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Identification of disease and therapeutic biomarkers remains a significant challenge in the early diagnosis and effective treatment of juvenile idiopathic arthritis (JIA). In this study, plasma metabolomic profiling was conducted to identify disease-related metabolic biomarkers associated with JIA. Plasma samples from treatment-naïve JIA patients and non-JIA reference patients underwent global metabolomic profiling across discovery (60 JIA, 60 non-JIA) and replication (49 JIA, 38 non-JIA) cohorts. Univariate analysis identified significant metabolites (q-value ≤ 0.05), followed by enrichment analysis using ChemRICH and metabolic network mapping with MetaMapp and Cytoscape. Receiver operating characteristic (ROC) analysis determined the top discriminating biomarkers based on area under the curve (AUC) values. A total of over 800 metabolites were measured, consisting of 714 known and 155 unknown compounds. In the discovery cohort, 587 metabolites were significantly altered in JIA patients compared with the reference population (q < 0.05). In the replication cohort, 288 metabolites were significantly altered, with 78 overlapping metabolites demonstrating the same directional change in both cohorts. JIA was associated with a notable increase in plasma levels of sphingosine metabolites and fatty acid ethanolamides and decreased plasma levels of sarcosine, iminodiacetate, and the unknown metabolite X-12462. Chemical enrichment analysis identified cycloparaffins in the form of naproxen and its metabolites, unsaturated lysophospholipids, saturated phosphatidylcholines, sphingomyelins, ethanolamines, and saturated ceramides as the top discriminating biochemical clusters. ROC curve analysis identified 11 metabolites classified as highly discriminatory based on an AUC > 0.90, with the top discriminating metabolite being sphinganine-1-phosphate (AUC = 0.98). This study identifies specific metabolic changes in JIA, particularly within sphingosine metabolism, through both discovery and replication cohorts. Plasma metabolomic profiling shows promise in pinpointing JIA-specific biomarkers, differentiating them from those in healthy controls and Crohn’s disease, which may improve diagnosis and treatment.

Abstract Background Inadequate systemic exposure to infliximab (IFX) is associated with treatment failure. This work evaluated factors associated with reduced IFX exposure in children with autoimmune disorders requiring IFX therapy. Methods In this single-center cross-sectional prospective study IFX trough concentrations and anti-drug antibodies (ADAs) were measured in serum from children diagnosed with inflammatory bowel disease (IBD) (n = 73), juvenile idiopathic arthritis (JIA) (n = 16), or uveitis (n = 8) receiving maintenance IFX infusions at an outpatient infusion clinic in a tertiary academic pediatric hospital. IFX concentrations in combination with population pharmacokinetic modeling were used to estimate IFX clearance. Patient demographic and clinical data were collected by chart review and evaluated for their relationship with IFX clearance. Results IFX trough concentrations ranged from 0 to > 40 μg/mL and were 3-fold lower in children with IBD compared to children with JIA (p = 0.0002) or uveitis (p = 0.001). Children with IBD were found to receive lower IFX doses with longer dosing intervals, resulting in dose intensities (mg/kg/day) that were 2-fold lower compared to children with JIA (p = 0.0002) or uveitis (p = 0.02). Use of population pharmacokinetic analysis to normalize for variation in dosing practices demonstrated that increased IFX clearance was associated with ADA positivity (p = 0.004), male gender (p = 0.02), elevated erythrocyte sedimentation rate (ESR) (p = 0.02), elevated c-reactive protein (CRP) (p = 0.001), reduced serum albumin concentrations (p = 0.0005), and increased disease activity in JIA (p = 0.009) and IBD (p ≤ 0.08). No significant relationship between diagnosis and underlying differences in IFX clearance was observed. Multivariable analysis by covariate population pharmacokinetic modeling confirmed increased IFX clearance to be associated with anti-IFX antibody positivity, increased ESR, and reduced serum albumin concentrations. Conclusions Enhanced IFX clearance is associated with immunogenicity and inflammatory burden across autoimmune disorders. Higher systemic IFX exposures observed in children with rheumatologic disorders are driven primarily by provider drug dose and interval selection, rather than differences in IFX pharmacokinetics across diagnoses. Despite maintenance IFX dosing at or above the standard recommended range for IBD (i.e., 5 mg/kg every 8 weeks), the dosing intensity used in the treatment of IBD is notably lower than dosing intensities used to treat JIA and uveitis, and may place some children with IBD at risk for suboptimal maintenance IFX exposures necessary for treatment response.

Abstract Although traditional approaches to biomarker discovery have elucidated key molecular markers that have improved drug selection (precision medicine), the discovery of biomarkers that inform optimal dose selection (precision dosing) continues to be a challenge in many therapeutic areas. Larger and more diverse study populations are necessary to discover additional biomarkers that provide the resolution needed for a more tailored dose. To generate and accommodate large datasets of drug response phenotypes, time‐ and cost‐efficient strategies are necessary. In particular, a multitude of technological advances that originated for purposes outside of biomedical research (electronic health records, direct‐to‐consumer genetic testing, social media, mobile devices, and machine learning) have made it easier to communicate, connect, and gather information from consumers. Although these technologies have been used with success in the health sciences for an array of purposes, these resources have not been fully capitalized on for precision dosing. This perspective will touch on how these innovations can be used as data sources, data collection tools, and data processing tools for drug‐response phenotypes with a unique focus on advancing biomarker‐driven precision dosing.

