Clinical pharmacogenetic model to predict response of MTX monotherapy in patients with established rheumatoid arthritis after DMARD failure

A Systematic Review of Polygenic Models for Predicting Drug Outcomes

Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.

Clinical predictors of response to methotrexate in patients with rheumatoid arthritis: a machine learning approach using clinical trial data

BACKGROUND

Methotrexate is the preferred initial disease-modifying antirheumatic drug (DMARD) for rheumatoid arthritis (RA). However, clinically useful tools for individualized prediction of response to methotrexate treatment in patients with RA are lacking. We aimed to identify clinical predictors of response to methotrexate in patients with rheumatoid arthritis (RA) using machine learning methods.

METHODS

Randomized clinical trials (RCT) of patients with RA who were DMARD-naïve and randomized to placebo plus methotrexate were identified and accessed through the Clinical Study Data Request Consortium and Vivli Center for Global Clinical Research Data. Studies with available Disease Activity Score with 28-joint count and erythrocyte sedimentation rate (DAS28-ESR) at baseline and 12 and 24 weeks were included. Latent class modeling of methotrexate response was performed. The least absolute shrinkage and selection operator (LASSO) and random forests methods were used to identify predictors of response.

RESULTS

A total of 775 patients from 4 RCTs were included (mean age 50 years, 80% female). Two distinct classes of patients were identified based on DAS28-ESR change over 24 weeks: "good responders" and "poor responders." Baseline DAS28-ESR, anti-citrullinated protein antibody (ACPA), and Health Assessment Questionnaire (HAQ) score were the top predictors of good response using LASSO (area under the curve [AUC] 0.79) and random forests (AUC 0.68) in the external validation set. DAS28-ESR ≤ 7.4, ACPA positive, and HAQ ≤ 2 provided the highest likelihood of response. Among patients with 12-week DAS28-ESR > 3.2, ≥ 1 point improvement in DAS28-ESR baseline-to-12-week was predictive of achieving DAS28-ESR ≤ 3.2 at 24 weeks.

CONCLUSIONS

We have developed and externally validated a prediction model for response to methotrexate within 24 weeks in DMARD-naïve patients with RA, providing variably weighted clinical features and defined cutoffs for clinical decision-making.

Toward Individualized Prediction of Response to Methotrexate in Early Rheumatoid Arthritis: A Pharmacogenomics-Driven Machine Learning Approach

OBJECTIVE

To test the ability of machine learning (ML) approaches with clinical and genomic biomarkers to predict methotrexate treatment response in patients with early rheumatoid arthritis (RA).

METHODS

Demographic, clinical, and genomic data from 643 patients of European ancestry with early RA (mean age 54 years; 70% female) subdivided into a training (n = 336) and validation cohort (n = 307) were used. The genomic data comprised 160 single-nucleotide polymorphisms (SNPs) previously associated with RA or methotrexate metabolism. Response to methotrexate monotherapy was defined as good or moderate by the European Alliance of Associations for Rheumatology (EULAR) response criteria at the 3-month follow-up. Supervised ML methods were trained with 5 repeats and 10-fold cross-validation using the training cohort. Prediction performance was validated in the independent validation cohort.

RESULTS

Supervised ML methods combining age, sex, smoking, rheumatoid factor, baseline Disease Activity Score in 28 joints (DAS28) scores and 160 SNPs predicted EULAR response at 3 months with the area under the receiver operating curve of 0.84 (P = 0.05) in the training cohort and achieved a prediction accuracy of 76% (P = 0.05) in the validation cohort (sensitivity 72%, specificity 77%). Intergenic SNPs rs12446816, rs13385025, rs113798271, and ATIC (rs2372536) had variable importance above 60.0 and along with baseline DAS28 scores were among the top predictors of methotrexate response.

CONCLUSION

Pharmacogenomic biomarkers combined with baseline DAS28 scores can be useful in predicting response to methotrexate in patients with early RA. Applying ML to predict treatment response holds promise for guiding effective RA treatment choices, including timely escalation of RA therapies.

