Pharmacogenomics of Drug Hypersensitivity

Phenotype-Genotype Correlation Applying a Cocktail Approach and an Exome Chip Analysis Reveals Further Variants Contributing to Variation of Drug Metabolism

Although great progress has been made in the fine-tuning of diplotypes, there is still a need to further improve the predictability of individual phenotypes of pharmacogenetically relevant enzymes. The aim of this study was to analyze the additional contribution of sex and variants identified by exome chip analysis to the metabolic ratio of five probe drugs. A cocktail study applying dextromethorphan, losartan, omeprazole, midazolam, and caffeine was conducted on 200 healthy volunteers. CYP2D6, 2C9, 2C19, 3A4/5, and 1A2 genotypes were analyzed and correlated with metabolic ratios. In addition, an exome chip analysis was performed. These SNPs correlating with metabolic ratios were confirmed by individual genotyping. The contribution of various factors to metabolic ratios was assessed by multiple regression analysis. Genotypically predicted phenotypes defined by CPIC discriminated very well the log metabolic ratios with the exception of caffeine. There were minor sex differences in the activity of CYP2C9, 2C19, 1A2, and CYP3A4/5. For dextromethorphan (CYP2D6), IP6K2 (rs61740999) and TCF20 (rs5758651) affected metabolic ratios, but only IP6K2 remained significant after multiple regression analysis. For losartan (CYP2C9), FBXW12 (rs17080138), ZNF703 (rs79707182), and SLC17A4 (rs11754288) together with CYP diplotypes, and sex explained 50% of interindividual variability. For omeprazole (CYP2C19), no significant influence of CYP2C:TG haplotypes was observed, but CYP2C19 rs12777823 improved the predictability. The comprehensive genetic analysis and inclusion of sex in a multiple regression model significantly improved the explanation of variability of metabolic ratios, resulting in further improvement of algorithms for the prediction of individual phenotypes of drug-metabolizing enzymes.

Novel insights into molecular and cellular aspects of delayed drug hypersensitivity reactions

INTRODUCTION

Delayed drug hypersensitivity reactions (DDHRs) represent a major health problem. They are unpredictable and can cause life-long disability or even death. The pathophysiology of DDHRs is complicated, multifactorial, and not well understood mainly due to the lack of validated animal models or in vitro systems. The role of the immune system is well demonstrated but its exact pathophysiology still a matter of debate.

AREA COVERED

This review summarizes the current understanding of DDHRs pathophysiology and abridges the available new evidence supporting each hypothesis. A comprehensive literature search for relevant publications was performed using PubMed, Google Scholar, and Medline databases with no date restrictions and focusing on the most recent 10 years.

EXPERT OPINION

Although multiple milestones have been achieved in our understanding of DDHRs pathophysiology as a result of the development of useful experimental models, many questions are yet to be fully answered. A deeper understanding of the mechanistic basis of DDHRs would not only facilitate the development of robust and reliable diagnostic assays for diagnosis, but would also inform therapy by providing specific target(s) for immunomodulation and potentially permit pre-therapeutic risk assessment to pursue the common goal of safe and effective drug therapy.

Pharmacogenomics: current status and future perspectives

Inter-individual variability in drug response, be it efficacy or safety, is common and likely to become an increasing problem globally given the growing elderly population requiring treatment. Reasons for this inter-individual variability include genomic factors, an area of study called pharmacogenomics. With genotyping technologies now widely available and decreasing in cost, implementing pharmacogenomics into clinical practice - widely regarded as one of the initial steps in mainstreaming genomic medicine - is currently a focus in many countries worldwide. However, major challenges of implementation lie at the point of delivery into health-care systems, including the modification of current clinical pathways coupled with a massive knowledge gap in pharmacogenomics in the health-care workforce. Pharmacogenomics can also be used in a broader sense for drug discovery and development, with increasing evidence suggesting that genomically defined targets have an increased success rate during clinical development.

Pathophysiology of drug hypersensitivity

Drug hypersensitivity reactions (DHRs) are type B adverse drug reactions (ADRs) traditionally defined as unpredictable, dose independent and not related to the drug pharmacology. DHRs, also called drug allergy if the immune system involvement is confirmed, represent around one-sixth of all ADRs and can cause major clinical problems due to their vague clinical presentation and irregular time course. Understanding the underlying pathophysiology of DHRs is very important for their diagnosis and management. Multiple layers of evidence exist pointing to the involvement of the immune system in DHRs. Recent data have led to a paradigm shift in our understanding of the exact pathophysiology of these reactions. Numerous hypotheses proposing explanation on how a low molecular weight drug molecule can elicit an immune reaction have been proposed. In addition to the classical "hapten" hypothesis, the reactive metabolite hypothesis, the pharmacological interaction with the immune system (p-i) concept, the danger/injury hypothesis and the altered peptide repertoire hypothesis have been proposed. We here introduce the inflammasome activation hypothesis and the cross-reactivity hypothesis as additional models explaining the pathophysiology of DHRs. Available data supporting these hypotheses are briefly summarized and discussed. We also introduced the cross-reactivity model, which may provide a platform to appreciate the potential role played by other factors leading to the activation of the immune system. We believe that although the drug in question could be the trigger of the reaction, the components of the immune system mediating the reaction do not act in isolation but rather are affected by the proinflammatory milieu occurring at the time of the reaction. This review attempts to summarize the available evidence to further illustrate the pathophysiology of DHRs.

LTT and HLA testing as diagnostic tools in Spanish vancomycin-induced DRESS cases: A case-control study

Drug reaction with eosinophilia and systemic symptoms (DRESS) is a severe T-cell-mediated off-target adverse reaction. DRESS cases caused by vancomycin have often been reported. The HLA-A*32:01 allele has been associated with genetic susceptibility to vancomycin-induced DRESS in US citizens of European descent. We have analyzed the association of the HLA-A*32:01 allele in 14 Spanish DRESS cases in which vancomycin was suspected as the culprit drug, and the lymphocyte transformation test (LTT) as an in vitro assay to evaluate vancomycin sensitization. The results were compared to vancomycin-tolerant control donors. LTT was performed in 12 DRESS cases with PBMCs from resolution samples available and in a group of 12 tolerant donors. ROC curves determined that LTT is a suitable tool to identify patients sensitized to vancomycin (AUC = 0.9646; p < 0.0001). When a stimulation index >3 was regarded as a positive result, contingency tables determined 91% sensitivity, 91.67% specificity, 91% positive predictive value, and 91.67% negative predictive value (p = 0.0001, Fisher’s exact test). The HLA A*32:01 allele was determined by an allele-specific PCR assay in 14 cases and 25 tolerant controls. Among the DRESS cases, five carriers were identified (35.7%), while it was detected in only one (4%) of the tolerant donors, [odds ratio (OR) = 13.33; 95% CI: 1.364–130.3; p = 0.016]. The strength of the association increased when only cases with positive LTT to vancomycin were considered (OR = 24.0; 95% CI: 2.28–252.6; p = 4.0 × 10⁻³). Our results confirm the association of the risk allele HLA-A*32:01 with vancomycin-induced DRESS in Spanish cases, and support LTT as a reliable tool to determine vancomycin sensitization.

Omics technologies in allergy and asthma research: An EAACI position paper

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.