Identification of drug-induced toxicity biomarkers for treatment determination.
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.
Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Department of Public Health Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan.
Tzu-Pin
TP
Lu
Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.
James J
JJ
Chen
D000818:Animals; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D064420:Drug-Related Side Effects and Adverse Reactions / Q000145:classification / Q000175:diagnosis / Q000453:epidemiology / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods / Q000706:statistics & numerical data; D005786:Gene Expression Regulation / Q000187:drug effects*; D005819:Genetic Markers; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D015233:Models, Statistical; D009010:Monte Carlo Method; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D015995:Prevalence; D012107:Research Design / Q000706:statistics & numerical data*; D018570:Risk Assessment; D018401:Sample Size
D005819:Genetic Markers
biomarkers of response; biomarkers of susceptibility; classification; safety assessment; subgroup identification; susceptible subpopulation