Abstract: PURPOSE: Increased susceptibility towards anorexia nervosa (AN) was reported with reduced levels of neuronatin (NNAT) gene. We sought to investigate the most pathogenic rare-coding missense mutations, non-synonymous single-nucleotide polymorphisms (nsSNPs) of NNAT and their potential damaging impact on protein function through transcript level sequence and structure based in silico approaches. METHODS: Gene sequence, single nucleotide polymorphisms (SNPs) of NNAT was retrieved from public databases and the putative post-translational modification (PTM) sites were analyzed. Distinctive in silico algorithms were recruited for transcript level SNPs analyses and to characterized high-risk rare-coding nsSNPs along with their impact on protein stability function. Ab initio 3D-modeling of wild-type, alternate model prediction for most deleterious nsSNP, validation and recognition of druggable binding pockets were also performed. AN 3D therapeutic compounds that followed rule of drug-likeness were docked with most pathogenic variant of NNAT to estimate the drugs' binding free energies. RESULTS: Conclusively, 10 transcript (201-205)-based nsSNPs from 3 rare-coding missense variants, i.e., rs539681368, rs542858994, rs560845323 out of 840 exonic SNPs were identified. Transcript-based functional impact analyses predicted rs539681368 (C30Y) from NNAT-204 as the high-risk rare-coding pathogenic nsSNP, deviating protein functions. The 3D-modeling analysis of AN drugs' binding energies indicated lowest binding free energy (DeltaG) and significant inhibition constant (Ki) with mutant models C30Y. CONCLUSIONS: Mutant model (C30Y) exhibiting significant drug binding affinity and the commonest interaction observed at the acetylation site K59. Thus, based on these findings, we concluded that the identified nsSNP may serve as potential targets for various studies, diagnosis and therapeutic interventions. LEVEL OF EVIDENCE: No level of evidence-open access bioinformatics research.

Abstract: Gastric cancer is the fourth cause of cancer death globally, and gastric adenocarcinoma is its most common type. Efforts for the treatment of gastric cancer have increased its median survival rate by only seven months. Due to the relatively low response of gastric cancer to surgery and adjuvant therapy, as well as the complex role of risk factors in its incidences, such as protein-pomp inhibitors (PPIs) and viral and bacterial infections, we aimed to study the pathological pathways involved in gastric cancer development and investigate possible medications by systems biology and bioinformatics tools. In this study, the protein-protein interaction network was analyzed based on microarray data, and possible effective compounds were discovered. Non-coding RNA versus coding RNA interaction network and gene-disease network were also reconstructed to better understand the underlying mechanisms. It was found that compounds such as amiloride, imatinib, omeprazole, troglitazone, pantoprazole, and fostamatinib might be effective in gastric cancer treatment. In a gene-disease network, it was indicated that diseases such as liver carcinoma, breast carcinoma, liver fibrosis, prostate cancer, ovarian carcinoma, and lung cancer were correlated with gastric adenocarcinoma through specific genes, including hgf, mt2a, mmp2, fbn1, col1a1, and col1a2. It was shown that signaling pathways such as cell cycle, cell division, and extracellular matrix organization were overexpressed, while digestion and ion transport pathways were underexpressed. Based on a multilevel systems biology analysis, hub genes in gastric adenocarcinoma showed participation in the pathways such as focal adhesion, platelet activation, gastric acid secretion, HPV infection, and cell cycle. PPIs are hypothesized to have a therapeutic effect on patients with gastric cancer. Fostamatinib seems a potential therapeutic drug in gastric cancer due to its inhibitory effect on two survival genes. However, these findings should be confirmed through experimental investigations.

Abstract: Drug metabolite identification is a bottleneck of drug metabolism studies due to the need for time-consuming chromatographic separation and structural confirmation. Ion mobility-mass spectrometry (IM-MS), on the other hand, separates analytes on a rapid (millisecond) time scale and enables the measurement of collision cross section (CCS), a unique physical property related to an ion's gas-phase size and shape, which can be used as an additional parameter for identification of unknowns. A current limitation to the application of IM-MS to the identification of drug metabolites is the lack of reference CCS values. In this work, we assembled a large-scale database of drug and drug metabolite CCS values using high-throughput in vitro drug metabolite generation and a rapid IM-MS analysis with automated data processing. Subsequently, we used this database to train a machine learning-based CCS prediction model, employing a combination of conventional 2D molecular descriptors and novel 3D descriptors, achieving high prediction accuracies (0.8-2.2% median relative error on test set data). The inclusion of 3D information in the prediction model enables the prediction of different CCS values for different protomers, conformers, and positional isomers, which is not possible using conventional 2D descriptors. The prediction models, dmCCS, are available at https://CCSbase.net/dmccs_predictions.