Complex Machine-Learning Algorithms and Multivariable Logistic Regression on Par in the Prediction of Insufficient Clinical Response to Methotrexate in Rheumatoid Arthritis

The goals of this study were to examine whether machine-learning algorithms outperform multivariable logistic regression in the prediction of insufficient response to methotrexate (MTX); secondly, to examine which features are essential for correct prediction; and finally, to investigate whether the best performing model specifically identifies insufficient responders to MTX (combination) therapy. The prediction of insufficient response (3-month Disease Activity Score 28-Erythrocyte-sedimentation rate (DAS28-ESR) > 3.2) was assessed using logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting (XGBoost). The baseline features of 355 rheumatoid arthritis (RA) patients from the “treatment in the Rotterdam Early Arthritis CoHort” (tREACH) and the U-Act-Early trial were combined for analyses. The model performances were compared using area under the curve (AUC) of receiver operating characteristic (ROC) curves, 95% confidence intervals (95% CI), and sensitivity and specificity. Finally, the best performing model following feature selection was tested on 101 RA patients starting tocilizumab (TCZ)-monotherapy. Logistic regression (AUC = 0.77 95% CI: 0.68–0.86) performed as well as LASSO (AUC = 0.76, 95% CI: 0.67–0.85), random forest (AUC = 0.71, 95% CI: 0.61 = 0.81), and XGBoost (AUC = 0.70, 95% CI: 0.61–0.81), yet logistic regression reached the highest sensitivity (81%). The most important features were baseline DAS28 (components). For all algorithms, models with six features performed similarly to those with 16. When applied to the TCZ-monotherapy group, logistic regression’s sensitivity significantly dropped from 83% to 69% (p = 0.03). In the current dataset, logistic regression performed equally well compared to machine-learning algorithms in the prediction of insufficient response to MTX. Models could be reduced to six features, which are more conducive for clinical implementation. Interestingly, the prediction model was specific to MTX (combination) therapy response.

The Potential Role of Genomic Medicine in the Therapeutic Management of Rheumatoid Arthritis

During the last decade, important advances have occurred regarding understanding of the pathogenesis and treatment of rheumatoid arthritis (RA). Nevertheless, response to treatment is not universal, and choosing among different therapies is currently based on a trial and error approach. The specific patient’s genetic background influences the response to therapy for many drugs: In this sense, genomic studies on RA have produced promising insights that could help us find an effective therapy for each patient. On the other hand, despite the great knowledge generated regarding the genetics of RA, most of the investigations performed to date have focused on identifying common variants associated with RA, which cannot explain the complete heritability of the disease. In this regard, rare variants could also contribute to this missing heritability as well as act as biomarkers that help in choosing the right therapy. In the present article, different aspects of genetics in the pathogenesis and treatment of RA are reviewed, from large-scale genomic studies to specific rare variant analyses. We also discuss the shared genetic architecture existing among autoimmune diseases and its implications for RA therapy, such as drug repositioning.

Assessing prognosis and prediction of treatment response in early rheumatoid arthritis: systematic reviews

BACKGROUND

Rheumatoid arthritis (RA) is a chronic, debilitating disease associated with reduced quality of life and substantial costs. It is unclear which tests and assessment tools allow the best assessment of prognosis in people with early RA and whether or not variables predict the response of patients to different drug treatments.

OBJECTIVE

To systematically review evidence on the use of selected tests and assessment tools in patients with early RA (1) in the evaluation of a prognosis (review 1) and (2) as predictive markers of treatment response (review 2).

DATA SOURCES

Electronic databases (e.g. MEDLINE, EMBASE, The Cochrane Library, Web of Science Conference Proceedings; searched to September 2016), registers, key websites, hand-searching of reference lists of included studies and key systematic reviews and contact with experts.

STUDY SELECTION

Review 1 - primary studies on the development, external validation and impact of clinical prediction models for selected outcomes in adult early RA patients. Review 2 - primary studies on the interaction between selected baseline covariates and treatment (conventional and biological disease-modifying antirheumatic drugs) on salient outcomes in adult early RA patients.

RESULTS

Review 1 - 22 model development studies and one combined model development/external validation study reporting 39 clinical prediction models were included. Five external validation studies evaluating eight clinical prediction models for radiographic joint damage were also included. c-statistics from internal validation ranged from 0.63 to 0.87 for radiographic progression (different definitions, six studies) and 0.78 to 0.82 for the Health Assessment Questionnaire (HAQ). Predictive performance in external validations varied considerably. Three models [(1) Active controlled Study of Patients receiving Infliximab for the treatment of Rheumatoid arthritis of Early onset (ASPIRE) C-reactive protein (ASPIRE CRP), (2) ASPIRE erythrocyte sedimentation rate (ASPIRE ESR) and (3) Behandelings Strategie (BeSt)] were externally validated using the same outcome definition in more than one population. Results of the random-effects meta-analysis suggested substantial uncertainty in the expected predictive performance of models in a new sample of patients. Review 2 - 12 studies were identified. Covariates examined included anti-citrullinated protein/peptide anti-body (ACPA) status, smoking status, erosions, rheumatoid factor status, C-reactive protein level, erythrocyte sedimentation rate, swollen joint count (SJC), body mass index and vascularity of synovium on power Doppler ultrasound (PDUS). Outcomes examined included erosions/radiographic progression, disease activity, physical function and Disease Activity Score-28 remission. There was statistical evidence to suggest that ACPA status, SJC and PDUS status at baseline may be treatment effect modifiers, but not necessarily that they are prognostic of response for all treatments. Most of the results were subject to considerable uncertainty and were not statistically significant.

