scholarly journals An Integrative Drug Repurposing Pipeline Using KNIME and Programmatic Data Access: A Case Study on COVID-19 Data

2020 ◽  
Author(s):  
Alzbeta Tuerkova ◽  
Barbara Zdrazil
Keyword(s):  
In Silico ◽  
Drug Targets ◽  
Open Data ◽  
Drug Repurposing ◽  
Data Access ◽  
Additional Advantage ◽  
Application Programming ◽  
Using Data ◽  
Chemical Abstracts Service ◽  
Programming Interfaces

Biomedical information mining is increasingly recognized as a promising technique to accelerate drug discovery and development. Especially, integrative approaches which mine data from several (open) data sources have become more attractive with the increasing possibilities to programmatically access data through Application Programming Interfaces. The use of open data in conjunction with free, platform-independent analytic tools provides the additional advantage<br>of flexibility, re-usability, and transparency. Here, we present a strategy for performing in silico drug repurposing with the analytics platform KNIME, using data for 38 suggested COVID-19 drug targets as a timely use case. The workflow includes a targeted download of data through web services, data curation (including chemical structure standardization), detection of enriched structural patterns, as well as substructure searches in DrugBank and a recently deposited dataset of antiviral drugs provided by Chemical Abstracts Service. Developed workflows, tutorials with detailed step-by-step instructions, and the information gained by the analysis of COVID-19 data are made freely available to the scientific community. The provided framework can be reused by researchers for other in silico drug repurposing projects, and it should serve as a valuable teaching resource for conveying integrative data mining strategies.

scholarly journals An Integrative Drug Repurposing Pipeline Using KNIME and Programmatic Data Access: A Case Study on COVID-19 Data

2020 ◽  
Author(s):  
Alzbeta Tuerkova ◽  
Barbara Zdrazil
Keyword(s):  
In Silico ◽  
Drug Targets ◽  
Open Data ◽  
Drug Repurposing ◽  
Data Access ◽  
Additional Advantage ◽  
Application Programming ◽  
Using Data ◽  
Chemical Abstracts Service ◽  
Programming Interfaces

Biomedical information mining is increasingly recognized as a promising technique to accelerate drug discovery and development. Especially, integrative approaches which mine data from several (open) data sources have become more attractive with the increasing possibilities to programmatically access data through Application Programming Interfaces. The use of open data in conjunction with free, platform-independent analytic tools provides the additional advantage<br>of flexibility, re-usability, and transparency. Here, we present a strategy for performing in silico drug repurposing with the analytics platform KNIME, using data for 38 suggested COVID-19 drug targets as a timely use case. The workflow includes a targeted download of data through web services, data curation (including chemical structure standardization), detection of enriched structural patterns, as well as substructure searches in DrugBank and a recently deposited dataset of antiviral drugs provided by Chemical Abstracts Service. Developed workflows, tutorials with detailed step-by-step instructions, and the information gained by the analysis of COVID-19 data are made freely available to the scientific community. The provided framework can be reused by researchers for other in silico drug repurposing projects, and it should serve as a valuable teaching resource for conveying integrative data mining strategies.

scholarly journals A ligand-based computational drug repurposing pipeline using KNIME and Programmatic Data Access: case studies for rare diseases and COVID-19

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Alzbeta Tuerkova ◽  
Barbara Zdrazil
Keyword(s):  
In Silico ◽  
Glucose Transporter ◽  
Open Data ◽  
Drug Repurposing ◽  
Data Access ◽  
Deficiency Syndrome ◽  
Data Set ◽  
Additional Advantage ◽  
Glut 1 ◽  
Chemical Abstracts Service

AbstractBiomedical information mining is increasingly recognized as a promising technique to accelerate drug discovery and development. Especially, integrative approaches which mine data from several (open) data sources have become more attractive with the increasing possibilities to programmatically access data through Application Programming Interfaces (APIs). The use of open data in conjunction with free, platform-independent analytic tools provides the additional advantage of flexibility, re-usability, and transparency. Here, we present a strategy for performing ligand-based in silico drug repurposing with the analytics platform KNIME. We demonstrate the usefulness of the developed workflow on the basis of two different use cases: a rare disease (here: Glucose Transporter Type 1 (GLUT-1) deficiency), and a new disease (here: COVID 19). The workflow includes a targeted download of data through web services, data curation, detection of enriched structural patterns, as well as substructure searches in DrugBank and a recently deposited data set of antiviral drugs provided by Chemical Abstracts Service. Developed workflows, tutorials with detailed step-by-step instructions, and the information gained by the analysis of data for GLUT-1 deficiency syndrome and COVID-19 are made freely available to the scientific community. The provided framework can be reused by researchers for other in silico drug repurposing projects, and it should serve as a valuable teaching resource for conveying integrative data mining strategies.

scholarly journals OpenFDA: an innovative platform providing access to a wealth of FDA’s publicly available data

