The DEER database: A bridge connecting drugs, environmental effects, and regulations

Fixed drug eruption in a patient of HLA-B*58:01 negative after allopurinol administration: A case report

WHAT IS KNOWN AND OBJECTIVE

Allopurinol is widely used for hyperuricemia and gouty arthritis, but is associated with cutaneous adverse drug reactions (CADRs). HLA-B*58:01 is a highly specific and effective genetic marker for the detection of allopurinol-induced CADRs, especially for Asian descents.

CASE SUMMARY

A 60-year-old Chinese Han male patient took allopurinol for lowering uric acid after the negative result from HLA-B*58:01 testing. Then, he experienced episodes of well-demarcated pruritic erythematous patches on the whole body that developed into blisters and pustular eruption. Fixed drug eruption (FDE) was diagnosed by skin biopsy and improved with withdrawal and hormone treatments.

WHAT IS NEW AND CONCLUSION

It should be kept in mind that cutaneous drug eruption might occur after allopurinol administration in Asians of HLA-B*58:01 negative. Awareness among medical practitioners about FDE can lead to correct diagnosis, treatment and decreased damage as well as lower therapeutic costs.

Computer-aided biomarker discovery for precision medicine: data resources, models and applications

Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.

Impact of HLA-B*58:01 allele and allopurinol-induced cutaneous adverse drug reactions: evidence from 21 pharmacogenetic studies

Allopurinol is widely used for hyperuricemia and gouty arthritis, but is associated with cutaneous adverse drug reactions (CADRs). Recently, HLA-B*58:01 allele was identified as a strong genetic marker for allopurinol-induced CADRs in Han Chinese. However, the magnitude of association and diagnosis value of HLA-B*58:01 in allopurinol-induced CADRs remain inconclusive. To investigate this inconsistency, we conducted a meta-analysis of 21 pharmacogenetic studies, including 551 patients with allopurinol-induced CADRs, and 2,370 allopurinol-tolerant controls as well as 9,592 healthy volunteers. The summary OR for allopurinol-induced CADRs among HLA-B*58:01 carriers was 82.77 (95% CI: 41.63 – 164.58, P < 10⁻⁵) and 100.87 (95% CI: 63.91 – 159.21, P < 10⁻⁵) in matched and population based studies, respectively. Significant results were also observed when stratified by outcomes and ethnicity. Furthermore, the summary estimates for quantitative analysis of HLA-B*58:01 allele carriers in allopurinol-induced CADRs screening were as follows: sensitivity, 0.93 (95% CI: 0.85 – 0.97); specificity, 0.89 (95% CI: 0.87 – 0.91); positive likelihood ratio, 8.24 (95% CI: 6.92 – 9.81); negative likelihood ratio, 0.084 (95% CI: 0.039 – 0.179); and diagnostic odds ratio, 98.59 (95% CI: 43.31 – 224.41). The AUSROC was 0.92 (95% CI: 0.89–0.94), indicating the high diagnostic performance. Our results indicated that allopurinol–SCAR is strongly associated with HLA-B*58:01, and HLA-B*58:01 is a highly specific and effective genetic marker for the detection allopurinol-induced CADRs, especially for Asian descents.

Interactions Between Genetics, Lifestyle, and Environmental Factors for Healthcare

The occurrence and progression of diseases are strongly associated with a combination of genetic, lifestyle, and environmental factors. Understanding the interplay between genetic and nongenetic components provides deep insights into disease pathogenesis and promotes personalized strategies for people healthcare. Recently, the paradigm of systems medicine, which integrates biomedical data and knowledge at multidimensional levels, is considered to be an optimal way for disease management and clinical decision-making in the era of precision medicine. In this chapter, epigenetic-mediated genetics-lifestyle-environment interactions within specific diseases and different ethnic groups are systematically discussed, and data sources, computational models, and translational platforms for systems medicine research are sequentially presented. Moreover, feasible suggestions on precision healthcare and healthy longevity are kindly proposed based on the comprehensive review of current studies.

Gene-environment interaction in autoimmune disease

Autoimmune disease manifests in numerous forms, but as a disease group is relatively common in the population. It is complex in aetiology, with genetic and environmental determinants. The involvement of gene variants in autoimmune disease is well established, and evidence for significant involvement of the environment in various disease forms is growing. These factors may act independently, or they may interact, with the effect of one factor influenced by the presence of another. Identifying combinations of genetic and environmental factors that interact in autoimmune disease has the capacity to more fully explain disease risk profile, and to uncover underlying molecular mechanisms contributing to disease pathogenesis. In turn, such knowledge is likely to contribute significantly to the development of personalised medicine, and targeted preventative approaches. In this review, we consider the current evidence for gene-environment (G-E) interaction in autoimmune disease. Large-scale G-E interaction research efforts, while well-justified, face significant practical and methodological challenges. However, it is clear from the evidence that has already been generated that knowledge on how genes and environment interact at a biological level will be crucial in fully understanding the processes that manifest as autoimmunity.