LIMITATIONS

The meta-analysis in review 1 was limited by the availability of only a small number of external validation studies. Studies rarely investigated the interaction between predictors and treatment.

SUGGESTED RESEARCH PRIORITIES

Collaborative research (including the use of individual participant data) is needed to further develop and externally validate the clinical prediction models. The clinical prediction models should be validated with respect to individual treatments. Future assessments of treatment by covariate interactions should follow good statistical practice.

CONCLUSIONS

Review 1 - uncertainty remains over the optimal prediction model(s) for use in clinical practice. Review 2 - in general, there was insufficient evidence that the effect of treatment depended on baseline characteristics.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42016042402.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

Validation of a clinical pharmacogenetic model to predict methotrexate nonresponse in rheumatoid arthritis patients

Aim: To study the performance of a clinical pharmacogenetic model for the prediction of nonresponse in rheumatoid arthritis (RA) patients treated with methotrexate (MTX) in combination with other synthetic or biologic disease-modifying anti-rheumatic drugs . This prediction model includes gender, smoking status, rheumatoid factor positivity and four genetic variants in AMPD1 (rs17602729), ATIC (rs2372536), ITPA (rs1127354) and MTHFD1 (rs17850560). Methods: A total of 314 RA patients from three Dutch studies were retrospectively included. Eligible patients were adults diagnosed with RA and had a treatment duration with MTX and follow-up for at least two study evaluation visits. Prediction model risk scores at the first and second evaluation were calculated and compared with the actual nonresponse (disease activity score >2.4). Regression and receiver operating characteristic curve analyses of the prediction model were performed. Also, the sensitivity, specificity and the positive and negative predictive values (PPV and NPV) were determined. Results: The receiver operating characteristic area under the curve was 75% at first and 70% after second evaluation. At the second evaluation, prediction nonresponse had a sensitivity of 67% (CI: 54–78%), specificity of 69% (CI: 60–77%), PPV of 52% (CI: 45–60%) and NPV of 80% (CI: 73–85%). Conclusions: This study demonstrates that the clinical pharmacogenetic model has an inadequate performance for the prediction of nonresponse to MTX in RA patients treated with combination therapies.

Development and validation of a prognostic multivariable model to predict insufficient clinical response to methotrexate in rheumatoid arthritis

Objective

The objective was to predict insufficient response to 3 months methotrexate (MTX) in DMARD naïve rheumatoid arthritis patients.

Methods

A Multivariable logistic regression model of rheumatoid arthritis patients starting MTX was developed in a derivation cohort with 285 patients starting MTX in a clinical multicentre, stratified single-blinded trial, performed in seven secondary care clinics and a tertiary care clinic. The model was validated in a validation cohort with 102 patients starting MTX at a tertiary care clinic. Outcome was insufficient response (disease activity score (DAS)28 >3.2) after 3 months of MTX treatment. Clinical characteristics, lifestyle variables, genetic and metabolic biomarkers were determined at baseline in both cohorts. These variables were dichotomized and used to construct a multivariable prediction model with backward logistic regression analysis.

Results

The prediction model for insufficient response in the derivation cohort, included: DAS28>5.1, Health Assessment Questionnaire>0.6, current smoking, BMI>25 kg/m², ABCB1 rs1045642 genotype, ABCC3 rs4793665 genotype, and erythrocyte-folate<750 nmol/L. In the derivation cohort, AUC of ROC curve was 0.80 (95%CI: 0.73–0.86), and 0.80 (95%CI: 0.69–0.91) in the validation cohort. Betas of the prediction model were transformed into total risk score (range 0–8). At cutoff of ≥4, probability for insufficient response was 44%. Sensitivity was 71%, specificity 72%, with positive and negative predictive value of 72% and 71%.

Conclusions

A prognostics prediction model for insufficient response to MTX in 2 prospective RA cohorts by combining genetic, metabolic, clinical and lifestyle variables was developed and validated. This model satisfactorily identified RA patients with high risk of insufficient response to MTX.

