Drug Databases for Development of Therapeutics Against Coronaviruses

Background: Traditional Chinese medicine (TCM) has been used for medical purposes since the ancient time and has gradually gained recognition worldwide. Nowadays, patients with thrombus presiding to anticoagulant/ antiplatelet drugs prefer taking TCM. However, an increasing number of studies on herb–drug interactions have been shown. Nevertheless, findings are frequently conflicting and vague. In this review, we discuss the herb–drug interactions between TCM and anticoagulant/antiplatelet drugs to provide guidance on concomitant ingestion with anticoagulant/antiplatelet drugs. Methods: We undertook a structured search of medicine and drug databases for peer-reviewed literature using focused review questions. Results: Danshen, Ginkgo, Ginger, H. Perforatum, SMY and Puerarin injection had directional regulation effects on the efficacy of anticoagulant drugs by altering the CYPs, pharmacokinetic indexs and hemorheological parameters. H. Perforatum inhibited the efficacy of Clopidogrel by enhancing the CYP3A4 activity and Ginkgo increased the efficacy of Ticlopidine. Additionally, Renshen, the formulae except SMY and injections except Puerarin injection could increase or decrease the efficacy of anticoagulant/antiplatelet drugs via regulating the CYPs, platelet aggregation, hemorheological parameters and others. Conclusion: Some cases have reported that TCMs may increase the bleeding risk or has no effect on coagulation when anticoagulant/antiplatelet drugs are concurrently used. However, pharmacokinetic studies have presented either consistent or slightly varying results. So it is difficult to ascertain whether the concurrent use of TCM may increase or reduce the pharmacologic effects of anticoagulant/antiplatelet drugs with adverse reactions. Therefore, herb–drug interactions of TCM and anticoagulant/antiplatelet drugs should be further explored and defined.

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Use of Disproportionality Analysis to Identify Previously Unknown Drug-Associated Causes of Cardiac Arrhythmias Using the Food and Drug Administration Adverse Event Reporting System (FAERS) Database

Journal of Cardiovascular Pharmacology and Therapeutics ◽

10.1177/1074248420984082 ◽

2021 ◽

pp. 107424842098408

Author(s):

Lindsay N. Moreland-Head ◽

James C. Coons ◽

Amy L. Seybert ◽

Matthew P. Gray ◽

Sandra L. Kane-Gill

Keyword(s):

Cardiac Arrhythmias ◽

Adverse Event Reporting System ◽

Adverse Event Reporting ◽

Reporting Odds Ratio ◽

Potential Drug ◽

Drug Induced ◽

Bundle Branch Block ◽

Search Terms ◽

Drug Databases ◽

Safety Consideration

Introduction: Drug-induced QTc-prolongation is a well-known adverse drug reaction (ADR), however there is limited knowledge of other drug-induced arrhythmias. Purpose: The objective of this study is to determine the drugs reported to be associated with arrhythmias other than QTc-prolongation using the FAERS database, possibly identifying potential drug causes that have not been reported previously. Methods: FAERS reports from 2004 quarter 1 through 2019 quarter 1 were combined to create a dataset of approximately 11.6 million reports. Search terms for arrhythmias of interest were selected from the Standardized MedDRA Queries (SMQ) Version 12.0. Frequency of the cardiac arrhythmias were determined for atrial fibrillation, atrioventricular block, bradyarrhythmia, bundle branch block, supraventricular tachycardia, and ventricular fibrillation and linked to the reported causal medications. Reports were further categorized by prior evidence associations using package inserts and established drug databases. A reporting odds ratio (ROR) and confidence interval (CI) were calculated for the ADRs for each drug and each of the 6 cardiac arrhythmias. Results: Of the 11.6 million reports in the FAERS database, 68,989 were specific to cardiac arrhythmias of interest. There were 61 identified medication-reported arrhythmia pairs for the 6 arrhythmia groups with 33 found to have an unknown reported association. Rosiglitazone was the most frequently medication reported across all arrhythmias [ROR 6.02 (CI: 5.82-6.22)]. Other medications with significant findings included: rofecoxib, digoxin, alendronate, lenalidomide, dronedarone, zoledronic acid, adalimumab, dabigatran, and interferon beta-1b. Conclusion: Upon retrospective analysis of the FAERS database, the majority of drug-associated arrhythmias reported were unknown suggesting new potential drug causes. Cardiac arrhythmias other than QTc prolongation are a new area of focus for pharmacovigilance and medication safety. Consideration of future studies should be given to using the FAERS database as a timely pharmacovigilance tool to identify unknown adverse events of medications.

