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Showing 2341 to 2344 of 5186 entries
Published on September 6, 2020
The Role of PARP1 in Monocyte and Macrophage Commitment and Specification: Future Perspectives and Limitations for the Treatment of Monocyte and Macrophage Relevant Diseases with PARP Inhibitors.
Abstract: Modulation of PARP1 expression, changes in its enzymatic activity, post-translational modifications, and inflammasome-dependent cleavage play an important role in the development of monocytes and numerous subtypes of highly specialized macrophages. Transcription of PARP1 is governed by the proliferation status of cells at each step of their development. Higher abundance of PARP1 in embryonic stem cells and in hematopoietic precursors supports their self-renewal and pluri-/multipotency, whereas a low level of the enzyme in monocytes determines the pattern of surface receptors and signal transducers that are functionally linked to the NFkappaB pathway. In macrophages, the involvement of PARP1 in regulation of transcription, signaling, inflammasome activity, metabolism, and redox balance supports macrophage polarization towards the pro-inflammatory phenotype (M1), which drives host defense against pathogens. On the other hand, it seems to limit the development of a variety of subsets of anti-inflammatory myeloid effectors (M2), which help to remove tissue debris and achieve healing. PARP inhibitors, which prevent protein ADP-ribosylation, and PARP1DNA traps, which capture the enzyme on chromatin, may allow us to modulate immune responses and the development of particular cell types. They can be also effective in the treatment of monocytic leukemia and other cancers by reverting the anti- to the proinflammatory phenotype in tumor-associated macrophages.
Published on September 3, 2020
Multiomics dissection of molecular regulatory mechanisms underlying autoimmune-associated noncoding SNPs.
Abstract: More than 90% of autoimmune-associated variants are located in noncoding regions, leading to challenges in deciphering the underlying causal roles of functional variants and genes and biological mechanisms. Therefore, to reduce the gap between traditional genetic findings and mechanistic understanding of disease etiologies and clinical drug development, it is important to translate systematically the regulatory mechanisms underlying noncoding variants. Here, we prioritized functional noncoding SNPs with regulatory gene targets associated with 19 autoimmune diseases by incorporating hundreds of immune cell-specific multiomics data. The prioritized SNPs are associated with transcription factor (TF) binding, histone modification, or chromatin accessibility, indicating their allele-specific regulatory roles. Their target genes are significantly enriched in immunologically related pathways and other known immunologically related functions. We found that 90.1% of target genes are regulated by distal SNPs involving several TFs (e.g., the DNA-binding protein CCCTC-binding factor [CTCF]), suggesting the importance of long-range chromatin interaction in autoimmune diseases. Moreover, we predicted potential drug targets for autoimmune diseases, including 2 genes (NFKB1 and SH2B3) with known drug indications on other diseases, highlighting their potential drug repurposing opportunities. Taken together, these findings may provide useful information for future experimental follow-up and drug applications on autoimmune diseases.
Published on September 2, 2020
Targeting the SARS-CoV-2 main protease using FDA-approved Isavuconazonium, a P2-P3 alpha-ketoamide derivative and Pentagastrin: An in-silico drug discovery approach.
Abstract: The SARS-CoV-2 main protease (M(pro)) is an attractive target towards discovery of drugs to treat COVID-19 because of its key role in virus replication. The atomic structure of M(pro) in complex with an alpha-ketoamide inhibitor (Lig13b) is available (PDB ID:6Y2G). Using 6Y2G and the prior knowledge that protease inhibitors could eradicate COVID-19, we designed a computational study aimed at identifying FDA-approved drugs that could interact with M(pro). We searched the DrugBank and PubChem for analogs and built a virtual library containing approximately 33,000 conformers. Using high-throughput virtual screening and ligand docking, we identified Isavuconazonium, a ketoamide inhibitor (alpha-KI) and Pentagastrin as the top three molecules (Lig13b as the benchmark) based on docking energy. The DeltaGbind of Lig13b, Isavuconazonium, alpha-KI, Pentagastrin was -28.1, -45.7, -44.7, -34.8 kcal/mol, respectively. Molecular dynamics simulation revealed that these ligands are stable within the M(pro) active site. Binding of these ligands is driven by a variety of non-bonded interaction, including polar bonds, H-bonds, van der Waals and salt bridges. The overall conformational dynamics of the complexed-M(pro) was slightly altered relative to apo-M(pro). This study demonstrates that three distinct classes molecules, Isavuconazonium (triazole), alpha-KI (ketoamide) and Pentagastrin (peptide) could serve as potential drugs to treat patients with COVID-19.