Prediction of primary non-response to methotrexate therapy using demographic, clinical and psychosocial variables: results from the UK Rheumatoid Arthritis Medication Study (RAMS)

Background

Methotrexate (MTX) remains the disease-modifying anti-rheumatic drug of first choice in rheumatoid arthritis (RA) but response varies. Predicting non-response to MTX could enable earlier access to alternative or additional medications and control of disease progression. We aimed to identify baseline predictors of non-response to MTX and combine these into a prediction algorithm.

Methods

This study included patients recruited to the Rheumatoid Arthritis Medication Study (RAMS), a UK multi-centre prospective observational study of patients with RA or undifferentiated polyarthritis, commencing MTX for the first time. Non-response to MTX at 6 months was defined as “no response” using the European League Against Rheumatism (EULAR) response criteria, discontinuation of MTX due to inefficacy or starting biologic therapy. The association of baseline demographic, clinical and psychosocial predictors with non-response was assessed using logistic regression. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC) and calibration plots.

Results

Of 1050 patients, 449 (43%) were classified as non-responders. Independent multivariable predictors of MTX non-response (OR (95% CI)) were rheumatoid factor (RF) negativity (0.62 (0.45, 0.86) for RF positivity versus negativity), higher Health Assessment Questionnaire score (1.64 (1.25, 2.15)), higher tender joint count (1.06 (1.02, 1.10)), lower Disease Activity score in 28 joints (0.29 (0.23, 0.39)) and higher Hospital Anxiety and Depression Scale anxiety score (1.07 (1.03, 1.12)). The optimism-corrected AUC was 0.74.

Conclusions

This is the first model for MTX non-response to be developed in a large contemporary study of patients commencing MTX in which demographic, clinical and psychosocial predictors were considered. Patient anxiety was a predictor of non-response and could be addressed at treatment commencement.

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1645-5) contains supplementary material, which is available to authorized users.

A novel gene-expression-signature-based model for prediction of response to Tripterysium glycosides tablet for rheumatoid arthritis patients

Background

Approximately 30% of rheumatoid arthritis (RA) patients treated with Tripterysium glycosides (TG) tablets fail to achieve clinical improvement, implying the essentiality of predictive biomarkers and tools. Herein, we aimed to identify possible biomarkers predictive of therapeutic effects of TG tablets in RA.

Methods

Gene expression profile in peripheral blood mononuclear cells obtained from a discovery cohort treated with TG tablets was detected by Affymetrix EG1.0 arrays. Then, a list of candidate gene biomarkers of response to TG tablets were identified by integrating differential expression data analysis and gene signal transduction network analysis. After that, a partial-least-squares (PLS) model based on the expression levels of the candidate gene biomarkers in RA patients was constructed and evaluated using a validation cohort.

Results

Six candidate gene biomarkers (MX1, OASL, SPINK1, CRK, GRAPL and RNF2) were identified to be predictors of TG therapy. Following the construction of a PLS-based model using their expression levels in peripheral blood, both the 5-fold cross-validation and independent dataset validations showed the high predictive efficiency of this model, and demonstrated a distinguished improvement of the PLS-model based on six candidate gene biomarkers’ expression in combination over the commonly used clinical and inflammatory parameters, as well as the gene biomarkers alone, in predicting RA patients’ response to TG tablets.

Conclusions

This hypothesis-generating study identified MX1, OASL, SPINK1, CRK, GRAPL and RNF2 as novel targets for RA therapeutic intervention, and the PLS model based on the expression levels of these candidate biomarkers may have a potential prognostic value in RA patients treated with TG tablets.

Electronic supplementary material

The online version of this article (10.1186/s12967-018-1549-9) contains supplementary material, which is available to authorized users.

Replication study of polymorphisms associated with response to methotrexate in patients with rheumatoid arthritis

About 70 genetic studies have already addressed the need of biomarkers to predict the response of patients with rheumatoid arthritis (RA) to methotrexate (MTX) treatment. However, no genetic biomarker has yet been sufficiently validated. Here, we aimed to replicate a selection of 25 SNPs in the largest collection of patients up to date, which consisted of 915 patients treated with MTX. The change in disease activity (measured as ΔDAS28) from baseline was considered the primary outcome. In addition, response according to widely used criteria (EULAR) was taken as secondary outcome. We considered consistency between outcomes, P values accounting for the number of SNPs, and independence from potential confounders for interpretation of the results. Only the rs1801394 SNP in MTRR fulfilled the high association standards. Its minor allele was associated with less improvement than the major allele according to ΔDAS28 (p = 0.0016), and EULAR response (p = 0.004), with independence of sex, age, baseline DAS28, smoking, seropositivity, concomitant corticosteroid use or previous treatments. In addition, previous evidence suggests the association of this SNP with response to MTX in another autoimmune disease, juvenile idiopathic arthritis, and with high intracellular folate levels, which could contribute to poor response.