Abstract: Promising drug development efforts may frequently fail due to unintended adverse reactions. Several methods have been developed to analyze such data, aiming to improve pharmacovigilance and drug safety. In this work, we provide a brief review of key directions to quantitatively analyzing adverse events and explore the potential of augmenting these methods using additional molecular data descriptors. We argue that molecular expansion of adverse event data may provide a path to improving the insights gained through more traditional pharmacovigilance approaches. Examples include the ability to assess statistical relevance with respect to underlying biomolecular mechanisms, the ability to generate plausible causative hypotheses and/or confirmation where possible, the ability to computationally study potential clinical trial designs and/or results, as well as the further provision of advanced features incorporated in innovative methods, such as machine learning. In summary, molecular data expansion provides an elegant way to extend mechanistic modeling, systems pharmacology, and patient-centered approaches for the assessment of drug safety. We anticipate that such advances in real-world data informatics and outcome analytics will help to better inform public health, via the improved ability to prospectively understand and predict various types of drug-induced molecular perturbations and adverse events.

Abstract: AIM: To identify pathogenic rare coding Mendelian/high-effect size variant(s) by whole-exome sequencing in familial polycystic ovary syndrome (PCOS) patients to elucidate PCOS-related pathways. METHODS: Twenty women and their affected available relatives diagnosed with PCOS according to Rotterdam criteria were recruited. Whole-exome sequencing on germ-line DNA from 31 PCOS probands and their affected relatives was performed. Whole-exome sequencing data were further evaluated by pathway and chemogenomics analyses. In-slico analysis of candidate variants were done by VarCards for functional predictions and VarSite for impact on three-dimensional (3D) structures in the candidate proteins. RESULTS: Two heterozygous rare FBN3 missense variants in three patients, and one FN1 missense variant in one patient from three different PCOS families were identified. CONCLUSION: We identified three novel FBN3 and FN1 variants for the first time in the literature and linked with PCOS. Further functional studies may identify causality of these newly discovered PCOS-related variants, and their role yet remains to be investigated. Our findings may improve our understanding of the biological pathways affected and identify new drug targets.

Abstract: Schizophrenia is a debilitating psychiatric disorder, leading to both physical and social morbidity. Worldwide 1% of the population is struggling with the disease, with 100,000 new cases annually only in the United States. Despite its importance, the goal of finding effective treatments for schizophrenia remains a challenging task, and previous work conducted expensive large-scale phenotypic screens. This work investigates the benefits of Machine Learning for graphs to optimize drug phenotypic screens and predict compounds that mitigate abnormal brain reduction induced by excessive glial phagocytic activity in schizophrenia subjects. Given a compound and its concentration as input, we propose a method that predicts a score associated with three possible compound effects, i.e., reduce, increase, or not influence phagocytosis. We leverage a high-throughput screening to prove experimentally that our method achieves good generalization capabilities. The screening involves 2218 compounds at five different concentrations. Then, we analyze the usability of our approach in a practical setting, i.e., prioritizing the selection of compounds in the SWEETLEAD library. We provide a list of 64 compounds from the library that have the most potential clinical utility for glial phagocytosis mitigation. Lastly, we propose a novel approach to computationally validate their utility as possible therapies for schizophrenia.

Abstract: Euphorbia plants have been identified as potential sources of antitumor lead compounds. The current study aimed to isolate and identify the main active constituents of Euphorbia abyssinica latex followed by a cytotoxic evaluation. A network pharmacology approach was employed to predict the underlying mechanism. Finally, drug-likeness and ADMET studies were conducted for active compounds. The phytochemical investigation of the latex of E. abyssinica resulted in the isolation of two triterpenes, 3-acetyloxy-(3alpha)-urs-12-en-28-oic methyl ester (1) and lup-20(29)-en-3alpha,23-diol (2). The dichloromethane extract displayed potent cytotoxic activity against the MCF7 cell line with an IC50 value of 4.27 +/- 0.12 mug/mL but weak activity against HepG2 and HeLa cell lines (IC50 = 20.47 +/- 1.17 and 26.73 +/- 2.99 mug/mL, respectively) compared to doxorubicin. Compound 1 showed an encouraging cytotoxic effect against MCF7 with IC50 = 4.20 +/- 0.20 mug/mL, followed by compound 2 (IC50 = 5.8 +/- 0.35 mug/mL). The network analysis revealed that the two isolated compounds are linked to 68 targets of human nature, among which 51 genes are linked to breast carcinomas and 5 targets (AR, CYP19A1, EGFR, PGR, and PTGS2) might be the top therapeutic targets of isolated compounds on breast cancer. Furthermore, the gene-enrichment analysis revealed that E. abyssinica could play a role in the treatment of breast cancer by striking 51 potential targets via mainly three signaling pathways: P13K-AKT, Wnt, and VEGF. Therefore, isolated triterpenes could be considered effective antitumor agents for breast cancer by elucidating their candidate target to alleviate breast cancer and related signaling pathways of the targets.

