Abstract: MOTIVATION: Clinical trials are the essential stage of every drug development program for the treatment to become available to patients. Despite the importance of well-structured clinical trial databases and their tremendous value for drug discovery and development such instances are very rare. Presently large-scale information on clinical trials is stored in clinical trial registers which are relatively structured, but the mappings to external databases of drugs and diseases are increasingly lacking. The precise production of such links would enable us to interrogate richer harmonized datasets for invaluable insights. RESULTS: We present a neural approach for medical concept normalization of diseases and drugs. Our two-stage approach is based on Bidirectional Encoder Representations from Transformers (BERT). In the training stage, we optimize the relative similarity of mentions and concept names from a terminology via triplet loss. In the inference stage, we obtain the closest concept name representation in a common embedding space to a given mention representation. We performed a set of experiments on a dataset of abstracts and a real-world dataset of trial records with interventions and conditions mapped to drug and disease terminologies. The latter includes mentions associated with one or more concepts (in-KB) or zero (out-of-KB, nil prediction). Experiments show that our approach significantly outperforms baseline and state-of-the-art architectures. Moreover, we demonstrate that our approach is effective in knowledge transfer from the scientific literature to clinical trial data. AVAILABILITY: We make code and data freely available at hidden\_during\_review\_process. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: Although several tools facilitating in silico drug design are available, their results are usually difficult to integrate with publicly available information or require further processing to be fully exploited. The rational design of multi-target ligands (polypharmacology) and the repositioning of known drugs towards unmet therapeutic needs (drug repurposing) have raised increasing attention in drug discovery, although they usually require careful planning of tailored drug design strategies. Computational tools and data-driven approaches can help to reveal novel valuable opportunities in these contexts, as they enable to efficiently mine publicly available chemical, biological, clinical, and disease-related data. Based on these premises, we developed LigAdvisor, a data-driven webserver which integrates information reported in DrugBank, Protein Data Bank, UniProt, Clinical Trials and Therapeutic Target Database into an intuitive platform, to facilitate drug discovery tasks as drug repurposing, polypharmacology, target fishing and profiling. As designed, LigAdvisor enables easy integration of similarity estimation results with clinical data, thereby allowing a more efficient exploitation of information in different drug discovery contexts. Users can also develop customizable drug design tasks on their own molecules, by means of ligand- and target-based search modes, and download their results. LigAdvisor is publicly available at https://ligadvisor.unimore.it/.

Abstract: Taste is one of the crucial organoleptic properties involved in the perception of food by humans. Taste of a chemical compound present in food stimulates us to take in food and avoid poisons. Bitter taste of drugs presents compliance problems and early flagging of potential bitterness of a drug candidate may help with its further development. Similarly, the taste of chemicals present in food is important for evaluation of food quality in the industry. In this work, we have implemented machine learning models to predict three different taste endpoints-sweet, bitter and sour. The VirtualTaste models achieved an overall accuracy of 90% and an AUC of 0.98 in 10-fold cross-validation and in an independent test set. The web server takes a two-dimensional chemical structure as input and reports the chemical's taste profile for three tastes-using molecular fingerprints along with confidence scores, including information on similar compounds with known activity from the training set and an overall radar chart. Additionally, insights into 25 bitter receptors are also provided via target prediction for the predicted bitter compounds. VirtualTaste, to the best of our knowledge, is the first freely available web-based platform for the prediction of three different tastes of compounds. It is accessible via http://virtualtaste.charite.de/VirtualTaste/without any login requirements and is free to use.

