Advances in the pharmacogenomics of adverse drug reactions

Statistical assessment of biomarker replicability using MAJAR method

In the era of precision medicine, many biomarkers have been discovered to be associated with drug efficacy and safety responses, which can be used for patient stratification and drug response prediction. Due to the small sample size and limited power of randomized clinical studies, meta-analysis is usually conducted to aggregate all available studies to maximize the power for identifying prognostic and predictive biomarkers. However, it is often challenging to find an independent study to replicate the discoveries from the meta-analysis (e.g. meta-analysis of pharmacogenomics genome-wide association studies (PGx GWAS)), which seriously limits the potential impacts of the discovered biomarkers. To overcome this challenge, we develop a novel statistical framework, MAJAR (meta-analysis of joint effect associations for biomarker replicability assessment), to jointly test prognostic and predictive effects and assess the replicability of identified biomarkers by implementing an enhanced expectation-maximization algorithm and calculating their posterior-probability-of-replicabilities and Bayesian false discovery rates (Fdr). Extensive simulation studies were conducted to compare the performance of MAJAR and existing methods in terms of Fdr, power, and computational efficiency. The simulation results showed improved statistical power with well-controlled Fdr of MAJAR over existing methods and robustness to outliers under different data generation processes. We further demonstrated the advantages of MAJAR over existing methods by applying MAJAR to the PGx GWAS summary statistics data from a large cardiovascular randomized clinical trial. Compared to testing main effects only, MAJAR identified 12 novel variants associated with the treatment-related low-density lipoprotein cholesterol reduction from baseline.

Patients' and physicians' perspectives on pharmacogenetic testing

INTRODUCTION

The integration of pharmacogenetic testing into routine care will, in part, depend upon the patients' and physicians' acceptance of these tests. Empirical data regarding patients' and physicians' views on pharmacogenetic testing are lacking.

OBJECTIVES

To explore patients' and physicians' perspectives on the potential implications of pharmacogenetic testing, particularly focusing on asthma, and to analyze the possible determinants of their expectations, hopes and fears.

METHODS

We conducted telephone interviews with patients with asthma or chronic obstructive pulmonary disease taking part in a larger pharmacogenetic study, in addition to general practitioners (GPs) from a different region in Germany. A total of 328 patients and 378 GPs were invited to participate. Determinants of their attitudes toward pharmacogenetic testing were assessed using logistic regression analysis.

RESULTS

Informed consent to participate in this study was given by 196 patients (60%) and 106 GPs (28%). Most patients (96%) and physicians (52%) appreciated the availability of pharmacogenetic tests for a disease such as asthma. Approximately a third of the patients worried about potential unfavorable test results (35%) and violation of privacy (36%). Female patients were more likely to have a fearful attitude (odds ratio [OR]=2.85; 95% confidence interval [CI]=1.58-5.12). Younger patients were generally more likely to be hopeful about the usefulness of pharmacogenetic testing (OR=2.12; CI=1.01-4.46). The GPs' concerns were mainly related to the possibility that patients might either be put under pressure to be tested (72%) or be disadvantaged at private health insurance agencies (61%). The nature of the responsible institution, the clarity of the research aim and explicit informed consent from patients influenced a physicians' decision regarding whether to support a pharmacogenetic study.

CONCLUSION

The concerns of patients and GPs differ somewhat with respect to negative psychosocial consequences, discrimination or violation of privacy. Development of information for physicians and patients would be helpful in preventing unrealistic fears or hopes.

Phenotyping-genotyping of alternatively spliced genes in one step: study of CYP3A5*3 polymorphism

Alternative splicing is required to increase the mRNA diversity of many genes, but can also be responsible for the abnormal expression of genes. For example, the CYP3A5*3 defective allele is caused by a single nucleotide polymorphism in intron 3. This mutation activates a cryptic acceptor splice site, which leads to the insertion of an intronic sequence containing premature termination codons in the mature mRNA, and hence the very low CYP3A5 protein expression in 75% of the Caucasian population. In the present study, we propose a novel strategy based on the quantitative real-time polymerase chain reaction with SYBR Green I chemistry, followed by melting curve analysis, to demonstrate and quantify the amount of splice variant mRNA. Using oligonucleotides flanking the insertion site, two products with different size can be obtained, which can be resolved by melting curve analysis. The relative ratio of differently spliced RNA can be estimated at the plateau phase by using the peak height ratio. For the CYP3A5 gene, the genotype, the level of expression and the proportion of alternatively spliced products were determined in a single reaction without DNA sequencing.

Toxicogenomics in predictive toxicology in drug development

The goal of toxicology is the assessment of possible risk to man. An emerging technology with the potential to have a major impact on risk assessment is toxicogenomics. In this review, we provide an overview of the many possibilities for toxicogenomics including technology platforms, data interpretation, and regulatory perspective and we give examples of toxicogenomics investigations. Toxicogenomics is a powerful tool for compound classification, for mechanistic studies, and for the detection of toxicity markers. Thus, toxicogenomics helps in the extrapolation of findings across species and increases predictability. Biomarkers are valuable in the evaluation of compounds at earlier development phases, improving clinical candidate selection. Caution regarding the interpretation of the results is still necessary. Nevertheless, toxicogenomics will accelerate preclinical safety assessments and improve the prediction of toxic liabilities, as well as of potential risk accumulation for drug-drug or drug-disease interactions.