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1.  Strategic Applications of Gene Expression: From Drug Discovery/Development to Bedside 
The AAPS Journal  2013;15(2):427-437.
ABSTRACT
Gene expression is useful for identifying the molecular signature of a disease and for correlating a pharmacodynamic marker with the dose-dependent cellular responses to exposure of a drug. Gene expression offers utility to guide drug discovery by illustrating engagement of the desired cellular pathways/networks, as well as avoidance of acting on the toxicological pathways. Successful employment of gene-expression signatures in the later stages of drug development depends on their linkage to clinically meaningful phenotypic characteristics and requires a biologically meaningful mechanism combined with a stringent statistical rigor. Much of the success in clinical drug development is hinged on predefining the signature genes for their fitness for purposes of application. Specific examples are highlighted to illustrate the breadth and depth of the potential utility of gene-expression signatures in drug discovery and clinical development to targeted therapeutics at the bedside.
doi:10.1208/s12248-012-9447-1
PMCID: PMC3675744  PMID: 23319288
clinical molecular signatures; molecular signatures of disease; signature genes; target engagement; toxicological pathways
2.  Analysis of randomized comparative clinical trial data for personalized treatment selections 
Biostatistics (Oxford, England)  2010;12(2):270-282.
Suppose that under the conventional randomized clinical trial setting, a new therapy is compared with a standard treatment. In this article, we propose a systematic, 2-stage estimation procedure for the subject-level treatment differences for future patient's disease management and treatment selections. To construct this procedure, we first utilize a parametric or semiparametric method to estimate individual-level treatment differences, and use these estimates to create an index scoring system for grouping patients. We then consistently estimate the average treatment difference for each subgroup of subjects via a nonparametric function estimation method. Furthermore, pointwise and simultaneous interval estimates are constructed to make inferences about such subgroup-specific treatment differences. The new proposal is illustrated with the data from a clinical trial for evaluating the efficacy and toxicity of a 3-drug combination versus a standard 2-drug combination for treating HIV-1–infected patients.
doi:10.1093/biostatistics/kxq060
PMCID: PMC3062150  PMID: 20876663
Cross-validation; HIV infection; Non-parametric function estimation; Personalized medicine; Subgroup analysis

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