Abstract: Monitoring adverse drug events or pharmacovigilance has been promoted by the World Health Organization to assure the safety of medicines through a timely and reliable information exchange regarding drug safety issues. We aim to discuss the application of machine learning methods as well as causal inference paradigms in pharmacovigilance. We first reviewed data sources for pharmacovigilance. Then, we examined traditional causal inference paradigms, their applications in pharmacovigilance, and how machine learning methods and causal inference paradigms were integrated to enhance the performance of traditional causal inference paradigms. Finally, we summarized issues with currently mainstream correlation-based machine learning models and how the machine learning community has tried to address these issues by incorporating causal inference paradigms. Our literature search revealed that most existing data sources and tasks for pharmacovigilance were not designed for causal inference. Additionally, pharmacovigilance was lagging in adopting machine learning-causal inference integrated models. We highlight several currently trending directions or gaps to integrate causal inference with machine learning in pharmacovigilance research. Finally, our literature search revealed that the adoption of causal paradigms can mitigate known issues with machine learning models. We foresee that the pharmacovigilance domain can benefit from the progress in the machine learning field.

Abstract: Immune-associated biomarkers can predict lung metastases from colorectal cancer. Differentially expressed genes (DEGs) were screened from sample data extracted from gene expression omnibus (GEO) database. The DEGs were screened from the lung metastasis (LM) and primary cancer (PC) groups of the Moffitt Cancer Center cohort dataset. Then, the tumor immune microenvironment and abundance of immune cell infiltration analyses were performed, and the immune-related DEGs were retrieved. In addition, the transcription factor (TF)-miRNA-mRNA network was constructed and enrichment analyses of the immune-related DEGs and upregulated and downregulated DEGs were carried out. Then, the protein-protein interaction (PPI) network was conducted and the drug-gene interaction was predicted. A total of 268 DEGs were screened. The Immune_Score of samples in the LM group was significantly higher compared with the PC group. The infiltration ratio of M0 macrophages and M2 macrophages of samples was higher than others. A total of 54 immune-related DEGs in M0 macrophages were screened. Moreover, the TF-miRNA-mRNA network was constructed among 8 miRNA-mRNA and 50 TF-mRNA, and the secreted phosphoprotein 1 was regulated by 12 TFs, and the oxidized low-density lipoprotein receptor 1 was regulated by 3 miRNAs and 3 TFs. The TF SAM pointed domain containing ETS TF was also a downregulated DEG. The Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that the DEGs in the TF-miRNA-mRNA network were mainly involved in the interleukin-7 signaling pathway and cell adhesion molecules. In total, 23 protein interactions in this PPI network of M0 macrophage cells were involved in 27 mRNAs. There were 38 drug-gene interactions of immune-related DEGs of M0 macrophage cells predicted to contain 34 small molecule drugs and 8 mRNAs. Finally, the CON cohort dataset verified that the infiltration ratio of M0 and M2 macrophages of the samples was higher.

Abstract: In rare diseases such as adrenocortical carcinoma (ACC), in silico analysis can help select promising therapy options. We screened all drugs approved by the FDA and those in current clinical studies to identify drugs that target genomic alterations, also known to be present in patients with ACC. We identified FDA-approved drugs in the My Cancer Genome and National Cancer Institute databases and identified genetic alterations that could predict drug response. In total, 155 FDA-approved drugs and 905 drugs in clinical trials were identified and linked to 375 genes of 89 TCGA patients. The most frequent potentially targetable genetic alterations included TP53 (20%), BRD9 (13%), TERT (13%), CTNNB1 (13%), CDK4 (7%), FLT4 (7%), and MDM2 (7%). We identified TP53-modulating drugs to be possibly effective in 20-26% of patients, followed by the Wnt signaling pathway inhibitors (15%), Telomelysin and INO5401 (13%), FHD-609 (13%), etc. According to our data, 67% of ACC patients exhibited genomic alterations that might be targeted by FDA-approved drugs or drugs being tested in current clinical trials. Although there are not many current therapy options directly targeting reported ACC alterations, this study identifies emerging options that could be tested in clinical trials.