Abstract: In the theory of traditional Chinese medicine (TCM), "liver-qi" stagnation and heat-induced toxicity represent the main etiologies of breast cancer. Recently, several TCMs with heat-clearing and detoxification efficacy have shown inhibitory effects on breast cancer. Jin'gan capsules (JGCs), initially approved to treat colds in China, are a heat-clearing and detoxification TCM formula. However, the anticancer activity of JGCs against breast cancer and its underlying mechanisms remain unclear. First, we assessed the antiproliferative activity of JGCs in breast cancer cell lines and evaluated their effects on cell apoptosis and the cell cycle by flow cytometry. Furthermore, we identified the potential bioactive components of JGCs and their corresponding target genes and constructed a bioactive compound-target interaction network by ultra-performance liquid chromatography-high-resolution tandem mass spectrometry (UPLC-HR-MS/MS) and network pharmacology analysis. Finally, the underlying mechanism was investigated through gene function enrichment analysis and experimental validation. We found that JGCs significantly inhibited breast cancer cell growth with IC50 values of 0.56 +/- 0.03, 0.16 +/- 0.03, and 0.94 +/- 0.09 mg/mL for MDA-MB-231, MDA-MB-468, and MCF-7, respectively. In addition, JGC treatment dramatically induced apoptosis and S phase cell cycle arrest in breast cancer cells. Western blot analysis confirmed that JGCs could regulate the protein levels of apoptosis- and cell cycle-related genes. Utilizing UPLC-HR-MS/MS analysis and network pharmacology, we identified 7 potential bioactive ingredients in JGCs and 116 antibreast cancer targets. Functional enrichment analysis indicated that the antitumor effects of JGCs were strongly associated with apoptosis and the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling pathway. Western blot analysis validated that JGC treatment markedly decreased the expression levels of p-JAK2, p-STAT3, and STAT3. Our findings suggest that JGCs suppress breast cancer cell proliferation and induce cell cycle arrest and apoptosis partly by inhibiting the JAK2/STAT3 signaling pathway, highlighting JGCs as a potential therapeutic candidate against breast cancer.

Abstract: The identification of optimal drug candidates is very important in drug discovery. Researchers in biology and computational sciences have sought to use machine learning (ML) to efficiently predict drug-target interactions (DTIs). In recent years, according to the emerging usefulness of pretrained models in natural language process (NLPs), pretrained models are being developed for chemical compounds and target proteins. This study sought to improve DTI predictive models using a Bidirectional Encoder Representations from the Transformers (BERT)-pretrained model, ChemBERTa, for chemical compounds. Pretraining features the use of a simplified molecular-input line-entry system (SMILES). We also employ the pretrained ProBERT for target proteins (pretraining employed the amino acid sequences). The BIOSNAP, DAVIS, and BindingDB databases (DBs) were used (alone or together) for learning. The final model, taught by both ChemBERTa and ProtBert and the integrated DBs, afforded the best DTI predictive performance to date based on the receiver operating characteristic area under the curve (AUC) and precision-recall-AUC values compared with previous models. The performance of the final model was verified using a specific case study on 13 pairs of subtrates and the metabolic enzyme cytochrome P450 (CYP). The final model afforded excellent DTI prediction. As the real-world interactions between drugs and target proteins are expected to exhibit specific patterns, pretraining with ChemBERTa and ProtBert could teach such patterns. Learning the patterns of such interactions would enhance DTI accuracy if learning employs large, well-balanced datasets that cover all relationships between drugs and target proteins.

Abstract: OBJECTIVES: To determine the potential molecular mechanisms underlying the therapeutic effect of curcumin on hepatocellular carcinoma (HCC) by network pharmacology and experimental in vitro validation. METHODS: The predictive targets of curcumin or HCC were collected from several databases. the identified overlapping targets were crossed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) platform. Two of the candidate pathways were selected to conduct an experimental verification. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide tetrazolium (MTT) assay was used to determine the effect of curcumin on the viability of HepG2 and LO2 cells. The apoptosis and autophagy of HepG2 cells were respectively detected by flow cytometry and transmission electron microscopy. Besides, western blot and real-time polymerase chain reaction (PCR) were employed to verify the p53 apoptotic pathway and adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) autophagy pathway. HepG2 cells were pretreated with pifithrin-alpha (PFT-alpha) and GSK690693 for further investigation. RESULTS: The 167 pathways analyzed by KEGG included apoptosis, autophagy, p53, and AMPK pathways. The GO enrichment analysis demonstrated that curcumin was involved in cellular response to drug, regulation of apoptotic pathway, and so on. The in vitro experiments also confirmed that curcumin can inhibit the growth of HepG2 cells by promoting the apoptosis of p53 pathway and autophagy through the AMPK pathway. Furthermore, the protein and messenger RNA (mRNA) of the two pathways were downregulated in the inhibitor-pretreated group compared with the experimental group. The damage-regulated autophagy modulator (DRAM) in the PFT-alpha-pretreated group was downregulated, and p62 in the GSK690693-pretreated group was upregulated. CONCLUSIONS: Curcumin can treat HCC through the p53 apoptotic pathway and the AMPK/Unc-51-like kinase 1 (ULK1) autophagy pathway, in which the mutual transformation of autophagy and apoptosis may occur through DRAM and p62.

