Novel Genetic Risk Variants Can Predict Anti-TNF Agent Response in Patients With Inflammatory Bowel Disease

Genetic factors that predict response and failure of biologic therapy in inflammatory bowel disease

Inflammatory bowel disease (IBD) represents a significant disease burden marked by chronic inflammation and complications that adversely affect patients’ quality of life. Effective diagnostic strategies involve clinical assessments, endoscopic evaluations, imaging studies, and biomarker testing, where early diagnosis is essential for effective management and prevention of long-term complications, highlighting the need for continual advancements in diagnostic methods. The intricate interplay between genetic factors and the outcomes of biological therapy is of critical importance. Unraveling the genetic determinants that influence responses and failures to biological therapy holds significant promise for optimizing treatment strategies for patients with IBD on biologics. Through an in-depth examination of current literature, this review article synthesizes critical genetic markers associated with therapeutic efficacy and resistance in IBD. Understanding these genetic actors paves the way for personalized approaches, informing clinicians on predicting, tailoring, and enhancing the effectiveness of biological therapies for improved outcomes in patients with IBD.

Pharmacogenetics in Response to Biological Agents in Inflammatory Bowel Disease: A Systematic Review

Inflammatory bowel diseases (IBDs) are chronic inflammatory disorders influenced by microbial, environmental, genetic, and immune factors. The introduction of biological agents has transformed IBD therapy, improving symptoms, reducing complications, and enhancing patients' quality of life. However, approximately 30% of patients exhibit primary non-response, and 50% experience a loss of response over time. Genetic and non-genetic factors contribute to variability in treatment outcomes. This systematic review aims to thoroughly analyze and assess existing studies exploring the relationships between genetic variations and individual responses to biologic drugs, in order to identify genetic markers that are predictive of treatment efficacy, risk of adverse effects, or drug toxicity, thereby informing clinical practice and guiding future research. PubMed and EMBASE papers were reviewed by three independent reviewers according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses [PRISMA] guidelines. Of the 883 records screened, 99 met the inclusion criteria. The findings of this review represent an initial step toward personalized medicine in IBD, with the potential to improve clinical outcomes in biological therapy.

Defining immune cell phenotypes that distinguish treatment responders and non-responders in RA

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TL1A: A model for a precision medicine approach in the treatment of Crohn's disease and ulcerative colitis

Inflammatory bowel disease (IBD) is a collective term for chronic inflammatory diseases of the intestinal tract. The term IBD encompasses two main forms, Crohn's disease (CD) and Ulcerative colitis (UC). CD is characterized by inflammation throughout the length of the gut, especially the ileum and colon, and is often complicated with fistulae and/or intestinal strictures. Ulcerative colitis (UC) is inflammatory disease restricted to the colon and rectum. In practice however, IBD is a heterogenous disease with CD and UC representing the extremes of a continuum of diseases with varied clinical presentation, including disease location, severity, and manifestation of extraintestinal diseases. This disease heterogeneity poses a challenge to successful and efficacious therapeutic treatment as the etiology driving disease in individual patients is unknown and likely to be multifactorial, including genetic predisposition, environmental factors such as the microbiota, as well as social behaviors such as smoking and diet. Precision medicine provides a strategy to account for disease heterogeneity and diverse etiology to select for patients most likely to respond to a given therapeutic. In this chapter we present an example of the development of a novel antibody therapeutic, Tulisokibart, as a model for a Precision Medicine approach to the successful treatment of patients with IBD.

Developing a Machine-Learning Prediction Model for Infliximab Response in Crohn's Disease: Integrating Clinical Characteristics and Longitudinal Laboratory Trends

BACKGROUND

Achieving long-term clinical remission in Crohn's disease (CD) with antitumor necrosis factor α (anti-TNF-α) agents remains challenging.

AIMS

This study aims to establish a prediction model based on patients' clinical characteristics using a machine-learning approach to predict the long-term efficacy of infliximab (IFX).

METHODS

Three cohorts comprising 746 patients with CD were included from 3 inflammatory bowel disease (IBD) centers between June 2013 and January 2022. Clinical records were collected from baseline, 14-, 30-, and 52-week post-IFX treatment. Three machine-learning approaches were employed to develop predictive models based on 23 baseline predictors. The SHapley Additive exPlanations (SHAP) algorithm was used to dissect underlying predictors, and latent class mixed model (LCMM) was applied for trajectory analysis of the longitudinal change of blood routine tests along with long-term IFX therapy.