Abstract: WHAT IS KNOWN AND OBJECTIVE: Hydroxychloroquine and protease inhibitors were widely used as off-label treatment options for COVID-19 but the safety data of these drugs among the COVID-19 population are largely lacking. Drug-induced QTc prolongation is a known adverse reaction of hydroxychloroquine, especially during chronic treatment. However, when administered concurrently with potential pro-arrhythmic drugs such as protease inhibitors, the risk of QTc prolongation imposed on these patients is not known. We aim to investigate the incidence of QTc prolongation events and potential factors associated with its occurrence in COVID-19 population. METHODS: We included 446 SARS-CoV-2 RT-PCR-positive patients taking at least one treatment drug for COVID-19 within a period of one month (March-April 2020). In addition to COVID-19-related treatment (HCQ/PI), concomitant drugs with risks of QTc prolongation were considered. We defined QTc prolongation as QTc interval of >/=470 ms in postpubertal males, and >/=480 ms in postpubertal females. RESULTS AND DISCUSSION: QTc prolongation events occurred in 28/446 (6.3%) patients with an incidence rate of 1 case per 100 person-days. A total of 26/28 (93%) patients who had prolonged QTc intervals received at least two pro-QT drugs. Multivariate analysis showed that HCQ and PI combination therapy had five times higher odds of QTc prolongation as compared to HCQ-only therapy after controlling for age, cardiovascular disease, SIRS and the use of concurrent QTc-prolonging agents besides HCQ and/or PI (OR 5.2; 95% CI, 1.11-24.49; p = 0.036). Independent of drug therapy, presence of SIRS resulted in four times higher odds of QTc prolongation (OR 4.3; 95% CI, 1.66-11.06; p = 0.003). In HCQ-PI combination group, having concomitant pro-QT drugs led to four times higher odds of QTc prolongation (OR 3.8; 95% CI, 1.53-9.73; p = 0.004). Four patients who had prolonged QTc intervals died but none were cardiac-related deaths. WHAT IS NEW AND CONCLUSION: In our cohort, hydroxychloroquine monotherapy had low potential to increase QTc intervals. However, when given concurrently with protease inhibitors which have possible or conditional risk, the odds of QTc prolongation increased fivefold. Interestingly, independent of drug therapy, the presence of systemic inflammatory response syndrome (SIRS) resulted in four times higher odds of QTc prolongation, leading to the postulation that some QTc events seen in COVID-19 patients may be due to the disease itself. ECG monitoring should be continued for at least a week from the initiation of treatment.

Abstract: Over the past decade, traditional Chinese medicine (TCM) has widely embraced systems biology and its various data integration approaches to promote its modernization. Thus, integrative pharmacology-based traditional Chinese medicine (TCMIP) was proposed as a paradigm shift in TCM. This review focuses on the presentation of this novel concept and the main research contents, methodologies and applications of TCMIP. First, TCMIP is an interdisciplinary science that can establish qualitative and quantitative pharmacokinetics-pharmacodynamics (PK-PD) correlations through the integration of knowledge from multiple disciplines and techniques and from different PK-PD processes in vivo. Then, the main research contents of TCMIP are introduced as follows: chemical and ADME/PK profiles of TCM formulas; confirming the three forms of active substances and the three action modes; establishing the qualitative PK-PD correlation; and building the quantitative PK-PD correlations, etc. After that, we summarize the existing data resources, computational models and experimental methods of TCMIP and highlight the urgent establishment of mathematical modeling and experimental methods. Finally, we further discuss the applications of TCMIP for the improvement of TCM quality control, clarification of the molecular mechanisms underlying the actions of TCMs and discovery of potential new drugs, especially TCM-related combination drug discovery.

Abstract: Even in the current age of advanced medicine, the prognosis of malignant peritoneal mesothelioma (MPM) remains abysmal. Molecular mechanisms responsible for the initiation and progression of MPM are still largely not understood. Adopting an integrated bioinformatics approach, this study aims to identify the key genes and pathways responsible for MPM. Genes that are differentially expressed in MPM in comparison with the peritoneum of healthy controls have been identified by analyzing a microarray gene expression dataset. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses of these differentially expressed genes (DEG) were conducted to gain a better insight. A protein-protein interaction (PPI) network of the proteins encoded by the DEGs was constructed using STRING and hub genes were detected analyzing this network. Next, the transcription factors and miRNAs that have possible regulatory roles on the hub genes were detected. Finally, survival analyses based on the hub genes were conducted using the GEPIA2 web server. Six hundred six genes were found to be differentially expressed in MPM; 133 are upregulated and 473 are downregulated. Analyzing the STRING generated PPI network, six dense modules and 12 hub genes were identified. Fifteen transcription factors and 10 miRNAs were identified to have the most extensive regulatory functions on the DEGs. Through bioinformatics analyses, this work provides an insight into the potential genes and pathways involved in MPM.

