Abstract: To explore the mechanism of Epimedii Folium (HF) and Notoginseng Radix (NR) intervention in vascular dementia (VD). This study used the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database to collect the active ingredients and potential drug targets of HF and NR, the Uniprot database to convert drug target names into gene names, GeneCards, Drugbank, Therapeutic Target Database, and Online Mendelian Inheritance in Man database to collect the potential disease targets of VD, and then combined them with the drug targets to construct the HF-NR-VD protein-protein interaction (PPI) network by Search Tool for the Retrieval of Interacting (STRING). Cytoscape (version 3.7.1) was used to perform cluster analysis of the PPI network. Metascape database was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The potential interaction of the main components of the HF-NR couplet medicine with core disease targets was revealed by molecular docking simulations. There were 23 predicted active ingredients in HF and NR, and 109 common drug targets that may be involved in the treatment of VD. Through PPI network analysis, 30 proteins were identified as core proteins owing to their topological importance. GO functional analysis revealed that the primary biological processes were mainly related to inflammation, apoptosis, and the response to oxidative stress. KEGG pathway enrichment analysis revealed that TNF and PI3K/Akt signaling pathways may occupy the core status in the anti-VD system. Molecular docking results confirmed that the core targets of VD had a high affinity for the main compounds of the HF-NR couplet medicine. We demonstrated the multi-component, multi-target, and multi-pathway characteristics of HF-NR couplet medicine for the treatment of VD and provided a foundation for further clinical application and experimental research.

Abstract: Clomiphene, a selective estrogen receptor modulator (SERM), has been used for the treatment of anovulation for more than 50 years. However, since (E)-clomiphene ((E)-Clom) and its metabolites are eliminated primarily via Cytochrome P450 (CYP) 2D6 and CYP3A4, exposure can be affected by CYP2D6 polymorphisms and concomitant use with CYP inhibitors. Thus, clomiphene therapy may be susceptible to drug-gene interactions (DGIs), drug-drug interactions (DDIs) and drug-drug-gene interactions (DDGIs). Physiologically based pharmacokinetic (PBPK) modeling is a tool to quantify such DGI and DD(G)I scenarios. This study aimed to develop a whole-body PBPK model of (E)-Clom including three important metabolites to describe and predict DGI and DD(G)I effects. Model performance was evaluated both graphically and by calculating quantitative measures. Here, 90% of predicted C(max) and 80% of AUC(last) values were within two-fold of the corresponding observed value for DGIs and DD(G)Is with clarithromycin and paroxetine. The model also revealed quantitative contributions of different CYP enzymes to the involved metabolic pathways of (E)-Clom and its metabolites. The developed PBPK model can be employed to assess the exposure of (E)-Clom and its active metabolites in as-yet unexplored DD(G)I scenarios in future studies.

Abstract: Drug-target association plays an important role in drug discovery, drug repositioning, drug synergy prediction, etc. Currently, a lot of drug-related databases, such as DrugBank and BindingDB, have emerged. However, these databases are separate, incomplete and non-uniform with different criteria. Here, we integrated eight drug-related databases; collected, filtered and supplemented drugs, target genes and experimentally validated (highly confident) associations and built a highly confident drug-target (HCDT: http://hainmu-biobigdata.com/hcdt) database. HCDT database includes 500 681 HCDT associations between 299 458 drugs and 5618 target genes. Compared to individual databases, HCDT database contains 1.1 to 254.2 times drugs, 1.8-5.5 times target genes and 1.4-27.7 times drug-target associations. It is normative, publicly available and easy for searching, browsing and downloading. Together with multi-omics data, it will be a good resource in analyzing the drug functional mechanism, mining drug-related biological pathways, predicting drug synergy, etc. Database URL: http://hainmu-biobigdata.com/hcdt.

Abstract: Rest has long been considered beneficial to patient healing; however, remarkably, there are no evidence-based experimental models determining how it benefits disease outcomes. Here, we created an experimental rest model in mice that briefly extends the morning rest period. We found in 2 major cardiovascular disease conditions (cardiac hypertrophy, myocardial infarction) that imposing a short, extended period of morning rest each day limited cardiac remodeling compared with controls. Mechanistically, rest mitigates autonomic-mediated hemodynamic stress on the cardiovascular system, relaxes myofilament contractility, and attenuates cardiac remodeling genes, consistent with the benefits on cardiac structure and function. These same rest-responsive gene pathways underlie the pathophysiology of many major human cardiovascular conditions, as demonstrated by interrogating open-source transcriptomic data; thus, patients with other conditions may also benefit from a morning rest period in a similar manner. Our findings implicate rest as a key driver of physiology, creating a potentially new field - as broad and important as diet, sleep, or exercise - and provide a strong rationale for investigation of rest-based therapy for major clinical diseases.

