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Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
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Identification of drug-induced toxicity biomarkers for treatment determination

Tzu-Pin Lu1,2, James J Chen1

  • 1Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.

Pharmaceutical Statistics
|April 28, 2015
PubMed

Insights

Identifying biomarkers for drug-induced organ toxicity (DIOT) is crucial. A statistical model effectively distinguishes susceptibility and response biomarkers, aiding in identifying at-risk patient groups for safer drug development.

Area of Science:

  • Pharmacogenomics
  • Biostatistics
  • Drug Safety

Background:

  • Drug-induced organ toxicity (DIOT) poses significant risks, leading to drug recalls and development terminations.
  • Genomic assays in safety studies aim to detect drug-induced adverse effects.
  • Biomarkers of susceptibility and response are key to understanding individual drug reactions.

Purpose of the Study:

  • To present a statistical model for differentiating susceptibility and response biomarkers.
  • To develop procedures for identifying susceptible subpopulations based on identified biomarkers.
  • To evaluate methods for identifying susceptibility biomarkers and their predictive performance.

Main Methods:

  • Developed a statistical model to distinguish between biomarker types.
  • Employed classification models to identify susceptible subpopulations.
  • Compared two methods for susceptibility biomarker identification: a traditional linear model with interaction and a single predictor model.
  • Utilized Monte Carlo simulations to assess method performance under varying conditions (prevalence, DIOT probability, sample size).

Main Results:

  • Method 2 (single predictor model) outperformed Method 1 (linear model with interaction) in identifying susceptible subpopulations.
  • The superior performance of Method 2 was attributed to the lack of statistical power for interaction effect testing in Method 1.
  • Subpopulation prevalence significantly impacts the identification of predictive biomarkers.
  • Very large sample sizes are required when the susceptible subpopulation is rare (≤1%).

Conclusions:

  • A statistical approach can effectively differentiate biomarker types and identify susceptible populations for DIOT.
  • The choice of statistical method and sample size are critical for successful biomarker and subpopulation identification.
  • Understanding subpopulation prevalence is essential for accurate preclinical DIOT biomarker discovery and drug safety assessment.
Keywords:
biomarkers of responsebiomarkers of susceptibilityclassificationsafety assessmentsubgroup identificationsusceptible subpopulation

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