Abstract: Currently, hundreds of herbal products with potential hepatotoxicity were available in the literature. A comprehensive summary and analysis focused on these potential hepatotoxic herbal products may assist in understanding herb-induced liver injury (HILI). In this work, we collected 335 hepatotoxic medicinal plants, 296 hepatotoxic ingredients, and 584 hepatoprotective ingredients through a systematic literature retrieval. Then we analyzed these data from the perspectives of phylogenetic relationship and structure-toxicity relationship. Phylogenetic analysis indicated that hepatotoxic medicinal plants tended to have a closer taxonomic relationship. By investigating the structures of the hepatotoxic ingredients, we found that alkaloids and terpenoids were the two major groups of hepatotoxicity. We also identified eight major skeletons of hepatotoxicity and reviewed their hepatotoxic mechanisms. Additionally, 15 structural alerts (SAs) for hepatotoxicity were identified based on SARpy software. These SAs will help to estimate the hepatotoxic risk of ingredients from herbs. Finally, a herb-ingredient network was constructed by integrating multiple datasets, which will assist to identify the hepatotoxic ingredients of herb/herb-formula quickly. In summary, a systemic analysis focused on HILI was conducted which will not only assist to identify the toxic molecular basis of hepatotoxic herbs but also contribute to decipher the mechanisms of HILI.

Abstract: The phenotypic effect of perturbing a gene's activity depends on the activity level of other genes, reflecting the notion that phenotypes are emergent properties of a network of functionally interacting genes. In the context of cancer, contemporary investigations have primarily focused on just one type of functional relationship between two genes-synthetic lethality (SL). Here, we define the more general concept of "survival-associated pairwise gene expression states" (SPAGEs) as gene pairs whose joint expression levels are associated with survival. We describe a data-driven approach called SPAGE-finder that when applied to The Cancer Genome Atlas (TCGA) data identified 71,946 SPAGEs spanning 12 distinct types, only a minority of which are SLs. The detected SPAGEs explain cancer driver genes' tissue specificity and differences in patients' response to drugs and stratify breast cancer tumors into refined subtypes. These results expand the scope of cancer SPAGEs and lay a conceptual basis for future studies of SPAGEs and their translational applications.

Abstract: BACKGROUND: Drug effects can be investigated through natural variation in the genes for their protein targets. The present study aimed to use this approach to explore the potential side effects and repurposing potential of antihypertensive drugs, which are among the most commonly used medications worldwide. METHODS: Genetic proxies for the effect of antihypertensive drug classes were identified as variants in the genes for the corresponding targets that associated with systolic blood pressure at genome-wide significance. Mendelian randomization estimates for drug effects on coronary heart disease and stroke risk were compared with randomized, controlled trial results. A phenome-wide association study in the UK Biobank was performed to identify potential side effects and repurposing opportunities, with findings investigated in the Vanderbilt University biobank (BioVU) and in observational analysis of the UK Biobank. RESULTS: Suitable genetic proxies for angiotensin-converting enzyme inhibitors, beta-blockers, and calcium channel blockers (CCBs) were identified. Mendelian randomization estimates for their effect on coronary heart disease and stroke risk, respectively, were comparable to results from randomized, controlled trials against placebo. A phenome-wide association study in the UK Biobank identified an association of the CCB standardized genetic risk score with increased risk of diverticulosis (odds ratio, 1.02 per standard deviation increase; 95% CI, 1.01-1.04), with a consistent estimate found in BioVU (odds ratio, 1.01; 95% CI, 1.00-1.02). Cox regression analysis of drug use in the UK Biobank suggested that this association was specific to nondihydropyridine CCBs (hazard ratio 1.49 considering thiazide diuretic agents as a comparator; 95% CI, 1.04-2.14) but not dihydropyridine CCBs (hazard ratio, 1.04; 95% CI, 0.83-1.32). CONCLUSIONS: Genetic variants can be used to explore the efficacy and side effects of antihypertensive medications. The identified potential effect of nondihydropyridine CCBs on diverticulosis risk could have clinical implications and warrants further investigation.