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Ontological Organization and Bioinformatic Analysis of Adverse Drug Reactions From Package Inserts: Development and Usability Study

Journal of Medical Internet Research ◽

10.2196/20443 ◽

2020 ◽

Vol 22 (7) ◽

pp. e20443

Author(s):

Xiaoying Li ◽

Xin Lin ◽

Huiling Ren ◽

Jinjing Guo

Keyword(s):

Language Processing ◽

Adverse Reactions ◽

Package Insert ◽

Bioinformatic Analysis ◽

Two Dimensions ◽

Semantic Relations ◽

Semantic Retrieval ◽

Formal Representation ◽

Package Inserts ◽

Drug Databases

Background Licensed drugs may cause unexpected adverse reactions in patients, resulting in morbidity, risk of mortality, therapy disruptions, and prolonged hospital stays. Officially approved drug package inserts list the adverse reactions identified from randomized controlled clinical trials with high evidence levels and worldwide postmarketing surveillance. Formal representation of the adverse drug reaction (ADR) enclosed in semistructured package inserts will enable deep recognition of side effects and rational drug use, substantially reduce morbidity, and decrease societal costs. Objective This paper aims to present an ontological organization of traceable ADR information extracted from licensed package inserts. In addition, it will provide machine-understandable knowledge for bioinformatics analysis, semantic retrieval, and intelligent clinical applications. Methods Based on the essential content of package inserts, a generic ADR ontology model is proposed from two dimensions (and nine subdimensions), covering the ADR information and medication instructions. This is followed by a customized natural language processing method programmed with Python to retrieve the relevant information enclosed in package inserts. After the biocuration and identification of retrieved data from the package insert, an ADR ontology is automatically built for further bioinformatic analysis. Results We collected 165 package inserts of quinolone drugs from the National Medical Products Administration and other drug databases in China, and built a specialized ADR ontology containing 2879 classes and 15,711 semantic relations. For each quinolone drug, the reported ADR information and medication instructions have been logically represented and formally organized in an ADR ontology. To demonstrate its usage, the source data were further bioinformatically analyzed. For example, the number of drug-ADR triples and major ADRs associated with each active ingredient were recorded. The 10 ADRs most frequently observed among quinolones were identified and categorized based on the 18 categories defined in the proposal. The occurrence frequency, severity, and ADR mitigation method explicitly stated in package inserts were also analyzed, as well as the top 5 specific populations with contraindications for quinolone drugs. Conclusions Ontological representation and organization using officially approved information from drug package inserts enables the identification and bioinformatic analysis of adverse reactions caused by a specific drug with regard to predefined ADR ontology classes and semantic relations. The resulting ontology-based ADR knowledge source classifies drug-specific adverse reactions, and supports a better understanding of ADRs and safer prescription of medications.

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Screening of Drug Databases

Bioinformatics ‐ From Genomes to Drugs - Methods and Principles in Medicinal Chemistry ◽

10.1002/3527601481.ch14 ◽

2001 ◽

pp. 137-170 ◽

Cited By ~ 2

Author(s):

Martin Stahl ◽

Matthias Rarey ◽

Gerhard Klebe

Keyword(s):

Drug Databases

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Multiple Virtual Screening Strategies for the Discovery of Novel Compounds Active Against Dengue Virus: A Hit Identification Study

Scientia Pharmaceutica ◽

10.3390/scipharm88010002 ◽

2019 ◽

Vol 88 (1) ◽

pp. 2 ◽

Cited By ~ 4

Author(s):

Kowit Hengphasatporn ◽

Arthur Garon ◽

Peter Wolschann ◽

Thierry Langer ◽

Shigeta Yasuteru ◽

...

