Next Article in Journal
Dimethyl Fumarate Suppresses Demyelination and Axonal Loss through Reduction in Pro-Inflammatory Macrophage-Induced Reactive Astrocytes and Complement C3 Deposition
Next Article in Special Issue
Cutaneous Manifestations in Biological-Treated Inflammatory Bowel Disease Patients: A Narrative Review
Previous Article in Journal
Judo Injuries Frequency in Europe’s Top-Level Competitions in the Period 2005–2020
Previous Article in Special Issue
Exploring the Relationship between Biologics and Postoperative Surgical Morbidity in Ulcerative Colitis: A Review
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

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

Giuseppe Privitera
Daniela Pugliese
Gian Ludovico Rapaccini
Antonio Gasbarrini
Alessandro Armuzzi
1,2 and
Luisa Guidi
Dipartimento Universitario di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, 00168 Roma, Italy
CEMAD—IBD UNIT—Dipartimento di Scienze Mediche e Chirurgiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy
Author to whom correspondence should be addressed.
J. Clin. Med. 2021, 10(4), 853;
Submission received: 8 January 2021 / Revised: 10 February 2021 / Accepted: 11 February 2021 / Published: 19 February 2021


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.

1. Introduction

Crohn’s disease (CD) and ulcerative colitis (UC) represent the two main forms of inflammatory bowel diseases (IBD). These are chronic conditions with a relapsing-remitting course that affect more than 5 million people worldwide, mostly in Western countries; their epidemiology is rapidly changing, with a sharp increase in incidence in Western countries registered in the previous decades [1]. IBD carries a significant direct and indirect health-care burden, which is mainly represented by drugs like biologics and small molecules, but also includes hospitalization-related costs and days of absence from work due to the disease [2]. In this scenario, there is an urgent need for a more effective approach than today’s trial and error method, when it comes to starting therapies.
To date, IBD treatment is based on corticosteroids (for the acute phases), mesalamine (only for UC), traditional immunosuppressants and targeted therapies; this last category includes: anti-tumor necrosis factor α (TNFα), anti-integrin, anti-interleukin (IL) 12/23 and Janus kinases (JAK) inhibitors [3,4,5]. It has been shown that different pathogenic pathways can sustain bowel damage in IBD [6,7], so that two patients with similar clinical phenotypes can have different inflammatory pathways activated and, thus, respond to different targeted therapies. There is also evidence that, within the same patient, the immune system can exhibit a significant plasticity and change the inflammatory pathways that are activated during the course of disease [8]. Such a complexity can easily explain why current therapies are only of limited efficacy. While the armamentarium for IBD treatment is constantly expanding—with new drugs targeting different pathogenetic pathways—there is still a significant proportion of patients who do not respond to therapy: data from clinical trials and real life report clinical efficacy of a single drug in up to 60% of patients [9,10]. Whether these patients would respond to another agent is not possible to foretell, but there is strong evidence that second- and third-line agents can be effective even in the case of primary failure, although to a lesser extent [11]. Furthermore, a substantial percentage of patients experience secondary failures [12]: in these cases, unless surgery is made mandatory by disease complications, the usual choice is to try another medical treatment.
Taken together, these considerations point to the necessity for the development of new prognostic tools able to identify those patients who would benefit from an early introduction of advanced therapies, to predict their response to a specific therapy and to assess response at an early point in treatment. Some clinical features have been identified so far, but they mainly identify patients who would most benefit from immunosuppressive therapies and/or patients who are more likely to respond to medical treatment, while they do not offer much information about patients’ response to a specific drug. Implementation of personalized medicine into IBD routine management represents one of the most compelling challenges of coming years, in order to provide patients with better clinical care in parallel with a reduction of costs for the health-care systems.
In this narrative review, we aim to summarize current knowledge regarding predictors and early biomarkers of response to biological therapies in IBD patients (Figure 1).

2. Traditional Markers

Associations of patients’ and disease characteristics with response to therapy have been widely investigated, but results have been generally discouraging. Age, gender, weight and smoking status have not been confirmed to correlate with response to anti-TNF or other targeted agents [13]. Two recent meta-analysis suggested that early treatment of CD is associated with better response rates [14,15], and one of those also observed an association with higher rates of mucosal healing [15]; however, no differences between different drugs have been observed so far. In CD patients, disease location has not been found to be associated with treatment outcomes of anti-TNF and ustekinumab [13]; of note, one study reported an association between colonic localization and better responses to vedolizumab [16], that was not confirmed in others. Some studies have reported a correlation, in CD patients, between inflammatory phenotype and better response rates with TNF antagonists, compared to stricturing or penetrating diseases [17,18,19,20]; such findings have not been confirmed for vedolizumab and ustekinumab.
Various serological biomarkers have been investigated for their potential predictive role. In a recent retrospective study on elderly (>60 years old) patients, a higher serum triiodothyronine-to-thyroxine (T3/T4) ratio was found to be associated to mucosal healing, regardless of the biological drug used [21]. C-reactive protein (CRP) levels haves been inconsistently associated with response to biological therapies. A correlation between higher CRP levels and better response to TNF antagonists has been reported in CD patients [22,23,24,25]; conversely, a negative correlation between CRP levels and response rates to TNF inhibitors has been observed in UC [26,27,28]. Similar findings have also been observed with vedolizumab, but not with ustekinumab [13]. However, such results have not been confirmed by many other studies. Higher CRP levels can help identify those patients whose symptoms are actually dependent on active IBD and, in CD, can help discriminate inflammatory vs. stricturing phenotypes; on the other hand, such high levels can also be expression of a higher inflammatory burden, that is comprehensively associated with poorer response to medical treatment. Faecal calprotectin has been tested as a potential predictor of response, with disappointing results. Of note, in a prospective observational study it has been found that lower post-induction calprotectin levels were able to predict sustained clinical response and mucosal healing in IBD patients receiving anti-TNF treatment [29].
Previous exposure to biologics has been associated with poorer response to subsequent lines of therapies. Reason for discontinuation seems to have an impact on the likelihood of responding to second-line therapies: a Spanish retrospective study and 2 meta-analysis concluded that discontinuation due to anti-TNF intolerance was associated with higher rates of response to both second anti-TNF or other biologic agents [11,30,31]. Primary non-response to TNF antagonists seems to correlate with an even lower likelihood of response to second-line therapies, when compared to secondary loss of response (LOR) [30]. A retrospective study on UC patients showed that, in case of primary failure, out-of-class swap seems to be superior to in-class switch with regard to rates of clinical response and remission [32]. However, such findings have not been consistently confirmed in literature.

3. Genetic Markers

More than 240 susceptibility loci for IBD have been identified so far [33]. Such genes have greatly contributed to the understanding of IBD pathogenesis and to the identification of novel therapeutic targets. However, genetic markers have usually performed quite poorly in predicting response to a specific drug [34]. In Table 1, there is an overview of the studies investigating the association between genetic markers and response to therapy.
Genome-wide association studies have reported that disease susceptibility loci do not seem to substantially contribute to anti-TNF non-response. For instance, polymorphisms in the genes encoding for TNF or molecules involved in the TNF receptor pathway have been inconsistently associated with treatment response. A 2013 meta-analysis reported an association between 2 polymorphisms in the TNF promoter and responsiveness to TNF inhibition in IBD patients: specifically, the more common alleles were associated with better response rates [35]. Another meta-analysis found a positive correlation between polymorphisms in FCGR3A, TLR4, TNFRSF1A, IFNG, IL6, IL1B genes and better clinical response, whereas variants of TLR2 and TLR9 were negatively correlated [34]. Polymorphism in the Nuclear Factor kappa-light-chain-enhancer of activated B cells (NFkB) pathway, TNF pathway and pathways of other cytokines have been linked to treatment response in IBD patients treated with anti-TNF agents [36,49]. IL23 receptor polymorphisms have been associated with response to infliximab in UC patients [37]. Moreover, the HLA-DQA1*05, the HLA-DRB1 allele and polymorphisms at the FCGR3A locus (encoding IgG Fc receptor IIIa) have been correlated with an increased risk of anti-drug antibodies (ADA) formation in CD patients treated with anti-TNF agents [38,39,40]. Being large complex proteins, monoclonal antibodies—especially infliximab, that is a chimeric antibody—can stimulate the production of ADA, which are associated with treatment inefficacy [50]. Identifying patients who are more likely to develop ADA would be of great help, as we know that concomitant immunosuppression (with thiopurines and methotrexate) reduces the risk of their formation [51].
Another marker previously identified by gene array studies in mucosal biopsies of IBD patients is the IL13 receptor alpha 2 (IL13RA2) [52]. This biomarker has been more recently evaluated as mRNA expression in the mucosa of IBD patients prior to therapy and found to be specifically predictive of non-response to anti-TNF in terms of mucosal healing at 6 months with an area under the receiver operating characteristic (AUROC)of 0.90 for infliximab and 0.94 for adalimumab, p < 0.001 [53].
The NOD2 gene is associated with CD susceptibility and with a more aggressive course of disease [54,55]; it encodes for a protein that plays a role in eliciting the immune response and is implicated in the inflammatory pathway of TNF. Some studies have found an association between NOD2 variants and worse response to anti-TNF therapy [41,42,43]. Polymorphisms in the ATG16L1 gene have been associated better response rates [44] and longer benefit [46] in CD patients treated with TNF antagonists. An apoptotic pharmacogenetic index (API) has been proposed to predict treatment response to anti-TNF in CD patients. The index was based on single nucleotide polymorphisms of 3 apoptotic genes (Fas, Fas-ligand and caspase-9). The authors elaborated a model, combining API with clinical features, that was able to predict response to infliximab in luminal and penetrating CD [45]. Predictive models combining clinical and genetic features have been shown to be superior to models based on clinical characteristics only for predicting primary non-response to anti-TNF agents in CD (Area Under the Receiver Operating Characteristics [AUROC] 0.93 vs. 0.70, p < 0.0001) [46], UC (AUROC 0.87 vs. 0.57, p < 0.0001) vs. [47] and IBD patients (AUROC 0.89 vs. 0.72, p < 0.0001) [48].
Data on genetic variants associated with response to anti-integrin is scarce. In the phase 2 trial of etrolizumab (anti-β7 integrin) in UC patients, αE gene expression was found to be predictive of clinical remission at week 10 [56]. This result was subsequently enhanced by the finding that also higher levels of Granzyme A (which is highly expressed by αE+ cells) mRNA in colonic biopsies taken at baseline could identify UC patients in clinical remission at week 10 of etrolizumab therapy [57]. No association between genetic markers and anti-IL12/23 response has been identified so far.

