Lack of association between CAG repeat polymorphism in the androgen receptor gene and the outcome of rheumatoid arthritis treatment with leflunomide

The association between CYB5A gene rs1790834 polymorphism and clinical improvement after 6 months of leflunomide treatment in women with rheumatoid arthritis

INTRODUCTION/OBJECTIVES

Rheumatoid arthritis (RA) is a disease affecting around 1% of the population in developed countries and can be treated with leflunomide. The higher prevalence of RA among women and numerous previous studies suggested the crucial role of sex hormones. Cytochrome CYB5A regulates the synthesis of androgens. Therefore, the aim of this study was to determine the association between common CYB5A gene polymorphism and the response to leflunomide in women with RA.

METHODS

This study included 111 patients. All of them received oral leflunomide monotherapy at a dose of 20 mg daily. Women were genotyped for the presence of CYB5A rs1790834 polymorphism and evaluated monthly for 6 months following the initiation of treatment.

RESULTS

After 6 months of therapy, patients with the GG genotype had higher DAS28 values and less improvement in DAS28 compared to patients with the GA and AA genotypes (p = 0.04). No statistically significant differences were found in relation to other disease activity parameters.

CONCLUSIONS

The results of the current study suggest a possible association of the CYB5A rs1790834 polymorphism with some disease activity parameters in RA patients treated with leflunomide during the initial therapy period. However, confirmation of the effect of this polymorphism on the efficacy of leflunomide treatment requires further studies. Key Points • Leflunomide is the synthetic disease-modifying anti-rheumatic drug used in the therapy of rheumatoid arthritis. • CYB5A rs1790834 gene polymorphism may influence the clinical improvement after 6 months of leflunomide treatment in women with rheumatoid arthritis.

An Improved Stacked Autoencoder for Metabolomic Data Classification

Naru3 (NR) is a traditional Mongolian medicine with high clinical efficacy and low incidence of side effects. Metabolomics is an approach that can facilitate the development of traditional drugs. However, metabolomic data have a high throughput, sparse, high-dimensional, and small sample nature, and their classification is challenging. Although deep learning methods have a wide range of applications, deep learning-based metabolomic studies have not been widely performed. We aimed to develop an improved stacked autoencoder (SAE) for metabolomic data classification. We established an NR-treated rheumatoid arthritis (RA) mouse model and classified the obtained metabolomic data using the Hessian-free SAE (HF-SAE) algorithm. During training, the unlabeled data were used for pretraining, and the labeled data were used for fine-tuning based on the HF algorithm for gradient descent optimization. The hybrid algorithm successfully classified the data. The results were compared with those of the support vector machine (SVM), k-nearest neighbor (KNN), and gradient descent SAE (GD-SAE) algorithms. A five-fold cross-validation was used to complete the classification experiment. In each fine-tuning process, the mean square error (MSE) and misclassification rates of the training and test data were recorded. We successfully established an NR animal model and an improved SAE for metabolomic data classification.

Biomarkers to Personalize the Treatment of Rheumatoid Arthritis: Focus on Autoantibodies and Pharmacogenetics

Rheumatoid arthritis (RA) is a chronic inflammatory disease that is very complex and heterogeneous. If not adequately treated, RA patients are likely to manifest excess of morbidity and disability with an important impact on the quality of life. Pharmacological treatment is based on the administration of the disease-modifying antirheumatic drugs (DMARDs), subdivided into conventional synthetic (csDMARDs), targeted synthetic (tsDMARDs), and biological (bDMARDs). bDMARDs are now frequently administered in patients, both as alternative treatment and together with csDMARDs. Unfortunately, there is a therapeutic response variability both to old and new drugs. Therefore, to identify pre-therapeutic and on-treatment predictors of response is a priority. This review aims to summarize recent advances in understanding the causes of the variability in treatment response in RA, with particular attention to predictive potential of autoantibodies and DMARD pharmacogenetics. In recent years, several biomarkers have been proposed to personalize the therapy. Unfortunately, a magic bullet does not exist, as many factors concur to disease susceptibility and treatment outcomes, acting around the patient’s congenital background. Models integrating demographic, clinical, biochemical, and genetic data are needed to enhance the predictive capacity of specific factors singularly considered to optimize RA treatment in light of multidisciplinary patient management.

The effect of gene polymorphisms on patient responses to rheumatoid arthritis therapy

INTRODUCTION

Rheumatoid arthritis (RA) is a systemic disease leading to joint destruction. The therapy of RA is mainly based on disease-modifying anti-rheumatic drugs (DMARDs) and biological drugs. The response to treatment is different among patients. Therefore, we have searched for factors that may predict the efficacy and toxicity during therapy in individual patients.

AREAS COVERED

This review presents the role of genetic polymorphisms as predictors of the efficacy and toxicity during the therapy of RA patients with DMARDs (methotrexate, leflunomide, sulfasalazine) and biological drugs (anti-TNF-alpha antagonists, Tocilizumab, Rituximab).

EXPERT OPINION

Despite studies having shown an association between genetic polymorphisms and response to therapy in RA patients, the majority of these findings are still inconclusive and inconsistent. We are still far from applying pharmacogenetic tests in routine clinical practice that can predict the outcome of treatment. Several factors, such as small sample size with low statistical power, variability in the outcome definitions and the heterogeneity of the cohorts, limited number of tested single nucleotide polymorphisms (SNPs), small effect for the selected variant, and a lack of consideration of epigenetic factors, may contribute to the inconsistency observed and may lead to limited success in personalizing therapy.