Pharmacogenetics
The current obesity epidemic represents a major international health problem. In the United States, the prevalence of childhood and adult obesity increased markedly in the past two decades. At present, nearly 2/3 of all adults living in the United States are overweight, and nearly ¼ are obese. This epidemic is accompanied by an unprecedented increase in obesity-related medical conditions. The risk of developing obesity and obesity-related metabolic diseases involves complex interactions between genetics, behaviors and socio-environmental factors. The treatment of these medical problems can therefore be quite challenging. Our research focuses on the identification of genetic markers that might improve the treatment of these problems.
1. Genetic variability in endocannabinergic (eCB) signaling pathways. Preclinical studies have clearly demonstrated that eCB signaling modulates food intake and utilization at multiple sites in the body. Phase III Clinical trials have recently shown that eCB receptor antagonists facilitate weight loss. A single nucleotide polymorphism (SNP) in one of the eCB receptor genes (CNR1) has recently been associated with altered anthropometric measures of body size (i.e., skin fold thickness and waist circumference). Another SNP in the fatty acid amide hydrolase (FAAH) gene, which encodes an enzyme known to metabolize endogenous cannabinoid ligands, has been shown to be associated with body mass index. This latter SNP has been shown to predict improvement in lipid parameters (e.g., cholesterol levels and triglyceride levels) in subjects on a low fat diet. Collectively, these observations suggest that genetic variation in eCB signaling may be used to direct the treatment of obesity and obesity-related metabolic complications. We are currently testing these hypotheses by the quantifying the distribution of haplotype tagging SNPs for three candidate eCB signaling genes (CNR1, FAAH, and MGLL) in 500 well-phenotyped families containing obese study subjects.
2. Genetic architecture underlying the efficacy and toxicity of cholesterol lowering therapy. Several large, multicenter trials have demonstrated that statin therapy (ie., clinical administration of an HMG CoA reductase inhibitor) reduces the likelihood of both primary and secondary coronary artery disease in patients at risk. Each of the six currently available statin drugs is regarded as both safe and efficacious. One of the more common statin-related adverse drug reactions (ADRs) is muscle toxicity. We have previously reported that genetic variability in one of the cytochromes P450 (CYP3A5) is associated with the severity of muscle toxicity (as determined by serum CK elevation), within the context of atorvastatin therapy. To validate this finding, and identify additional genes impacting statin-induced ADRs, we are collaborating with five other academic centers to conduct a case-control pharmacogenetic association study of 450 subjects previously exposed to statins. Three drugs are being characterized (atorvastatin, simvastatin and pravastatin). This study will employ both whole genome scanning and a candidate gene approach. Candidate genes include Phase I drug metabolizing enzymes (e.g., CYPs), Phase II drug metabolizing enzymes (e.g., UGTs), and a series of membrane transporters (e.g., OATs) known to impact statin outcomes. Analyses will be both univariate and multivariate.
3. Characterizing gene-gene interaction in the context of treatment outcomes. The candidate gene approach to pharmacogenetics is hypothesis driven, and anchored in biological plausibility. Whole genome scanning is hypothesis generating, and it may lead to new biology. Both approaches are important. We are therefore exploring the advantages of a step-wise approach to large-scale pharmacogenetic association studies. Our approach begins with candidate genes, then uses a pathway-based intermediate step, to inform the subsequent design and analysis of studies that use whole genome scanning. Two pathway-based models are being developed: (1) a linear signaling pathway, and (2) and a more robust spatial signaling pathway. Both can be adapted to fit the known biology underlying any given pharmacologic process. For instance, cholesterol synthesis (and the attenuation of cholesterol synthesis by statin drugs) can be modeled using a fairly simple pathway. On the other hand, the synthesis of membrane-derived lipid signaling molecules (e.g., eCBs) may require the development of more complex models that consider a number of biosynthetic pathways in parallel. One such pathway is shown below, to illustrate the impact of gene-environment and gene-gene interactions.
Recent publications:
Hillman MA, Wilke RA, Caldwell MD, Berg R Glurich I, Burmester JK: Relative impact of covariates in prescribing warfarin according to CYP2C9-based genotype. Pharmacogenetics 14: 539-47, 2004.
Ritchie MD, Carillo MW, Wilke RA: Computational approaches for pharmacogenomics: In Pacific Symposium on Biocomputing: Altman RB, Dunker AK, Hunter L, Jung TA, Klein TE, Editors. 2005.
Wilke RA, Berg RL, Vidaillet H, Caldwell MD, Burmester JK, Hillman MA: Impact of age, CYP2C9 genotype, and concomitant medication on the rate of rise for prothrombin time, during the first 30 days of warfarin therapy. Clinical Medicine and Research 3: 207-213, 2005.
Hillman MA, Wilke RA, Yale S, Vidaillet H, Caldwell MD, Glurich I, Berg RL, Schmelzer J, Burmester JK: A prospective, randomized pilot trial of model-based warfarin dose initiation using CYP2C9 genotype and clinical data. Clinical Medicine and Research 3: 137-145, 2005.
McCarty C, Wilke RA, Giampietro P, Wesbrook S, Caldwell MD (on behalf of the Personalized Medicine Research Project team): The Marshfield Clinic Personalized Medicine Research Project (PMRP) – design, methods and initial recruitment results for a population-based DNA Biobank. Personalized Medicine 2: 49-79, 2005.
Wilke RA, Moore JH, Burmester JK: Relative impact of CYP3A genotype and concomitant medication on the severity of atorvastatin-induced muscle damage. Pharmacogenet Genomics 15:415-421, 2005.
Wilke RA, Reif DG, Moore JH: Combinatorial pharmacogenetics. Nature Reviews Drug Discovery 4: 911-918, 2005.
Wilke RA, AK Musana, Weber WW: Cytochrome P450 gene-based drug prescribing, and factors impacting translation into routine clinical practice. Personalized Medicine 2: 213-224, 2005.
Carillo MW, Wilke RA, Ritchie MD: Computational approaches for pharmacogenomics: In Pacific Symposium on Biocomputing: Altman RB, Dunker AK, Hunter L, Jung TA, Klein TE, Editors. 2006.
Wilke RA: Translational pharmacogenetics and risk management in the cardiovascular arena - using the CYP 3A5*3 allele as a model for gene-based drug selection. Personalized Medicine 3: 385-390, 2006.
Wilke RA, Berg RL, PeissigP, Kitchner T, Sijercic B, McCarty CA, McCarty DJ: Use of an electronic medical record for the identification of research subjects with diabetes mellitus. Clinical Medicine and Research 5: 1-7, 2007.
McCarty CA, Mukesh BN, Giampietro PF, Wilke RA. Healthy People 2010 disease prevalence in the Marshfield Clinic Personalized Medicine Research Project: Opportunities for public health genomic research. Personalized Medicine 4: 183-190, 2007.
Peissig P, Sirohi E, Berg RL, Brown-Switzer C, Ghebranious N, McCarty CA, Wilke RA: Construction of atorvastatin dose-response relationships using data from a large population-based DNA Biobank. Basic Clin Pharmacol Toxicol 100: 286-288, 2007.
Wilke RA, Lin DW, Roden DM, Watkins PB, Flockheart D, Zineh I, Giacomini KM, Krauss RM: Identifying genetic risk factors for serious adverse drug reactions: current progress and challenges. Nat Rev Drug Discov. 2007 Nov;6(11):904-16.Review. Erratum in: Nat Rev Drug Discov. 2008 Feb;7(2):185.