4. Other Markers: Transcriptomics, Proteomics and Immunological Markers

Transcriptomics studies have provided some more pleasing results, suggesting that therapy response seems to rely on differential expression of significant genes, more than on genetic variants.
A putative biomarker identified by transcriptomics studies is the triggering receptor expressed on myeloid cells 1 (TREM-1), although with some discordant findings: a study of gene expression described a down-regulation in whole blood of non-responders to anti-TNF. However, these patients showed an up-regulation of TREM-1 and of chemokine receptor type 2 (CCR2)–chemokine ligand 7 (CCL7) in intestinal biopsies. In the same study, plasma cell frequencies were examined in intestinal biopsies by CD138+ staining and were considered able to predict anti-TNF response, being higher in non-responders (p = 0.0005). [58] A different study analyzed the expression of TREM1 in whole blood and in mucosal tissue and as protein level in the serum and found a significant reduction in IBD patients who achieved mucosal healing [59]. This pathway seems to be specific for anti-TNF response as no modifications was detected in patients treated with vedolizumab or ustekinumab.
Measures of TNF production have been studied as putative biomarkers of response to anti-TNF. In vivo imaging by endomicroscopy revealed higher numbers of mTNF+ cells in short-term (12 weeks) responders, after local fluorescent adalimumab administration [60]. A recent study analyzed in 42 IBD patients the in vitro production of TNF from peripheral blood mononuclear cells (PBMC) stimulated with lipopolysaccharide (LPS) and found it to be predictive of clinical response after 6 weeks of infliximab therapy. A cutoff of 500 pg/mL was identified in CD for short-term response with 100% sensitivity and 82% specificity [61].
High expression of a member of the IL6 family, oncostatin M (OSM), in the intestinal mucosa was found to predictive of refractoriness to anti-TNF therapy. The clinical response was assessed at week 8 and 30 in a cohort of patients treated with infliximab (from ACT1/2 studies) and at 6 weeks in a cohort of patients treated with golimumab (from the Program of Ulcerative Colitis Research Studies Utilizing an Investigational Treatment−Subcutaneous, PURSUIT study) [62]. This biomarker was also studied at baseline in serum and found to be predictive of mucosal healing at 54 weeks of infliximab treatment with an AUC of 0.91 in a study on 45 CD patients [63].
Assays of α4β7 occupancy in peripheral blood T cells showed almost complete blocking of this integrin in patients after vedolizumab treatment, irrespective of clinical response and also of circulating drug levels [64]. However, in a small study, vedolizumab responders had higher pre-treatment α4β7 expression on T effector memory cells (p = 0.0009 for CD4 and.0043 for CD8) and on natural killer (NK) cells (p = 0.0047) [65]. These results were partly confirmed at the tissue level by a preliminary study with confocal endomicroscopy with fluorescein isothiocyanate (FITC) labelled vedolizumab, where only CD patients were responders to subsequent therapy with the anti-integrin showed α4β7+ cells in the mucosa [66]. A study examined the in vitro assay of baseline peripheral blood CD4+ cells dynamic adhesion to recombinant MAdCAM-1 and the decrease of this effect by vedolizumab: these parameters have been suggested as predictors of clinical response at 15 weeks in a study on 21 UC patients [67]. Another small study explored serum markers of response in anti-TNF refractory patients, before starting vedolizumab. They found higher levels of IL6 in IBD patients who were subsequently non-responder, of sCD40L in CD non-responder patients and higher osteocalcin levels in UC responders [68]. More recently serum IL6 and IL8 measured at baseline and at week 10 of vedolizumab treatment were suggested as early markers of clinical response at 12 months [69]. The prognostic value of serum biomarkers measured at baseline and at early intervals during vedolizumab therapy was also explored in two studies. In UC, an increase of s-α4β7 an a decrease of s-MAdCAM-1, s-VCAM-1, s-ICAM-1, and s-TNF were found in clinical and endoscopic remitters at 26 weeks [70]. In CD patients, higher early serum levels of s-ICAM-1 and s-VCAM-1 and lower values of s-α4β7 were found in endoscopic remitters [71].
Limited data of transcriptomic and immunologic predictive biomarkers of response to IL23 inhibition are available. IL22 exerts a role of positive regulation on IL23 signalling. In the phase 2a trial of brazikumab (anti-IL 23) in CD patients, higher concentration of IL22 at baseline had an association with increased likelihood of response, even though the association did not test statistically significant [72].
Results from transcriptomic and immunologic studies are resumed in Table 2.

