Helen R. Gosselt

Introduction and Objective The recent rise in acute kidney injury (AKI) incidence, with approximately 30% attributed to potentially preventable adverse drug events (ADEs), poses challenges in evaluating drug-induced AKI due to polypharmacy and other risk factors. This study seeks to consolidate knowledge on the drugs with AKI potential from four distinct sources: (i) bio(medical) peer-reviewed journals; (ii) spontaneous reporting systems (SRS); (iii) drug information databases (DIDs); and (iv) NephroTox website. By harnessing the potential of these underutilised sources, our objective is to bridge gaps and enhance the understanding of drug-induced AKI. Methods By searching Medline, studies with lists of drugs with AKI potential established through consensus amongst medical experts were selected. A final list of 63 drugs was generated aggregating the original studies. For these 63 drugs, the AKI reporting odds ratios (RORs) using three SRS databases, the average frequency of ADEs from four different DIDs and the number of published studies identified via NephroTox was reported. Results Drugs belonging to the antivirals, antibacterials, and non-steroidal anti-inflammatory pharmacological classes exhibit substantial consensus on AKI potential, which was also reflected in strong ROR signals, frequent to very frequent AKI-related ADEs and a high number of published studies reporting adverse kidney events as identified via NephroTox. Renin-angiotensin aldosterone system inhibitors and diuretics also display comparable signal strengths, but this can be attributed to expected haemodynamic changes. More variability is noted for proton-pump inhibitors. Conclusions By integrating four disjointed sources of knowledge, we have created a novel, comprehensive resource on drugs with AKI potential, contributing to kidney safety improvement efforts.

There is a lack of knowledge on patient perspectives on adverse drug reactions (ADRs) attributed to the use of biologics. The aim of this study is to quantify the burden over time of ADRs attributed to TNF-α inhibitors in patients with inflammatory rheumatic diseases (IRDs) and investigate whether the burden over time differs between different types of ADRs. Data were used from the Dutch Biologic Monitor (DBM), an observational prospective cohort study for patient-reported ADRs attributed to biologics. Patients with an IRD using a TNF-α inhibitor reporting an ADR, lasting for three consecutive questionnaires, were included. Questionnaires were sent every two months and burden was scored on a 5-point Likert-type scale. Burden scores were analyzed using linear mixed models. Data from 166 unique patients reporting 274 ADRs were included. The burden score decreased every month by 0.29 points (95% CI -0.34 - -0.24) on average on a 5-point Likert-type scale. The burden score for infections and infestations decreased significantly faster than the burden score for injection site reactions. Patient-reported burden of ADRs attributed to the use of a TNF-α inhibitor in patients with IRDs decreased significantly over time, especially for infections and infestations.

We aimed to describe the burden of adverse drug reactions (ADRs) reported by patients participating in the Dutch ADR Monitor using a multifactorial burden measurement instrument. The Dutch ADR Monitor is a cohort event monitoring system that collects information on ADR experiences, including burden. This study includes the initial data (November 2022 until May 2023). Patients were asked if experienced ADRs impacted 7 domains of burden: appearance, medical treatment, daily life, fatigue, physical consequences, mental consequences and the course of ADRs. Burden was scored from 0 to 10 on impacted domains. The distributions of these burden scores were demonstrated in Likert plots. The burden between persistent and recurrent ADRs was compared. 92 patients reported 199 ADRs. Impact on the domains fatigue and daily life were experienced most frequently, except for skin and subcutaneous tissue ADRs, where impact on appearance and mental consequences were experienced most frequently. Fatigue was considered the most burdensome domain. No difference in burden was found between persistent (median = 7, IQR = 4) and recurrent ADRs (median = 6, IQR = 4, p = 0.59). This is the first study investigating burden of ADRs on 7 domains in patients with chronic diseases. Impact on the domain fatigue was considered most burdensome.