RESULTS

The XGBoost model exhibited the best discrimination between long-term responders and nonresponders. In the internal training and testing set, the model achieved an AUC of 0.91 (95% CI, 0.86-0.95) and 0.71 (95% CI, 0.66-0.87), respectively. Moreover, it achieved a moderate predictive performance in the independent external cohort, with an AUC of 0.68 (95% CI, 0.59-0.77). The SHAP algorithm revealed disease-relevant laboratory measurements, notably hemoglobin (HB), white blood cells (WBC), erythrocyte sedimentation rate (ESR), albumin (ALB), and platelets (PLT), alongside age at diagnosis and the Montreal classification, as the most influential predictors. Furthermore, 2 distinct patient clusters based on dynamic laboratory tests were identified for monitoring the long-term remission.

CONCLUSIONS

The established prediction model demonstrated remarkable discriminatory power in distinguishing long-term responders from nonresponders to IFX therapy. The identification of distinct patient clusters further emphasizes the need for tailored therapeutic approaches in CD management.

Genetic Variants Associated with Biological Treatment Response in Inflammatory Bowel Disease: A Systematic Review

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the digestive tract usually characterized by diarrhea, rectal bleeding, and abdominal pain. IBD includes Crohn's disease and ulcerative colitis as the main entities. IBD is a debilitating condition that can lead to life-threatening complications, involving possible malignancy and surgery. The available therapies aim to achieve long-term remission and prevent disease progression. Biologics are bioengineered therapeutic drugs that mainly target proteins. Although they have revolutionized the treatment of IBD, their potential therapeutic benefits are limited due to large interindividual variability in clinical response in terms of efficacy and toxicity, resulting in high rates of long-term therapeutic failure. It is therefore important to find biomarkers that provide tailor-made treatment strategies that allow for patient stratification to maximize treatment benefits and minimize adverse events. Pharmacogenetics has the potential to optimize biologics selection in IBD by identifying genetic variants, specifically single nucleotide polymorphisms (SNPs), which are the underlying factors associated with an individual's drug response. This review analyzes the current knowledge of genetic variants associated with biological agent response (infliximab, adalimumab, ustekinumab, and vedolizumab) in IBD. An online literature search in various databases was conducted. After applying the inclusion and exclusion criteria, 28 reports from the 1685 results were employed for the review. The most significant SNPs potentially useful as predictive biomarkers of treatment response are linked to immunity, cytokine production, and immunorecognition.

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.

Novel Genetic Variant Predicts Surgical Recurrence Risk in Crohn's Disease Patients

BACKGROUND

We aimed to identify a model of clinical and genetic risk factors through hypothesis-free search across genome that can predict the surgical recurrence risk after the first abdominal surgery in CD patients.

MATERIALS AND METHODS

Two independent inflammatory bowel disease (IBD) cohort studies were used to derive and validate the genetic risk profile. The study subjects were genotyped using Illumina Immunochip custom genotyping array. Surgical recurrence was defined as having the second or more abdominal bowel resections after the first abdominal surgery at the time of study enrollment; nonsurgical recurrence was defined as having no further abdominal resection after the first abdominal surgery.

RESULTS

Among 372 CD patients who had at least 1 abdominal surgery at the study enrollment, 132 (35.5%) had subsequent surgical recurrence after their first abdominal surgery, and 240 (64.5%) required no subsequent abdominal surgery at the end of follow up. Among clinical factors, multivariable analysis showed that history of immunomodulatory use (odds ratio [OR], 3.96; P = 0.002) and early era of CD first surgery (OR, 1.12; P = 1.01E-04) remained significant. Genotypic association tests identified a genome-wide significant locus rs2060886 in TCF4 at chr18q21.2 associated with surgical recurrence risk (OR, dom, 4.10 [2.37-7.11]; P = 4.58E-08).

CONCLUSIONS

Novel genetic locus rs2060886 in TCF4 was associated with surgical recurrence risk at genome-wide significance level among CD patients after their first abdominal surgery. Early era of CD first intestinal surgery predicts higher surgical recurrence risk. These results suggest that genetic variants may help guide the CD management strategy in patients at the highest risk of repeated abdominal surgeries.