5. Therapeutic Drug Monitoring

Therapeutic drug monitoring (TDM) consists in the dosage of drug trough levels (TLs) and ADA titres. To date, reactive TDM (performed in patients with active intestinal symptoms) is recommended for the management of LOR to anti-TNF agents, in particular with infliximab and adalimumab [73]. Furthermore, it has been shown that a tailored management—based on reactive TDM—in the case of secondary non-response is more cost-effective than symptom-based dose escalation [74,75].
An association between higher TLs and better clinical outcomes has been observed in retrospective studies and post-hoc analysis of RCTs (Table 3) [76,77,78,79,80,81,82,83,84,85,86]. The PANTS study, where anti-TNF naïve patients with luminal CD were enrolled at the time of anti-TNF initiation and prospectively observed until treatment discontinuation, reported that low TLs of infliximab (<7 mg/L) or adalimumab (<12 mg/L) at week 14 correlated with primary non-response at week 14 (odds ratio [OR] 0.35, 95% confidence interval [CI] 0.20–0.62, p = 0.00038 for infliximab; OR 0.13, 95% CI 0.06–0.28, p < 0.0001 for adalimumab) and non-remission at week 54 (OR 0.29, 95% CI 0.16–0.52, p < 0.0001 for infliximab; OR 0.03, 95% CI 0.10–0.12, p < 0.0001 for adalimumab) [87]. Similar associations between TLs and therapeutic outcomes have also been observed for golimumab, both in the post-hoc analysis of the registration trial [88] and in some observational studies [89,90,91,92]. Some studies have suggested that specific disease phenotypes might need higher TLs to be adequately controlled. For instance, it has been proposed that, in patients with perianal fistulizing CD, higher than usual infliximab TLs are required to induce healing [93,94,95]. It has also been suggested that patients with acute severe ulcerative colitis (ASUC) might benefit from an optimized induction regimen (with an additional infusion at week 1), even though results have been inconsistent so far [96,97,98]. Furthermore, there is evidence that therapeutic levels depend also on the target considered: more ambitious targets might require higher TLs [99,100].
In the case of LOR to infliximab or adalimumab, TDM can predict the outcomes of subsequent therapeutic interventions and, therefore, help in choosing the correct management. In a retrospective work, Yanai et al. [115] investigated the outcomes of different interventions in patients experiencing LOR to infliximab or adalimumab, according to TLs and ADA. Patients with low TLs and negative ADA were the most likely to respond to therapy escalation. Out-of-class switch in patients with adequate TLs (>3.8 µg/mL for infliximab and >4.5 µg/mL for adalimumab) was associated with an increased likelihood of early response compared to therapy optimization (OR 7.8; 95% CI, 1.5–42; p = 0.02) and a trend towards longer duration of therapy response (log rank test p = 0.09); conversely, in patients with high titres of antibodies, switching to another anti-TNF was associated with longer duration of response (log rank test p = 0.03), compared to therapy optimization [115]. In a 2018 work, Roblin and colleagues found that pharmacokinetic status upon first anti-TNF failure could help predicting the outcome of second-line anti-TNF treatment: patients with adequate TLs at LOR were significantly less likely to achieve clinical remission at week 30, compared to patients with subtherapeutic or undetectable TLs, regardless of ADA titre (p ≤ 0.05 against each group). Notably, at week 102, patients with undetectable TLs and negative ADA and patients with subtherapeutic TLs showed higher rates of clinical remission, compared to those with therapeutic TLs or undetectable TLs and positive ADA; furthermore, patients with undetectable TLs and positive ADA at LOR were the most likely to develop ADA against the second anti-TNF [116]. These observations are in line with some more recent findings from the same group: in IBD patients switched to a second-line anti-TNF therapy due to immunogenicity, the addition of an immunomodulator seems to prevent ADA formation and to be associated with lower rates of clinical failure [117].
Using TDM proactively—to manage stable patients and optimize therapy based on TDM results—has not been shown to provide significant benefits. The Trough Concentration Adapted Infliximab Treatment (TAXIT) and the randomized controlled trial investigating tailored treatment with infliximab for active luminal Crohn’s disease (TAILORIX) RCTs, testing a proactive TDM approach in IBD patients, both failed to meet their primary endpoint and demonstrate the efficacy of proactive TDM for IBD management [118,119]. Conversely, in a recent RCT on 78 biologic-naïve paediatric CD patients receiving adalimumab, proactive TDM was found to be superior to reactive TDM for maintaining sustained steroid-free clinical remission from week 8 to week 72 (82% vs. 48%, p = 0.002) [120]. Evidence is emerging that highlight an association between TLs during induction phase and treatment efficacy, thus suggesting a potential role of early proactive TDM for the prevention of primary non-response [121].
Data on TDM with vedolizumab and ustekinumab is scarcer. Both agents are have lower immunogenicity compared to anti-TNF [50], and the presence of ADA has not been consistently associated with therapy inefficacy in registration trials [122,123,124,125]. Early vedolizumab TLs (mainly at weeks 6 and 14) have been variously associated with clinical remission [101,102], treatment persistence [103], biochemical remission [103,104,105], endoscopic response [126], mucosal healing [104,105] and even histological healing [106]; due to the heterogeneity of assays used, timepoints assessed and outcomes investigated, different threshold concentrations have been proposed. An association between vedolizumab TLs during maintenance periods and treatment outcomes has been reported in multiple observational studies [102,106,107]. Interestingly, in a retrospective analysis of IBD patients on maintenance therapy with vedolizumab, TLs <7.4 µg/mL, in patients experiencing LOR was associated with a significantly increased likelihood to respond to dose escalation compared to higher concentrations (OR 3.7, 95% CI 1.1–13, p = 0.04) [108]. A similar exposure-efficacy relationship has been observed for ustekinumab: early TLs (week 4 and 8) have been associated with clinical remission [109,110], decrease of faecal calprotectin [111] and biochemical remission [112], while TLs during maintenance have been correlated with clinical remission [109,110], endoscopic response [111,113] and need for optimization [114]. In a recent review, Alsoud et al. [127] proposed target TLs for vedolizumab and ustekinumab. During induction, TLs >20 µg/mL at week 6 and >14 µg/mL at week 14 have been suggested for vedolizumab, whereas for ustekinumab >14 µg/mL and >4 µg/mL at weeks 4 and 8, respectively; during maintenance phase, TLs >12 µg/mL for vedolizumab and >2.5 µg/mL for ustekinumab have been proposed [127].
In conclusion, TDM can prove extremely useful to inform the management of patients with loss of response. In case of secondary failure, objective confirmation of disease activity (preferably with endoscopy, or with biomarkers) should be quickly obtained [73], and endoscopic re-evaluation should be performed within 3-6 months after therapy adjustment [128]. In consideration of the well-established dose-response relationship observed with biologics, loss of response is frequently managed via therapy optimization (i.e.: dose-escalation or interval shortening) [76]. However, pharmacokinetic failure—which is therapy inefficacy due to inadequate TLs—only accounts for some cases of loss of response, and TDM can help to identify those patients who would benefit from optimization. Indeed, TDM is recommended to manage loss of response to anti-TNF agents, in order to discriminate patients who might need higher doses to achieve clinical remission from those who need to change therapy [73,76]. With regard to infliximab, both dose-escalation and interval shortening are considered valid options, and it has been previously demonstrated that either strategy is equally effective: in a retrospective study including 94 CD patients, shortening dose to 6 weeks was found to be as effective as doubling the dose to manage loss of response [129]. TDM for vedolizumab and ustekinumab has not been incorporated in routine clinical practice, yet; however, based on the evidence presented above, it is plausible that reactive TDM could contribute to identify patients who would benefit from vedolizumab or ustekinumab optimization.

6. Gut Microbiota

While it is well established that microbiota alterations play a role in IBD pathogenesis, whether dysbiosis is a cause or rather a consequence of intestinal inflammation is not clear yet. Furthermore, beside alterations in microbiota compositions, it has been increasingly recognized that changes in microbiota metabolic functions have a meaningful impact on IBD pathophysiology [130]. Table 4 summarizes the main findings on the associations between microbiological features and response to therapy.
Different studies reported that microbial diversity at baseline, as well as higher abundance of specific taxa or species, could predict response to anti-TNF treatment [131,139,140]. It has been shown that anti-TNF treatment can alleviate intestinal dysbiosis in IBD patients, with enrichment in species diversity and an increased relative abundance of short-chain fatty acids (SCFAs) producing bacteria [132]. However, such an improvement has not been found to consistently correlate with treatment efficacy. It has also been shown that longitudinal changes in microbiota composition correlate with response to anti-TNF therapy: in particular, an increase in the Firmicutes phylum (most notably, F. prausnitzii) has been observed in responders [131,133,140].
A longitudinal prospective study on a paediatric cohort of IBD patients, who received immunomodulators or biologics, could not find an association between the degree of dysbiosis at baseline and treatment response, measured as mucosal healing after approximately one year; however, the authors observed significant differences, between responders and non-responders, in the relative abundance of specific microbial genera (namely Akkermansia, Coprococcus, Fusobacterium, Veillonella, Faecalibacterium, and Adlercreutzia), and found that baseline microbial data could predict treatment response with a 75.6% accuracy [134].
Zhuang and colleagues found that a prediction model, based on the combination of microbiota data, clinical activity and FCP at baseline, could predict infliximab response at week 30 (AUC = 0.938). In a recent work on biologic-naïve CD patients treated with infliximab, enrichment of Lachnospiraceaea and Blautia, after infliximab initiation, was found to be a predictor of treatment efficacy. In particular, Lachnospiraceaea and Blautia combined increase at week 6 was able to predict clinical remission at week 14 (AUC = 0.924, 95% CI 0.707–0.962; p < 0.0001), as well as clinical remission (area under the curve (AUC) 0.842, 95% CI 0.715–0.968; p < 0.001) and endoscopic response (AUC 0.891, 95% CI 0.789–0.993; p < 0.0001) at week 30 [135]. In a study by Wang et al., a positive correlation between sustained response and increase in SCFAs-producing bacteria was found in infliximab-treated paediatric IBD patients [141].
Aden and colleagues investigated the association between metabolic changes of gut microbiota and anti-TNF treatment in IBD patients. Via an in silico model, they showed that, at baseline, metabolic interchanges between microbes were more reduced in patients not achieving clinical remission; non-remitters were characterized by a significantly reduced number of mutualistic and antagonistic interactions between bacteria (which contribute to enhance the stability of microbial ecosystem) and by an increased number of competitive interactions (which reduce microbiota stability). Furthermore, they were able to identify bacterial stool metabolites that were associated with clinical remission at week 14: butyrate and its substrates were more frequently exchanged in patients achieving responding to anti-TNF therapy [136]. Ding et al. recently presented their results on metabolomics analysis on CD patients treated with anti-TNF agents, showing a distinct metabolic profile of serum, faecal and urinary metabolites that was able to discriminate between responders and non-responders at baseline. In particular, they identified higher levels of primary bile acids and lower levels of secondary and tertiary acids to be associated with non-response to anti-TNF treatment. Since bile acid metabolism is known to be dependent on specific intestinal bacteria, it has been advanced that the aforementioned differences between responders and non-responders might be related to differences in the gut microbiota [137].
In a study investigating the effect of infliximab therapy in IBD patients in clinical remission, it was found that infliximab TLs ≥ 5 µg/mL and MH were associated with increased bacterial diversity, richness and relative abundance of F. prausnitzii in gut microbiota at baseline [138]. Whether intestinal eubiosis should be considered a prognostic marker or might represent a therapeutic target associated with better clinical outcomes is yet to clarified. Ananthakrishnan and colleagues investigated the role of gut microbiome functions in predicting response to vedolizumab in IBD patients. They found that a predictive model incorporating clinical and microbial data (on composition and function) at baseline was able to predict clinical remission at week 14 (AUC 0.872); in particular, they found abundance of butyrate-producing bacteria and enrichment of branched chain amino acid biosynthesis pathways at baseline in remitters. Furthermore, they observed early changes in microbiota composition and functions, especially the reduction in oxidative stress pathways, in patients achieving remission [142]. Doherty et al. investigated gut microbiota in CD patients treated with ustekinumab. A predictive model based on clinical and microbiome data was able to predict response and remission at week 6 with 84.4% and 73.3% accuracy, respectively. They also observed enrichment in microbial diversity during ustekinumab therapy in responders, but not in non-responders [143].