Published in August 2020
Delta-9-Tetrahydrocannabinol and Cannabidiol Drug-Drug Interactions.
Abstract: Although prescribing information (PI) is often the initial source of information when identifying potential drug-drug interactions, it may only provide a limited number of exemplars or only reference a class of medications without providing any specific medication examples. In the case of medical cannabis and medicinal cannabinoids, this is further complicated by the fact that the increased therapeutic use of marijuana extracts and cannabidiol oil will not have regulatory agency approved PI. The objective of this study was to provide a detailed and comprehensive drug-drug interaction list that is aligned with cannabinoid manufacturer PI. The cannabinoid drug-drug interaction information is listed in this article and online supplementary material as a PRECIPITANT (cannabinoid) medication that either INHIBITS/INDUCES the metabolism or competes for the same SUBSTRATE target (metabolic enzyme) of an OBJECT (OTHER) medication. In addition to a comprehensive list of drug-drug interactions, we also provide a list of 57 prescription medications displaying a narrow therapeutic index that are potentially impacted by concomitant cannabinoid use (whether through prescription use of cannabinoid medications or therapeutic/recreational use of cannabis and its extracts).
Published in August 2020
A machine learning and network framework to discover new indications for small molecules.
Abstract: Drug repurposing, identifying novel indications for drugs, bypasses common drug development pitfalls to ultimately deliver therapies to patients faster. However, most repurposing discoveries have been led by anecdotal observations (e.g. Viagra) or experimental-based repurposing screens, which are costly, time-consuming, and imprecise. Recently, more systematic computational approaches have been proposed, however these rely on utilizing the information from the diseases a drug is already approved to treat. This inherently limits the algorithms, making them unusable for investigational molecules. Here, we present a computational approach to drug repurposing, CATNIP, that requires only biological and chemical information of a molecule. CATNIP is trained with 2,576 diverse small molecules and uses 16 different drug similarity features, such as structural, target, or pathway based similarity. This model obtains significant predictive power (AUC = 0.841). Using our model, we created a repurposing network to identify broad scale repurposing opportunities between drug types. By exploiting this network, we identified literature-supported repurposing candidates, such as the use of systemic hormonal preparations for the treatment of respiratory illnesses. Furthermore, we demonstrated that we can use our approach to identify novel uses for defined drug classes. We found that adrenergic uptake inhibitors, specifically amitriptyline and trimipramine, could be potential therapies for Parkinson's disease. Additionally, using CATNIP, we predicted the kinase inhibitor, vandetanib, as a possible treatment for Type 2 Diabetes. Overall, this systematic approach to drug repurposing lays the groundwork to streamline future drug development efforts.
Published on August 31, 2020
Genomic landscape of the immune microenvironments of brain metastases in breast cancer.
Abstract: BACKGROUND: This study was intended to investigate the genomic landscape of the immune microenvironments of brain metastases in breast cancer. METHODS: Three gene expression profile datasets (GSE76714, GSE125989 and GSE43837) of breast cancer with brain metastases were downloaded from Gene Expression Omnibus (GEO) database. After differential expression analysis, the tumor immune microenvironment and immune cell infiltration were analyzed. Then immune-related genes were identified, followed by function analysis, transcription factor (TF)-miRNA-mRNA co-regulatory network analysis, and survival analysis of metastatic recurrence. RESULTS: The present results showed that the tumor immune microenvironment in brain metastases was immunosuppressed compared with primary caner. Compared with primary cancer samples, the infiltration ratio of plasma cells in brain metastases samples was significantly higher, while the infiltration ratio of macrophages M2 cells in brain metastases samples was significantly lower. Total 42 immune-related genes were identified, such as THY1 and NEU2. CD1B, THY1 and DOCK2 were found to be implicated in the metastatic recurrence of breast cancer. CONCLUSIONS: Targeting macrophages or plasma cells may be new strategies for immunotherapy of breast cancer with brain metastases. THY1 and NEU2 may be potential therapeutic targets for breast cancer with brain metastases, and THY1, CD1B and DOCK2 may serve as potential prognostic markers for improvement of brain metastases survival.
Published in August 2020
Supportive care medications coinciding with chemotherapy among children with hematologic malignancy.
