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1.  Pharmacogenomic test that predicts response to inhaled corticosteroids in adults with asthma likely to be cost-saving 
Pharmacogenomics  2015;16(6):591-600.
Aim
To identify the clinical and economic circumstances under which a pharmacogenomic test that predicts response to inhaled corticosteroids might be a cost-effective option for individuals with asthma.
Materials & methods
We synthesized published data on clinical and economic outcomes to project 10-year costs, quality-adjusted life-years and cost–effectiveness of pharmacogenomic testing for inhaled corticosteroid response. We assumed the pharmacogenomic test cost was $500 with a sensitivity and specificity of 84 and 98%, respectively. These were varied in sensitivity analyses.
Results
Both strategies, pharmacogenomic testing for inhaled corticosteroid response and no testing conferred 7.1 quality-adjusted life-years. Compared with no testing, pharmacogenomic testing costs less.
Conclusion
Pharmacogenomic testing for asthma is cost-saving and noninferior in improving health.
doi:10.2217/pgs.15.28
PMCID: PMC4545673  PMID: 25880024
asthma; cost–effectiveness; inhaled corticosteroids; pharmacogenomics; predictive test
2.  Predicting response to short-acting bronchodilator medication using Bayesian networks 
Pharmacogenomics  2009;10(9):1393-1412.
Aims
Bronchodilator response tests measure the effect of β2-agonists, the most commonly used short-acting reliever drugs for asthma. We sought to relate candidate gene SNP data with bronchodilator response and measure the predictive accuracy of a model constructed with genetic variants.
Materials & methods
Bayesian networks, multivariate models that are able to account for simultaneous associations and interactions among variables, were used to create a predictive model of bronchodilator response using candidate gene SNP data from 308 Childhood Asthma Management Program Caucasian subjects.
Results
The model found that 15 SNPs in 15 genes predict bronchodilator response with fair accuracy, as established by a fivefold cross-validation area under the receiver-operating characteristic curve of 0.75 (standard error: 0.03).
Conclusion
Bayesian networks are an attractive approach to analyze large-scale pharmacogenetic SNP data because of their ability to automatically learn complex models that can be used for the prediction and discovery of novel biological hypotheses.
doi:10.2217/pgs.09.93
PMCID: PMC2804237  PMID: 19761364
asthma; Bayesian networks; β2-agonists; bronchodilator response; prediction
3.  Predictors of poor response during asthma therapy differ with definition of outcome 
Pharmacogenomics  2009;10(8):1231-1242.
Aims
To evaluate phenotypic and genetic variables associated with a poor long-term response to inhaled corticosteroid therapy for asthma, based independently on lung function changes or asthma exacerbations.
Materials & methods
We tested 17 phenotypic variables and polymorphisms in FCER2 and CRHR1 in 311 children (aged 5–12 years) randomized to a 4-year course of inhaled corticosteroid during the Childhood Asthma Management Program (CAMP).
Results
Predictors of recurrent asthma exacerbations are distinct from predictors of poor lung function response. A history of prior asthma exacerbations, younger age and a higher IgE level (p < 0.05) are associated with recurrent exacerbations. By contrast, lower bronchodilator response to albuterol and the minor alleles of RS242941 in CRHR1 and T2206C in FCER2 (p < 0.05) are associated with poor lung function response. Poor lung function response does not increase the risk of exacerbations and vice versa (p = 0.72).
Conclusion
Genetic and phenotypic predictors of a poor long-term response to inhaled corticosteroids differ markedly depending on definition of outcome (based on exacerbations vs lung function). These findings are important in comparing outcomes of clinical trials and in designing future pharmacogenetic studies.
doi:10.2217/PGS.09.86
PMCID: PMC2746392  PMID: 19663668
asthma; corticosteroid; exacerbation; lung function; pharmacogenetics

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