7. Conclusions

The one size fits all paradigm for medical therapies has been strongly rejected by academics; however, we are still far behind in finding valid prognostic tool that can be implemented in real-life clinical practice. So far, the only tool of personalized medicine that has been widely incorporated in IBD clinical practice in regard to biotechnological therapies is represented by TDM for secondary non-response to infliximab and adalimumab.
Patients’ and disease’s markers probably have a role in predicting the course and aggressiveness of IBD in a certain patient, but have not proven sufficient to accurately foretell if a patient will respond to a specific therapy. Genetic and transcriptomic markers seemingly hold the potential to help predicting response to IBD therapies: a significant number of different loci has been investigated so far, but no ideal candidate for prediction of response has been identified. The susceptibility to ADA formation conferred by some genetic polymorphisms could prove extremely useful in real-life clinical practice, as it could help identify patients who might benefit from the association of an immunomodulators. With regards to TDM, whether higher TLs are causatively correlated to better clinical outcomes or are just a marker of decreasing inflammatory burden and treatment efficacy is still unknown. To date, there is not enough evidence to conclude that TLs should be considered a target per se. The immunological landscape of IBD is characterized by remarkable inter- and intra-patient variability and plasticity; thus, a greater comprehension of the immune pathways activated in a specific patient at a specific time-point—and the identification of reliable biomarkers thereof—could substantially contribute in tailoring the treatment for the patient. Finally, microbiological markers are undoubtedly attractive: gut microbiota seems to change in parallel with IBD phases and could, therefore, be regarded as dynamic, non-invasive biomarker that could inform about IBD activity and, potentially, predict response to therapy. Notably, most of recent studies seem to suggest that multiparametric models, incorporating different features, hold the highest predictive power. It is also worth mentioning that most of the findings discussed in this review represent just association, meaning that they need to be cautiously interpreted, as they do not prove causation. Indeed, it is of the utmost importance that predictive markers and models need to be validated in external cohorts, so as to prove their strength.
In conclusion, the implementation of personalized medicine represents one of the most crucial unmet needs in IBD. Given the growing expansion of the IBD population, the significant health-care costs associated with the disease and the expanding pipeline of therapies, future studies will need to look at the development of new prognostic tools that can enable us to choose the right drug, for the right patient, at the right time.

Author Contributions

G.P., D.P. and L.G. designed the project, performed literature research and drafted the manuscript. G.L.R., A.G. and A.A. contributed to literature research and revised the manuscript critically. L.G. oversaw the project and guarantees for the integrity of the work. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were waived for this study, due to the article type (narrative review).

Informed Consent Statement

Patient consent was waived due to the article type (narrative review).

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare the following conflicts of interest: Giuseppe Privitera received consultancy fees from Alphasigma. Daniela Pugliese received speaker fees from AbbVie, Janssen, MSD, Pfizer, Takeda. Antonio Gasbarrini reports personal fees for consultancy for Eisai S.r.l., 3PSolutions, Real Time Meeting, Fondazione Istituto Danone, Sinergie S.r.l. Board MRGE, and Sanofi S.p.A, personal fees for acting as a speaker for Takeda S.p.A, AbbVie, and Sandoz S.p.A, and personal fees for acting on advisory boards for VSL3 and Eisai. Alessandro Armuzzi: consulting and/or advisory board fees from AbbVie, Allergan, Amgen, Arena, Biogen, Bristol-Myers Squibb, Celgene, Celltrion, Ferring, Gilead, Janssen, Lilly, MSD, Mylan, Pfizer, Samsung Bioepis, Sandoz, Takeda; lecture and/or speaker bureau fees from AbbVie, Amgen, Biogen, Ferring, Gilead, Janssen, MSD, Mitsubishi-Tanabe, Nikkiso, Novartis, Pfizer, Sandoz, Samsung Bioepis, Takeda; and research grants from MSD, Pfizer, Takeda. Luisa Guidi: consultancies and/or speaker fees from: AbbVie, Janssen, MSD, Mundipharma, Pfizer, Takeda, Vifor Pharma, Zambon. All the other authors have no conflict of interest to declare.


