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Showing 2271 to 2274 of 5186 entries
Published on October 29, 2020
Historical Evolution and Provider Awareness of Inactive Ingredients in Oral Medications.
Abstract: PURPOSE: A multitude of different versions of the same medication with different inactive ingredients are currently available. It has not been quantified how this has evolved historically. Furthermore, it is unknown whether healthcare professionals consider the inactive ingredient portion when prescribing medications to patients. METHODS: We used data mining to track the number of available formulations for the same medication over time and correlate the number of available versions in 2019 to the number of manufacturers, the years since first approval, and the number of prescriptions. A focused survey among healthcare professionals was conducted to query their consideration of the inactive ingredient portion of a medication when writing prescriptions. RESULTS: The number of available versions of a single medication have dramatically increased in the last 40 years. The number of available, different versions of medications are largely determined by the number of manufacturers producing this medication. Healthcare providers commonly do not consider the inactive ingredient portion when prescribing a medication. CONCLUSIONS: A multitude of available versions of the same medications provides a potentially under-recognized opportunity to prescribe the most suitable formulation to a patient as a step towards personalized medicine and mitigate potential adverse events from inactive ingredients.
Published on October 28, 2020
Binding mechanism and structural insights into the identified protein target of COVID-19 and importin-alpha with in-vitro effective drug ivermectin.
Abstract: While an FDA approved drug Ivermectin was reported to dramatically reduce the cell line of SARS-CoV-2 by approximately 5000 folds within 48 h, the precise mechanism of action and the COVID-19 molecular target involved in interaction with this in-vitro effective drug are unknown yet. Among 12 different COVID-19 targets along with Importin-alpha studied here, the RNA dependent RNA polymerase (RdRp) with RNA and Helicase NCB site show the strongest affinity to Ivermectin amounting -10.4 kcal/mol and -9.6 kcal/mol, respectively, followed by Importin-alpha with -9.0 kcal/mol. Molecular dynamics of corresponding protein-drug complexes reveals that the drug bound state of RdRp with RNA has better structural stability than the Helicase NCB site and Importin-alpha, with MM/PBSA free energy of -187.3 kJ/mol, almost twice that of Helicase (-94.6 kJ/mol) and even lower than that of Importin-alpha (-156.7 kJ/mol). The selectivity of Ivermectin to RdRp is triggered by a cooperative interaction of RNA-RdRp by ternary complex formation. Identification of the target and its interaction profile with Ivermectin can lead to more powerful drug designs for COVID-19 and experimental exploration.
Published on October 27, 2020
Chemoinformatics-based enumeration of chemical libraries: a tutorial.
Abstract: Virtual compound libraries are increasingly being used in computer-assisted drug discovery applications and have led to numerous successful cases. This paper aims to examine the fundamental concepts of library design and describe how to enumerate virtual libraries using open source tools. To exemplify the enumeration of chemical libraries, we emphasize the use of pre-validated or reported reactions and accessible chemical reagents. This tutorial shows a step-by-step procedure for anyone interested in designing and building chemical libraries with or without chemoinformatics experience. The aim is to explore various methodologies proposed by synthetic organic chemists and explore affordable chemical space using open-access chemoinformatics tools. As part of the tutorial, we discuss three examples of design: a Diversity-Oriented-Synthesis library based on lactams, a bis-heterocyclic combinatorial library, and a set of target-oriented molecules: isoindolinone based compounds as potential acetylcholinesterase inhibitors. This manuscript also seeks to contribute to the critical task of teaching and learning chemoinformatics.
Published on October 27, 2020
Prediction of Suicide-Related Events by Analyzing Electronic Medical Records from PTSD Patients with Bipolar Disorder.