Abstract: The PerMM web server and database were developed for quantitative analysis and visualization of passive translocation of bioactive molecules across lipid membranes. The server is the first physics-based web tool that calculates membrane binding energies and permeability coefficients of diverse molecules through artificial and natural membranes (phospholipid bilayers, PAMPA-DS, blood-brain barrier, and Caco-2/MDCK cell membranes). It also visualizes the transmembrane translocation pathway as a sequence of translational and rotational positions of a permeant as it moves across the lipid bilayer, along with the corresponding changes in solvation energy. The server can be applied for prediction of permeability coefficients of compounds with diverse chemical scaffolds to facilitate selection and optimization of potential drug leads. The complementary PerMM database allows comparison of computationally and experimentally determined permeability coefficients for more than 500 compounds in different membrane systems. The website and database are freely accessible at https://permm.phar.umich.edu/ .

Abstract: Epilepsy, that comprises a wide spectrum of neuronal disorders and accounts for about one percent of global disease burden affecting people of all age groups, is recognised as apasmara in the traditional medicinal system of Indian antiquity commonly known as Ayurveda. Towards exploring the molecular level complex regulatory mechanisms of 63 anti-epileptic Ayurvedic herbs and thoroughly examining the multi-targeting and synergistic potential of 349 drug-like phytochemicals (DPCs) found therein, in this study, we develop an integrated computational framework comprising of network pharmacology and molecular docking studies. Neuromodulatory prospects of anti-epileptic herbs are probed and, as a special case study, DPCs that can regulate metabotropic glutamate receptors (mGluRs) are inspected. A novel methodology to screen and systematically analyse the DPCs having similar neuromodulatory potential vis-a-vis DrugBank compounds (NeuMoDs) is developed and 11 NeuMoDs are reported. A repertoire of 74 DPCs having poly-pharmacological similarity with anti-epileptic DrugBank compounds and those under clinical trials is also reported. Further, high-confidence PPI-network specific to epileptic protein-targets is developed and the potential of DPCs to regulate its functional modules is investigated. We believe that the presented schema can open-up exhaustive explorations of indigenous herbs towards meticulous identification of clinically relevant DPCs against various diseases and disorders.

Abstract: Members of genus Pteris have their established role in the traditional herbal medicine system. In the pursuit to identify its biologically active constituents, the specie Pteris cretica L. (P. cretica) was selected for the bioassay-guided isolation. Two new maleates (F9 and CB18) were identified from the chloroform extract and the structures of the isolates were elucidated through their spectroscopic data. The putative targets, that potentially interact with both of these isolates, were identified through reverse docking by using in silico tools PharmMapper and ReverseScreen3D. On the basis of reverse docking results, both isolates were screened for their antioxidant, acetylcholinesterase (AChE) inhibition, alpha-glucosidase (GluE) inhibition and antibacterial activities. Both isolates depicted moderate potential for the selected activities. Furthermore, docking studies of both isolates were also studied to investigate the binding mode with respective targets followed by molecular dynamics simulations and binding free energies. Thereby, the current study embodies the poly-pharmacological potential of P. cretica.

Abstract: While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

Abstract: Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.

Abstract: Hepatocellular carcinoma (HCC) is one major cause of cancer-related death worldwide. But now, the systematic therapy for the advanced stages of HCC is rather limited. Thus, the discovery of novel drug targets and thereafter targeted drugs against HCC is continuously needed. In this study, we combined clinical association data, gene expression profiles and manually collected drug target genes with the human protein-protein interaction (PPI) network to establish an in-silico HCC drug target predictor. First, we found drug target genes (DTGs), disease-associated genes (DAGs), prognostic unfavorable genes (PUGs) and cancer up-regulated genes (URGs) have higher degree, betweenness, closeness centrality, while cancer down-regulated genes (DRGs), prognostic favorable genes (PFGs) have lower degrees, in comparison with background genes. Moreover, DTG nodes were shown to be closer to DAG, PUG and URG nodes, but farther away from PFG and DRG nodes. Compared to the background, PFGs and DRGs were shown to have relatively bigger genetic dependency scores, while PUGs and URGs have smaller genetic dependency scores. Finally, based on the observed features of DTGs, we constructed a drug target predictor using one-class support vector machine (one-class SVM). Performance evaluation results suggested our predictor could effectively identify putative drug target genes for further research.

Abstract: Cannabigerol (CBG) and cannabichromene (CBC) are non-psychoactive cannabinoids that have raised increasing interest in recent years. These compounds exhibit good tolerability and low toxicity, representing promising candidates for drug repositioning. To identify novel potential therapeutic targets for CBG and CBC, an integrated ligand-based and structure-based study was performed. The results of the analysis led to the identification of CBG as a low micromolar inhibitor of the Enoyl acyl carrier protein (ACP) reductase (InhA) enzyme.