Variation in methotrexate (MTX) efficacy represents a significant barrier to early and effective disease control in the treatment of autoimmune arthritis. We hypothesize that the utilization of metabolomic techniques will allow for an improved understanding of the biochemical basis for the pharmacological activity of MTX, and can promote the identification and evaluation of novel molecular biomarkers of MTX response. In this work, erythroblastoid cells were exposed to MTX at the physiologic concentration of 1,000 nM and analyzed using three metabolomic platforms to give a broad spectrum of cellular metabolites. MTX pharmacological activity, defined as cellular growth inhibition, was associated with an altered cellular metabolomic profile based on the analysis of 724 identified metabolites. By discriminant analysis, MTX treatment was associated with increases in ketoisovaleric acid, fructose, galactose, and 2‐deoxycytidine, and corresponding reductions in 2‐deoxyuridine, phosphatidylinositol 32:0, orotic acid, and inosine monophosphate. Inclusion of data from analysis of folate metabolism in combination with chemometric and metabolic network analysis demonstrated that MTX treatment is associated with dysregulated folate metabolism and nucleotide biosynthesis, which is in line with its known mechanism of action. However, MTX treatment was also associated with alterations in a diversity of metabolites, including intermediates of amino acid, carbohydrate, and lipid metabolism. Collectively, these findings support a robust metabolic response following exposure to physiologic concentrations of MTX. They also identify various metabolic intermediates that are associated with the pharmacological activity of MTX, and are, therefore, potential molecular biomarker candidates in future preclinical and clinical studies of MTX efficacy in autoimmune arthritis.

Methotrexate (MTX) is widely used in the treatment of autoimmune arthritis but is limited by its unpredictable and variable response profile. Currently, no biomarkers exist to predict or monitor early therapeutic responses to MTX. Using a collagen-induced arthritis (CIA) mouse model, this study aimed to identify biochemical pathways and biomarkers associated with MTX efficacy in autoimmune arthritis. Following arthritis disease induction, DBA/1J mice were treated with subcutaneous MTX (20 mg/kg/week) and disease activity was assessed based on disease activity scores (DAS) and paw volume (PV) measurements. Red blood cell (RBC) and plasma samples were collected at the end of the study and were assessed for folate and MTX content. Plasma samples were analyzed by semitargeted global metabolomic profiling and analyzed by univariate and multivariate analysis. Treatment with MTX was associated with significant reductions in disease activity based on both DAS (p = 0.0006) and PV (p = 0.0006). MTX therapy resulted in significant reductions in 5-methyltetrahydrofolate (5mTHF) levels in plasma (p = 0.02) and RBCs (p = 0.001). Reductions in both RBC and plasma 5mTHF were associated with lower DAS (p = 0.0007, p = 0.01, respectively) and PV (p = 0.001, p = 0.005, respectively). Increases in RBC MTX were associated with lower DAS (p = 0.003) but not PV (p = 0.23). Metabolomic analysis identified N-methylisoleucine (NMI) and quinolone as metabolites significantly altered in disease mice, which were corrected towards healthy control levels in mice treated with MTX. Reductions in plasma NMI were associated with lower DAS (p = 0.0002) and PV (p = 9.5 × 10−6). Increases in plasma quinolone were associated with lower DAS (p = 0.02) and PV (p = 0.01). Receiver-operating characteristic curve analysis identified plasma NMI (AUC = 1.00, p = 2.4 × 10−8), RBC 5mTHF (AUC = 0.99, p = 2.4 × 10−5), and plasma quinolone (AUC = 0.89, p = 0.01) as top discriminating metabolites of MTX treatment. Our data support a relationship between MTX efficacy and its effect on circulating folates and identified 5mTHF, NMI, and quinolone as potential therapeutic biomarkers of disease activity and MTX response in the CIA mouse model of autoimmune arthritis.

Methotrexate (MTX) efficacy in the treatment of rheumatoid arthritis (RA) is variable and unpredictable, resulting in a need to identify biomarkers to guide drug therapy. This study evaluates changes in the plasma metabolome associated with response to MTX in RA with the goal of understanding the metabolic basis for MTX efficacy towards the identification of potential metabolic biomarkers of MTX response. Plasma samples were collected from healthy control subjects (n = 20), and RA patients initiating MTX therapy (n = 20, 15 mg/week) before and after 16 weeks of treatment. The samples were analyzed by a semi-targeted metabolomic analysis, and then analyzed by univariate and multivariate methods, as well as an enrichment analysis. An MTX response was defined as a clinically significant reduction in the disease activity score in 28 joints (DAS-28) of greater than 1.2; achievement of clinical remission, defined as a DAS-28 < 2.6, was also utilized as an additional measure of response. In this study, RA is associated with an altered plasma metabolome that is normalized following initiation of MTX therapy. Metabolite classes found to be altered in RA and corrected by MTX therapy were diverse and included triglycerides (p = 1.1 × 10−16), fatty acids (p = 8.0 × 10−12), and ceramides (p = 9.8 × 10−13). Stratification based on responses to MTX identified various metabolites differentially impacted in responders and non-responders including glucosylceramides (GlcCer), phosphatidylcholines (PC), sphingomyelins (SM), phosphatidylethanolamines (PE), choline, inosine, hypoxanthine, guanosine, nicotinamide, and itaconic acid (p < 0.05). In conclusion, RA is associated with significant alterations to the plasma metabolome displaying at least partial normalization following 16 weeks of MTX therapy. Changes in multiple metabolites were found to be associated with MTX efficacy, including metabolites involved in fatty acid/lipid, nucleotide, and energy metabolism.