Timely Monitoring of Inflammation by Fecal Lactoferrin Rapidly Predicts Therapeutic Response in Inflammatory Bowel Disease

Predictive, preventive and personalised approach as a conceptual and technological innovation in primary and secondary care of inflammatory bowel disease benefiting affected individuals and populations

Inflammatory bowel disease (IBD) is a global health burden which carries lifelong morbidity affecting all age groups in populations with the disease-specific peak of the age groups ranging between 15 and 35 years, which are of great economic importance for the society. An accelerating incidence of IBD is reported for newly industrialised countries, whereas stabilising incidence but increasing prevalence is typical for countries with a Westernised lifestyle, such as the European area and the USA. Although the aetiology of IBD is largely unknown, the interplay between the genetic, environmental, immunological, and microbial components is decisive for the disease manifestation, course, severity and individual outcomes. Contextually, the creation of an individualised patient profile is crucial for the cost-effective disease management in primary and secondary care of IBD. The proposed pathomechanisms include intestinal pathoflora and dysbiosis, chronic inflammation and mitochondrial impairments, amongst others, which collectively may reveal individual molecular signatures defining IBD subtypes and leading to clinical phenotypes, patient stratification and cost-effective protection against health-to-disease transition and treatments tailored to individualised patient profiles-all the pillars of an advanced 3PM approach. The paradigm change from reactive medical services to predictive diagnostics, cost-effective targeted prevention and treatments tailored to individualised patient profiles in overall IBD management holds a promise to meet patient needs in primary and secondary care, to increase the life-quality of affected individuals and to improve health economy in the area of IBD management. This article analyses current achievements and provides the roadmap for future developments in the area in the context of 3P medicine benefiting society at large.

Predictors and optimal management of tumor necrosis factor antagonist nonresponse in inflammatory bowel disease: A literature review

Tumor necrosis factor-α (TNF-α) antagonists, the first biologics approved for treating patients with inflammatory bowel disease (IBD), are effective for the induction and maintenance of remission and significantly improving prognosis. However, up to one-third of treated patients show primary nonresponse (PNR) to anti-TNF-α therapies, and 23%-50% of IBD patients experience loss of response (LOR) to these biologics during subsequent treatment. There is still no recognized predictor for evaluating the efficacy of anti-TNF drugs. This review summarizes the existing predictors of PNR and LOR to anti-TNF in IBD patients. Most predictors remain controversial, and only previous surgical history, disease manifestations, drug concentrations, antidrug antibodies, serum albumin, some biologic markers, and some genetic markers may be potentially predictive. In addition, we also discuss the next steps of treatment for patients with PNR or LOR to TNF antagonists. Therapeutic drug monitoring plays an important role in treatment selection. Dose escalation, combination therapy, switching to a different anti-TNF drug, or switching to a biologic with a different mechanism of action can be selected based on the concentration of the drug and/or antidrug antibodies.

Value of dynamic analysis of fecal lactoferrin for prediction of response to biotherapy in patients with ulcerative colitis

BACKGROUND

Ulcerative colitis (UC) is a common intestinal inflammatory disease. Biological agents are an effective treatment for UC patients, but some patients are still unresponsive to biological therapy. Therefore, it is of great significance to explore the factors affecting the response to biological therapy.

AIM

To analyze the value of dynamic analysis of fecal lactoferrin (FL) in predicting the response to biotherapy in patients with UC.

METHODS

A total of 168 UC patients admitted to our hospital from July 2018 to September 2021 were selected as the research subjects. All patients were treated with adalimumab. After 12 wk of continuous treatment, they were divided into an effective group and an ineffective group according to the response to treatment. The rates of changes in general data, serum biomarkers, and fecal biomarkers were compared between the two groups. Multivariate logistic regression analysis was used to identify the factors related to biotherapy responsiveness, and the area under the curve was calculated by ROC curve analysis.

RESULTS

There were 132 cases in the effective group and 36 in the ineffective group. After 2 wk of treatment, the rates of changes of CRP, ESR, and PCT in the effective group were greater than those in the ineffective group. The change rates of FC, FL, M2-PK and MMP-9 were also higher than those of the ineffective group (P < 0.05). Logistic regression analysis showed that ESR, PCT, FC and FL were all predictive factors for the response to biotherapy in patients with UC (odds ratio = 0.943, 0.859, 0.964 and 0.875, respectively, P < 0.05 for all). The area under the curve (0.831) of FL was higher than those of ESR, CRP, PCT, FL, MMP-9 and M2-PK (P < 0.05 for all).

CONCLUSION

ESR, PCT, FC and FL can all predict the therapeutic response to biological therapy in UC patients, among which FL has the highest predictive value.

Fecal Lactoferrin Predicts Primary Nonresponse to Biologic Agents in Inflammatory Bowel Disease

INTRODUCTION

Fecal lactoferrin (FL) is a timely and accurate marker of inflammation in ulcerative colitis (UC) and Crohn's disease (CD). The aim of this study was to verify whether FL can predict primary nonresponse (PNR) to biologic agents during induction.

METHODS

Retrospective outcome review in 27 patients (13 with CD and 14 with UC) tested for baseline FL and retested within a week after the first and second induction doses. Clinical/biochemical outcomes were evaluated at end of induction and at follow-up (3-24 months).

RESULTS

Compared to baseline, changes of the Harvey-Bradshaw (CD) and Partial Mayo Scoring (UC) indices at end of induction separated responders (18/27 or 67%) from nonresponders (9/17 or 33%). In all patients, the initial FL value at induction decreased compared to baseline, continuing to decrease after the following dose in clinical responders while bouncing back in the others. Models targeting the 2 consecutively decreased FL values or the second FL value compared to baseline or the second FL value compared to the first were able to accurately predict response at end of induction. Follow-up assessment confirmed clinical remission in initial responders (with FL values reduced on the average by 94 ± 10% compared to baseline).

CONCLUSIONS

In CD and UC patients during induction with biologic agents, early FL measurements accurately separate clinical responders from those experiencing PNR. The method described here offers several potential advantages over other strategies to assess and manage these patients.

A Practical Guide to Therapeutic Drug Monitoring of Biologic Medications for Inflammatory Bowel Disease

Therapeutic drug monitoring (TDM) is a useful strategy to optimize biologic medications for inflammatory bowel disease not responsive to standard dosing regimens. TDM is cost effective for anti-tumor necrosis factor agents in the setting of loss of response (reactive TDM). Optimizing drug dosing when patients are in remission (proactive TDM) may be beneficial in certain circumstances. However, frequently the serum drug concentration in isolation becomes the focus TDM. Additionally, the lines of reactive and proactive TDM can quickly blur in many common clinical settings. Physicians employing a TDM based strategy need to place the drug concentration in context with the inflammatory status of the patient, the underlying pharmacokinetics and pharmacodynamics of the drug, the risk of immunogenicity, and the therapeutic goals for the patient. Physicians should understand the limits of TDM and feel comfortable making therapeutic decisions with imperfect information. The goal of this narrative review is to provide a framework of questions that physicians can use to employ TDM effectively in practice.