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

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.

Alterations in platelet function have been implicated in the pathophysiology of COVID-19 since the beginning of the pandemic. While early reports linked hyperactivated platelets to thromboembolic events in COVID-19, subsequent investigations demonstrated hyporeactive platelets with a procoagulant phenotype. Mitochondria are important for energy metabolism and the function of platelets. Here, we sought to map the energy metabolism of platelets in a cohort of noncritically ill COVID-19 patients and assess platelet mitochondrial function, activation status, and responsiveness to external stimuli. We enrolled hospitalized COVID-19 patients and controls between October 2020 and December 2021. Platelets function and metabolism was analyzed by flow cytometry, metabolomics, glucose fluxomics, electron and fluorescence microscopy and western blot. Platelets from COVID-19 patients showed increased phosphatidylserine externalization indicating a procoagulant phenotype and hyporeactivity to ex vivo stimuli, associated with profound mitochondrial dysfunction characterized by mitochondrial depolarization, lower mitochondrial DNA–encoded transcript levels, an altered mitochondrial morphology consistent with increased mitochondrial fission, and increased pyruvate/lactate ratios in platelet supernatants. Metabolic profiling by untargeted metabolomics revealed NADH, NAD⁺, and ATP among the top decreased metabolites in patients’ platelets, suggestive of energy metabolism failure. Consistently, platelet fluxomics analyses showed a strongly reduced utilization of ¹³C-glucose in all major energy pathways together with a rerouting of glucose to de novo generation of purine metabolites. Patients’ platelets further showed evidence of oxidative stress, together with increased glutathione oxidation and synthesis. Addition of plasma from COVID-19 patients to normal platelets partially reproduced the phenotype of patients’ platelets and disclosed a temporal relationship between mitochondrial decay and (subsequent) phosphatidylserine exposure and hyporeactivity. These data link energy metabolism failure in platelets from COVID-19 patients with a prothrombotic platelet phenotype with features matching cell death.

Background/Objectives: Methotrexate (MTX) is a cornerstone drug used to treat immune-mediated inflammatory diseases (IMIDs) in low doses (10–30 mg/week), and malignancies in high doses (5000 mg/m²/2 weeks). Its active metabolites, Methotrexate polyglutamates (MTX-PG_2–5), quantified in erythrocytes, are associated with efficacy. This study aimed to compare erythrocyte MTX-PG concentrations in patients with IMIDs and pediatric acute lymphoblastic leukemia (ped-ALL) treated with low-dose or high-dose MTX, respectively, and to identify clinical, demographic, and treatment-related factors influencing their concentration. Methods: A total of 567 patients with rheumatoid arthritis, juvenile idiopathic arthritis, Crohn’s disease, sarcoidosis, and ped-ALL were included. Erythrocyte MTX-PG concentration data was collected after 3 months (2.5 months for ped-ALL patients) of MTX-use. Multivariate linear regressing modelling adjusting for age, sex, body mass index (BMI), smoking status, starting MTX dose, route of MTX administration, use of predniso(lo)ne, disease-modifying anti-rheumatic drugs (DMARDs), and folic (or folinic) acid was performed. Results: Intravenous high-dose MTX increased MTX-PG_4&5 accumulation. Despite 50-fold higher doses in ped-ALL, MTX-PG_2–5sum concentrations were similar to those seen with subcutaneous low-dose MTX used in IMIDs. Age positively influenced MTX-PG concentrations, while DMARD use reduced MTX-PG_2–3&5 concentrations. Interestingly, predniso(lo)ne use was associated with higher MTX-PG_4&5 concentrations and folic (or folinic) acid with higher MTX-PG_3–5 concentrations. Conclusions: This is the first study to compare erythrocyte MTX-PG concentration in low-dose and high-dose patients. Intravenous high-dose MTX administration increased long-chain MTX-PG_4&5 concentrations, with MTX-PG_2–5sum concentrations similar compared to low-dose subcutaneous MTX use. This study demonstrated that route of administration, age, and concomitant therapies such as DMARDs, predniso(lo)ne, and folic (or folinic) acid significantly influence MTX-PG concentrations.

Methotrexate is pivotal in treating immune-mediated inflammatory diseases. Intracellularly, methotrexate is metabolized to methotrexate-polyglutamates (MTX-PG_1-7), comprising up to six additional glutamate moieties, crucial for cellular retention and therapeutic efficacy. Hitherto, quantification of MTX-PG_1-6 in peripheral blood mononuclear cells (PBMCs) from methotrexate-treated patients was challenging due to their low abundance in blood and matrix effects. We present a robust validated UPLC-MS/MS method to quantify individual MTX-PG_1-6 in PBMCs. Stable-isotope labelled internal standard mixture of MTX-PG_1-6 was added to 5 million PBMCs, followed by deproteinization with perchloric acid, and additional sample clean-up using solid phase extraction columns. MTX-PG_1-6 were detected and quantified using UPLC-MS/MS. The method was validated for lower limit of quantification (LLOQ), linearity, carryover, recovery, matrix effects, precision and stability. We assessed MTX-PG_1-6 in PBMCs derived from five methotrexate-treated rheumatoid arthritis patients. For all MTX-PG_1-6, LLOQs were < 1 fmol-MTX-PG_1-6/million cells with linearities R² > 0.995. The recoveries, carryover and stability were acceptable and no matrix effects were observed. The intraday and interday precision %CVs of quality controls ranged from 2.7 % to 11.4 % and 3.5-14.9 % respectively. Interday precision using nine PBMCs aliquots from a single MTX-treated patient aligned similarly (%CV <15 %). In patient-derived PBMC samples, MTX-PG₁ was the highest, with decreasing concentrations of MTX-PG₂ to MTX-PG₅. No signal for MTX-PG₆ was detected in the patient samples. We validated a new UPLC-MS/MS method to quantify MTX-PG_1-6 in PBMCs, thus facilitating PBMC-based therapeutic drug monitoring studies and understand associations between MTX-PG_1-6 concentration and therapy efficacy or adherence.