Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

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Idiosyncratic drug-induced liver injury (DILI) is a common cause for drug withdrawal from the market and although infrequent, DILI can result in serious clinical outcomes including acute liver failure and the need for liver transplantation. Eliminating the iatrogenic "harm" caused by a therapeutic intent is a priority in patient care. However, identifying culprit drugs and individuals at risk for DILI remains challenging. Apart from genetic factors predisposing individuals at risk, the role of the drugs' physicochemical and toxicological properties and their interactions with host and environmental factors need to be considered. The influence of these factors on mechanisms involved in DILI is multi-layered. In this review, we summarize current knowledge on 1) drug properties associated with hepatotoxicity, 2) host factors considered to modify an individuals' risk for DILI and clinical phenotypes, and 3) drug-host interactions. We aim at clarifying knowledge gaps needed to be filled in as to improve risk stratification in patient care. We therefore broadly discuss relevant areas of future research. Emerging insight will stimulate new investigational approaches to facilitate the discovery of clinical DILI risk modifiers in the context of disease complexity and associated interactions with drug properties, and hence will be able to move towards safety personalized medicine.