Prediction of response to methotrexate in rheumatoid arthritis

INTRODUCTION

Methotrexate (MTX) is the first-line disease-modifying drug of choice in controlling active inflammation of the synovium that characterises rheumatoid arthritis, a chronic autoimmune inflammatory condition. However, many patients do not respond to treatment with MTX or cannot tolerate the medication. Pre-treatment characteristics that predict response to MTX are, therefore, of particular interest and potential clinical utility. Areas covered: This narrative review seeks to cover various genotypic and phenotypic characteristics that have been investigated as predictors of treatment response to MTX in RA. Ovid Medline searches (1946 to January 2018) were carried out for 'methotrexate' and 'rheumatoid arthritis', in combination with relevant terms. All papers identified were English language, with abstracts. Relevant references were also reviewed. Expert commentary: Despite the introduction of biologic medication and targeted therapies, MTX is likely to remain the mainstay of RA treatment, largely due to its much cheaper cost. Development of a multifactorial predictive algorithm for response to MTX may be of clinical utility, as well as routine MTX drug level testing to improve medication adherence and persistence.

Evaluation of a clinical pharmacogenetics model to predict methotrexate response in patients with rheumatoid arthritis

Variability of response to treatment hinders successful management of rheumatoid arthritis (RA). Consequently, a clinical pharmacogenetics model for predicting response to methotrexate (CP-MTX) has been previously proposed that includes four clinical variables (disease activity, sex, the presence of rheumatoid factor and smoking status) and four SNPs (rs2236225, rs17602729, rs1127354, and rs2372536) in genes of the folate pathway. It showed good performance, but failed to attract attention, likely, in relation with lack of clear clinical benefit. Here, we have revised the value of the CP-MTX model directly addressing its clinical benefit by focusing on the expected benefit-cost of the predictions. In addition, our study included a much larger number of RA patients (n = 720) in MTX monotherapy than previous studies. Benefit of CP-MTX prediction was defined as the patients that would have received combination therapy as first treatment because they were correctly predicted as non-responders to MTX monotherapy. In contrast, cost of CP-MTX prediction was defined as the responder patients that were wrongly predicted as non-responders. Application of CP-MTX predictions to our patients showed a good benefit-cost relationship, with half of the 66.7% non-responders to MTX monotherapy rightly directed to alternative treatments (a benefit of 33.3%) at the cost of 8.5% wrongly predicted non-responders. These benefits-costs were consistent with reanalysis of the previously published studies. Therefore, predictions of CP-MTX showed a good benefit-cost relationship for informing MTX prescription.

Clinical Pharmacogenetic Models of Treatment Response to Methotrexate Monotherapy in Slovenian and Serbian Rheumatoid Arthritis Patients: Differences in Patient's Management May Preclude Generalization of the Models

Objectives: Methotrexate (MTX) is the first line treatment for rheumatoid arthritis (RA), but nevertheless 30% of patients experience MTX inefficacy. Our aim was to develop a clinical pharmacogenetic model to predict which RA patients will not respond to MTX monotherapy. We also assessed whether this model can be generalized to other populations by validating it on a group of Serbian RA patients.

Methods: In 110 RA Slovenian patients, data on clinical factors and 34 polymorphisms in MTX pathway were analyzed by Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression to select variables associated with the disease activity as measured by Disease Activity Score (DAS28) score after 6 months of MTX monotherapy. A clinical pharmacogenetic index was constructed from penalized regression coefficients with absolute value above 0.05. This index was cross-validated and also independently validated on 133 Serbian RA patients.

Results: A clinical pharmacogenetic index for prediction of DAS28 after 6 months of MTX monotherapy in Slovenian RA patients consisted of DAS28 score at diagnosis, presence of erosions, MTX dose, Solute Carrier Family 19 Member 1 (SLC19A1) rs1051266, Solute Carrier Organic Anion Transporter Family Member 1B1 (SLCO1B1) rs2306283, Thymidylate Synthase (TYMS), and Adenosine Monophosphate Deaminase 1 (AMPD1) rs17602729. It correctly classified 69% of Slovenian patients as responders or nonresponders and explained 30% of variability in DAS28 after 6 months of MTX monotherapy. Testing for validity in another population showed that it classified correctly only 22.5% of Serbian RA patients.

