tissue/cell type; (4) health endpoints with or without mutagenicity as a key event; and finally, (5) determinant factors of toxicity in mutagenicity. At the end of this review, a consensus statement of what is known, what is believed to be factual but requires confirmation, and existing data gaps, as well as future research needs and directions, is provided

The Helicobacter pylori Genome Project : insights into H. pylori population structure from analysis of a worldwide collection of complete genomes

Helicobacter pylori, a dominant member of the gastric microbiota, shares co-evolutionary history with humans. This has led to the development of genetically distinct H. pylori subpopulations associated with the geographic origin of the host and with differential gastric disease risk. Here, we provide insights into H. pylori population structure as a part of the Helicobacter pylori Genome Project (HpGP), a multi-disciplinary initiative aimed at elucidating H. pylori pathogenesis and identifying new therapeutic targets. We collected 1011 well-characterized clinical strains from 50 countries and generated high-quality genome sequences. We analysed core genome diversity and population structure of the HpGP dataset and 255 worldwide reference genomes to outline the ancestral contribution to Eurasian, African, and American populations. We found evidence of substantial contribution of population hpNorthAsia and subpopulation hspUral in Northern European H. pylori. The genomes of H. pylori isolated from northern and southern Indigenous Americans differed in that bacteria isolated in northern Indigenous communities were more similar to North Asian H. pylori while the southern had higher relatedness to hpEastAsia. Notably, we also found a highly clonal yet geographically dispersed North American subpopulation, which is negative for the cag pathogenicity island, and present in 7% of sequenced US genomes. We expect the HpGP dataset and the corresponding strains to become a major asset for H. pylori genomics