Abstract: Gastric cancer (GC) is one of the most common cancers in the world, and the tumor microenvironment (TME) plays a vital role in the occurrence and development of GC. In this study, we obtained differential expressed genes in GC tissues from The Cancer Genome Atlas. After ESTIMATE and weighted correlation network analysis, we obtained differentially expressed genes (DEGs). With further screening DEGs of immune infiltration and then through Kaplan-Meier survival analysis, least absolute shrinkage and selection operator regression analysis and COX analysis, we found that VCAN was a gene positively correlated with high immune infiltration and poor prognosis of patients in GC. In addition, we selected a Gene Expression Omnibus dataset (GSE84433) to verify the effect of VCAN on the patient's prognosis, and analyzed the value of VCAN in immunotherapy through TIMER database and TISIDB. In conclusion, we hold the view that VCAN may affect the development of GC by regulating the TME, which may act as a potential therapeutic target for GC.

Abstract: In this article we summarize the cardiovascular adverse events that were observed in three patients during their treatment for COVID-19 and discuss their association with lopinavir/ ritonavir (LPV/r) and hydroxychloroquine (HCQ). The cases were reported to our regional pharmacovigilance centre in April 2020. All three patients were above 75 years in age, male and multimorbid, and had been hospitalized for treatment of COVID-19. As part of their treatment, all of them received a very strictly monitored off-label therapy with LPV/r and HCQ, for which they had given their prior, written, informed consent. In one patient, erythromycin was also administered. All three patients developed a significant QTc time prolongation during or shortly after therapy with the above drugs. On account of this, the treatment had to be discontinued early in each case and QTc time recovered in all three patients.

Abstract: Waddlia chondrophila is an emerging pathogen that has been implicated in numerous unpropitious pregnancy events in humans and ruminants. Taking into account its association with abortigenic events, possible modes of transmission, and future risk, immediate clinical measures are required to prevent widespread damage caused by this organism and hence this study. Here, a subtractive proteomics approach was employed to identify druggable proteins of W. chondrophila. Considering the essential genes, antibiotic resistance proteins, and virulence factors, 676 unique important proteins were initially identified for this bacterium. Afterward, NCBI BLASTp performed against human proteome identified 223 proteins that were further pushed into KEGG Automatic Annotation Server (KAAS) for automatic annotation. Using the information from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database 14 Waddlia specific metabolic pathways were identified with respect to humans. Analyzing the data from KAAS and KEGG databases, forty-eight metabolic pathway-dependent, and seventy metabolic pathway independent proteins were identified. Standalone BLAST search against DrugBank FDA approved drug targets revealed eight proteins that are finally considered druggable proteins. Prediction of three-dimensional structures was done for the eight proteins through homology modeling and the Ramachandran plot model showed six models as a valid prediction. Finally, virtual screening against MurB protein was performed using FDA approved drugs to employ the drug repositioning strategy. Three drugs showed promising docking results that can be used for therapeutic purposes against W. chondrophila following the clinical validation of the study.

Abstract: Positively charged groups that mimic arginine or lysine in a natural substrate of trypsin are necessary for drugs to inhibit the trypsin-like serine protease TMPRSS2 that is involved in the viral entry and spread of coronaviruses, including SARS-CoV-2. Based on this assumption, we identified a set of 13 approved or clinically investigational drugs with positively charged guanidinobenzoyl and/or aminidinobenzoyl groups, including the experimentally verified TMPRSS2 inhibitors Camostat and Nafamostat. Molecular docking using the C-I-TASSER-predicted TMPRSS2 catalytic domain model suggested that the guanidinobenzoyl or aminidinobenzoyl group in all the drugs could form putative salt bridge interactions with the side-chain carboxyl group of Asp435 located in the S1 pocket of TMPRSS2. Molecular dynamics simulations further revealed the high stability of the putative salt bridge interactions over long-time (100 ns) simulations. The molecular mechanics/generalized Born surface area-binding free energy assessment and per-residue energy decomposition analysis also supported the strong binding interactions between TMPRSS2 and the proposed drugs. These results suggest that the proposed compounds, in addition to Camostat and Nafamostat, could be effective TMPRSS2 inhibitors for COVID-19 treatment by occupying the S1 pocket with the hallmark positively charged groups.