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  3. Epidemiology and Preventive Medicine / 流行病學與預防醫學研究所
  4. Identification of drug-induced toxicity biomarkers for treatment determination
 
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Identification of drug-induced toxicity biomarkers for treatment determination

Journal
Pharmaceutical Statistics
Journal Volume
14
Journal Issue
4
Pages
284-293
Date Issued
2015
Author(s)
TZU-PIN LU  
Chen J.J.
DOI
10.1002/pst.1684
URI
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937022848&doi=10.1002%2fpst.1684&partnerID=40&md5=ab92887e613e55b39b6ef2b5c55e108d
https://scholars.lib.ntu.edu.tw/handle/123456789/520969
Abstract
Drug-induced organ toxicity (DIOT) that leads to the removal of marketed drugs or termination of candidate drugs has been a leading concern for regulatory agencies and pharmaceutical companies. In safety studies, the genomic assays are conducted after the treatment so that drug-induced adverse effects can occur. Two types of biomarkers are observed: biomarkers of susceptibility and biomarkers of response. This paper presents a statistical model to distinguish two types of biomarkers and procedures to identify susceptible subpopulations. The biomarkers identified are used to develop classification model to identify susceptible subpopulation. Two methods to identify susceptibility biomarkers were evaluated in terms of predictive performance in subpopulation identification, including sensitivity, specificity, and accuracy. Method 1 considered the traditional linear model with a variable-by-treatment interaction term, and Method 2 considered fitting a single predictor variable model using only treatment data. Monte Carlo simulation studies were conducted to evaluate the performance of the two methods and impact of the subpopulation prevalence, probability of DIOT, and sample size on the predictive performance. Method 2 appeared to outperform Method 1, which was due to the lack of power for testing the interaction effect. Important statistical issues and challenges regarding identification of preclinical DIOT biomarkers were discussed. In summary, identification of predictive biomarkers for treatment determination highly depends on the subpopulation prevalence. When the proportion of susceptible subpopulation is 1% or less, a very large sample size is needed to ensure observing sufficient number of DIOT responses for biomarker and/or subpopulation identifications.
SDGs

[SDGs]SDG3

Publisher
John Wiley and Sons Ltd
Type
journal article

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