Methotrexate polyglutamates (MTX‐PG) concentrations in red blood cells (RBCs) have been suggested as a biomarker of response in patients with rheumatoid arthritis (RA) receiving low‐dose MTX therapy. We investigated the association and interpatient variability between RBC‐MTX‐PG_3‐5‐exposure and response in patients with RA starting MTX. Data of three prospective cohorts were available. The relationship between exposure and Disease Activity Score in 28 joints (DAS28) was analyzed using a population pharmacokinetic‐pharmacodynamic model. Relevant covariates were tested using full covariate modeling and backward elimination. From 395 patients, 3,401 MTX‐PG concentrations and 1,337 DAS28 measurements were available between 0 and 300 days after MTX treatment onset. The developed model adequately described the time course of MTX‐PG_3‐5 and DAS28. The median MTX‐PG_3‐5 level at month 1 was 30.9 nmol/L (interquartile range (IQR): 23.6–43.7; n = 41) and at month 3: 69.3 nmol/L (IQR: 17.9–41.2; n = 351). Clearance of MTX‐PG_3‐5 from RBCs was 28% lower (95% confidence interval (CI): 23.6–32.8%) in a woman and 10% lower (95% CI: 7.7–12.4%) in a 65‐year‐old compared with a 35‐year‐old patient. MTX‐PG_3‐5 concentrations associated with DAS28: half‐maximal effective concentration (EC₅₀) was 9.14 nmol/L (95% CI: 4.2 nmol/L‐14.1 nmol/L). EF at 80% (EC₈₀) above 47 nmol/L was regarded as the optimal response. Independent of the MTX‐PG _3–5 – response association, co‐administration of disease‐modifying antirheumatic drugs and corticosteroids improved response (additive effect on maximum effect (E_max)), whereas smoking, high body mass index and low albumin decreased E_max. In patients with RA starting MTX, RBC‐MTX‐PG_3‐5 was associated with clinical response. A dose increase is suggested when MTX‐PG_3‐5 at month 1 is below 9.15 nmol/L, continued with the same dose when the concentration is above 47 nmol/L, and consider other treatment options above 78 nmol/L from 3 months onwards.

To examine sex differences in relation to the nature, frequency and burden of patient-reported adverse drug reactions (ADRs) in patients with inflammatory rheumatic diseases.Rheumatoid arthritis, p...

Background: The number of Individual Case Safety Reports (ICSRs) in pharmacovigilance databases are rapidly increasing world‐wide. The majority of ICSRs at the Netherlands Pharmacovigilance Centre Lareb is reviewed manually to identify potential signal triggering reports (PSTR) or ICSRs which need further clinical assessment for other reasons.Objectives: To develop a prediction model to identify ICSRs that require clinical review, including PSTRs. Secondly, to identify the most important features of these reports.Methods: All ICSRs (n = 30 424) received by Lareb between October 1, 2017 and February 26, 2021 were included. ICSRs originating from marketing authorisation holders and ICSRs reported on vaccines were excluded. The outcome was defined as PSTR (yes/no), where PSTR ‘yes’ was defined as an ICSR discussed at a signal detection meeting. Nineteen features were included, concerning structured information on: patients, adverse drug reactions (ADR) or drugs. Data were divided into a training (70%) and test set (30%) using a stratified split to maintain the PSTR/no PSTR ratio. Logistic regression, elastic net logistic regression and eXtreme Gradient Boosting models were trained and tuned on a training set. Random down‐sampling of negative controls was applied on the training set to adjust for the imbalanced dataset. Final models were evaluated on the test set. Model performances were assessed using the area under the curve (AUC) with 95% confidence interval of a receiver operating characteristic (ROC), and specificity and precision were assessed at a threshold for perfect sensitivity (100%, to not miss any PSTRs). Feature importance plots were inspected and a selection of features was used to re‐train and test model performances with fewer features.Results: 1439 (4.7%) of reports were PSTR. All three models performed equally with a highest AUC of 0.75 (0.73–0.77). Despite moderate model performances, specificity (5%) and precision (5%) were low. Most important features were: ‘absence of ADR in the Summary of product characteristics’, ‘ADR reported as serious’, ‘ADR labelled as an important medical event’, ‘ADR reported by physician’ and ‘positive rechallenge’. Model performances were similar when using only nine of the most important features.Conclusions: We developed a prediction model with moderate performances to identify PSTRs with nine commonly available features. Optimisation of the model using more ICSR information (e.g., free text fields) to increase model precision is required before implementation.

