Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome

December 10, 2015
Free for AMIA members and students of Academic Forum member institutions; Others: $50
Alexander Tropsha, PhD - Associate Dean for Pharmacoinformatics and Data Science, University of North Carolina at Chapel Hill; Yen Sia Low, PhD - Postdoctoral Researcher, Stanford Biomedical Informatics

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.


  • Follow @AMIAinformatics and #JAMIAJC for Journal Club information.
  • Participants also receive short feedback surveys to evaluate the JAMIA JC webinar.


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]

Fee Statement

Students who are not AMIA members, but whose academic institutions are members of the Academic Forum, are eligible for a complimentary JAMIA Journal Club registration. Please contact Susanne Arnold at for the discount code. In the email, please include: full name, Academic Department, and the primary Academic Forum representative of that Academic Department. Note that AMIA Student memberships are $45, which allow access to JAMIA, all JAMIA Journal Clubs, and other webinars of interest to the biomedical informatics community. 

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. 

Target Audience

The target audience for this activity is professionals and students interested in biomedical and health informatics.

Learning Objective

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
Postdoctoral Researcher
Stanford Biomedical Informatics

Accreditation Statement

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. 

Criteria for Successful Completion

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 and 
  • Verification of attendance through the participant's electronic report through the individual login at 

The physician participant will be able to generate a CME certificate through the AMIA automated system. 
For a certificate of completion, contact

Commercial Support

No commercial support was received for this activity.

Disclosure Policy

As a provider accredited by the ACCME, AMIA requires that everyone who is in a position to control the content of an educational activity disclose all relevant financial relationships with any commercial interest for 12 months prior to the educational activity.

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

Instructions for Claiming CME/CE Credit

CME site (MyAMIA) works best with IE 8 or above version, Chrome, Safari, and Firefox.

• Login to your AMIA account on the website
• Go to “My Profile”
• Click “Invoices & Transactions” tab
• Scroll down to Events section and click ‘Credits’ next to “Webinar: JAMIA Journal Club - December 2015”
• Physicians: for “Select Credit Type” click “Physician” in the drop-down menu.
• For “Select Physician Credit Type” click “Physician” (not MOC-II)
• Click Submit
• Click on the AMIA Activities tab in your account; click “download” in the row for “Webinar: JAMIA Journal Club - December 2015”; you may print out your certificate
Other attendees: if you require a certificate of participation, please contact

Contact Info

For questions about content or CE, email