Biomarkers of olanzapine-induced weight gain in mice

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DESCRIPTION (provided by applicant): One-third of patients taking the antipsychotic olanzapine experience severe weight gain and a metabolic syndrome consisting of increased visceral fat stores, insulin resistance, impaired glucose tolerance, dyslipidemia and hypertension. In a large clinical trial following 1,039 schizophrenia patients treated with olanzapine for at least 6 months, the average weight change was +7.6 kg or nearly 10 percent of initial body weight. The population, however, followed a normal distribution with the upper quartile gaining >20 kg and the lower quartile experiencing no weight gain. These data are consistent with a complex genetic trait and suggest that it may be possible to identify clinically-useful biomarkers of extreme sensitivity to this ADR. Several recent publications have demonstrated an ability to model olanzapine-induced weight gain in mice and suggest that genetic background influences susceptibility to this adverse drug reaction. At UNC, we have at our disposal a next-generation systems biology platform, the Collaborative Cross (CC), which was designed to tease out mechanisms regulating complex phenotypes, such as drug response. In year one, we expose 100 genetically diverse CC mouse strains to human-like concentrations of olanzapine under rigorous experimental conditions and carefully measure changes in body mass, adiposity, blood chemistry and locomotor activity. As with the human data, we expect to identify extreme responders ideal for biomarker discovery. In year two, we focus on the extreme responder strains (10 high and 10 low) to identify gene networks regulating susceptibility to weight gain. First, we will re-create these 20 lines and re-phenotype them to measure robustness of the drug response phenotype. Second, we will use next generation sequencing technologies to assess the transcriptome and methylome of the hypothalamus in drug and placebo treated animals. Third, we will validate the most promising transcript and methylation changes with a distinct methodology. Finally, we will evaluate the predictive validity of these biomarkers using RNA and DNA collected from the peripheral blood of the extreme mice. Biomarkers that emerge could then be tested in human samples to determine replication across species. We expect to identify a manageable list of high probability biomarkers for such work in humans, providing the pharmacogenomics field with an alternative to the typical Candidate gene or under-powered genome-wide search.