Abstract: Pain is one of the most common symptoms experienced by patients. The use of current analgesics is limited by low efficacy and important side effects. Transient receptor potential vanilloid-1 (TRPV1) is a non-selective cation channel, activated by capsaicin, heat, low pH or pro-inflammatory agents. Since TRPV1 is a potential target for the development of novel analgesics due to its distribution and function, we aimed to develop an in silico drug repositioning framework to predict potential TRPV1 ligands among approved drugs as candidates for treating various types of pain. Structures of known TRPV1 agonists and antagonists were retrieved from ChEMBL databases and three datasets were established: agonists, antagonists and inactive molecules (pIC50 or pEC50 < 5 M). Structures of candidates for repurposing were retrieved from the DrugBank database. The curated active/inactive datasets were used to build and validate ligand-based predictive models using Bemis-Murcko structural scaffolds, plain ring systems, flexophore similarities and molecular descriptors. Further, molecular docking studies were performed on both active and inactive conformations of the TRPV1 channel to predict the binding affinities of repurposing candidates. Variables obtained from calculated scaffold-based activity scores, molecular descriptors criteria and molecular docking were used to build a multi-class neural network as an integrated machine learning algorithm to predict TRPV1 antagonists and agonists. The proposed predictive model had a higher accuracy for classifying TRPV1 agonists than antagonists, the ROC AUC values being 0.980 for predicting agonists, 0.972 for antagonists and 0.952 for inactive molecules. After screening the approved drugs with the validated algorithm, repaglinide (antidiabetic) and agomelatine (antidepressant) emerged as potential TRPV1 antagonists, and protokylol (bronchodilator) as an agonist. Further studies are required to confirm the predicted activity on TRPV1 and to assess the candidates' efficacy in alleviating pain.

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a human coronaviruses that emerged in China at Wuhan city, Hubei province during December 2019. Subsequently, SARS-CoV-2 has spread worldwide and caused millions of deaths around the globe. Several compounds and vaccines have been proposed to tackle this crisis. Novel recommended in silico approaches have been commonly used to screen for specific SARS-CoV-2 inhibitors of different types. Herein, the phytochemicals of Pakistani medicinal plants (especially Artemisia annua) were virtually screened to identify potential inhibitors of the SARS-CoV-2 main protease enzyme. The X-ray crystal structure of the main protease of SARS-CoV-2 with an N3 inhibitor was obtained from the protein data bank while A. annua phytochemicals were retrieved from different drug databases. The docking technique was carried out to assess the binding efficacy of the retrieved phytochemicals; the docking results revealed that several phytochemicals have potential to inhibit the SARS-CoV-2 main protease enzyme. Among the total docked compounds, the top-10 docked complexes were considered for further study and evaluated for their physiochemical and pharmacokinetic properties. The top-3 docked complexes with the best binding energies were as follows: the top-1 docked complex with a -7 kcal/mol binding energy score, the top-2 docked complex with a -6.9 kcal/mol binding energy score, and the top-3 docked complex with a -6.8 kcal/mol binding energy score. These complexes were subjected to a molecular dynamic simulation analysis for further validation to check the dynamic behavior of the selected top-complexes. During the whole simulation time, no major changes were observed in the docked complexes, which indicated complex stability. Additionally, the free binding energies for the selected docked complexes were also estimated via the MM-GB/PBSA approach, and the results revealed that the total delta energies of MMGBSA were -24.23 kcal/mol, -26.38 kcal/mol, and -25 kcal/mol for top-1, top-2, and top-3, respectively. MMPBSA calculated the delta total energy as -17.23 kcal/mol (top-1 complex), -24.75 kcal/mol (top-2 complex), and -24.86 kcal/mol (top-3 complex). This study explored in silico screened phytochemicals against the main protease of the SARS-CoV-2 virus; however, the findings require an experimentally based study to further validate the obtained results.