Conclusions: We developed a clinical pharmacogenetic model for DAS28 after 6 months of MTX monotherapy in Slovenian RA patients by combining clinical and genetic variables. The clinical pharmacogenetic index developed for Slovenian patients did not perform well on Serbian patients, presumably due to the differences in patients' characteristics and clinical management between the two groups.

Clinical-pharmacogenetic models for personalized cancer treatment: application to malignant mesothelioma

Large interindividual differences in treatment outcome are observed in cancer patients undergoing chemotherapy. Our aim was to develop and validate clinical-pharmacogenetic prediction models of gemcitabine/cisplatin or pemetrexed/cisplatin treatment outcome and develop an algorithm for genotype-based treatment recommendations in malignant mesothelioma (MM). We genotyped 189 MM patients for polymorphisms in gemcitabine, pemetrexed and cisplatin metabolism, transport and drug target genes and DNA repair pathways. To build respective clinical-pharmacogenetic models, pharmacogenetic scores were assigned by rounding regression coefficients. Gemcitabine/cisplatin model was based on training group of 71 patients and included CRP, histological type, performance status, RRM1 rs1042927, ERCC2 rs13181, ERCC1 rs3212986, and XRCC1 rs25487. Patients with higher score had shorter progression-free (PFS) and overall survival (P < 0.001). This model’s sensitivity was 0.615 and specificity 0.812. In independent validation group of 66 patients the sensitivity and specificity were 0.667 and 0.641, respectively. Pemetrexed/cisplatin model was based on 57 patients and included CRP, MTHFD1 rs2236225, and ABCC2 rs2273697. Patients with higher score had worse response and shorter PFS (P < 0.001). This model’s sensitivity was 0.750 and specificity 0.607. In independent validation group of 20 patients the sensitivity and specificity were 0.889 and 0.500, respectively. The proposed algorithm based on these models could enable the choice of the most effective chemotherapy for 85.5% of patients and lead to improved treatment outcome in MM.

Mechanism of action of methotrexate in rheumatoid arthritis, and the search for biomarkers

The treatment and outcomes of patients with rheumatoid arthritis (RA) have been transformed over the past two decades. Low disease activity and remission are now frequently achieved, and this success is largely the result of the evolution of treatment paradigms and the introduction of new therapeutic agents. Despite the rapid pace of change, the most commonly used drug in RA remains methotrexate, which is considered the anchor drug for this condition. In this Review, we describe the known pharmacokinetic properties and putative mechanisms of action of methotrexate. Consideration of the pharmacodynamic perspective could inform the development of biomarkers of responsiveness to methotrexate, enabling therapy to be targeted to specific groups of patients. Such biomarkers could revolutionize the management of RA.

Moving toward personalized medicine in rheumatoid arthritis: SNPs in methotrexate intracellular pathways are associated with methotrexate therapeutic outcome

AIM

Evaluate the potential of selected SNPs as predictors of methotrexate (MTX) therapeutic outcome.

PATIENTS & METHODS

In total, 35 SNPs in 14 genes involved in MTX intracellular pathways and Phase II reactions were genotyped in 233 rheumatoid arthritis (RA) patients treated with MTX. Binary logistic regressions were performed by genotype/haplotype-based approaches. Non-Response- and Toxicity-Genetic Risk Indexes (Non-RespGRI and ToxGRI) were created.

RESULTS

MTX nonresponse was associated to eight genotypes and three haplotypes: MTHFR rs1801131 AA and rs1801133 TT; MS rs1805087 AA; MTRR rs1801394 A carriers; ATIC rs2372536 C carriers, rs4673993 T carriers, rs7563206 T carriers and rs12995526 T carriers; CC for GGH rs3758149 and rs12681874; CGTTT for ATIC combination 1; and CTTTC for ATIC combination 2. From overall Non-RespGRI patients with indexes 6-8 had more than sixfold increased risk for MTX nonresponse than those patients with indexes 0-5. MTX-related toxicity was associated to five genotypes and two haplotypes: ATIC rs2372536 G carriers, rs3821353 T carriers, rs7563206 CC and rs12995526 CC; ADORA2A rs2267076 T; CTTCC for ATIC combination 1; and TC for ADORA2A rs2267076 and rs2298383. From overall ToxGRI, patients with indexes 3-4 had more than sevenfold increased risk for MTX-related toxicity than those patients with indexes 1-2.