Abstract: Around 800,000 people worldwide die from suicide every year and it's the 10th leading cause of death in the US. It is of great value to build a mathematic model that can accurately predict suicide especially in high-risk populations. Several different ML-based models were trained and evaluated using features obtained from electronic medical records (EMRs). The contribution of each feature was calculated to determine how it impacted the model predictions. The best-performing model was selected for analysis and decomposition. Random forest showed the best performance with true positive rates (TPR) and positive predictive values (PPV) of greater than 80%. The use of Aripiprazole, Levomilnacipran, Sertraline, Tramadol, Fentanyl, or Fluoxetine, a diagnosis of autistic disorder, schizophrenic disorder, or substance use disorder at the time of a diagnosis of both PTSD and bipolar disorder, were strong indicators for no SREs within one year. The use of Trazodone and Citalopram at baseline predicted the onset of SREs within one year. Additional features with potential protective or hazardous effects for SREs were identified by the model. We constructed an ML-based model that was successful in identifying patients in a subpopulation at high-risk for SREs within a year of diagnosis of both PTSD and bipolar disorder. The model also provides feature decompositions to guide mechanism studies. The validation of this model with additional EMR datasets will be of great value in resource allocation and clinical decision making.
Published on October 25, 2020
High-throughput virtual screening of drug databanks for potential inhibitors of SARS-CoV-2 spike glycoprotein.
Abstract: COVID-19, which is caused by a novel coronavirus known as SARS-CoV-2, has spread rapidly around the world, and it has infected more than 29 million individuals as recorded on 16 September 2020. Much effort has been made to stop the virus from spreading, and there are currently no approved pharmaceutical products to treat COVID-19. Here, we apply an in silico approach to investigate more than 3800 FDA approved drugs on the viral RBD S1-ACE2 interface as a target. The compounds were investigated through flexible ligand docking, ADME property calculations and protein-ligand interaction maps. Molecular dynamics (MD) simulations were also performed on eleven compounds to study the stability and the interactions of the protein-ligand complexes. The MD simulations show that bagrosin, chidamide, ebastine, indacaterol, regorafenib, salazosulfadimidine, silodosin and tasosartan are relatively stable near the C terminal domain (CTD1) of the S1 subunit of the viral S protein. The relative MMGBSA binding energies show that silodosin has the best binding to the target. The constant velocity steered molecular dynamics (SMD) simulations show that silodosin preferentially interacts with the RBD S1 and has potential to act as an interfering compound between viral spike-host ACE2 interactions. Communicated by Ramaswamy H. Sarma.
Published on October 25, 2020
Linking Pharmacogenomic Information on Drug Safety and Efficacy with Ethnic Minority Populations.
Abstract: Numerous prescription drugs' labeling contains pharmacogenomic (PGx) information to aid health providers and patients in the safe and effective use of drugs. However, clinical studies for such PGx biomarkers and related drug doses are generally not conducted in diverse ethnic populations. Thus, it is urgently important to incorporate PGx information with genetic characteristics of racial and ethnic minority populations and utilize it to promote minority health. In this project a bioinformatics approach was developed to enhance the collection of PGx information related to ethnic minorities to pave the way toward understanding the population-wide utility of PGx information. To address this challenge, we first gathered PGx information from drug labels. Second, we extracted data on the allele frequency information of genetic variants in ethnic minority groups from public resources. Then, we collected published research articles on PGx biomarkers and related drugs for reference. Finally, the data were integrated and formatted to build a new PGx database containing information on known drugs and biomarkers for ethnic minority groups. This database provides scientific information needed to evaluate available PGx information to enhance drug dose selection and drug safety for ethnic minority populations.
Published on October 23, 2020
Understanding Severe Acute Respiratory Syndrome Coronavirus 2 Replication to Design Efficient Drug Combination Therapies.
Abstract: BACKGROUND: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its disease CO-VID-19 has strongly encouraged the search for antiviral compounds. Most of the evaluated drugs against SARS-CoV-2 derive from drug repurposing of Food and Drug Administration-approved molecules. These drugs have as target three major processes: (1) early stages of virus-cell interaction, (2) viral proteases, and (3) the viral RNA-dependent RNA polymerase. SUMMARY: This review focused on the basic principles of virology and pharmacology to understand the importance of early stages of virus-cell interaction as therapeutic targets and other main processes vital for SARS-CoV-2 replication. Furthermore, we focused on describing the main targets associated with SARS-CoV-2 antiviral therapy and the rationale of drug combinations for efficiently suppressing viral replication. Key Messages: We hypothesized that blocking of both entry mechanisms could allow a more effective antiviral effect compared to the partial results obtained with chloroquine or its derivatives alone. This approach, already used to achieve an antiviral effect higher than that offered by every single drug administered separately, has been successfully applied in several viral infections such as HIV and HCV. This review will contribute to expanding the perception of the possible therapeutic targets in SARS-CoV-2 infection and highlight the benefits of using combination therapies.
