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J Am Med Inform Assoc. 2016 Sep;23(5):968-78. doi: 10.1093/jamia/ocv127. Epub 2015 Oct 24.

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome.

Author information

1
Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA Department of Environmental Sciences and Engineering, Gillings School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA.
2
Uppsala Monitoring Centre, Uppsala, Sweden Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
3
Uppsala Monitoring Centre, Uppsala, Sweden.
4
Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA.
5
Uppsala Monitoring Centre, Uppsala, Sweden Department of Mathematics, Stockholm University, Stockholm, Sweden.
6
Department of Environmental Sciences and Engineering, Gillings School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA.
7
Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA alex_tropsha@unc.edu.

Abstract

OBJECTIVE:

Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), 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. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models.

MATERIALS AND METHODS:

Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan).

RESULTS:

We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%-81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature.

DISCUSSION:

Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts.

CONCLUSIONS:

We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations.

KEYWORDS:

QSAR; Stevens-Johnson Syndrome; adverse drug reactions; cheminformatics; pharmacovigilance

PMID:
26499102
PMCID:
PMC4997030
DOI:
10.1093/jamia/ocv127
[Indexed for MEDLINE]
Free PMC Article

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