Alexander Tropsha and Yen Sia Low will discuss this month's JAMIA Journal Club selection:
Cheminformatics-aided Pharmacovigilance: application to Stevens-Johnson Syndrome
Low YS, Caster O, Bergvall T, Fourches D, Zang X, Noren GN, Rusyn I, Edwards R, Tropsha A. Cheminformatics-aided Pharmacovigilance: application to Stevens-Johnson Syndrome. J J Am Med Inform Assoc. 2015 Oct. 24 pii: ocv127.doi: 10.1093/jamia/ocv127. [Epub ahead of print]
Alexander Tropsha, PhD, is K.H. Lee Distinguished Professor and Associate Dean for Pharmacoinformatics and Data Science at the UNC Eshelman School of Pharmacy, UNC-Chapel Hill. Prof. Tropsha obtained his PhD in Chemical Enzymology in 1986 from Moscow State University, Russia. He came to UNC-Chapel Hill in 1989 as a postdoctoral fellow and became faculty in the School of Pharmacy in 1991. His research interests are in the areas of Computer-Assisted Drug Design, Cheminformatics, Structural Bioinformatics and Computational Toxicology. He has authored or co-authored nearly 200 peer-reviewed research papers, reviews and book chapters and co-edited two monographs. His research has been supported by multiple grants from the NIH, NSF, EPA, DOD, and private companies.
Yen Sia Low, PhD, is a postdoctoral researcher at the Stanford Center for Biomedical Research, where she combines machine learning approaches with epidemiological study designs to evaluate patient behavior and outcomes. She did her PhD with Dr. Alex Tropsha at the University of North Carolina - Chapel Hill during which she combined cheminformatics and bioinformatics methods to examine how the molecular features and bioassays profiles of chemicals were predictors of drug potency and chemical toxicity.
- 40-minute discussion between the authors and the JAMIA Student Editorial Board moderators including salient features of the published study and its potential impact on practice.
- 20-minute discussion of questions submitted by listeners via the webinar tools.
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JAMIA Journal Club managers are JAMIA Student Editorial Board members:
Mary Regina Boland, MA, Department of Biomedical Informatics, Columbia University
Matthew K. Breitenstein, PhD, Department of Health Sciences Research, Mayo Clinic
The PubMed citation for the paper under discussion is:
Low YS, Caster O, Bergvall T, et al. Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome. J Am Med Inform Assoc. 2015 Oct. 24. pii: ocv127. doi: 10.1093/jamia/ocv127. [Epub ahead of print]
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Statement of Purpose
Pharmacovigilance data could serve as a source of information linking drugs and their adverse side effects. This data could be reformatted to enable the development of Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs) from the chemical structure of the drugs. These QSAR models can be used to predict possible ADRs for any marketable drug and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. This article focuses on the use of ADR prediction by QSAR models to identify active and inactive drugs associated with Stevens-Johnson syndrome.
It is of interest to researchers and healthcare providers to consider whether QSAR models may provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations.
The target audience for this activity is professionals and students interested in biomedical and health informatics.
After this live activity, the participant should be better able to:
- Weigh the utility of ADR prediction by QSAR models to accurately identify drugs associated with Stevens-Johnson Syndrome
- Consider the utility of QSAR models to predict ADRs for other disease states
Alexander Tropsha, PhD
Associate Dean for Pharmacoinformatics and Data Science
K.H. Lee Distinguished Professor
Division of Chemical Biology and Medicinal Chemistry
Adjunct Professor, Department of Biomedical Engineering
Adjunct Professor, Department of Computer Science
Member, Lineberger Comprehensive Cancer Center
UNC Eshelman School of Pharmacy
Ujniversity of North Carolina at Chapel Hill
Chapel Hill, NC
Yen Sia Low, PhD
Stanford Biomedical Informatics
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.
Credit Designation Statement
The American Medical Informatics Association designates this live activity for a maximum of 1 AMA PRA Category 1 Credit(s). Physicians should claim only the credit commensurate with the extent of their participation in the activity.
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Completion of this live activity is demonstrated by:
- Viewing the live webinar
- Optional submission of questions via webinar feature; option to follow @AMIAinformatics and tweet via #JAMIAJC
- Completion of the evaluation survey at https://www.surveymonkey.com/r/JJC_Dec2015 and
- Verification of attendance through the participant's electronic report through the individual login at www.amia.org.
The physician participant will be able to generate a CME certificate through the AMIA automated system.
For a certificate of completion, contact Pesha@amia.org.
No commercial support was received for this activity.
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The ACCME considers relationships of the person involved in the CME activity to include financial relationships of a spouse or partner.
Faculty and planners who refuse to disclose relevant financial relationships will be disqualified from participating in the CME activity. For an individual with no relevant financial relationship(s), the participants must be informed that no conflicts of interest or financial relationship(s) exist.
AMIA uses a number of methods to resolve potential conflicts of interest, including: limiting content of the presentation to that which has been reviewed by one or more peer reviewers; ensuring that all scientific research referred to conforms to generally accepted standards of experimental design, data collection, and analysis; undertaking review of the educational activity by a content reviewer to evaluate for potential bias, balance in presentation, evidence-based content or other indicators of integrity, and absence of bias; monitoring the educational activity to evaluate for commercial bias in the presentation; and/or reviewing participant feedback to evaluate for commercial bias in the activity.
Disclosures for this Activity
These faculty, planners, and staff who are in a position to control the content of this activity disclose that they and their life partners have no relevant financial relationships with commercial interests:
Faculty: Alexander Tropsha, Yen Sia Low
JAMIA Journal Club planners: Mary Regina Boland, Matthew Breitenstein
AMIA staff: Susanne Arnold, Pesha Rubinstein
JAMIA Journal Club planner Michael Chiang discloses the following:
- Received Grant/Research support from the National Institutes of Health
- Is an unpaid member of the Scientific Advisory Board of Clarity Medical Systems
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