Aim: To identify differentially methylated positions (DMPs) and regions (DMRs) that predict response to Methotrexate (MTX) in early rheumatoid arthritis (RA) patients.Materials and methods: DNA from baseline peripheral blood mononuclear cells was extracted from 72 RA patients. DNA methylation, quantified using the Infinium MethylationEPIC, was assessed in relation to response to MTX (combination) therapy over the first 3 months.Results: Baseline DMPs associated with response were identified; including hits previously described in RA. Additionally, 1309 DMR regions were observed. However, none of these findings were genome-wide significant. Likewise, no specific pathways were related to response, nor could we replicate associations with previously identified DMPs.Conclusion: No baseline genome-wide significant differences were identified as biomarker for MTX (combination) therapy response; hence meta-analyses are required.

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.

This study aimed to identify baseline metabolic biomarkers for response to methotrexate (MTX) therapy in rheumatoid arthritis (RA) using an untargeted method. In total, 82 baseline plasma samples (41 insufficient responders and 41 sufficient responders to MTX) were selected from the Treatment in the Rotterdam Early Arthritis Cohort (tREACH, trial number: ISRCTN26791028) based on patients’ EULAR response at 3 months. Metabolites were assessed using high-performance liquid chromatography-quadrupole time of flight mass spectrometry. Differences in metabolite concentrations between insufficient and sufficient responders were assessed using partial least square regression discriminant analysis (PLS-DA) and Welch’s t-test. The predictive performance of the most significant findings was assessed in a receiver operating characteristic plot with area under the curve (AUC), sensitivity and specificity. Finally, overrepresentation analysis was performed to assess if the best discriminating metabolites were enriched in specific metabolic events. Baseline concentrations of homocystine, taurine, adenosine triphosphate, guanosine diphosphate and uric acid were significantly lower in plasma of insufficient responders versus sufficient responders, while glycolytic intermediates 1,3-/2,3-diphosphoglyceric acid, glycerol-3-phosphate and phosphoenolpyruvate were significantly higher in insufficient responders. Homocystine, glycerol-3-phosphate and 1,3-/2,3-diphosphoglyceric acid were independent predictors and together showed a high AUC of 0.81 (95% CI: 0.72–0.91) for the prediction of insufficient response, with corresponding sensitivity of 0.78 and specificity of 0.76. The Warburg effect, glycolysis and amino acid metabolism were identified as underlying metabolic events playing a role in clinical response to MTX in early RA. New metabolites and potential underlying metabolic events correlating with MTX response in early RA were identified, which warrant validation in external cohorts.

Introduction Methotrexate (MTX) constitutes the first-line therapy in rheumatoid arthritis (RA), yet approximately 30% of the patients do not benefit from MTX. Recently, we reported a prognostic multivariable prediction model for insufficient clinical response to MTX at 3 months of treatment in the treatment in the Rotterdam Early Arthritis Cohort (tREACH), including baseline predictors: Disease activity score 28 (DAS28), Health Assessment Questionnaire (HAQ), erythrocyte folate, single-nucleotide polymorphisms (SNPs; ABCB1, ABCC3), smoking, and BMI. The purpose of the current study was (1) to externally validate the model and (2) to enhance the model’s clinical applicability. Methods Erythrocyte folate and SNPs were assessed in 91 early disease-modifying antirheumatic drug (DMARD)-naïve RA patients starting MTX in the external validation cohort (U-Act-Early). Insufficient response (DAS28 > 3.2) was determined after 3 months and non-response after 6 months of therapy. The previously developed prediction model was considered successfully validated in the U-Act-Early (validation cohort) if the area under the curve (AUC) of the receiver operating characteristic (ROC) was not significantly lower than in the tREACH (derivation cohort). Results The AUCs in U-Act-Early at three and 6 months were 0.75 (95% CI 0.64–0.85) and 0.71 (95% CI 0.60–0.82) respectively, similar to the tREACH. Baseline DAS28 > 5.1 and HAQ > 0.6 were the strongest predictors. The model was simplified by excluding the SNPs, while still classifying 73% correctly. Furthermore, interaction terms between BMI and HAQ and BMI and erythrocyte folate significantly improved the model increasing correct classification to 75%. Results were successfully implemented in Evidencio online platform assisting clinicians in shared decision-making to intensify treatment when appropriate. Conclusions We successfully externally validated our recently reported prediction model for MTX non-response and enhanced its clinical application thus enabling its evaluation in a clinical trial. Trial Registration The U-Act-Early is registered at ClinicalTrials.gov. number: NCT01034137. tREACH is registered retrospectively at ISRCTN registry, number: ISRCTN26791028 at 23 August 2007.