CONCLUSION

Genotyping may be helpful to identify which RA patients will not benefit from MTX treatment and, consequently, important to personalized medicine in RA. Nevertheless, further studies are required to validate these findings.

Preliminary study for predicting better methotrexate efficacy in Japanese patients with rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by systemic inflammatory status, joint destruction, disability, and pain. Methotrexate (MTX) has been confirmed to reduce disease activity and delay or stabilize the development of bone erosions. However, major drawbacks are that patients show great interindividual variability in response to MTX and the unpredictable occurrence of side effects. A strategy for personalized MTX treatment to predict its efficacy and toxicity has not yet been determined. To establish personalized MTX therapy in Japanese patients with rheumatoid arthritis, we performed a preliminary study for predicting better methotrexate efficacy including single-nucleotide polymorphisms (SNPs) for MTX-related transporters/enzymes.

METHODS

Disease control status (good or poor) was judged by the number of Disease Activity Scores (DAS28) of <2 for 6-12 months. The response index R was calculated by the improved area under the curve (AUC) of the DAS28 score for 0-3 or 0-6 months by dividing the cumulative dose of MTX during 0-3 or 0-6 months, respectively. Genotyping of alleles of RFC1 80G > A, RFC1 -43 T > C, FPGS 1994G > A, GGH 401C > T, MTHFR 1298A > C, and TYMS 3'-UTR (-6/+6) was performed using the real-time PCR system.

RESULTS

Seven of 21 patients were judged as good responders in terms of disease control, and the remainder as poor responders. For 0-3 months after starting MTX administration, the median cumulative dose and improved DAS28 AUC in the good and poor response groups were 96.0 mg and 25.4 and 118.0 mg and 23.4, respectively. For 0-6 months, the median cumulative dose and improved DAS28 AUC in the good and poor response groups were 192.0 mg and 51.0 and 214.0 mg and 47.6, respectively. Statistically significant differences between the 2 groups in the 0-6-month period were observed in DAS28 AUC improvement and index R. A slight tendency for a correlation between G/G genotypes and A allele genotypes in RFC1 80 genotypes was observed, although it did not reach statistical significance.

CONCLUSION

This study suggested that aggressive RA treatment with MTX from the early period of administration is necessary to obtain a good response after 6 months, although no SNPs predicting a better treatment response to MTX were identified.

Response to methotrexate predicts long-term patient-related outcomes in rheumatoid arthritis

This study was conducted to investigate the predictive value of the initial response to methotrexate (MTX) on long-term patient-related outcomes (PROs) in rheumatoid arthritis (RA). All RA patients starting MTX treatment between 1980 and 1987 in our department were enrolled in a prospective observational study. After an average of 18 years, patient-related outcomes were assessed in three dimensions according to the International Classification of Functioning, Disability and Health (ICF). Statistical analyses employed multivariable models with baseline values for age, gender, disease duration, rheumatoid factor positivity, disease activity, response to MTX after 1 year and continuous use of MTX as covariates. The 271 patients enrolled had a mean disease duration of 8.5 years, a mean number of swollen joints of 18 (out of 32), and a mean erythrocyte sedimentation rate of 55 mm/h. After 18 years, PRO was available in 89 patients (33 %). A clinical improvement of at least 20 % 1 year after the initiation of MTX was associated with a favourable outcome in all three dimensions of the ICF, independent of continuation of MTX (p < 0.05). The initial response to MTX is an independent predictor of PRO in RA as assessed after an average of 18 years.

ATIC 347C>G gene polymorphism may be associated with methotrexate-induced adverse events in south Indian Tamil rheumatoid arthritis

Aim: To find the association of ATIC 347C>G gene polymorphism with methotrexate (MTX) treatment response and MTX-induced adverse events in south Indian Tamil patients with rheumatoid arthritis. Patients & methods: A total of 319 rheumatoid arthritis and 310 healthy controls were recruited for the study and ATIC 347C>G gene polymorphism was analyzed by PCR-RFLP method. Results: The genotype and allele frequencies of ATIC 347 C>G SNP did not differ between good and nonresponders and hence this SNP was not found to be associated with MTX treatment response. However, the ATIC 347 GG genotype (p = 0.02; odds ratio [OR]: 4.46; 95% CI: 1.28–15.52) and mutant G allele was associated with MTX-induced gastrointestinal adverse events (p = 0.01; OR: 2.60; 95% CI: 1.27–5.35). Conclusion: ATIC 347C>G gene polymorphism may be associated with the development of MTX induced gastrointestinal adverse events.

