Publications Search
Explore how scientists all over the world use DrugBank in their research.
Showing 871 to 874 of 5186 entries
Published in April 2022
Human/SARS-CoV-2 genome-scale metabolic modeling to discover potential antiviral targets for COVID-19.
Abstract: Background: Coronavirus disease 2019 (COVID-19) has caused a substantial increase in mortality and economic and social disruption. The absence of US Food and Drug Administration-approved drugs for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) highlights the need for new therapeutic drugs to combat COVID-19. Methods: The present study proposed a fuzzy hierarchical optimization framework for identifying potential antiviral targets for COVID-19. The objectives in the decision-making problem were not only to evaluate the elimination of the virus growth, but also to minimize side effects causing treatment. The identified candidate targets could promote processes of drug discovery and development. Significant findings: Our gene-centric method revealed that dihydroorotate dehydrogenase (DHODH) inhibition could reduce viral biomass growth and metabolic deviation by 99.4% and 65.6%, respectively, and increase cell viability by 70.4%. We also identified two-target combinations that could completely block viral biomass growth and more effectively prevent metabolic deviation. We also discovered that the inhibition of two antiviral metabolites, cytidine triphosphate (CTP) and uridine-5'-triphosphate (UTP), exhibits effects similar to those of molnupiravir, which is undergoing phase III clinical trials. Our predictions also indicate that CTP and UTP inhibition blocks viral RNA replication through a similar mechanism to that of molnupiravir.
Published in April 2022
Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs.
Abstract: Natural compounds constitute a rich resource of potential small molecule therapeutics. While experimental access to this resource is limited due to its vast diversity and difficulties in systematic purification, computational assessment of structural similarity with known therapeutic molecules offers a scalable approach. Here, we assessed functional similarity between natural compounds and approved drugs by combining multiple chemical similarity metrics and physicochemical properties using a machine-learning approach. We computed pairwise similarities between 1410 drugs for training classification models and used the drugs shared protein targets as class labels. The best performing models were random forest which gave an average area under the ROC of 0.9, Matthews correlation coefficient of 0.35, and F1 score of 0.33, suggesting that it captured the structure-activity relation well. The models were then used to predict protein targets of circa 11k natural compounds by comparing them with the drugs. This revealed therapeutic potential of several natural compounds, including those with support from previously published sources as well as those hitherto unexplored. We experimentally validated one of the predicted pair's activities, viz., Cox-1 inhibition by 5-methoxysalicylic acid, a molecule commonly found in tea, herbs and spices. In contrast, another natural compound, 4-isopropylbenzoic acid, with the highest similarity score when considering most weighted similarity metric but not picked by our models, did not inhibit Cox-1. Our results demonstrate the utility of a machine-learning approach combining multiple chemical features for uncovering protein binding potential of natural compounds.
Published on April 30, 2022
A Single-Cell Network-Based Drug Repositioning Strategy for Post-COVID-19 Pulmonary Fibrosis.
Abstract: Post-COVID-19 pulmonary fibrosis (PCPF) is a long-term complication that appears in some COVID-19 survivors. However, there are currently limited options for treating PCPF patients. To address this problem, we investigated COVID-19 patients' transcriptome at single-cell resolution and combined biological network analyses to repurpose the drugs treating PCPF. We revealed a novel gene signature of PCPF. The signature is functionally associated with the viral infection and lung fibrosis. Further, the signature has good performance in diagnosing and assessing pulmonary fibrosis. Next, we applied a network-based drug repurposing method to explore novel treatments for PCPF. By quantifying the proximity between the drug targets and the signature in the interactome, we identified several potential candidates and provided a drug list ranked by their proximity. Taken together, we revealed a novel gene expression signature as a theragnostic biomarker for PCPF by integrating different computational approaches. Moreover, we showed that network-based proximity could be used as a framework to repurpose drugs for PCPF.
Published in April 2022
Abnormal global alternative RNA splicing in COVID-19 patients.
Abstract: Viral infections can alter host transcriptomes by manipulating host splicing machinery. Despite intensive transcriptomic studies on SARS-CoV-2, a systematic analysis of alternative splicing (AS) in severe COVID-19 patients remains largely elusive. Here we integrated proteomic and transcriptomic sequencing data to study AS changes in COVID-19 patients. We discovered that RNA splicing is among the major down-regulated proteomic signatures in COVID-19 patients. The transcriptome analysis showed that SARS-CoV-2 infection induces widespread dysregulation of transcript usage and expression, affecting blood coagulation, neutrophil activation, and cytokine production. Notably, CD74 and LRRFIP1 had increased skipping of an exon in COVID-19 patients that disrupts a functional domain, which correlated with reduced antiviral immunity. Furthermore, the dysregulation of transcripts was strongly correlated with clinical severity of COVID-19, and splice-variants may contribute to unexpected therapeutic activity. In summary, our data highlight that a better understanding of the AS landscape may aid in COVID-19 diagnosis and therapy.
Published in April 2022
Protecting effect of emodin in experimental autoimmune encephalomyelitis mice by inhibiting microglia activation and inflammation via Myd88/PI3K/Akt/NF-kappaB signalling pathway.
Abstract: Experimental autoimmune encephalomyelitis (EAE) is characterized by demyelination of the central nervous system. Emodin is an anthraquinone derivative with comprehensive anti-inflammatory, anti-cancer, and immunomodulatory effects and is widely used in the treatment of inflammatory, tumor, and immune system diseases. However, none of the clinical or experimental studies have explored the therapeutic efficacy of emodin in EAE/multiple sclerosis (MS). Thus, we evaluated the protective effect of emodin on EAE mediated via inhibition of microglia activation and inflammation. Wild-type mice were randomly divided into the normal control, EAE, low-dose emodin, and high-dose emodin groups. Clinical scores and pathological changes were assessed 21 days after immunization. The network pharmacology approach was used to elucidate the underlying mechanisms by using an online database. Molecular docking, polymerase-chain reaction tests, western blotting, and immunofluorescence were performed to verify the network pharmacology results. An in vivo experiment showed that high-dose emodin ameliorated clinical symptoms, inflammatory cell infiltration, and myelination. Pharmacological network analysis showed AKT1 was the main target and that emodin played a key role in MS treatment mainly via the PI3K-Akt pathway. Molecular docking showed that emodin bound well with PI3K, AKT1, and NFKB1. Emodin decreased the expression of phosphorylated(p)-PI3K, p-Akt, NF-kappaB, and myeloid differentiation factor 88 and the levels of markers (CD86 and CD206) in M1- and M2-phenotype microglia in EAE. Thus, the emodin inhibited microglial activation and exhibited anti-inflammatory and neuroprotective effects against EAE via the Myd88/PI3K/Akt/NF-kappaB signalling pathway. In conclusion, emodin has a promising role in EAE/MS treatment, warranting further detailed studies.
Published in April 2022
Pharmacogenomic landscape of Indian population using whole genomes.
Abstract: Ethnic differences in pharmacogenomic (PGx) variants have been well documented in literature and could significantly impact variability in response and adverse events to therapeutics. India is a large country with diverse ethnic populations of distinct genetic architecture. India's national genome sequencing initiative (IndiGen) provides a unique opportunity to explore the landscape of PGx variants using population-scale whole genome sequences. We have analyzed the IndiGen variation dataset (N = 1029 genomes) along with global population scale databases to map the most prevalent clinically actionable and potentially deleterious PGx variants among Indians. Differential frequencies for the known and novel variants were studied and interaction of the disrupted PGx genes affecting drug responses were analyzed by performing a pathway analysis. We have highlighted significant differences in the allele frequencies of clinically actionable PGx variants in Indians when compared to the global populations. We identified 134 mostly common (allele frequency [AF] > 0.1) potentially deleterious PGx variants that could alter or inhibit the function of 102 pharmacogenes in Indians. We also estimate that on, an average, each Indian individual carried eight PGx variants (single nucleotide variants) that have a direct impact on the choice of treatment or drug dosing. We have also highlighted clinically actionable PGx variants and genes for which preemptive genotyping is most recommended for the Indian population. The study has put forward the most comprehensive PGx landscape of the Indian population from whole genomes that could enable optimized drug selection and genotype-guided prescriptions for improved therapeutic outcomes and minimizing adverse events.
Published on April 29, 2022
Prescription drugs and mitochondrial metabolism.
Abstract: Mitochondria are central to the physiology and survival of nearly all eukaryotic cells and house diverse metabolic processes including oxidative phosphorylation, reactive oxygen species buffering, metabolite synthesis/exchange, and Ca2+ sequestration. Mitochondria are phenotypically heterogeneous and this variation is essential to the complexity of physiological function among cells, tissues, and organ systems. As a consequence of mitochondrial integration with so many physiological processes, small molecules that modulate mitochondrial metabolism induce complex systemic effects. In the case of many commonly prescribed drugs, these interactions may contribute to drug therapeutic mechanisms, induce adverse drug reactions, or both. The purpose of this article is to review historical and recent advances in the understanding of the effects of prescription drugs on mitochondrial metabolism. Specific 'modes' of xenobiotic-mitochondria interactions are discussed to provide a set of qualitative models that aid in conceptualizing how the mitochondrial energy transduction system may be affected. Findings of recent in vitro high-throughput screening studies are reviewed, and a few candidate drug classes are chosen for additional brief discussion (i.e. antihyperglycemics, antidepressants, antibiotics, and antihyperlipidemics). Finally, recent improvements in pharmacokinetics models that aid in quantifying systemic effects of drug-mitochondria interactions are briefly considered.
Published on April 29, 2022
Anaplastic thyroid cancer: genome-based search for new targeted therapy options.