Keyword(s):

Virtual Screening ◽

Dengue Infection ◽

Md Simulations ◽

Viral Envelope ◽

Screening Process ◽

Active Inhibitor ◽

Effective Prevention ◽

E Protein ◽

Drug Databases ◽

Promising Target

Dengue infection is caused by a mosquito-borne virus, particularly in children, which may even cause death. No effective prevention or therapeutic agents to cure this disease are available up to now. The dengue viral envelope (E) protein was discovered to be a promising target for inhibition in several steps of viral infection. Structure-based virtual screening has become an important technique to identify first hits in a drug screening process, as it is possible to reduce the number of compounds to be assayed, allowing to save resources. In the present study, pharmacophore models were generated using the common hits approach (CHA), starting from trajectories obtained from molecular dynamics (MD) simulations of the E protein complexed with the active inhibitor, flavanone (FN5Y). Subsequently, compounds presented in various drug databases were screened using the LigandScout 4.2 program. The obtained hits were analyzed in more detail by molecular docking, followed by extensive MD simulations of the complexes. The highest-ranked compound from this procedure was then synthesized and tested on its inhibitory efficiency by experimental assays.

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Practical application of whole genome and transcriptome tumour analysis to guide chemotherapy decision-making for patients with advanced cancers.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.e22020 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. e22020-e22020

Author(s):

Janessa J. Laskin ◽

Howard John Lim ◽

Karen A. Gelmon ◽

Cheryl Ho ◽

Daniel John Renouf ◽

...

Keyword(s):

Decision Making ◽

Drug Targets ◽

Cell Cancer ◽

Copy Number Variations ◽

Whole Genome ◽

Incurable Cancer ◽

Dna And Rna ◽

Drug Databases ◽

Tumour Biopsy ◽

The Impact

e22020 Background: We propose that applying personal genomic information prospectively, in a clinically realistic timeframe can aid chemotherapy decision-making and result in more effective cancer treatment. We are investigating this approach in a variety of cancers to examine timeliness, deliverability, and rate of actionable targets identified. Methods: Eligible subjects with incurable cancer and limited chemo options have a tumour biopsy and “normal” blood taken for analysis. Archival specimens are concurrently analyzed to look for changes with time and treatment. Samples are subject to both an Ampliseq amplicon panel and in-depth whole genome DNA and RNA sequencing (WGS). Bioinformatics approaches identity genes with somatic and copy number variations, and expression changes. Variants are integrated into a pathway analysis to identify tumour specific processes that may drive the tumour, these are then matched to drug databases, with manual literature reviews, to indentify drugs that may be useful or even contra-indicated. Results: Between July 2012 -Jan 2013, 9 subjects (of 30 planned) are enrolled: 2 cases each of: colorectal and breast and 1 each of: squamous skin, squamous ethmoid sinus, nasopharyngeal, lung, and CLL-peripheral mantle cell cancer. 5 have completed analyses. Cancer panel results correlated well with WGS; although the panel is more rapid, it provides less comprehensive information and has not been as informative for identifying candidate druggable drivers. Extensive pathway mapping uncovered potential drug targets in each case that would not have necessarily been considered without this analyses. To date, 4 subjects have started chemo based on the analyses and 1 patient has had his diagnosis radically changed. There are significant genomic differences between archival and fresh tumour samples. Conclusions: This approach is feasible and yields actionable targets that can inform real-time chemotherapy decision-making. Archival samples do not appear to adequately represent post-treatment cancers. The impact of WGS vs. panel sequencing will require more subjects but it appears a panel may be insufficient for detailed treatment guidance.

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Clinical utility of comprehensive genomic pathway and integrated network analyses in personalized oncology.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e14051 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e14051-e14051

Author(s):

Nikhil Shri Sahajpal ◽

Ashis Mondal ◽

Meenakshi Ahluwalia ◽

Allan Njoroge Njau ◽

Vamsi Kota ◽

...

