Dataset Information

Accelerated induction regimens of TNF-alpha inhibitors in patients with inflammatory bowel disease: a scoping review protocol.

ABSTRACT: INTRODUCTION:Tumour necrosis factor (TNF)-alpha inhibitors are commonly used to treat inflammatory bowel disease (IBD). In patients with IBD who are unresponsive to their first induction dose, the implementation of an 'accelerated' induction dose schedule (doses more frequent than recommended in product monographs) is becoming increasingly common. It is unclear whether this practice results in favourable patient outcomes, such as avoidance of surgery and disease remission. As such, there is a need to identify and map the current evidence base on accelerated induction schedules of these medications in the treatment of IBD. METHODS AND ANALYSIS:A scoping review will be employed to systematically identify and characterise the nature of scientific literature on accelerated induction regimens of TNF-alpha inhibitors. MEDLINE, Embase, International Pharmaceutical Abstracts and grey literature will be searched to identify relevant studies. The titles/abstracts of all records and full text of potentially relevant articles will be independently screened for inclusion by two reviewers. Data will be abstracted from included studies by one reviewer and verified for accuracy by another. The findings will be synthesised descriptively. ETHICS AND DISSEMINATION:We intend to report the findings of this scoping review in a peer-reviewed journal and a scientific conference. TRIAL REGISTRATION:This research was registered prospectively with the Open Science Framework (https://osf.io/z7n2d/).

SUBMITTER: Johnston A

PROVIDER: S-EPMC5829849 | biostudies-other | 2018 Jan

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Accelerated induction regimens of TNF-alpha inhibitors in patients with inflammatory bowel disease: a scoping review protocol.

Johnston Amy A Natarajan Sabrina S Hayes Meghan M MacDonald Erika E Shorr Risa R

BMJ open 20180130 1

<h4>Introduction</h4>Tumour necrosis factor (TNF)-alpha inhibitors are commonly used to treat inflammatory bowel disease (IBD). In patients with IBD who are unresponsive to their first induction dose, the implementation of an 'accelerated' induction dose schedule (doses more frequent than recommended in product monographs) is becoming increasingly common. It is unclear whether this practice results in favourable patient outcomes, such as avoidance of surgery and disease remission. As such, there ...[more]

PMID: 29382683

Similar Datasets

Project description:Colorectal cancer (CRC) is one of the most common malignancies worldwide. Early stage CRC patients have a good prognosis. If distant metastasis occurs, the 5-year survival drops below 10%. Despite treatment success over the last decades, treatment options for metastatic disease are still limited. Therefore, novel targets are needed to foster therapy of advanced stage CRC patients and hinder progression of early stage patients into metastasis. A novel target is the crucial oncogene Metastasis-Associated in Colon Cancer 1 (MACC1) involved in molecular pathogenesis of CRC metastasis. MACC1 induces cell proliferation and motility, supports cellular survival and rewires metabolism resulting in increased metastasis in vivo. MACC1 is a prognostic biomarker not only for CRC but for more than 20 solid cancer entities. Inflammation plays a pivotal role in tumorigenesis, tumor progression and metastasis. For CRC, inflammatory bowel disease and ulcerative colitis are important inflammation associated risk factors. Certain cytokines, such as TNF-α and IFN-γ, are key factors in determining the contribution of the inflammatory process to CRC. Knowledge of the connection between inflammation and MACC1 driven tumors remains unclear. Gene expression analysis of CRC cells after cytokine stimulation was analyzed by qRT-PCR and Western blot. Cellular motility was assessed by Boyden chamber assays. MACC1 promoter activity after stimulation with pro-inflammatory cytokines was measured using promoter-luciferase constructs. To investigate signal transduction from receptor to effector molecules, blocking experiments using neutralizing antibodies and knockdown experiments were performed. Following TNF-α stimulation, MACC1 and c-Jun expression were significantly increased at the mRNA and protein level. Knockdown of c-Jun reduced MACC1 inducibility following TNF-α stimulation. TNF-α promoted MACC1-induced cell migration that was reverted following MACC1 knockdown. Moreover, MACC1 and c-Jun expression were downregulated by blocking TNFR1, but not TNFR2. Knock down of the NF-κB subunit, p65, reduced basal MACC1 and c-Jun mRNA expression levels. Adalimumab, a clinically approved monoclonal anti-TNF-α antibody, hindered MACC1 induction. The present study highlights that TNF-α regulates the induction of MACC1 via the NF-κB subunit p65 and the transcription factor c-Jun in CRC cells. This finding unravels a novel signaling pathway upstream of MACC1 and provides a potential therapeutic target for the treatment of CRC patients with an associated inflammation.

