Innovations In Clinical Neuroscience

Hot Topics in Drug Development Apr 2016

A peer-reviewed, evidence-based journal for clinicians in the field of neuroscience

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Hot Topics in Drug Development [April 2016] 10 Click * at the end of each synopsis to access the abstract on Pubmed "BIG DATA" AND DATA SHARING Use of "big data" in drug discovery and clinical trials. Taglang G, Jackson DB. Gynecol Oncol. 2016 Apr;141(1):17-23. doi: 10.1016/j.ygyno.2016.02.022. Summary: In this review article, the authors explore the current and potential impact the use of "big data" can have on oncology research. Accordinging to the authors, use of big data is expediting biomedical discovery and transforming the practice of oncology into an information science. The authors go on to discuss how, based on the collected genetic information from patients, clinico- molecular data analytics can provide a deeper understanding of the mechanisms controlling the biological and clinical response to available therapeutic options. * PMID: 27016224 Can data repositories help find effective treatments for complex diseases? Farber GK. Prog Neurobiol. 2016 Mar 24. pii: S0301-0082(15)30025-3. doi: 10.1016/j.pneurobio.2016.03. 008. [Epub ahead of print] Summary: Here the researcher describes a theoretical data repository that could potentially facilitate the development of effective medical treatments. The author describes the amount of data that might be needed for such a data repository and whether the existing data storage infrastructure is enough. The author then examines several successful data repositories for common characteristics, and provides an example of an unsuccessful attempt, focusing on potential lessons learned. The author then describes a variety of issues related to sharing data. * PMID: 27018167 Curating and integrating data from multiple sources to support healthcare analytics. Ng K, Kakkanatt C, Benigno M, et al. Stud Health Technol Inform. 2015;216:1056. Summary: Here, researchers describe their approach to and highlight their experiences with the development of a robust data collection, curation and integration infrastructure that supports healthcare analytics. The authors describe how their system has been successfully applied to the processing of a variety of data types including clinical data from electronic health records and observational studies, genomic data, microbiomic data, self- reported data from surveys and self- tracked data from wearable devices from over 600 subjects. * PMID: 26262355 Sharing clinical trial data on patient level: opportunities and challenges. Biom J. 2015 Jan;57(1):8–26. doi: 10.1002/bimj.201300283. Epub 2014 Jun 18. Summary: The authors examine the implementation of sharing patient- level clinical trial data initiatives. Several ongoing initiatives for the open reporting of patient-level clinical trial data are described. The potential benefits and challenges of such initiatives are described. * PMID: 24942505 Sharing clinical trial data: maximizing benefits, minimizing risk. Committee on Strategies for Responsible Sharing of Clinical Trial Data; Board on Health Sciences Policy; Institute of Medicine. Washington (DC): National Academies Press (US); 2015 Apr. Summary: The committee describes how data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. The committee also describes risks of data sharing. This committee presents activities and strategies for the responsible sharing of clinical trial data. The authors identify guiding principles and make recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each Highlights of noteworthy, peer- reviewed, evidence-based articles on drug discovery and development Journal Watch

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