Genetic polymorphisms in low-dose methotrexate transporters: current relevance as methotrexate therapeutic outcome biomarkers

Methotrexate (MTX) is used in low doses to treat a variety of diseases. Although the mechanism responsible for its therapeutic action is unknown, MTX membrane transport proteins (influx and/or efflux) can be major determinants of pharmacokinetics, adverse drug reactions and clinical response profiles. With progess in pharmacogenomics, the improvement of the prediction of patients' therapeutic outcome treated with low doses of MTX will offer a powerful tool for the translation of transporter SNPs into clinical practice and will be essential to sustain a breakthrough in the field of personalized medicine. Therefore, this paper provides an update on the current data on SNPs in genes encoding low-dose MTX membrane transport proteins and their relevance as possible biomarkers of MTX therapeutic outcome.

Current and future trends in biomarker discovery and development of companion diagnostics for arthritis

Musculoskeletal diseases such as rheumatoid arthritis are complex multifactorial disorders that are chronic in nature and debilitating for patients. A number of drug families are available to clinicians to manage these disorders but few tests exist to target these to the most responsive patients. As a consequence, drug failure and switching to drugs with alternate modes of action is common. In parallel, a limited number of laboratory tests are available which measure biological indicators or 'biomarkers' of disease activity, autoimmune status, or joint damage. There is a growing awareness that assimilating the fields of drug selection and diagnostic tests into 'companion diagnostics' could greatly advance disease management and improve outcomes for patients. This review aims to highlight: the current applications of biomarkers in rheumatology with particular focus on companion diagnostics; developments in the fields of proteomics, genomics, microbiomics, imaging and bioinformatics and how integration of these technologies into clinical practice could support therapeutic decisions.

Polymorphisms in genes involved in the mechanism of action of methotrexate: are they associated with outcome in rheumatoid arthritis patients?

Background: Methotrexate (MTX) is the first-line treatment option for newly diagnosed rheumatoid arthritis (RA) patients. However, 50–70% of the patients respond to treatment and 30% suffer toxicity. Aim: To identify pharmacogenetic markers of outcome in RA patients treated with MTX. Patients & methods: We analyzed 27 genetic variants in DHFR, TYMS, MTHFR, ATIC and CCND1 genes. Results: We included 124 RA patients treated with MTX monotherapy. In multivariate analyses two variants in the MTHFR gene were associated with response, rs17421511 (p = 0.024) and rs1476413 (p = 0.0086), as well as one in the DHFR gene, rs1643650 (p = 0.026). The ATIC rs16853826 variant was associated with toxicity (p = 0.039). Conclusion: MTHFR, DHFR and ATIC genetic variants can be considered as pharmacogenetic markers of outcome in RA patients under MTX monotherapy.

Original submitted 10 January 2014; Revision submitted 28 March 2014

Pharmacogenetics and pharmacogenomics for rheumatoid arthritis responsiveness to methotrexate treatment: the 2013 update

Rheumatoid arthritis (RA) is a complex, systemic autoimmune disease characterized by chronic inflammation of multiple peripheral joints, which leads to serious destruction of cartilage and bone, progressive deformity and severe disability. Methotrexate (MTX) is one of the first-line drugs commonly used in RA therapy owing to its excellent long-term efficacy and cheapness. However, the efficacy and toxicity of MTX treatment have significant interpatient variability. Genetic factors contribute to this variability. In this review, we have summarized and updated the progress of RA response to MTX treatment since 2009 by focusing on the fields of pharmacogenetics and pharmacogenomics. Identification of genetic factors involved in MTX treatment response will increase the understanding of RA pathology and the development of new personalized treatments.

Pharmacogenetics of disease-modifying antirheumatic drugs in rheumatoid arthritis: towards personalized medicine

Rheumatoid arthritis is a disease showing considerable heterogeneity in all its aspects, including response to therapy. The efficacy of disease-modifying antirheumatic drugs (DMARDs), with or without biological activity, has been unambiguously established. DMARDs improve the symptoms associated with the disease, and, even more importantly, are capable of stagnating the joint damage associated with the disease. Nonetheless, a considerable proportion of patients fail to achieve an adequate response and/or experience toxicity. This variability in treatment response between individuals has given rise to an extensive search for prognostic markers in order to personalize and optimize therapy in rheumatoid arthritis patients. Pharmacogenetics, the study of genetic variation underlying differential responses to drugs, is a rapidly progressing field in rheumatology that might enable personalized therapy in rheumatic diseases. This review will summarize the pharmacogenetics of commonly used synthetic and biological DMARDs.