Novel approaches to develop biomarkers predicting treatment responses to TNF-blockers

Introduction: Chronic inflammatory diseases (CIDs) cause significant morbidity and are a considerable burden for the patients in terms of pain, impaired function, and diminished quality of life. Important progress in CID treatment has been obtained with biological therapies, such as tumor-necrosis-factor blockers. However, more than a third of the patients fail to respond to these inhibitors and are exposed to the side effects of treatment, without the benefits. Therefore, there is a strong interest in developing tools to predict response of patients to biologics. Areas covered: The authors searched PubMed for recent studies on biomarkers for disease assessment and prediction of therapeutic responses, focusing on the effect of TNF blockers on immune responses in spondyloarthritis (SpA), and other CID, in particular rheumatoid arthritis and inflammatory bowel disease. Conclusions will be drawn about the possible development of predictive biomarkers for response to treatment. Expert opinion: No validated biomarker is currently available to predict treatment response in CID. New insight could be generated through the development of new bioinformatic modeling approaches to combine multidimensional biomarkers that explain the different genetic, immunological and environmental determinants of therapeutic responses.

Evaluation of anti-TNF therapeutic response in patients with inflammatory bowel disease: Current and novel biomarkers

Neutralizing tumour necrosis factor (TNF) antibodies have been widely used to treat inflammatory bowel disease (IBD) in the clinical practice. In this review, the principal biomarker analysis revealed that faecal calprotectin, C-reactive protein, serum or mucosal concentrations of anti-TNF monoclonal antibodies (mAbs) and antibodies to anti-TNF mAbs are commonly used as current biomarkers in the evaluation of anti-TNF therapeutic efficacy. However, mucosal cytokine transcripts. microRNAs, proteomics and faecal and mucosal gut microbiota profile and mucosal histological features are reported to be novel candidates of biomarkers with high clinical utility in the evaluation of anti-TNF therapeutic efficacy in patients with IBD. Therefore, a robust validation of novel promising biomarkers and comparison studies between current used and novel biomarkers are urgently required to improve their value in the evaluation of therapeutic efficacy and optimization of personalized medicine and identification of IBD candidates for anti-TNF therapy in future clinical practice.

Predictors and Early Markers of Response to Biological Therapies in Inflammatory Bowel Diseases

Inflammatory bowel diseases (IBD) are chronic conditions that primarily affect the gastrointestinal tract, with a complex pathogenesis; they are characterized by a significant heterogeneity of clinical presentations and of inflammatory pathways that sustain intestinal damage. After the introduction of the first biological therapies, the pipeline of therapies for IBD has been constantly expanding, and a significant number of new molecules is expected in the next few years. Evidence from clinical trials and real-life experiences has taught us that up to 40% of patients do not respond to a specific drug. Unfortunately, to date, clinicians lack a valid tool that can predict each patient’s response to therapies and that could help them in choosing what drug to administer. Several candidate biomarkers have been investigated so far, with conflicting results: clinical, genetic, immunological, pharmacokinetic and microbial markers have been tested, but no ideal marker has been identified so far. Based on recent evidence, multiparametric models seemingly hold the greatest potential for predicting response to therapy. In this narrative review, we aim to summarize the current knowledge on predictors and early markers of response to biological therapies in IBD.

Personalized Medicine of Monoclonal Antibodies in Inflammatory Bowel Disease: Pharmacogenetics, Therapeutic Drug Monitoring, and Beyond

The pharmacotherapy of inflammatory bowel diseases (Crohn’s disease and ulcerative colitis) has experienced significant progress with the advent of monoclonal antibodies (mABs). As therapeutic proteins, mABs display peculiar pharmacokinetic characteristics that differentiate them from chemical drugs, such as aminosalicylates, antimetabolites (i.e., azathioprine, 6-mercaptopurine, and methotrexate), and immunosuppressants (corticosteroids and cyclosporine). However, clinical trials have demonstrated that biologic agents may suffer from a pharmacokinetic variability that could influence the desired clinical outcome, beyond primary resistance phenomena. Therefore, therapeutic drug monitoring (TDM) protocols have been elaborated and applied to adaptation drug doses according to the desired plasma concentrations of mABs. This activity is aimed at maximizing the beneficial effects of mABs while sparing patients from toxicities. However, some aspects of TDM are still under discussion, including time-changing therapeutic ranges, proactive and reactive approaches, the performance and availability of instrumental platforms, the widely varying individual characteristics of patients, the severity of the disease, and the coadministration of immunomodulatory drugs. Facing these issues, personalized medicine in IBD may benefit from a combined approach, made by TDM protocols and pharmacogenetic analyses in a timeline that necessarily considers the frailty of patients, the chronic administration of drugs, and the possible worsening of the disease. Therefore, the present review presents and discusses the activities of TDM protocols using mABs in light of the most recent results, with special attention on the integration of other actions aimed at exploiting the most effective and safe therapeutic effects of drugs prescribed in IBD patients.