  1. Kaplan, G.G. The global burden of IBD: From 2015 to 2025. Nat. Rev. Gastroenterol. Hepatol. 2015, 12, 720–727. [Google Scholar] [CrossRef]
  2. Park, K.T.; Ehrlich, O.G.; Allen, J.I.; Meadows, P.; Szigethy, E.M.; Henrichsen, K.; Kim, S.C.; Lawton, R.C.; Murphy, S.M.; Regueiro, M.; et al. The Cost of Inflammatory Bowel Disease: An Initiative from the Crohn’s & Colitis Foundation. Inflamm. Bowel Dis. 2020, 26, 1–10. [Google Scholar] [CrossRef] [Green Version]
  3. Harbord, M.; Eliakim, R.; Bettenworth, D.; Karmiris, K.; Katsanos, K.; Kopylov, U.; Kucharzik, T.; Molnár, T.; Raine, T.; Sebastian, S.; et al. Third European Evidence-based Consensus on Diagnosis and Management of Ulcerative Colitis. Part 2: Current Management. J. Crohn’s Colitis 2017, 11, 769–784. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Torres, J.; Bonovas, S.; Doherty, G.; Kucharzik, T.; Gisbert, J.P.; Raine, T.; Adamina, M.; Armuzzi, A.; Bachmann, O.; Bager, P.; et al. ECCO Guidelines on Therapeutics in Crohn’s Disease: Medical Treatment. J. Crohn’s Colitis 2020, 14, 4–22. [Google Scholar] [CrossRef] [PubMed]
  5. Sandborn, W.J.; Su, C.; Sands, B.E.; D’Haens, G.R.; Vermeire, S.; Schreiber, S.; Danese, S.; Feagan, B.G.; Reinisch, W.; Niezychowski, W.; et al. Tofacitinib as Induction and Maintenance Therapy for Ulcerative Colitis. N. Engl. J. Med. 2017, 376, 1723–1736. [Google Scholar] [CrossRef]
  6. Verdier, J.; Bègue, B.; Cerf-Bensussan, N.; Ruemmele, F.M. Compartmentalized Expression of Th1 and Th17 Cytokines in Pediatric Inflammatory Bowel Diseases. Inflamm. Bowel Dis. 2012, 18, 1260–1266. [Google Scholar] [CrossRef] [PubMed]
  7. Atreya, R.; Neurath, M.F. Mechanisms of molecular resistance and predictors of response to biological therapy in inflammatory bowel disease. Lancet Gastroenterol. Hepatol. 2018, 3, 790–802. [Google Scholar] [CrossRef]
  8. Zorzi, F.; Monteleone, I.; Sarra, M.; Calabrese, E.; Marafini, I.; Cretella, M.; Sedda, S.; Biancone, L.; Pallone, F.; Monteleone, G. Distinct Profiles of Effector Cytokines Mark the Different Phases of Crohn’s Disease. PLoS ONE 2013, 8, e54562. [Google Scholar] [CrossRef]
  9. Singh, S.; Murad, M.H.; Fumery, M.; Dulai, P.S.; Sandborn, W.J. First- and Second-Line Pharmacotherapies for Patients with Moderate to Severely Active Ulcerative Colitis: An Updated Network Meta-Analysis. Clin. Gastroenterol. Hepatol. 2020, 18, 2179–2191.e6. [Google Scholar] [CrossRef]
  10. Singh, S.; Fumery, M.; Sandborn, W.J.; Murad, M.H. Systematic review and network meta-analysis: First- and second-line biologic therapies for moderate-severe Crohn’s disease. Aliment. Pharmacol. Ther. 2018, 48, 394–409. [Google Scholar] [CrossRef] [PubMed]
  11. Singh, S.; George, J.; Boland, B.S.; Casteele, N.V.; Sandborn, W.J. Primary Non-Response to Tumor Necrosis Factor Antagonists is Associated with Inferior Response to Second-line Biologics in Patients with Inflammatory Bowel Diseases: A Systematic Review and Meta-analysis. J. Crohn’s Colitis 2018, 12, 635–643. [Google Scholar] [CrossRef]
  12. Ben-Horin, S.; Kopylov, U.; Chowers, Y. Optimizing anti-TNF treatments in inflammatory bowel disease. Autoimmun. Rev. 2014, 13, 24–30. [Google Scholar] [CrossRef]
  13. Gisbert, J.P.; Chaparro, M. Predictors of Primary Response to Biologic Treatment [Anti-TNF, Vedolizumab, and Ustekinumab] in Patients with Inflammatory Bowel Disease: From Basic Science to Clinical Practice. J. Crohn’s Colitis 2020, 14, 694–709. [Google Scholar] [CrossRef]
  14. Hamdeh, S.; Aziz, M.; Altayar, O.; Olyaee, M.; Murad, M.H.; Hanauer, S.B. Early vs Late Use of Anti-TNFa Therapy in Adult Patients with Crohn Disease: A Systematic Review and Meta-Analysis. Inflamm. Bowel Dis. 2020, 26, 1808–1818. [Google Scholar] [CrossRef]
  15. Ungaro, R.C.; Aggarwal, S.; Topaloglu, O.; Lee, W.-J.; Clark, R.; Colombel, J.-F. Systematic review and meta-analysis: Efficacy and safety of early biologic treatment in adult and paediatric patients with Crohn’s disease. Aliment. Pharmacol. Ther. 2020, 51, 831–842. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Singh, N.; Rabizadeh, S.; Jossen, J.; Pittman, N.; Check, M.; Hashemi, G.; Phan, B.L.; Hyams, J.S.; Dubinsky, M.C. Multi-Center Experience of Vedolizumab Effectiveness in Pediatric Inflammatory Bowel Disease. Inflamm. Bowel Dis. 2016, 22, 2121–2126. [Google Scholar] [CrossRef] [PubMed]
  17. Vermeire, S.; Louis, E.; Carbonez, A.; Van Assche, G.; Noman, M.; Belaiche, J.; De Vos, M.; Van Gossum, A.; Pescatore, P.; Fiasse, R.; et al. Demographic and clinical parameters influencing the short-term outcome of anti-tumor necrosis factor (infliximab) treatment in Crohn’s disease. Am. J. Gastroenterol. 2002, 97, 2357–2363. [Google Scholar] [CrossRef] [PubMed]
  18. Sprakes, M.B.; Ford, A.C.; Warren, L.; Greer, D.; Hamlin, J. Efficacy, tolerability, and predictors of response to infliximab therapy for Crohn’s disease: A large single centre experience. J. Crohn’s Colitis 2012, 6, 143–153. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Zorzi, F.; Zuzzi, S.; Onali, S.; Calabrese, E.; Condino, G.; Petruzziello, C.; Ascolani, M.; Pallone, F.; Biancone, L. Efficacy and safety of infliximab and adalimumab in Crohn’s disease: A single centre study. Aliment. Pharmacol. Ther. 2012, 35, 1397–1407. [Google Scholar] [CrossRef]
  20. Kiss, L.S.; Szamosi, T.; Molnár, T.; Miheller, P.; Lakatos, L.; Vincze, A.; Palatka, K.; Barta, Z.; Gasztonyi, B.; Salamon, A.; et al. Early clinical remission and normalisation of CRP are the strongest predictors of efficacy, mucosal healing and dose escalation during the first year of adalimumab therapy in Crohn’s disease. Aliment. Pharmacol. Ther. 2011, 34, 911–922. [Google Scholar] [CrossRef] [Green Version]
  21. Bertani, L.; Tricò, D.; Pugliese, D.; Privitera, G.; Linsalata, G.; Zanzi, F.; Mumolo, M.G.; Barberio, B.; Monzani, F.; Marchi, S.; et al. Serum triiodothyronine-to-thyroxine (T3/T4) ratio predicts therapeutic outcome to biological therapies in elderly IBD patients. Aliment. Pharmacol. Ther. 2020, 53. [Google Scholar] [CrossRef]
  22. Colombel, J.; Sandborn, W.J.; Rutgeerts, P.; Enns, R.; Hanauer, S.B.; Panaccione, R.; Schreiber, S.; Byczkowski, D.; Li, J.; Kent, J.D.; et al. Adalimumab for Maintenance of Clinical Response and Remission in Patients with Crohn’s Disease: The CHARM Trial. Gastroenterology 2007, 132, 52–65. [Google Scholar] [CrossRef] [Green Version]
  23. Peters, C.P.; Eshuis, E.J.; Toxopeüs, F.M.; Hellemons, M.E.; Jansen, J.M.; D’Haens, G.R.A.M.; Fockens, P.; Stokkers, P.C.F.; Tuynman, H.A.R.E.; Van Bodegraven, A.A.; et al. Adalimumab for Crohn’s disease: Long-term sustained benefit in a population-based cohort of 438 patients. J. Crohn’s Colitis 2014, 8, 866–875. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Jürgens, M.; John, J.M.M.; Cleynen, I.; Schnitzler, F.; Fidder, H.; Van Moerkercke, W.; Ballet, V.; Noman, M.; Hoffman, I.; Van Assche, G.; et al. Levels of C-reactive Protein Are Associated with Response to Infliximab Therapy in Patients with Crohn’s Disease. Clin. Gastroenterol. Hepatol. 2011, 9, 421–427.e1. [Google Scholar] [CrossRef] [PubMed]
  25. Reinisch, W.; Wang, Y.; Oddens, B.J.; Link, R. C-reactive protein, an indicator for maintained response or remission to infliximab in patients with Crohn’s disease: A post-hoc analysis from ACCENT I. Aliment. Pharmacol. Ther. 2012, 35, 568–576. [Google Scholar] [CrossRef] [PubMed]