Abstract: BACKGROUND: Extraction of drug drug interactions from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around relation extraction using bidirectional long short-term memory networks (BiLSTM) and BERT model which does not attain the best feature representations. RESULTS: Our proposed DDI (drug drug interaction) prediction model provides multiple advantages: (1) The newly proposed attention vector is added to better deal with the problem of overlapping relations, (2) The molecular structure information of drugs is integrated into the model to better express the functional group structure of drugs, (3) We also added text features that combined the T-distribution and chi-square distribution to make the model more focused on drug entities and (4) it achieves similar or better prediction performance (F-scores up to 85.16%) compared to state-of-the-art DDI models when tested on benchmark datasets. CONCLUSIONS: Our model that leverages state of the art transformer architecture in conjunction with multiple features can bolster the performances of drug drug interation tasks in the biomedical domain. In particular, we believe our research would be helpful in identification of potential adverse drug reactions.

Abstract: BACKGROUND: Adverse drug effects (ADEs) in children are common and may result in disability and death, necessitating post-marketing monitoring of their use. Evaluating drug safety is especially challenging in children due to the processes of growth and maturation, which can alter how children respond to treatment. Current drug safety-signal-detection methods do not account for these dynamics. METHODS: We recently developed a method called disproportionality generalized additive models (dGAMs) to better identify safety signals for drugs across child-development stages. FINDINGS: We used dGAMs on a database of 264,453 pediatric adverse-event reports and found 19,438 ADEs signals associated with development and validated these signals against a small reference set of pediatric ADEs. Using our approach, we can hypothesize on the ontogenic dynamics of ADE signals, such as that montelukast-induced psychiatric disorders appear most significant in the second year of life. Additionally, we integrated pediatric enzyme expression data and found that pharmacogenes with dynamic childhood expression, such as CYP2C18 and CYP27B1, are associated with pediatric ADEs. CONCLUSIONS: We curated KidSIDES, a database of pediatric drug safety signals, for the research community and developed the Pediatric Drug Safety portal (PDSportal) to facilitate evaluation of drug safety signals across childhood growth and development. FUNDING: This study was supported by grants from the National Institutes of Health (NIH).

Abstract: From inadequate prior antidepressants that targeted monoamine neurotransmitter systems emerged the discovery of alternative drugs for depression. For instance, drugs targeted interleukin 6 receptor (IL6R) in inflammatory system. Genomic analysis-based drug repurposing using single nucleotide polymorphism (SNP) inclined a promising method for several diseases. However, none of the diseases was depression. Thus, we aimed to identify drug repurposing candidates for depression treatment by adopting a genomic-analysis-based approach. The 5885 SNPs obtained from the machine learning approach were annotated using HaploReg v4.1. Five sets of functional annotations were applied to determine the depression risk genes. The STRING database was used to expand the target genes and identify drug candidates from the DrugBank database. We validated the findings using the ClinicalTrial.gov and PubMed databases. Seven genes were observed to be strongly associated with depression (functional annotation score = 4). Interestingly, IL6R was auspicious as a target gene according to the validation outcome. We identified 20 drugs that were undergoing preclinical studies or clinical trials for depression. In addition, we identified sarilumab and satralizumab as drugs that exhibit strong potential for use in the treatment of depression. Our findings indicate that a genomic-analysis-based approach can facilitate the discovery of drugs that can be repurposed for treating depression.

Abstract: Blocking the catalytic activity of urease has been shown to have a key role in different diseases as well as in different agricultural applications. A vast array of molecules have been tested against ureases of different species, but the clinical translation of these compounds has been limited due to challenges of potency, chemical and metabolic stability as well as promiscuity against other proteins. The design and development of new compounds greatly benefit from insights from previously tested compounds; however, no large-scale studies surveying the urease inhibitors' chemical space exist that can provide an overview of developed compounds to data. Therefore, given the increasing interest in developing new compounds for this target, we carried out a comprehensive analysis of the activity landscape published so far. To do so, we assembled and curated a data set of compounds tested against urease. To the best of our knowledge, this is the largest data set of urease inhibitors to date, composed of 3200 compounds of diverse structures. We characterized the data set in terms of chemical space coverage, molecular scaffolds, distribution with respect to physicochemical properties, as well as temporal trends of drug development. Through these analyses, we highlighted different substructures and functional groups responsible for distinct activity and inactivity against ureases. Furthermore, activity cliffs were assessed, and the chemical space of urease inhibitors was compared to DrugBank. Finally, we extracted meaningful patterns associated with activity using a decision tree algorithm. Overall, this study provides a critical overview of urease inhibitor research carried out in the last few decades and enabled finding underlying SAR patterns such as under-reported chemical functional groups that contribute to the overall activity. With this work, we propose different rules and practical implications that can guide the design or selection of novel compounds to be screened as well as lead optimization.