Keyword(s):

Synthetic Lethality ◽

Cancer Case ◽

Precision Oncology ◽

Levels Of Evidence ◽

Downstream Targets ◽

Network Analyses ◽

Drug Databases ◽

Clinical Literature ◽

Holistic Understanding ◽

Pathway Analyses

e14051 Background: Adoption of next-generation sequencing (NGS) technology in routine clinical practice has enabled the detection of genetic aberrations such as single nucleotide variants, copy number alterations, and gene fusions. Pathway and network analyses (PNA) are key components for evaluation of NGS data in a clinical setting to explain findings involving thousands of altered genes and proteins with a smaller and more interpretable set of altered processes. Though PNA have been applied to identify driver genes and pathways in cohort-based analyses, its application in precision oncology remains unexplored. We investigate the potential utility of the Watson for Genomics (WfG) pathway analyses tool in interpreting complex and multiple genomic alterations in individual cancers. Methods: DNA and RNA isolated from 70 patient tumors across 30 different cancer types were processed with Illumina’s TST170 NGS platform. WfG’s feature of pathway analyses was used to identify gene variants, signaling pathways, networks, and the drugs targeting these alterations based on evidence in the clinical literature and FDA drug databases. Results: Analyses defined 5 different pathway/network models: 1) downstream therapeutic targets, 2) synthetic lethality, 3) combinatorial downstream targets + synthetic lethality, 4) two or more pathways converging to downstream targets, and 5) complex profile analyses. The five PNA models are illustrated by the following unique cases. 1) A thyroid cancer case with HRAS variant and activated RAF1 downstream pathway showed MAPK1/3 were suggestive of relevant targets. 2) An acute myeloid leukemia case with BRCA1, BRCA2 and PTEN variants, targeting a common synthetic lethal partner PARP1 was ideal for therapy. 3) A penile carcinoma case with BRAF, CDKN2A and TP53 variants, targeting the BRAF downstream pathway in combination with either CDKN2A or TP53 were the likely choice for therapy. 4) A glioma case with activated PI3K and MEK downstream pathway, targeting a common downstream marker would block both pathways. 5) A breast carcinoma case with a complex pathogenic variant profile provided relevant clinical information and levels of evidence for multiple drug targets. Conclusions: We discovered that the integrated WfG pathway analyses tool is ideal for visualization of the variants with levels of evidence from clinical literature and FDA drug databases that can help inform treatment options and provides a holistic understanding of a specific tumor profile allowing the treating clinician to select personalized targeted therapy.

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The Prediction of Drug-Disease Correlation Based on Gene Expression Data

BioMed Research International ◽

10.1155/2018/4028473 ◽

2018 ◽

Vol 2018 ◽

pp. 1-6 ◽

Cited By ~ 3

Author(s):

Hui Cui ◽

Menghuan Zhang ◽

Qingmin Yang ◽

Xiangyi Li ◽

Michael Liebman ◽

...

Keyword(s):

Gene Expression ◽

High Throughput ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Drug Combinations ◽

Target Tissue ◽

Specific Patient ◽

Computational Approaches ◽

Drug Databases ◽

Optimal Drug

The explosive growth of high-throughput experimental methods and resulting data yields both opportunity and challenge for selecting the correct drug to treat both a specific patient and their individual disease. Ideally, it would be useful and efficient if computational approaches could be applied to help achieve optimal drug-patient-disease matching but current efforts have met with limited success. Current approaches have primarily utilized the measureable effect of a specific drug on target tissue or cell lines to identify the potential biological effect of such treatment. While these efforts have met with some level of success, there exists much opportunity for improvement. This specifically follows the observation that, for many diseases in light of actual patient response, there is increasing need for treatment with combinations of drugs rather than single drug therapies. Only a few previous studies have yielded computational approaches for predicting the synergy of drug combinations by analyzing high-throughput molecular datasets. However, these computational approaches focused on the characteristics of the drug itself, without fully accounting for disease factors. Here, we propose an algorithm to specifically predict synergistic effects of drug combinations on various diseases, by integrating the data characteristics of disease-related gene expression profiles with drug-treated gene expression profiles. We have demonstrated utility through its application to transcriptome data, including microarray and RNASeq data, and the drug-disease prediction results were validated using existing publications and drug databases. It is also applicable to other quantitative profiling data such as proteomics data. We also provide an interactive web interface to allow our Prediction of Drug-Disease method to be readily applied to user data. While our studies represent a preliminary exploration of this critical problem, we believe that the algorithm can provide the basis for further refinement towards addressing a large clinical need.

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