Mixture models for gene expression experiments with two species
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Mixture models for gene expression experiments with two species Yuhua Su1* , Lei Zhu2 , Alan Menius2 and Jason Osborne3 Abstract Cross-species research in drug development is novel and challenging. A bivariate mixture model utilizing information across two species was proposed to solve the fundamental problem of identifying differentially expressed genes in microarray experiments in order to potentially improve the understanding of translation between preclinical and clinical studies for drug development. The proposed approach models the joint distribution of treatment effects estimated from independent linear models. The mixture model posits up to nine components, four of which include groups in which genes are differentially expressed in both species. A comprehensive simulation to evaluate the model performance and one application on a real world data set, a mouse and human type II diabetes experiment, suggest that the proposed model, though highly structured, can handle various configurations of differential gene expression and is practically useful on identifying differentially expressed genes, especially when the magnitude of differential expression due to different treatment intervention is weak. In the mouse and human application, the proposed mixture model was able to eliminate unimportant genes and identify a list of genes that were differentially expressed in both species and could be potential gene targets for drug development. Keywords: Orthology, Drug development, Drug response prediction, Type II diabetes
Introduction Background
Pharmaceutical medicine is an industry with huge upfront investment for rewards that may or may not come years later. A complete drug development process, including drug discovery, preclinical research (on animals) and clinical trials (on humans), is lengthy, expensive, and risky. Determined by the US Food and Drug Administration (FDA) [1], the average total cost per drug development is about $1.9 billion. The typical development time is 10 to 15 years. The overall attrition rate of a drug compound from first-in-man to registration is approximately 80%–90% [2,3]. FDA [1] calls the preclinical and clinical research together as the ‘critical path’ development phase, where most investment required for a successful drug launch occurs. Currently, this development phase is inherently inefficient. The goal of preclinical research is to assess how a drug is absorbed, distributed, metabolized, and *Correspondence: [email protected] 1 Dr. Su’s Statistics & Department of Human Nutrition, Food, and Animal Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA Full list of author information is available at the end of the article
excreted in animals, and to use the findings to determine potential human outcomes before starting clinical trials. Yet the rate of success after a drug candidate enters Phase I is undesirably low. As mentioned in FDA [1] and Kola and Landis [3], animal models with poor clinical relevance may be accountable for this
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