Abstract: BACKGROUND: Recently, drug repositioning has received considerable attention for its advantage to pharmaceutical industries in drug development. Artificial intelligence techniques have greatly enhanced drug reproduction by discovering therapeutic drug profiles, side effects, and new target proteins. However, as the number of drugs increases, their targets and enormous interactions produce imbalanced data that might not be preferable as an input to a prediction model immediately. METHODS: This paper proposes a novel scheme for predicting drug-target interactions (DTIs) based on drug chemical structures and protein sequences. The drug Morgan fingerprint, drug constitutional descriptors, protein amino acid composition, and protein dipeptide composition were employed to extract the drugs and protein's characteristics. Then, the proposed approach for extracting negative samples using a support vector machine one-class classifier was developed to tackle the imbalanced data problem feature sets from the drug-target dataset. Negative and positive samplings were constructed and fed into different prediction algorithms to identify DTIs. A 10-fold CV validation test procedure was applied to assess the predictability of the proposed method, in addition to the study of the effectiveness of the chemical and physical features in the evaluation and discovery of the drug-target interactions. RESULTS: Our experimental model outperformed existing techniques concerning the curve for receiver operating characteristic (AUC), accuracy, precision, recall F-score, mean square error, and MCC. The results obtained by the AdaBoost classifier enhanced prediction accuracy by 2.74%, precision by 1.98%, AUC by 1.14%, F-score by 3.53%, and MCC by 4.54% over existing methods.

Abstract: Diabetic nephropathy (DN) is one of the most serious complications of diabetes and the main cause of end-stage renal failure. Rhubarb is a widely used traditional Chinese herb, and it has exhibited efficacy in reducing proteinuria, lowering blood sugar levels and improving kidney function in patients with DN. However, the exact pharmacological mechanism by rhubarb improves DN remain unclear due to the complexity of its ingredients. Hence, we systematically explored the underlying mechanisms of rhubarb in the treatment of DN. We adopted a network pharmacology approach, focusing on the identification of active ingredients, drug target prediction, gene collection, Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes enrichment. Molecular docking technology was used to verify the binding ability between the main active compounds and central therapeutic targets, and screen out the core active ingredients in rhubarb for the treatment of DN. Finally, molecular dynamics simulation was performed for the optimal core protein-ligand obtained by molecular docking using GROMACS software. The network analysis identified 16 active compounds in rhubarb that were linked to 37 possible therapeutic targets related to DN. Through protein-protein interaction analysis, TP53, CASP8, CASP3, MYC, JUN and PTGS2 were identified as the key therapeutic targets. By validation of molecular docking, finding that the central therapeutic targets have good affinities with the main active compounds of rhubarb, and rhein, beta-sitosterol and aloe-emodin were identified as the core active ingredients in rhubarb for the treatment of DN. Results from molecular dynamics simulations showed that TP53 and aloe-emodin bound very stably with a binding free energy of - 26.98 kcal/mol between the two. The results of the gene enrichment analysis revealed that the PI3K-Akt signalling pathway, p53 signalling pathway, AGE-RAGE signalling pathway and MAPK signalling pathway might be the key pathways for the treatment of DN, and these pathways were involved in podocyte apoptosis, glomerular mesangial cell proliferation, inflammation and renal fibrosis. Based on the network pharmacology approach and molecular docking technology, we successfully predicted the active compounds and their respective targets. In addition, we illustrated the molecular mechanisms that mediate the therapeutic effects of rhubarb against DN. These findings provided an important scientific basis for further research of the mechanism of rhubarb in the treatment of DN.

Abstract: Hydrophilic interaction liquid chromatography (HILIC) has inherent merits over RP-HPLC in the analyzing of hydrophilic substances. Accordingly, an innovative HILIC-UV methodology is proposed for the simultaneous estimation of ethyl paraben (PRN), fluconazole (FLZ) and moxifloxacin hydrochloride (MOX) in raw materials and pharmaceutical eye gel. The separation process was conducted using Waters XBridge HILIC column (100 mm x 4.6 mm, 3.5 mum particle size) at room temperature. Isocratic mobile phase containing acetonitrile: 0.1% triethylamine buffer (90:10, v/v, pH 5.0), was pumped at flow rate 1.0 mL/min and detected at 260 nm. Under these optimized conditions, PRN, FLZ and MOX showed rectilinear relationships with the concentration ranges (0.5-6.0), (5.0-50.0) and (5.0-60.0) mug/mL, respectively. The developed method offered at least fivefold increase in sensitivity within shorter time than the reported methods. Three greenness assessment tools namely: Analytical eco-scale, GAPI and AGREE were exploited to investigate the method's impact on the environment and conduct a comparative study with the reported methods. International council of Harmonization (ICH) guidelines have been followed to calculate validation parameters. The statistical comparison between results of the suggested method and the comparison method showed no discrepancy confirming accuracy of the method.