Identification of CYP2C9*2 allele in HepG2 cell line

Liver three-dimensional cellular models for high-throughput chemical testing

Drug-induced hepatotoxicity is a leading cause of drug withdrawal from the market. High-throughput screening utilizing in vitro liver models is critical for early-stage liver toxicity testing. Traditionally, monolayer human hepatocytes or immortalized liver cell lines (e.g., HepG2, HepaRG) have been used to test compound liver toxicity. However, monolayer-cultured liver cells sometimes lack the metabolic competence to mimic the in vivo condition and are therefore largely appropriate for short-term toxicological testing. They may not, however, be adequate for identifying chronic and recurring liver damage caused by drugs. Recently, several three-dimensional (3D) liver models have been developed. These 3D liver models better recapitulate normal liver function and metabolic capacity. This review describes the current development of 3D liver models that can be used to test drugs/chemicals for their pharmacologic and toxicologic effects, as well as the advantages and limitations of using these 3D liver models for high-throughput screening.

Bayesian penalized cumulative logit model for high-dimensional data with an ordinal response

Many previous studies have identified associations between gene expression, measured using high-throughput genomic platforms, and quantitative or dichotomous traits. However, we note that health outcome and disease status measurements frequently appear on an ordinal scale, that is, the outcome is categorical but has inherent ordering. Identification of important genes may be useful for developing novel diagnostic and prognostic tools to predict or classify stage of disease. Gene expression data are usually high-dimensional, meaning that the number of genes is much larger than the sample size or number of patients. Herein we describe some existing frequentist methods for modeling an ordinal response in a high-dimensional predictor space. Following Tibshirani (1996), who described the LASSO estimate as the Bayesian posterior mode when the regression coefficients have independent Laplace priors, we propose a new approach for high-dimensional data with an ordinal response that is rooted in the Bayesian paradigm. We show that our proposed Bayesian approach outperforms existing frequentist methods through simulation studies. We then compare the performance of frequentist and Bayesian approaches using a study evaluating progression to hepatocellular carcinoma in hepatitis C infected patients.