Published on October 21, 2020
Unravelling the antimicrobial action of antidepressants on gut commensal microbes.
Abstract: Over the past decade, there has been increasing evidence highlighting the implication of the gut microbiota in a variety of brain disorders such as depression, anxiety, and schizophrenia. Studies have shown that depression affects the stability of gut microbiota, but the impact of antidepressant treatments on microbiota structure and metabolism remains underexplored. In this study, we investigated the in vitro antimicrobial activity of antidepressants from different therapeutic classes against representative strains of human gut microbiota. Six different antidepressants: phenelzine, venlafaxine, desipramine, bupropion, aripiprazole and (S)-citalopram have been tested for their antimicrobial activity against 12 commensal bacterial strains using agar well diffusion, microbroth dilution method, and colony counting. The data revealed an important antimicrobial activity (bacteriostatic or bactericidal) of different antidepressants against the tested strains, with desipramine and aripiprazole being the most inhibitory. Strains affiliating to most dominant phyla of human microbiota such as Akkermansia muciniphila, Bifidobacterium animalis and Bacteroides fragilis were significantly altered, with minimum inhibitory concentrations (MICs) ranged from 75 to 800 mug/mL. A significant reduction in bacterial viability was observed, reaching 5 logs cycle reductions with tested MICs ranged from 400 to 600 mug/mL. Our findings demonstrate that gut microbiota could be altered in response to antidepressant drugs.
Published on October 21, 2020
System biological investigations of hydroxychloroquine and azithromycin targets and their implications in QT interval prolongation.
Abstract: COVID-2019 pandemic is affecting people worldwide in the absence of an effective treatment strategy. Several suggestive therapeutic options through drug repurposing are recommended, but a complete consensus is not reached. A combination of Hydroxychloroquine (HCQ) and Azithromycin (AZM) has been widely tried and discussed but its administration has also led to potential adversities in patients. Studies are suggesting that most prominent adverse event with HCQ and AZM combination is QT interval prolongation. We studied interaction of HCQ with AZM and subsequent effect of this drug combination on QT interval prolongation. We performed system biological investigation of HCQ and AZM targets and screened important targets and pathways possibly involved in QT interval prolongation. The best core hub protein drug targets involved in QT interval prolongation were identified as HSP90AA1 exclusively associated with HCQ, while AKT1 exclusively associated with AZM. It was found that PI3K/Akt, VEGF, ERBB2 pathways must be given consideration for understanding the role of HCQ and AZM in QT interval prolongation. Conclusion: Computational methods have certain limitations based on source database coverage and prediction algorithms and therefore this data needs experimental correlation to draw final conclusion, but current findings screen targets for QT interval prolongation associated with HCQ and AZM. These proteins and pathways may provide ways to reduce this major risk associated with this combination.
Published on October 20, 2020
The Clinical Kinase Index: A Method to Prioritize Understudied Kinases as Drug Targets for the Treatment of Cancer.
Abstract: The approval of the first kinase inhibitor, Gleevec, ushered in a paradigm shift for oncological treatment-the use of genomic data for targeted, efficacious therapies. Since then, over 48 additional small-molecule kinase inhibitors have been approved, solidifying the case for kinases as a highly druggable and attractive target class. Despite the role deregulated kinase activity plays in cancer, only 8% of the kinome has been effectively "drugged." Moreover, 24% of the 634 human kinases are understudied. We have developed a comprehensive scoring system that utilizes differential gene expression, pathological parameters, overall survival, and mutational hotspot analysis to rank and prioritize clinically relevant kinases across 17 solid tumor cancers from The Cancer Genome Atlas. We have developed the clinical kinase index (CKI) app (http://cki.ccs.miami.edu) to facilitate interactive analysis of all kinases in each cancer. Collectively, we report that understudied kinases have potential clinical value as biomarkers or drug targets that warrant further study.
Showing 2271 to 2274 of 5186 entries