Please use this identifier to cite or link to this item: https://doi.org/10.1002/minf.201200086
Title: Determination of the potential of drug candidates to cause severe skin disorders using computational modeling
Authors: He, Y.
Chong, F.H.T.
Lim, J.
Lee, R.J.T.
Yap, C.W. 
Keywords: Adverse reaction
Applicability domain
Classification
Ensemble
QSAR
Issue Date: Mar-2013
Source: He, Y., Chong, F.H.T., Lim, J., Lee, R.J.T., Yap, C.W. (2013-03). Determination of the potential of drug candidates to cause severe skin disorders using computational modeling. Molecular Informatics 32 (3) : 303-312. ScholarBank@NUS Repository. https://doi.org/10.1002/minf.201200086
Abstract: Efficient and accurate prediction for drugs' potential to cause rare and severe adverse drug reactions (ADRs) is needed to facilitate the evaluation of risk-benefit ratio of drug candidates during drug development. Severe skin disorders like the Stevens Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN), which are life-threatening dermatological conditions, are such ADRs that have not received sufficient attention so far. In this study, a total of 1127 marketed drugs were screened for their potential to cause SJS/TEN, of which 255 were found to cause SJS/TEN and 239 were unlikely to cause SJS/TEN. One-class classification method was used to develop multiple prediction models. An applicability domain was determined to define the applicability of the model. Ensemble method was used to develop ensemble models to improve prediction ability. The final ensemble model achieved a sensitivity and specificity of 81 % and 67.4 %, respectively, when estimated using the external 5-fold cross validation method, and a sensitivity of 66.7 % when assessed using an external positive set. The results suggest the methods used in this study are potentially useful for facilitating the prediction of rare and severe ADRs. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Source Title: Molecular Informatics
URI: http://scholarbank.nus.edu.sg/handle/10635/105809
ISSN: 18681743
DOI: 10.1002/minf.201200086
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

25
checked on Jan 21, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.