Project description:In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA) respond insufficiently to TNF-α inhibitors (TNFis). The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients' response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124). The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI). The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28), erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP). Out of 139 metabolites, the best-performing predictors were sn1-LPC(18:3-ω3/ω6), sn1-LPC(15:0), ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01). The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23) and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05). Our study established an accurate prediction model for response to TNFi therapy, containing metabolites and clinical parameters. Associations between metabolites and disease activity may help elucidate additional pathologic mechanisms behind RA.

Project description:The objective of this study was to identify pre-treatment colonic gene expression patterns in patients with inflammatory bowel disease (Ulcerative Colitis and colonic Crohn’s disease) that are predictive of a good versus poor response to treatment with the bioloic anti-TNFa. By identifying key pathways that are altered in responders versus non-responders we also aimed to propose novel targets for therapy. Pre-treatment colonic endoscopic biopsies were collected in RNAlater from 22 patients (14 CD and 8 UC) who were starting anti-TNFa therapy. RNA was extracted using RNeasy columns (Qiagen) and quantified by Qubit (Life Technologies). 0.5ng RNA was used to prepare uniquely indexed cDNA QIAseq UPX 3’ Transcriptome libraries according to manufacturer’s instructions (Qiagen). Libraries were quantified and quality-controlled using the QIAseq Library Quant Assay Kit and tapestation analysis and sequenced on the Miseq and Nextseq Illumina platforms using the illumina sequencing primer and PhiX 15% to a depth of 1-3milion reads/sample. Read numbers were set as 101 bp (R1) x 51 bp (R2). Fastq files were obtained through BaseSpace and reads de-multiplexed, aligned to human genome GRCh38, quantified and normalised by the TPM method using the CLC Genomics Workbench (Qiagen). The following differential expression procedure was performed for participants classified as responders, partial responders or non-responders according to their endoscopic presentation 12 weeks after therapy. For UC patients the Mayo score was used: Responders Mayo= 0, Partial responders 1<= Mayo <3 and Non Responders Mayo >=3. For CD patients % reduction in the SES-CD score was used: Responders >=50% of SES-CD, Partial responders 50%<SES-CD<75%, Non responders SES-CD > 75%. Normalised values were log-transformed with a pseudocount of 1 added. Transcripts with < 1 transformed count in any sample were excluded from further analysis, as were transcripts with low variance (defined as less than 10% unique counts across both conditions and greater than a 19:1 ratio of the most frequent count to the second most frequent across both conditions). Differential expression analysis between conditions was conducted with the limma package. Library size was estimated using reduced maximum likelihood estimator with 500 iterations. Initial fitting was performed using a robust M-estimation, and moderated test statistics computed by empirical Bayes. A FDR corrected p value <0.05 was considered and filtered for further downstream data analysis. Partial least squares discriminant analysis (PLS-DA) modelling was performed on these genes to calculate their variable Importance in Projection (VIP) score. To understand the biological significance and pathways represented in the DEGS, we also performed enrichment analysis using the DAVID Gene Functional Classification Tool (REF I,II). Finally, strongest indicators of response were predicted by Random Forest Area Under the Curve (AUC) analysis.

Dataset Information

Accelerated induction regimens of TNF-alpha inhibitors in patients with inflammatory bowel disease: a scoping review protocol.

Publications

Accelerated induction regimens of TNF-alpha inhibitors in patients with inflammatory bowel disease: a scoping review protocol.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets