United States National Library of Medicine Drug Information Portal

Hochstein, Colette; Goshorn, Jeanne; Chang, Florence

doi:10.1080/02763860902816784

Cited by 5 publications

(5 citation statements)

References 1 publication

(1 reference statement)

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“…In total, we identified 1570 lncRNA-TF-gene triplets of 282 drugs, and 16 307 relationships were formed between drugs and triplets. Next, we divided 282 drugs into 15 classes, which are Antineoplastic Agents, Enzyme Inhibitors, Antimitotic Agents, Anti-Infective Agents and so on according to the Drug Information Portal [ 36 ] ( Supplementary Figure S1 , see Supplementary Data available online at https://academic.oup.com/bib ). In addition, a total of 1059 lncRNAs were involved in the drug sensitivity-related triplets, most of which were related to the antineoplastic agents sensitivity ( Figure 2B ).…”

Section: Resultsmentioning

confidence: 99%

Identifying and characterizing drug sensitivity-related lncRNA-TF-gene regulatory triplets

et al. 2022

Briefings in Bioinformatics

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Recently, many studies have shown that lncRNA can mediate the regulation of TF-gene in drug sensitivity. However, there is still a lack of systematic identification of lncRNA-TF-gene regulatory triplets for drug sensitivity. In this study, we propose a novel analytic approach to systematically identify the lncRNA-TF-gene regulatory triplets related to the drug sensitivity by integrating transcriptome data and drug sensitivity data. Totally, 1570 drug sensitivity-related lncRNA-TF-gene triplets were identified, and 16 307 relationships were formed between drugs and triplets. Then, a comprehensive characterization was performed. Drug sensitivity-related triplets affect a variety of biological functions including drug response-related pathways. Phenotypic similarity analysis showed that the drugs with many shared triplets had high similarity in their two-dimensional structures and indications. In addition, Network analysis revealed the diverse regulation mechanism of lncRNAs in different drugs. Also, survival analysis indicated that lncRNA-TF-gene triplets related to the drug sensitivity could be candidate prognostic biomarkers for clinical applications. Next, using the random walk algorithm, the results of which we screen therapeutic drugs for patients across three cancer types showed high accuracy in the drug-cell line heterogeneity network based on the identified triplets. Besides, we developed a user-friendly web interface-DrugSETs (http://bio-bigdata.hrbmu.edu.cn/DrugSETs/) available to explore 1570 lncRNA-TF-gene triplets relevant with 282 drugs. It can also submit a patient’s expression profile to predict therapeutic drugs conveniently. In summary, our research may promote the study of lncRNAs in the drug resistance mechanism and improve the effectiveness of treatment.

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Section: Resultsmentioning

confidence: 99%

Identifying and characterizing drug sensitivity-related lncRNA-TF-gene regulatory triplets

et al. 2022

Briefings in Bioinformatics

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“…Drug names, categories, and synonyms coming from the Drug Information Portal of the National Library of Medicine (NIH DIP) were used to identify and categorize drugs used in clinical trials ( Hochstein et al, 2009 ). The NIH Drug Information Portal was an online platform containing information about drugs, alternative names, categories, etc.…”

Section: Methodsmentioning

confidence: 99%

“…Initially, it contained the columns Category and EntryTerms, which are synonym terms for each disease or condition. MESH Categories table was merged with the conditions section of the ICTRP Drug names, categories, and synonyms coming from the Drug Information Portal of the National Library of Medicine (NIH DIP) were used to identify and categorize drugs used in clinical trials (Hochstein et al, 2009). The NIH Drug Information Portal was an online platform containing information about drugs, alternative names, categories, etc.…”

Section: Introductionmentioning

confidence: 99%

The international clinical trials registry platform (ICTRP): data integrity and the trends in clinical trials, diseases, and drugs

Namiot,

Smirnovová,

Sokolov

et al. 2023

Front. Pharmacol.

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Introduction: Clinical trials are the gold standard for testing new therapies. Databases like ClinicalTrials.gov provide access to trial information, mainly covering the US and Europe. In 2006, WHO introduced the global ICTRP, aggregating data from ClinicalTrials.gov and 17 other national registers, making it the largest clinical trial platform by June 2019. This study conducts a comprehensive global analysis of the ICTRP database and provides framework for large-scale data analysis, data preparation, curation, and filtering.Materials and methods: The trends in 689,793 records from the ICTRP database (covering trials registered from 1990 to 2020) were analyzed. Records were adjusted for duplicates and mapping of agents to drug classes was performed. Several databases, including DrugBank, MESH, and the NIH Drug Information Portal were used to investigate trends in agent classes.Results: Our novel approach unveiled that 0.5% of the trials we identified were hidden duplicates, primarily originating from the EUCTR database, which accounted for 82.9% of these duplicates. However, the overall number of hidden duplicates within the ICTRP seems to be decreasing. In total, 689 793 trials (478 345 interventional) were registered in the ICTRP between 1990 and 2020, surpassing the count of trials in ClinicalTrials.gov (362 500 trials by the end of 2020). We identified 4 865 unique agents in trials with DrugBank, whereas 2 633 agents were identified with NIH Drug Information Portal data. After the ClinicalTrials.gov, EUCTR had the most trials in the ICTRP, followed by CTRI, IRCT, CHiCTR, and ISRCTN. CHiCTR displayed a significant surge in trial registration around 2015, while CTRI experienced rapid growth starting in 2016.Conclusion: This study highlights both the strengths and weaknesses of using the ICTRP as a data source for analyzing trends in clinical trials, and emphasizes the value of utilizing multiple registries for a comprehensive analysis.

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“…One obvious strategy is to investigate existing drugs for the biochemical properties of interest. In this regard, the National Library of Medicine’s Drug Information Portal is an online dictionary that could be utilized when initially searching for drugs-of-interest ( 6 ). In addition, online encyclopedias, such as PubChem, allow the search of information including chemical and physical properties, biological activities, safety and toxicity profiles, patents, and relevant literature ( 7 ).…”

Section: Emerging Computational Approachesmentioning

confidence: 99%

Computational approaches for drug repurposing in oncology: untapped opportunity for high value innovation

et al. 2023

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Historically, the effort by academia and industry to develop new chemical entities into lifesaving drugs has limited success in meeting the demands of today’s healthcare. Repurposing drugs that are originally approved by the United States Food and Drug Administration or by regulatory authorities around the globe is an attractive strategy to rapidly develop much-needed therapeutics for oncologic indications that extend from treating cancer to managing treatment-related complications. This review discusses computational approaches to harness existing drugs for new therapeutic use in oncology.

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United States National Library of Medicine Drug Information Portal

Cited by 5 publications

References 1 publication

Identifying and characterizing drug sensitivity-related lncRNA-TF-gene regulatory triplets

Identifying and characterizing drug sensitivity-related lncRNA-TF-gene regulatory triplets

The international clinical trials registry platform (ICTRP): data integrity and the trends in clinical trials, diseases, and drugs

Computational approaches for drug repurposing in oncology: untapped opportunity for high value innovation

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