Abstract: Approximately 50% of Alzheimer's disease (AD) patients will develop psychotic symptoms and these patients will experience severe rapid cognitive decline compared with those without psychosis (AD-P). Currently, no medication has been approved by the Food and Drug Administration for AD with psychosis (AD+P) specifically, although atypical antipsychotics are widely used in clinical practice. These drugs have demonstrated modest efficacy in managing psychosis in individuals with AD, with an increased frequency of adverse events, including excess mortality. We compared the differences between the genetic variations/genes associated with AD+P and schizophrenia from existing Genome-Wide Association Study and differentially expressed genes (DEGs). We also constructed disease-specific protein-protein interaction networks for AD+P and schizophrenia. Network efficiency was then calculated to characterize the topological structures of these two networks. The efficiency of antipsychotics in these two networks was calculated. A weight adjustment based on binding affinity to drug targets was later applied to refine our results, and 2013 and 2123 genes were identified as related to AD+P and schizophrenia, respectively, with only 115 genes shared. Antipsychotics showed a significantly lower efficiency in the AD+P network than in the schizophrenia network (P < 0.001) indicating that antipsychotics may have less impact in AD+P than in schizophrenia. AD+P may be caused by mechanisms distinct from those in schizophrenia which result in a decreased efficacy of antipsychotics in AD+P. In addition, the network analysis methods provided quantitative explanations of the lower efficacy of antipsychotics in AD+P.

Abstract: Identification of new chemical compounds with desired structural diversity and biological properties plays an essential role in drug discovery, yet the construction of such a potential space with elements of 'near-drug' properties is still a challenging task. In this work, we proposed a multimodal chemical information reconstruction system to automatically process, extract and align heterogeneous information from the text descriptions and structural images of chemical patents. Our key innovation lies in a heterogeneous data generator that produces cross-modality training data in the form of text descriptions and Markush structure images, from which a two-branch model with image- and text-processing units can then learn to both recognize heterogeneous chemical entities and simultaneously capture their correspondence. In particular, we have collected chemical structures from ChEMBL database and chemical patents from the European Patent Office and the US Patent and Trademark Office using keywords 'A61P, compound, structure' in the years from 2010 to 2020, and generated heterogeneous chemical information datasets with 210K structural images and 7818 annotated text snippets. Based on the reconstructed results and substituent replacement rules, structural libraries of a huge number of near-drug compounds can be generated automatically. In quantitative evaluations, our model can correctly reconstruct 97% of the molecular images into structured format and achieve an F1-score around 97-98% in the recognition of chemical entities, which demonstrated the effectiveness of our model in automatic information extraction from chemical patents, and hopefully transforming them to a user-friendly, structured molecular database enriching the near-drug space to realize the intelligent retrieval technology of chemical knowledge.

Abstract: Chronic myelogenous leukemia (CML) is a serious threat to human health, while drugs for CML are limited. Herbal medicines with structural diversity, low toxicity and low drug resistance are always the most important source for drug discoveries. Gynura divaricata (L.) DC. is a well-known herbal medicine whose non-alkaline ingredients (GD-NAIs) were isolated. The GD-NAIs demonstrated potential anti-CML activity in our preliminary screening tests. However, the chemical components and underlying mechanism are still unknown. In this study, GD-NAIs were tentatively characterized using UHPLC-HRMS combined with molecular networking, which were composed of 75 sesquiterpenoids. Then, the anti-CML activities of GD-NAIs were evaluated and demonstrated significant suppression of proliferation and promotion of apoptosis in K562 cells. Furthermore, the mechanism of GD-NAIs against CML were elucidated using network pharmacology combined with RNA sequencing. Four sesquiterpenoids would be the main active ingredients of GD-NAIs against CML, which could regulate PD-L1 expression and the PD-1 checkpoint pathway in cancer, PI3K/AKT, JAK/STAT, TGF-beta, estrogen, Notch and Wnt signaling pathways. In conclusion, our study reveals the composition of GD-NAIs, confirms its anti-CML activity and elucidates their underlying mechanism, which is a potential countermeasure for the treatment of CML.

Abstract: Inflammatory bowel disease (IBD) is characterized by chronic inflammation of the gastrointestinal system. Omega-3 (omega(3)) fatty acids are polyunsaturated fatty acids (PUFAs) that are largely obtained from diet and have been speculated to decrease the inflammatory response that is involved in IBD; however, the causality of this association has not been established. A two-sample Mendelian randomization (MR) was used to assess genetic associations between 249 circulating metabolites measured in the UK Biobank as exposures and IBD as the outcome. The genome-wide association study summary level data for metabolite measurements and IBD were derived from large European ancestry cohorts. We observed omega(3) fatty acids as a significant protective association with IBD, with multiple modes of MR evidence replicated in three IBD summary genetic datasets. The instrumental variables that were involved in the causal association of omega(3) fatty acids with IBD highlighted an intronic SNP, rs174564, in FADS2, a protein engaged in the first step of alpha-linolenic acid desaturation leading to anti-inflammatory EPA and thence DHA production. A low ratio of omega(3) to omega(6) fatty acids was observed to be a causal risk factor, particularly for Crohn's disease. omega(3) fatty acid supplementation may provide anti-inflammatory responses that are required to attenuate inflammation that is involved in IBD.