  26. Ferrante, M.; Vermeire, S.; Fidder, H.; Schnitzler, F.; Noman, M.; Van Assche, G.; De Hertogh, G.; Hoffman, I.; D’Hoore, A.; Van Steen, K.; et al. Long-term outcome after infliximab for refractory ulcerative colitis. J. Crohn’s Colitis 2008, 2, 219–225. [Google Scholar] [CrossRef] [Green Version]
  27. Arias, M.T.; Casteele, N.V.; Vermeire, S.; Overstraeten, A.D.B.V.; Billiet, T.; Baert, F.; Wolthuis, A.; Van Assche, G.; Noman, M.; Hoffman, I.; et al. A Panel to Predict Long-term Outcome of Infliximab Therapy for Patients with Ulcerative Colitis. Clin. Gastroenterol. Hepatol. 2015, 13, 531–538. [Google Scholar] [CrossRef]
  28. Reinisch, W.; Sandborn, W.J.; Hommes, D.W.; D’Haens, G.; Hanauer, S.; Schreiber, S.; Panaccione, R.; Fedorak, R.N.; Tighe, M.B.; Huang, B.; et al. Adalimumab for induction of clinical remission in moderately to severely active ulcerative colitis: Results of a randomised controlled trial. Gut 2011, 60, 780–787. [Google Scholar] [CrossRef]
  29. Guidi, L.; Marzo, M.; Andrisani, G.; Felice, C.; Pugliese, D.; Mocci, G.; Nardone, O.; De Vitis, I.; Papa, A.; Rapaccini, G.; et al. Faecal calprotectin assay after induction with anti-Tumour Necrosis Factor α agents in inflammatory bowel disease: Prediction of clinical response and mucosal healing at one year. Dig. Liver Dis. 2014, 46, 974–979. [Google Scholar] [CrossRef]
  30. Gisbert, J.P.; Marín, A.C.; McNicholl, A.G.; Chaparro, M. Systematic review with meta-analysis: The efficacy of a second anti-TNF in patients with inflammatory bowel disease whose previous anti-TNF treatment has failed. Aliment. Pharmacol. Ther. 2015, 41, 613–623. [Google Scholar] [CrossRef]
  31. Casanova, M.J.; Chaparro, M.; Mínguez, M.; Ricart, E.; Taxonera, C.; García-López, S.; Guardiola, J.; Román, A.L.-S.; Iglesias, E.; Beltrán, B.; et al. Effectiveness and Safety of the Sequential Use of a Second and Third Anti-TNF Agent in Patients with Inflammatory Bowel Disease: Results from the Eneida Registry. Inflamm. Bowel Dis. 2020, 26, 606–616. [Google Scholar] [CrossRef] [PubMed]
  32. Papamichael, K.; Rivals-Lerebours, O.; Billiet, T.; Casteele, N.V.; Gils, A.; Ferrante, M.; Van Assche, G.; Rutgeerts, P.; Mantzaris, G.J.; Peyrin-Biroulet, L.; et al. Long-Term Outcome of Patients with Ulcerative Colitis and Primary Non-response to Infliximab. J. Crohn’s Colitis 2016, 10, 1015–1023. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Park, S.C.; Jeen, Y.T. Genetic Studies of Inflammatory Bowel Disease-Focusing on Asian Patients. Cells 2019, 8, 404. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Bek, S.; Nielsen, J.V.; Bojesen, A.B.; Franke, A.; Bank, S.; Vogel, U.; Andersen, V. Systematic review: Genetic biomarkers associated with anti-TNF treatment response in inflammatory bowel diseases. Aliment. Pharmacol. Ther. 2016, 44, 554–567. [Google Scholar] [CrossRef]
  35. Tong, Q.; Zhao, L.; Qian, X.-D.; Zhang, L.-L.; Xu, X.; Dai, S.-M.; Cai, Q.; Zhao, D. Association ofTNF-α polymorphism with prediction of response to TNF blockers in spondyloarthritis and inflammatory bowel disease: A meta-analysis. Pharmacogenomics 2013, 14, 1691–1700. [Google Scholar] [CrossRef] [Green Version]
  36. Bank, S.; Andersen, P.S.; Burisch, J.; Pedersen, N.; Roug, S.; Galsgaard, J.; Turino, S.Y.; Brodersen, J.B.; Rashid, S.; Rasmussen, B.K.; et al. Associations between functional polymorphisms in the NFκB signaling pathway and response to anti-TNF treatment in Danish patients with inflammatory bowel disease. Pharm. J. 2014, 14, 526–534. [Google Scholar] [CrossRef] [Green Version]
  37. Jürgens, M.; Laubender, R.P.; Hartl, F.; Weidinger, M.; Seiderer, J.; Wagner, J.; Wetzke, M.; Beigel, F.; Pfennig, S.; Stallhofer, J.; et al. Disease Activity, ANCA, and IL23R Genotype Status Determine Early Response to Infliximab in Patients with Ulcerative Colitis. Am. J. Gastroenterol. 2010, 105, 1811–1819. [Google Scholar] [CrossRef]
  38. Sazonovs, A.; Kennedy, N.A.; Moutsianas, L.; Heap, G.A.; Rice, D.L.; Reppell, M.; Bewshea, C.M.; Chanchlani, N.; Walker, G.J.; Perry, M.H.; et al. HLA-DQA1*05 Carriage Associated with Development of Anti-Drug Antibodies to Infliximab and Adalimumab in Patients with Crohn’s Disease. Gastroenterology 2020, 158, 189–199. [Google Scholar] [CrossRef] [Green Version]
  39. Billiet, T.; Casteele, N.V.; Van Stappen, T.; Princen, F.; Singh, S.; Gils, A.; Ferrante, M.; Van Assche, G.; Cleynen, I.; Vermeire, S. Immunogenicity to infliximab is associated with HLA-DRB1. Gut 2015, 64, 1344–1345. [Google Scholar] [CrossRef]
  40. Louis, E.; El Ghoul, Z.; Vermeire, S.; Dall’Ozzo, S.; Rutgeerts, P.; Paintaud, G.; Belaiche, J.; De Vos, M.; Van Gossum, A.; Colombel, J.F.; et al. Association between polymorphism in IgG Fc receptor IIIa coding gene and biological response to infliximab in Crohn’s disease. Aliment. Pharmacol. Ther. 2004, 19, 511–519. [Google Scholar] [CrossRef]
  41. Niess, J.H.; Klaus, J.; Stephani, J.; Pflüger, C.; Degenkolb, N.; Spaniol, U.; Mayer, B.; Lahr, G.; Von Boyen, G.B.T. NOD2 Polymorphism Predicts Response to Treatment in Crohn’s Disease—First Steps to a Personalized Therapy. Dig. Dis. Sci. 2012, 57, 879–886. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Juanola, O.; Moratalla, A.; Gutierrez, A.; Sempere, L.; Zapater, P.; Giménez, P.; Almenta, I.; Peiró, G.; González-Navajas, J.M.; Such, J.F.; et al. Anti-TNF-alpha loss of response is associated with a decreased percentage of FoxP3+ T cells and a variant NOD2 genotype in patients with Crohn’s disease. J. Gastroenterol. 2015, 50, 758–768. [Google Scholar] [CrossRef]
  43. Schäffler, H.; Geiss, D.; Gittel, N.; Rohde, S.; Huth, A.; Glass, Ä; Brandhorst, G.; Jaster, R.; Lamprecht, G. Mutations in theNOD2gene are associated with a specific phenotype and lower anti-tumor necrosis factor trough levels in Crohn’s disease. J. Dig. Dis. 2018, 19, 678–684. [Google Scholar] [CrossRef]
  44. Koder, S.; Repnik, K.; Ferkolj, I.; Pernat, C.; Skok, P.; Weersma, R.; Potočnik, U. Genetic polymorphism inATG16L1gene influences the response to adalimumab in Crohn’s disease patients. Pharmacogenomics 2015, 16, 191–204. [Google Scholar] [CrossRef] [PubMed]
  45. Hlavaty, T.; Ferrante, M.; Henckaerts, L.; Pierik, M.; Rutgeerts, P.; Vermeire, S. Predictive Model for the Outcome of Infliximab Therapy in Crohn’s Disease Based on Apoptotic Pharmacogenetic Index and Clinical Predictors. Inflamm. Bowel Dis. 2007, 13, 372–379. [Google Scholar] [CrossRef]
  46. Barber, G.E.; Yajnik, V.; Khalili, H.; Giallourakis, C.; Garber, J.; Xavier, R.; Ananthakrishnan, A.N. Genetic Markers Predict Primary Non-Response and Durable Response to Anti-TNF Biologic Therapies in Crohn’s Disease. Am. J. Gastroenterol. 2016, 111, 1816–1822. [Google Scholar] [CrossRef] [Green Version]
  47. Burke, K.E.; Khalili, H.; Garber, J.; Haritunians, T.; McGovern, D.P.; Xavier, R.J.; Ananthakrishnan, A.N. Genetic Markers Predict Primary Nonresponse and Durable Response to Anti–Tumor Necrosis Factor Therapy in Ulcerative Colitis. Inflamm. Bowel Dis. 2018, 24, 1840–1848. [Google Scholar] [CrossRef] [PubMed]
  48. Wang, M.-H.; Friton, J.J.; Raffals, L.E.; Leighton, J.A.; Pasha, S.F.; Picco, M.F.; Cushing, K.C.; Monroe, K.; Nix, B.D.; Newberry, R.D.; et al. Novel Genetic Risk Variants Can Predict Anti-TNF Agent Response in Patients with Inflammatory Bowel Disease. J. Crohn’s Colitis 2019, 13, 1036–1043. [Google Scholar] [CrossRef]
  49. Atreya, I.; Atreya, R.; Neurath, M.F. NF-κB in inflammatory bowel disease. J. Intern. Med. 2008, 263, 591–596. [Google Scholar] [CrossRef]