2015 ◽  
Vol 23 (3) ◽  
pp. 596-600 ◽  
Author(s):  
Taha A Kass-Hout ◽  
Zhiheng Xu ◽  
Matthew Mohebbi ◽  
Hans Nelsen ◽  
Adam Baker ◽  
...  
Keyword(s):  
Open Data ◽  
The United States ◽  
Important Food ◽  
The Public ◽  
New Public ◽  
Application Programming ◽  
Public Datasets ◽  
Programming Interfaces ◽  
Apparent Association

Objective The objective of openFDA is to facilitate access and use of big important Food and Drug Administration public datasets by developers, researchers, and the public through harmonization of data across disparate FDA datasets provided via application programming interfaces (APIs). Materials and Methods Using cutting-edge technologies deployed on FDA’s new public cloud computing infrastructure, openFDA provides open data for easier, faster (over 300 requests per second per process), and better access to FDA datasets; open source code and documentation shared on GitHub for open community contributions of examples, apps and ideas; and infrastructure that can be adopted for other public health big data challenges. Results Since its launch on June 2, 2014, openFDA has developed four APIs for drug and device adverse events, recall information for all FDA-regulated products, and drug labeling. There have been more than 20 million API calls (more than half from outside the United States), 6000 registered users, 20,000 connected Internet Protocol addresses, and dozens of new software (mobile or web) apps developed. A case study demonstrates a use of openFDA data to understand an apparent association of a drug with an adverse event. Conclusion With easier and faster access to these datasets, consumers worldwide can learn more about FDA-regulated products.

scholarly journals Do transportation network companies decrease or increase congestion?

2019 ◽  
Vol 5 (5) ◽  
pp. eaau2670 ◽  
Author(s):  
Gregory D. Erhardt ◽  
Sneha Roy ◽  
Drew Cooper ◽  
Bhargava Sana ◽  
Mei Chen ◽  
...  
Keyword(s):  
San Francisco ◽  
Traffic Congestion ◽  
Transportation Network ◽  
Time Data ◽  
Travel Time Data ◽  
Application Programming ◽  
Using Data ◽  
Programming Interfaces ◽  
Insight Into ◽  
Transportation Network Companies

This research examines whether transportation network companies (TNCs), such as Uber and Lyft, live up to their stated vision of reducing congestion in major cities. Existing research has produced conflicting results and has been hampered by a lack of data. Using data scraped from the application programming interfaces of two TNCs, combined with observed travel time data, we find that contrary to their vision, TNCs are the biggest contributor to growing traffic congestion in San Francisco. Between 2010 and 2016, weekday vehicle hours of delay increased by 62% compared to 22% in a counterfactual 2016 scenario without TNCs. The findings provide insight into expected changes in major cities as TNCs continue to grow, informing decisions about how to integrate TNCs into the existing transportation system.

scholarly journals Open science practices and risks arising from misuse of biological research

2021 ◽  
Author(s):  
James Andrew Smith ◽  
Jonas Sandbrink
Keyword(s):  
Risk Mitigation ◽  
Open Data ◽  
Open Science ◽  
Biological Research ◽  
Science Practices ◽  
Societal Benefit ◽  
General Importance ◽  
Science Community ◽  
Application Programming ◽  
Programming Interfaces

The proliferation of open science may inadvertently increase the chance of deliberate or accidental misuse of research. Here, we examine the interaction between open science practices and biosecurity and biosafety to identify risks and opportunities for risk mitigation. We argue that open data, code, and materials may increase risks from research with misuse potential, despite their general importance. For instance, increased access to protocols, datasets, and computational methods for viral engineering may increase the risk of release of enhanced pathogens. For this dangerous subset of research, both open science and biosecurity goals may be achieved by using access-controlled repositories or application programming interfaces. The increased use of preprints could challenge any strategy for risk mitigation that relies on assessment at the publication stage, emphasising the need for earlier oversight in the research lifecycle. Preregistration of research, a practice promoted by the open science community, provides an opportunity for achieving biosecurity risk assessment at the conception of research. Open science and biosecurity experts have an important role to play in enabling responsible research with maximal societal benefit.

scholarly journals Recent Advances in In Silico Target Fishing

Molecules ◽  
2021 ◽  
Vol 26 (17) ◽  
pp. 5124 ◽  
Author(s):  
Salvatore Galati ◽  
Miriana Di Stefano ◽  
Elisa Martinelli ◽  
Giulio Poli ◽  
Tiziano Tuccinardi
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Drug Targets ◽  
Biological Activities ◽  
Protein Structures ◽  
Drug Repurposing ◽  
Specific Protein ◽  
Protein Targets ◽  
Drug Candidates ◽  
Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

scholarly journals Application Programming Interfaces in Health Care: Findings from a Current-State Sociotechnical Assessment

