Identification of drug-induced toxicity biomarkers for treatment determination

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.

Subgroup identification for treatment selection in biomarker adaptive design

“…We use the DLDA algorithm [ 24 ] to classify patients into g + and g − subgroups. The DLDA algorithm has been shown to perform well for high-dimensional data [ 26 ], and is robust against imbalanced data [ 27 , 28 ], a common problem encountered in subgroup classification where the numbers of patients in the g + and g − subgroups differ substantially. Other classifiers, such as random forests [ 21 ] and support vector machine [ 22 , 23 ], may not perform as well if the number of g + patients is much smaller than the number of g − patients.…”