  50. Vermeire, S.; Gils, A.; Accossato, P.; Lula, S.; Marren, A. Immunogenicity of biologics in inflammatory bowel disease. Ther. Adv. Gastroenterol. 2018, 11. [Google Scholar] [CrossRef] [Green Version]
  51. Colombel, J.-F.; Adedokun, O.J.; Gasink, C.; Gao, L.-L.; Cornillie, F.J.; D’Haens, G.R.; Rutgeerts, P.J.; Reinisch, W.; Sandborn, W.J.; Hanauer, S.B. Combination Therapy with Infliximab and Azathioprine Improves Infliximab Pharmacokinetic Features and Efficacy: A Post Hoc Analysis. Clin. Gastroenterol. Hepatol. 2019, 17, 1525–1532.e1. [Google Scholar] [CrossRef] [Green Version]
  52. Arijs, I.; Li, K.; Toedter, G.; Quintens, R.; Van Lommel, L.; Van Steen, K.; Leemans, P.; De Hertogh, G.; Lemaire, K.; Ferrante, M.; et al. Mucosal gene signatures to predict response to infliximab in patients with ulcerative colitis. Gut 2009, 58, 1612–1619. [Google Scholar] [CrossRef]
  53. Verstockt, B.; Verstockt, S.; Creyns, B.; Tops, S.; Van Assche, G.; Gils, A.; Ceuppens, J.L.; Vermeire, S.; Ferrante, M.; Breynaert, C. Mucosal IL13RA2 expression predicts nonresponse to anti-TNF therapy in Crohn’s disease. Aliment. Pharmacol. Ther. 2019, 49, 572–581. [Google Scholar] [CrossRef]
  54. Ogura, Y.; Bonen, D.K.; Inohara, N.; Nicolae, D.L.; Chen, F.F.; Ramos, R.; Britton, H.; Moran, T.; Karaliuskas, R.; Duerr, R.H.; et al. A frameshift mutation in NOD2 associated with susceptibility to Crohn’s disease. Nat. Cell Biol. 2001, 411, 603–606. [Google Scholar] [CrossRef]
  55. Weersma, R.K.; Stokkers, P.C.F.; Van Bodegraven, A.A.; Van Hogezand, R.A.; Verspaget, H.W.; De Jong, D.J.; Van Der Woude, C.J.; Oldenburg, B.; Linskens, R.K.; Festen, E.A.M.; et al. Molecular prediction of disease risk and severity in a large Dutch Crohn’s disease cohort. Gut 2009, 58, 388–395. [Google Scholar] [CrossRef] [Green Version]
  56. Vermeire, S.; O’Byrne, S.; Keir, M.; Williams, M.; Lu, T.T.; Mansfield, J.C.; Lamb, C.A.; Feagan, B.G.; Panes, J.; Salas, A.; et al. Etrolizumab as induction therapy for ulcerative colitis: A randomised, controlled, phase 2 trial. Lancet 2014, 384, 309–318. [Google Scholar] [CrossRef] [Green Version]
  57. Tew, G.W.; Hackney, J.A.; Gibbons, D.; Lamb, C.A.; Luca, D.; Egen, J.G.; Diehl, L.; Anderson, J.E.; Vermeire, S.; Mansfield, J.C.; et al. Association Between Response to Etrolizumab and Expression of Integrin αE and Granzyme A in Colon Biopsies of Patients with Ulcerative Colitis. Gastroenterology 2016, 150, 477–487.e9. [Google Scholar] [CrossRef] [Green Version]
  58. Gaujoux, R.; Starosvetsky, E.; Maimon, N.; Vallania, F.; Bar-Yoseph, H.; Pressman, S.; Weisshof, R.; Goren, I.; Rabinowitz, K.; Waterman, M.; et al. Cell-centred meta-analysis reveals baseline predictors of anti-TNFα non-response in biopsy and blood of patients with IBD. Gut 2019, 68, 604–614. [Google Scholar] [CrossRef] [PubMed]
  59. Verstockt, B.; Verstockt, S.; Dehairs, J.; Ballet, V.; Blevi, H.; Wollants, W.-J.; Breynaert, C.; Van Assche, G.; Vermeire, S.; Ferrante, M. Low TREM1 expression in whole blood predicts anti-TNF response in inflammatory bowel disease. EBioMedicine 2019, 40, 733–742. [Google Scholar] [CrossRef] [Green Version]
  60. Atreya, R.; Neumann, H.; Neufert, C.; Waldner, M.J.; Billmeier, U.; Zopf, Y.; Willma, M.; App, C.; Münster, T.; Kessler, H.; et al. In vivo imaging using fluorescent antibodies to tumor necrosis factor predicts therapeutic response in Crohn’s disease. Nat. Med. 2014, 20, 313–318. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Jessen, B.; Rodriguez-Sillke, Y.; Sonnenberg, E.; Schumann, M.; Kruglov, A.; Freise, I.; Schmidt, F.; Maul, J.; Kühl, A.A.; Glauben, R.; et al. Level of Tumor Necrosis Factor Production by Stimulated Blood Mononuclear Cells Can Be Used to Predict Response of Patients with Inflammatory Bowel Diseases to Infliximab. Clin. Gastroenterol. Hepatol. 2020. [Google Scholar] [CrossRef] [PubMed]
  62. West, N.R.; Hegazy, A.N.; Owens, B.M.J.; Bullers, S.J.; Linggi, B.; Buonocore, S.; Coccia, M.; Görtz, D.; This, S.; Stockenhuber, K.; et al. Oncostatin M drives intestinal inflammation and predicts response to tumor necrosis factor–neutralizing therapy in patients with inflammatory bowel disease. Nat. Med. 2017, 23, 579–589. [Google Scholar] [CrossRef] [PubMed]
  63. Bertani, L.; Fornai, M.; Fornili, M.; Antonioli, L.; Benvenuti, L.; Tapete, G.; Svizzero, G.B.; Ceccarelli, L.; Mumolo, M.G.; Baglietto, L.; et al. Serum oncostatin M at baseline predicts mucosal healing in Crohn’s disease patients treated with infliximab. Aliment. Pharmacol. Ther. 2020, 52, 284–291. [Google Scholar] [CrossRef]
  64. Ungar, B.; Kopylov, U.; Yavzori, M.; Fudim, E.; Picard, O.; Lahat, A.; Coscas, D.; Waterman, M.; Haj-Natour, O.; Orbach-Zingboim, N.; et al. Association of Vedolizumab Level, Anti-Drug Antibodies, and α4β7 Occupancy with Response in Patients with Inflammatory Bowel Diseases. Clin. Gastroenterol. Hepatol. 2018, 16, 697–705.e7. [Google Scholar] [CrossRef] [Green Version]
  65. Boden, E.K.; Shows, D.M.; Chiorean, M.V.; Lord, J.D. Identification of Candidate Biomarkers Associated with Response to Vedolizumab in Inflammatory Bowel Disease. Dig. Dis. Sci. 2018, 63, 2419–2429. [Google Scholar] [CrossRef] [PubMed]
  66. Rath, T.; Bojarski, C.; Neurath, M.; Atreya, R. Molecular imaging of mucosal α4β7 integrin expression with the fluorescent anti-adhesion antibody vedolizumab in Crohn’s disease. Gastrointest. Endosc. 2017, 86, 406–408. [Google Scholar] [CrossRef]
  67. Allner, C.; Melde, M.; Becker, E.; Fuchs, F.; Mühl, L.; Klenske, E.; Müller, L.; Morgenstern, N.; Fietkau, K.; Hirschmann, S.; et al. Baseline levels of dynamic CD4+ T cell adhesion to MAdCAM-1 correlate with clinical response to vedolizumab treatment in ulcerative colitis: A cohort study. BMC Gastroenterol. 2020, 20, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  68. Soendergaard, C.; Seidelin, J.B.; Steenholdt, C.; Nielsen, O.H. Putative biomarkers of vedolizumab resistance and underlying inflammatory pathways involved in IBD. BMJ Open Gastroenterol. 2018, 5, e000208. [Google Scholar] [CrossRef]
  69. Bertani, L.; Caviglia, G.P.; Antonioli, L.; Pellicano, R.; Sharmila, F.; Astegiano, M.; Saracco, G.M.; Bugianesi, E.; Blandizzi, C.; Costa, F.; et al. Serum Interleukin-6 and -8 as Predictors of Response to Vedolizumab in Inflammatory Bowel Diseases. J. Clin. Med. 2020, 9, 1323. [Google Scholar] [CrossRef] [PubMed]
  70. Battat, R.; Dulai, P.S.; Casteele, N.V.; Evans, E.; Hester, K.D.; Webster, E.; Jain, A.; Proudfoot, J.A.; Mairalles, A.; Neill, J.; et al. Biomarkers Are Associated with Clinical and Endoscopic Outcomes with Vedolizumab Treatment in Ulcerative Colitis. Inflamm. Bowel Dis. 2019, 25, 410–420. [Google Scholar] [CrossRef] [PubMed]
  71. Holmer, A.K.; Battat, R.; Dulai, P.S.; Casteele, N.V.; Nguyen, N.; Jain, A.; Miralles, A.; Neill, J.; Le, H.; Singh, S.; et al. Biomarkers are associated with clinical and endoscopic outcomes with vedolizumab treatment in Crohn’s disease. Ther. Adv. Gastroenterol. 2020, 13. [Google Scholar] [CrossRef]