2020 ◽  
Vol 11 (01) ◽  
pp. 059-069 ◽  
Author(s):  
Prashila Dullabh ◽  
Lauren Hovey ◽  
Krysta Heaney-Huls ◽  
Nithya Rajendran ◽  
Adam Wright ◽  
...  
Keyword(s):  
Health Care ◽  
Clinical Data ◽  
Data Access ◽  
Use Cases ◽  
Data Set ◽  
Application Programming Interfaces ◽  
Current State ◽  
Application Programming ◽  
The Common ◽  
Programming Interfaces

Abstract Objective Interest in application programming interfaces (APIs) is increasing as key stakeholders look for technical solutions to interoperability challenges. We explored three thematic areas to assess the current state of API use for data access and exchange in health care: (1) API use cases and standards; (2) challenges and facilitators for read and write capabilities; and (3) outlook for development of write capabilities. Methods We employed four methods: (1) literature review; (2) expert interviews with 13 API stakeholders; (3) review of electronic health record (EHR) app galleries; and (4) a technical expert panel. We used an eight-dimension sociotechnical model to organize our findings. Results The API ecosystem is complicated and cuts across five of the eight sociotechnical model dimensions: (1) app marketplaces support a range of use cases, the majority of which target providers' needs, with far fewer supporting patient access to data; (2) current focus on read APIs with limited use of write APIs; (3) where standards are used, they are largely Fast Healthcare Interoperability Resources (FHIR); (4) FHIR-based APIs support exchange of electronic health information within the common clinical data set; and (5) validating external data and data sources for clinical decision making creates challenges to provider workflows. Conclusion While the use of APIs in health care is increasing rapidly, it is still in the pilot stages. We identified five key issues with implications for the continued advancement of API use: (1) a robust normative FHIR standard; (2) expansion of the common clinical data set to other data elements; (3) enhanced support for write implementation; (4) data provenance rules; and (5) data governance rules. Thus, while APIs are being touted as a solution to interoperability challenges, they remain an emerging technology that is only one piece of a multipronged approach to data access and use.

scholarly journals Exploring Cytotoxic Potential of Ciclopirox on Colorectal Cancer Cells by In-Silico Methodology

2021 ◽  
Vol 12 (6) ◽  
pp. 7287-7310
Keyword(s):  
Colorectal Cancer ◽  
In Silico ◽  
Drug Targets ◽  
High Mobility ◽  
Target Prediction ◽  
Drug Repurposing ◽  
Pharmacokinetic Studies ◽  
Standard Drug ◽  
Bioisosteric Replacement ◽  
In Silico Studies

Colorectal cancer (CRC) stands 3rd among male cancer cases and the second most prevalent disease in women, accounting for 10% of all cancer cases globally. Ciclopirox (CPX) is a broad-spectrum, synthetic, off-patent antifungal drug recommended in dermatological conditions of mycoses of the skin and nails. There are no important molecular docking studies on inhibitory aspects of CPX against CRC targets. The main objective of this study was to explore the potential of CPX as an anti-CRC agent by using in-silico approaches with the help of published literature on downregulation of overhead protein expression in CRC and combining this information in order to recognize novel drug targets [Cell division cycle 25A (Cdc25A), Protein deglycase DJ-1 (DJ-1), Retinoblastoma protein (p-Rb/Rb), Cyclin-dependent kinase-4 (CDK4), High-mobility group AT-hook-2 (HMGA2), and Catenin β-1 (Wnt/-catenin)] and to identify the perspectives for drug repurposing and comparing this with oxaliplatin; one of the standard drug used in CRC. Also, in silico drug-likeliness studies, bioavailability studies, pharmacokinetic studies, drug target prediction, and bioisosteric replacement have been performed for CPX using online SwissADME tools (SwissADME, SwissTargetPrediction, and SwissBioisostere). The in silico studies revealed that CPX successfully inhibited all the molecular targets, which suggested plausible re-utilization of CPX for treating CRC.

scholarly journals In-Silico Drug Repurposing for Targeting SARS-CoV-2 Mpro

2020 ◽  
Author(s):  
Shilpa Sharma ◽  
Shashank Deep
Keyword(s):  
In Silico ◽  
Drug Targets ◽  
Md Simulation ◽  
Chemical Constituents ◽  
Drug Repurposing ◽  
Viral Disease ◽  
Ayurvedic Drugs ◽  
Main Protease ◽  
Novel Coronavirus ◽  
Potential Inhibitors

<p>COVID-19, caused by novel coronavirus or SARS-CoV-2, is a viral disease which has infected millions worldwide. Considering the urgent need of the drug for fighting against this infectious disease, we performed in-silico drug repurposing. The main protease (M<sup>pro</sup>) is one of the best characterized drug targets among coronaviruses, therefore, this was screened for already known drugs, including chemical constituents of Ayurvedic drugs, using docking and MD simulation. The results suggest EGCG, withaferin A and artesunate may act as potential inhibitors of the main protease (M<sup>pro</sup>).</p>

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