Abstract: Pharmacokinetic (PK) conflicts can arise between supportive care medications (SCM) and chemotherapy in children with hematologic malignancy (HM). In this retrospective study, medical records for children (28 days-18 years) diagnosed with HM and receiving an SCM antimicrobial were collected from a hospital network between 1 May 2000 and 31 December 2014. PK drug-gene associations were obtained from a curated pharmacogenomics database. Among 730 patients (median age of 7.5 (IQR 3.7-13.9) years), primarily diagnosed with lymphoid leukemia (52%), lymphoma (28%), or acute myeloid leukemia (16%), chemotherapy was administered in 2846 hospitalizations. SCM accounted for 90.5% (n = 448) of distinct drugs with 93% (n = 679) of children, receiving >/=5 different SCM/hospitalization. Same-day SCM/chemotherapeutic PK gene overlap occurred in 48.3% of hospitalizations and was associated with age (p = 0.026), number of SCM, HM subtype, surgery, and hematopoietic stem cell transplant (p < 0.0001). A high and variable SCM burden among children with HM receiving chemotherapy poses a risk for unanticipated PK conflicts.
Published in August 2020
Predicting novel drugs for SARS-CoV-2 using machine learning from a >10 million chemical space.
Abstract: There is an urgent need for the identification of effective therapeutics for COVID-19 and we have developed a machine learning drug discovery pipeline to identify several drug candidates. First, we collect assay data for 65 target human proteins known to interact with the SARS-CoV-2 proteins, including the ACE2 receptor. Next, we train machine learning models to predict inhibitory activity and use them to screen FDA registered chemicals and approved drugs (~100,000) and ~14 million purchasable chemicals. We filter predictions according to estimated mammalian toxicity and vapor pressure. Prospective volatile candidates are proposed as novel inhaled therapeutics since the nasal cavity and respiratory tracts are early bottlenecks for infection. We also identify candidates that act across multiple targets as promising for future analyses. We anticipate that this theoretical study can accelerate testing of two categories of therapeutics: repurposed drugs suited for short-term approval, and novel efficacious drugs suitable for a long-term follow up.
Published on August 31, 2020
Investigating Core Signaling Pathways of Hepatitis B Virus Pathogenesis for Biomarkers Identification and Drug Discovery via Systems Biology and Deep Learning Method.
Abstract: Hepatitis B Virus (HBV) infection is a major cause of morbidity and mortality worldwide. However, poor understanding of its pathogenesis often gives rise to intractable immune escape and prognosis recurrence. Thus, a valid systematic approach based on big data mining and genome-wide RNA-seq data is imperative to further investigate the pathogenetic mechanism and identify biomarkers for drug design. In this study, systems biology method was applied to trim false positives from the host/pathogen genetic and epigenetic interaction network (HPI-GEN) under HBV infection by two-side RNA-seq data. Then, via the principal network projection (PNP) approach and the annotation of KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, significant biomarkers related to cellular dysfunctions were identified from the core cross-talk signaling pathways as drug targets. Further, based on the pre-trained deep learning-based drug-target interaction (DTI) model and the validated pharmacological properties from databases, i.e., drug regulation ability, toxicity, and sensitivity, a combination of promising multi-target drugs was designed as a multiple-molecule drug to create more possibility for the treatment of HBV infection. Therefore, with the proposed systems medicine discovery and repositioning procedure, we not only shed light on the etiologic mechanism during HBV infection but also efficiently provided a potential drug combination for therapeutic treatment of Hepatitis B.
Published in August 2020
Predictive features of gene expression variation reveal mechanistic link with differential expression.
Abstract: For most biological processes, organisms must respond to extrinsic cues, while maintaining essential gene expression programmes. Although studied extensively in single cells, it is still unclear how variation is controlled in multicellular organisms. Here, we used a machine-learning approach to identify genomic features that are predictive of genes with high versus low variation in their expression across individuals, using bulk data to remove stochastic cell-to-cell variation. Using embryonic gene expression across 75 Drosophila isogenic lines, we identify features predictive of expression variation (controlling for expression level), many of which are promoter-related. Genes with low variation fall into two classes reflecting different mechanisms to maintain robust expression, while genes with high variation seem to lack both types of stabilizing mechanisms. Applying this framework to humans revealed similar predictive features, indicating that promoter architecture is an ancient mechanism to control expression variation. Remarkably, expression variation features could also partially predict differential expression after diverse perturbations in both Drosophila and humans. Differential gene expression signatures may therefore be partially explained by genetically encoded gene-specific features, unrelated to the studied treatment.
Showing 2341 to 2344 of 5186 entries