Russell A. Wilke

Case Definition and Phenotype Standardization in Drug-Induced Liver Injury

Drug‐induced liver injury (DILI) is the most frequent reason cited for the withdrawal of approved drugs from the market and accounts for up to 15% of the cases of acute liver failure. Investigators around the globe have begun to identify and study patients with DILI; several large registries and tissue banks are being established. In order to gain the maximum scientific benefit from these efforts, the definitions and terminology related to the clinical phenotypes of DILI must be harmonized. For this purpose, an international DILI Expert Working Group of clinicians and scientists reviewed current DILI terminology and diagnostic criteria so as to develop more uniform criteria that would define and characterize the spectrum of clinical syndromes that constitute DILI. Consensus was established with respect to the threshold criteria for definition of a case as being DILI, the pattern of liver injury, causality assessment, severity, and chronicity. Consensus was also reached on approaches to characterizing DILI in the setting of chronic liver diseases, including autoimmune hepatitis (AIH).

Identifying genetic risk factors for serious adverse drug reactions: current progress and challenges

Serious adverse drug reactions (SADRs) are a major cause of morbidity and mortality worldwide. Some SADRs may be predictable, based upon a drugs pharmacodynamic and pharmacokinetic properties. Many, however, appear to be idiosyncratic. Genetic factors may underlie susceptibility to SADRs and the identification of predisposing genotypes may improve patient management through the prospective selection of appropriate candidates. Here we discuss three specific SADRs with an emphasis on genetic risk factors. These SADRs, selected based on wide-sweeping clinical interest, are drug-induced liver injury, statin-induced myotoxicity and drug-induced long QT and torsades de pointes. Key challenges for the discovery of predictive risk alleles for these SADRs are also considered.

Operational Implementation of Prospective Genotyping for Personalized Medicine: The Design of the Vanderbilt PREDICT Project

The promise of “personalized medicine” guided by an understanding of each individuals genome has been fostered by increasingly powerful and economical methods to acquire clinically relevant information. We describe the operational implementation of prospective genotyping linked to an advanced clinical decision‐support system to guide individualized health care in a large academic health center. This approach to personalized medicine entails engagement between patient and health‐care provider, identification of relevant genetic variations for implementation, assay reliability, point‐of‐care decision support, and necessary institutional investments. In one year, approximately 3,000 patients, most of whom were scheduled for cardiac catheterization, were genotyped on a multiplexed platform that included genotyping for CYP2C19 variants that modulate response to the widely used antiplatelet drug clopidogrel. These data are deposited into the electronic medical record (EMR), and point‐of‐care decision support is deployed when clopidogrel is prescribed for those with variant genotypes. The establishment of programs such as this is a first step toward implementing and evaluating strategies for personalized medicine.

The Clinical Pharmacogenomics Implementation Consortium: CPIC Guideline for SLCO1B1 and Simvastatin‐Induced Myopathy

Cholesterol reduction from statin therapy has been one of the greatest public health successes in modern medicine. Simvastatin is among the most commonly used prescription medications. A non‐synonymous coding single‐nucleotide polymorphism (SNP), rs4149056, in SLCO1B1 markedly increases systemic exposure to simvastatin and the risk of muscle toxicity. This guideline explores the relationship between rs4149056 (c.521T>C, p.V174A) and clinical outcome for all statins. The strength of the evidence is high for myopathy with simvastatin. We limit our recommendations accordingly.

Genome-Wide Association of Lipid-Lowering Response to Statins in Combined Study Populations

Background Statins effectively lower total and plasma LDL-cholesterol, but the magnitude of decrease varies among individuals. To identify single nucleotide polymorphisms (SNPs) contributing to this variation, we performed a combined analysis of genome-wide association (GWA) results from three trials of statin efficacy. Methods and Principal Findings Bayesian and standard frequentist association analyses were performed on untreated and statin-mediated changes in LDL-cholesterol, total cholesterol, HDL-cholesterol, and triglyceride on a total of 3932 subjects using data from three studies: Cholesterol and Pharmacogenetics (40 mg/day simvastatin, 6 weeks), Pravastatin/Inflammation CRP Evaluation (40 mg/day pravastatin, 24 weeks), and Treating to New Targets (10 mg/day atorvastatin, 8 weeks). Genotype imputation was used to maximize genomic coverage and to combine information across studies. Phenotypes were normalized within each study to account for systematic differences among studies, and fixed-effects combined analysis of the combined sample were performed to detect consistent effects across studies. Two SNP associations were assessed as having posterior probability greater than 50%, indicating that they were more likely than not to be genuinely associated with statin-mediated lipid response. SNP rs8014194, located within the CLMN gene on chromosome 14, was strongly associated with statin-mediated change in total cholesterol with an 84% probability by Bayesian analysis, and a p-value exceeding conventional levels of genome-wide significance by frequentist analysis (P = 1.8×10−8). This SNP was less significantly associated with change in LDL-cholesterol (posterior probability = 0.16, P = 4.0×10−6). Bayesian analysis also assigned a 51% probability that rs4420638, located in APOC1 and near APOE, was associated with change in LDL-cholesterol. Conclusions and Significance Using combined GWA analysis from three clinical trials involving nearly 4,000 individuals treated with simvastatin, pravastatin, or atorvastatin, we have identified SNPs that may be associated with variation in the magnitude of statin-mediated reduction in total and LDL-cholesterol, including one in the CLMN gene for which statistical evidence for association exceeds conventional levels of genome-wide significance. Trial Registration PRINCE and TNT are not registered. CAP is registered at Clinicaltrials.gov NCT00451828

The emerging role of electronic medical records in pharmacogenomics.