Personalizing Treatment in IBD: Hype or Reality in 2020? Can We Predict Response to Anti-TNF?

The advent of anti-TNF agents as the first approved targeted therapy in the treatment of inflammatory bowel disease (IBD) patients has made a major impact on our existing therapeutic algorithms. They have not only been approved for induction and maintenance treatment in IBD patients, but have also enabled us to define and achieve novel therapeutic outcomes, such as combination of clinical symptom control and endoscopic remission, as well as mucosal healing. Nevertheless, approximately one third of treated patients do not respond to initiated anti-TNF therapy and these treatments are associated with sometimes severe systemic side-effects. There is therefore the currently unmet clinical need do establish predictive markers of response to identify the subgroup of IBD patients, that have a heightened probability of response. There have so far been approaches from different fields of IBD research, to descry markers that would empower us to apply TNF-inhibitors in a more rational manner. These markers encompass findings from disease-related and clinical factors, pharmacokinetics, biochemical markers, blood and stool derived parameters, pharmacogenomics, microbial species, metabolic compounds, and mucosal factors. Furthermore, changes in the intestinal immune cell composition in response to therapeutic pressure of anti-TNF treatment have recently been implicated in the process of molecular resistance to these drugs. Insights into factors that determine resistance to anti-TNF therapy give reasonable hope, that a more targeted approach can then be utilized in these non-responders. Here, IL-23 could be identified as one of the key factors determining resistance to TNF-inhibitors. Growing insights into the molecular mechanism of action of TNF-inhibitors might also enable us to derive critical molecular markers that not only mediate the clinical effects of anti-TNF therapy, but which level of expression might also correlate with its therapeutic efficacy. In this narrative review, we present an overview of currently identified possible predictive markers for successful anti-TNF therapy and discuss identified molecular pathways that drive resistance to these substances. We will also point out the necessity and difficulty of developing and validating a diagnostic marker concerning clinically relevant outcome parameters, before they can finally enter daily clinical practice and enable a more personalized therapeutic approach.

ZNF133 is associated with infliximab responsiveness in patients with inflammatory bowel diseases

BACKGROUND AND AIM

Infliximab has been widely prescribed for treating inflammatory bowel disease (IBD). However, the response rates to infliximab differ among patients. Therefore, we aimed to identify the genetic and clinical markers that predict infliximab response.

METHODS

A total of 139 Korean patients with IBD who received infliximab were classified according to infliximab response as follows: (i) primary response vs nonresponse and (ii) sustained response vs loss of response. We performed an association study using whole-exome sequencing data to identify genetic variants associated with infliximab response. Candidate variants were validated in 77 German patients with IBD. Stepwise multivariate logistic regression was performed to identify predictors.

RESULTS

We found five candidate variants that were associated with primary nonresponse to infliximab (P < 5 × 10^-6 ). Of the five variants, rs2228273 in ZNF133 was validated in German (combined P = 6.49 × 10^-7 ). We also identified the best genetic variant (rs9144, P = 4.60 × 10^-6 ) associated with the loss of infliximab response. In multivariate regression analysis, rs2228273 (P = 2.10 × 10^-5 ), concurrent azathioprine/6-mercaptopurine use, and bodyweight at the first infliximab use (< 50 kg) were associated with primary nonresponse. In addition, the Crohn's disease activity index at the first infliximab use and rs9144 (P = 0.001) were independently associated with the loss of response in patients with Crohn's disease.

CONCLUSIONS

We identified clinical and genetic markers associated with infliximab response in IBD patients. Our findings could provide insights to maximize the efficacy of infliximab therapy in IBD patients.