Abstract: Objective: Anaplastic thyroid cancer (ATC) is one of the most lethal human cancers with meager treatment options. We aimed to identify the targeted drugs already approved by the Food and Drug Administration (FDA) for solid cancer in general, which could be effective in ATC. Design: Database mining. Methods: FDA-approved drugs for targeted therapy were identified by screening the databases of MyCancerGenome and the National Cancer Institute. Drugs were linked to the target genes by querying Drugbank. Subsequently, MyCancerGenome, CIViC, TARGET and OncoKB were mined for genetic alterations which are predicted to lead to drug sensitivity or resistance. We searched the Cancer Genome Atlas database (TCGA) for patients with ATC and probed their sequencing data for genetic alterations which predict a drug response. Results: In the study,155 FDA-approved drugs with 136 potentially targetable genes were identified. Seventeen (52%) of 33 patients found in TCGA had at least one genetic alteration in targetable genes. The point mutation BRAF V600E was seen in 45% of patients. PIK3CA occurred in 18% of cases. Amplifications of ALK and SRC were detected in 3% of cases, respectively. Fifteen percent of the patients displayed a co-mutation of BRAF and PIK3CA. Besides BRAF-inhibitors, the PIK3CA-inhibitor copanlisib showed a genetically predicted response. The 146 (94%) remaining drugs showed no or low (under 4% cases) genetically predicted drug response. Conclusions: While ATC carrying BRAF mutations can benefit from BRAF inhibitors and this effect might be enhanced by a combined strategy including PIK3CA inhibitors in some of the patients, alterations in BRAFWT ATC are not directly targeted by currently FDA-approved options.
Published on April 29, 2022
Chemical Diversity and Potential Target Network of Woody Peony Flower Essential Oil from Eleven Representative Cultivars (Paeonia x suffruticosa Andr.).
Abstract: Woody peony (Paeonia x suffruticosa Andr.) has many cultivars with genetic variances. The flower essential oil is valued in cosmetics and fragrances. This study was to investigate the chemical diversity of essential oils of eleven representative cultivars and their potential target network. Hydro-distillation afforded yields of 0.11-0.25%. Essential oils were analyzed by GC-MS and GC-FID which identified 105 compounds. Three clusters emerged from multivariate analysis, representative of phloroglucinol trimethyl ether ('Caihui'), citronellol ('Jingyu', 'Zhaofen' and 'Baiyuan Zhenghui') and mixed (the rest of the cultivars) chemotypes. 'Zhaofen' and 'Jingyu' also exhibited low levels of other rose-related compounds. The main components were subjected to a target network approach. Drug-likeness screening gave 20 compounds with predictive blood-brain barrier permeation. Compound target network identified six key compounds, namely nerol, citronellol, geraniol, geranic acid, cis-3-hexen-1-ol and 1-hexanol. Top enriched terms in GO, KEGG and DisGeNET were mostly related to the central nervous system (CNS). Protein-protein interactions revealed a core network of 14 targets, 11 of which were CNS-related (targets for antidepressants, analgesics, antipsychotics, anti-Alzheimer's and anti-Parkinson's agents). This work provides useful information on the production of woody peony essential oils with specific chemotypes and reveals their potential importance in aromatherapy for alternative treatment of CNS disorders.
Published on April 27, 2022
A multi-task Gaussian process self-attention neural network for real-time prediction of the need for mechanical ventilators in COVID-19 patients.
Abstract: OBJECTIVE: The Coronavirus Disease 2019 (COVID-19) pandemic has overwhelmed the capacity of healthcare resources and posed a challenge for worldwide hospitals. The ability to distinguish potentially deteriorating patients from the rest helps facilitate reasonable allocation of medical resources, such as ventilators, hospital beds, and human resources. The real-time accurate prediction of a patient's risk scores could also help physicians to provide earlier respiratory support for the patient and reduce the risk of mortality. METHODS: We propose a robust real-time prediction model for the in-hospital COVID-19 patients' probability of requiring mechanical ventilation (MV). The end-to-end neural network model incorporates the Multi-task Gaussian Process to handle the irregular sampling rate in observational data together with a self-attention neural network for the prediction task. RESULTS: We evaluate our model on a large database with 9,532 nationwide in-hospital patients with COVID-19. The model demonstrates significant robustness and consistency improvements compared to conventional machine learning models. The proposed prediction model also shows performance improvements in terms of area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) compared to various deep learning models, especially at early times after a patient's hospital admission. CONCLUSION: The availability of large and real-time clinical data calls for new methods to make the best use of them for real-time patient risk prediction. It is not ideal for simplifying the data for traditional methods or for making unrealistic assumptions that deviate from observation's true dynamics. We demonstrate a pilot effort to harmonize cross-sectional and longitudinal information for mechanical ventilation needing prediction.
Showing 871 to 874 of 5186 entries