Health‐care information technology and genotyping technology are both advancing rapidly, creating new opportunities for medical and scientific discovery. The convergence of these two technologies is now facilitating genetic association studies of unprecedented size within the context of routine clinical care. As a result, the medical community will soon be presented with a number of novel opportunities to bring functional genomics to the bedside in the area of pharmacotherapy. By linking biological material to comprehensive medical records, large multi‐institutional biobanks are now poised to advance the field of pharmacogenomics through three distinct mechanisms: (i) retrospective assessment of previously known findings in a clinical practice‐based setting, (ii) discovery of new associations in huge observational cohorts, and (iii) prospective application in a setting capable of providing real‐time decision support. This review explores each of these translational mechanisms within a historical framework.

A statin-dependent QTL for GATM expression is associated with statin-induced myopathy.

Statins are prescribed widely to lower plasma low-density lipoprotein (LDL) concentrations and cardiovascular disease risk and have been shown to have beneficial effects in a broad range of patients. However, statins are associated with an increased risk, albeit small, of clinical myopathy and type 2 diabetes. Despite evidence for substantial genetic influence on LDL concentrations, pharmacogenomic trials have failed to identify genetic variations with large effects on either statin efficacy or toxicity, and have produced little information regarding mechanisms that modulate statin response. Here we identify a downstream target of statin treatment by screening for the effects of in vitro statin exposure on genetic associations with gene expression levels in lymphoblastoid cell lines derived from 480 participants of a clinical trial of simvastatin treatment. This analysis identified six expression quantitative trait loci (eQTLs) that interacted with simvastatin exposure, including rs9806699, a cis-eQTL for the gene glycine amidinotransferase (GATM) that encodes the rate-limiting enzyme in creatine synthesis. We found this locus to be associated with incidence of statin-induced myotoxicity in two separate populations (meta-analysis odds ratio = 0.60). Furthermore, we found that GATM knockdown in hepatocyte-derived cell lines attenuated transcriptional response to sterol depletion, demonstrating that GATM may act as a functional link between statin-mediated lowering of cholesterol and susceptibility to statin-induced myopathy.

Quality Control Procedures for Genome‐Wide Association Studies

Genome‐wide association studies (GWAS) are being conducted at an unprecedented rate in population‐based cohorts and have increased our understanding of the pathophysiology of complex disease. Regardless of context, the practical utility of this information will ultimately depend upon the quality of the original data. Quality control (QC) procedures for GWAS are computationally intensive, operationally challenging, and constantly evolving. Here we enumerate some of the challenges in QC of GWAS data and describe the approaches that the electronic MEdical Records and Genomics (eMERGE) network is using for quality assurance in GWAS data, thereby minimizing potential bias and error in GWAS results. We discuss common issues associated with QC of GWAS data, including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We propose best practices and discuss areas of ongoing and future research. Curr. Protoc. Hum. Genet. 68:1.19.1‐1.19.18

Relative impact of CYP3A genotype and concomitant medication on the severity of atorvastatin-induced muscle damage.

Atorvastatin is metabolized through enzymes encoded by members of the cytochrome P-450 (CYP) 3A gene family. Some patients who take atorvastatin along with concomitant medications known to inhibit CYP3A enzyme activity (e.g. itraconazole) develop rhabdomyolysis secondary to a severe drug-induced myopathy. The present study aimed to characterize the relationship between CYP3A gene polymorphisms and atorvastatin-induced muscle damage in the context of concomitant medication. The study employed a retrospective case–control (n=137) design. Study subjects were recruited from the general patient population served by Marshfield Clinic, a large horizontally integrated multispecialty group practice located in central Wisconsin, and case assignment was based upon both subjective (myalgia) and objective inclusion criteria [elevated serum creatine kinase (CK) levels]. The primary outcome was the relationship between serum CK level and CYP3A genotype. CYP3A genotype was not associated with an increased risk for the development of atorvastatin-induced muscle damage. CYP3A4*1B and CYP3A5*3 allele frequencies were similar in cases (n=68) and controls (n=69). Conversely, CYP3A genotype was associated with an increased severity of atorvastatin-induced muscle damage. An association was identified between the non-functional CYP3A5*3 allele and the magnitude of serum CK elevation in case patients experiencing myalgia. Patients who were homozygous for CYP3A5*3 demonstrated greater serum CK levels than patients who were heterozygous for CYP3A5*3, when concomitant lipid-lowering agents were sequentially removed from the analysis (P=0.025 without gemfibrozil, P=0.010 without gemfibrozil and niacin). The study demonstrates that patients who develop myalgia while taking atorvastatin are more likely to experience a greater degree of muscle damage if they express two copies of CYP3A5*3.

Optimizing Drug Outcomes Through Pharmacogenetics: A Case for Preemptive Genotyping

Routine integration of genotype data into drug decision making could improve patient safety, particularly if many relevant genetic variants can be assayed simultaneously before prescribing the target drug. The frequency of opportunities for pharmacogenetic prescribing and the potential adverse events (AEs) mitigated are unknown. We examined the frequency with which 56 medications with known outcomes influenced by variant alleles were prescribed in a cohort of 52,942 medical home patients at Vanderbilt University Medical Center (VUMC). Within a 5‐year window, we estimated that 64.8% (95% confidence interval (CI): 64.4–65.2%) of individuals were exposed to at least one medication with an established pharmacogenetic association. Using previously published results for six medications with severe, well‐characterized, genetically linked AEs, we estimated that 383 events (95% CI, 212–552) could have been prevented with an effective preemptive genotyping program. Our results suggest that multiplexed, preemptive genotyping may represent an efficient alternative approach to current single‐use (“reactive”) methods and may also improve safety.