  72. Sands, B.E.; Chen, J.; Feagan, B.G.; Penney, M.; Rees, W.A.; Danese, S.; Higgins, P.D.; Newbold, P.; Faggioni, R.; Patra, K.; et al. Efficacy and Safety of MEDI2070, an Antibody Against Interleukin 23, in Patients with Moderate to Severe Crohn’s Disease: A Phase 2a Study. Gastroenterology 2017, 153, 77–86.e6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Mitrev, N.; Casteele, N.V.; Seow, C.H.; Andrews, J.M.; Connor, S.J.; Moore, G.T.; Barclay, M.; Begun, J.; Bryant, R.; Chan, W.; et al. Review article: Consensus statements on therapeutic drug monitoring of anti-tumour necrosis factor therapy in inflammatory bowel diseases. Aliment. Pharmacol. Ther. 2017, 46, 1037–1053. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Guidi, L.; Pugliese, D.; Tonucci, T.P.; Berrino, A.; Tolusso, B.; Basile, M.; Cantoro, L.; Balestrieri, P.; Civitelli, F.; Bertani, L.; et al. Therapeutic Drug Monitoring is More Cost-Effective than a Clinically Based Approach in the Management of Loss of Response to Infliximab in Inflammatory Bowel Disease: An Observational Multicentre Study. J. Crohn’s Colitis 2018, 12, 1079–1088. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Steenholdt, C.; Brynskov, J.; Thomsen, O. Østergaard; Munck, L.K.; Fallingborg, J.; Christensen, L.A.; Pedersen, G.; Kjeldsen, J.; Jacobsen, B.A.; Oxholm, A.S.; et al. Individualised therapy is more cost-effective than dose intensification in patients with Crohn’s disease who lose response to anti-TNF treatment: A randomised, controlled trial. Gut 2014, 63, 919–927. [Google Scholar] [CrossRef]
  76. Argollo, M.; Kotze, P.G.; Kakkadasam, P.; D’Haens, G. Optimizing biologic therapy in IBD: How essential is therapeutic drug monitoring? Nat. Rev. Gastroenterol. Hepatol. 2020, 17, 1–9. [Google Scholar] [CrossRef]
  77. De Cruz, P.; Kamm, M.A.; Prideaux, L.; Allen, P.B.; Moore, G. Mucosal Healing in Crohn’s Disease. Inflamm. Bowel Dis. 2013, 19, 429–444. [Google Scholar] [CrossRef]
  78. Papamichael, K.; Rakowsky, S.; Rivera, C.; Cheifetz, A.S.; Osterman, M.T. Infliximab trough concentrations during maintenance therapy are associated with endoscopic and histologic healing in ulcerative colitis. Aliment. Pharmacol. Ther. 2018, 47, 478–484. [Google Scholar] [CrossRef] [Green Version]
  79. Shah, S.C.; Colombel, J.-F.; Sands, B.E.; Narula, N. Mucosal Healing Is Associated with Improved Long-term Outcomes of Patients with Ulcerative Colitis: A Systematic Review and Meta-analysis. Clin. Gastroenterol. Hepatol. 2016, 14, 1245–1255.e8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  80. Pugliese, D.; Felice, C.; Papa, A.; Gasbarrini, A.; Rapaccini, G.L.; Guidi, L.; Armuzzi, A. Anti TNF-α therapy for ulcerative colitis: Current status and prospects for the future. Expert Rev. Clin. Immunol. 2017, 13, 223–233. [Google Scholar] [CrossRef]
  81. Aharie, D.; Filippi, J.; Roblin, X.; Nancey, S.; Chevaux, J.-B.; Hébuterne, X.; Flourié, B.; Capdepont, M.; Peyrin-Biroulet, L. Impact of mucosal healing on long-term outcomes in ulcerative colitis treated with infliximab: A multicenter experience. Aliment. Pharmacol. Ther. 2013, 37, 998–1004. [Google Scholar] [CrossRef]
  82. Adedokun, O.J.; Sandborn, W.J.; Feagan, B.G.; Rutgeerts, P.; Xu, Z.; Marano, C.W.; Johanns, J.; Zhou, H.; Davis, H.M.; Cornillie, F.; et al. Association Between Serum Concentration of Infliximab and Efficacy in Adult Patients with Ulcerative Colitis. Gastroenterology 2014, 147, 1296–1307.e5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  83. Yarur, A.J.; Kubiliun, M.J.; Czul, F.; Sussman, D.A.; Quintero, M.A.; Jain, A.; Drake, K.A.; Hauenstein, S.I.; Lockton, S.; Deshpande, A.R.; et al. Concentrations of 6-Thioguanine Nucleotide Correlate with Trough Levels of Infliximab in Patients with Inflammatory Bowel Disease on Combination Therapy. Clin. Gastroenterol. Hepatol. 2015, 13, 1118–1124.e3. [Google Scholar] [CrossRef] [PubMed]
  84. Roblin, X.; Marotte, H.; Rinaudo, M.; Del Tedesco, E.; Moreau, A.; Phelip, J.M.; Genin, C.; Peyrin-Biroulet, L.; Paul, S. Association Between Pharmacokinetics of Adalimumab and Mucosal Healing in Patients with Inflammatory Bowel Diseases. Clin. Gastroenterol. Hepatol. 2014, 12, 80–84.e2. [Google Scholar] [CrossRef] [PubMed]
  85. Bodini, G.; Giannini, E.G.; Savarino, V.; Del Nero, L.; Pellegatta, G.; De Maria, C.; Baldissarro, I.; Savarino, E. Adalimumab trough serum levels and anti-adalimumab antibodies in the long-term clinical outcome of patients with Crohn’s disease. Scand. J. Gastroenterol. 2016, 51, 1081–1086. [Google Scholar] [CrossRef] [PubMed]
  86. Paul, S.; Del Tedesco, E.; Marotte, H.; Rinaudo-Gaujous, M.; Moreau, A.; Phelip, J.M.; Genin, C.; Peyrin-Biroulet, L.; Roblin, X. Therapeutic Drug Monitoring of Infliximab and Mucosal Healing in Inflammatory Bowel Disease. Inflamm. Bowel Dis. 2013, 19, 2568–2576. [Google Scholar] [CrossRef]
  87. Kennedy, N.A.; Heap, G.A.; Green, H.D.; Hamilton, B.; Bewshea, C.; Walker, G.J.; Thomas, A.; Nice, R.; Perry, M.H.; Bouri, S.; et al. Predictors of anti-TNF treatment failure in anti-TNF-naive patients with active luminal Crohn’s disease: A prospective, multicentre, cohort study. Lancet Gastroenterol. Hepatol. 2019, 4, 341–353. [Google Scholar] [CrossRef] [Green Version]
  88. Adedokun, O.J.; Xu, Z.; Marano, C.W.; Strauss, R.; Zhang, H.; Johanns, J.; Zhou, H.; Davis, H.M.; Reinisch, W.; Feagan, B.G.; et al. Pharmacokinetics and Exposure-response Relationship of Golimumab in Patients with Moderately-to-Severely Active Ulcerative Colitis: Results from Phase 2/3 PURSUIT Induction and Maintenance Studies. J. Crohn’s Colitis 2017, 11, 35–46. [Google Scholar] [CrossRef] [Green Version]
  89. Samaan, M.A.; Cunningham, G.; Tamilarasan, A.G.; Beltran, L.; Pavlidis, P.; Ray, S.; Mawdsley, J.; Sanderson, J.D.; Arkir, Z.; Irving, P.M. Therapeutic thresholds for golimumab serum concentrations during induction and maintenance therapy in ulcerative colitis: Results from the GO-LEVEL study. Aliment. Pharmacol. Ther. 2020, 52, 292–302. [Google Scholar] [CrossRef]
  90. Magro, F.; Lopes, S.; Silva, M.; Coelho, R.; Portela, F.; Branquinho, D.; Correia, L.; Fernandes, S.; Cravo, M.; Caldeira, P.; et al. Low Golimumab Trough Levels at Week 6 Are Associated with Poor Clinical, Endoscopic and Histological Outcomes in Ulcerative Colitis Patients: Pharmacokinetic and Pharmacodynamic Sub-analysis of the Evolution Study. J. Crohn’s Colitis 2019, 13, 1387–1393. [Google Scholar] [CrossRef]
  91. Boland, K.; Greener, T.; Kabakchiev, B.; Stempak, J.; Tessolini, J.; Li, R.; Soriano, J.; Croitoru, K.; Nguyen, G.; Steinhart, A.H.; et al. Identification of Target Golimumab Levels in Maintenance Therapy of Crohn’s Disease and Ulcerative Colitis Associated with Mucosal Healing. Inflamm. Bowel Dis. 2019, 26, 766–773. [Google Scholar] [CrossRef]
  92. Dreesen, E.; Kantasiripitak, W.; Detrez, I.; Stefanović, S.; Vermeire, S.; Ferrante, M.; Bouillon, T.; Drobne, D.; Gils, A. A Population Pharmacokinetic and Exposure-Response Model of Golimumab for Targeting Endoscopic Remission in Patients with Ulcerative Colitis. Inflamm. Bowel Dis. 2019, 26, 570–580. [Google Scholar] [CrossRef]
  93. Bouguen, G.; Siproudhis, L.; Gizard, E.; Wallenhorst, T.; Billioud, V.; Bretagne, J.-F.; Bigard, M.-A.; Peyrin-Biroulet, L. Long-term Outcome of Perianal Fistulizing Crohn’s Disease Treated with Infliximab. Clin. Gastroenterol. Hepatol. 2013, 11, 975–981.e4. [Google Scholar] [CrossRef] [Green Version]
  94. Davidov, Y.; Ungar, B.; Bar-Yoseph, H.; Carter, D.; Haj-Natour, O.; Yavzori, M.; Chowers, Y.; Eliakim, R.; Ben-Horin, S.; Kopylov, U. Association of Induction Infliximab Levels with Clinical Response in Perianal Crohn’s Disease. J. Crohn’s Colitis 2016, 11, 549–555. [Google Scholar] [CrossRef]
  95. Yarur, A.J.; Kanagala, V.; Stein, D.J.; Czul, F.; Quintero, M.A.; Agrawal, D.; Patel, A.; Best, K.; Fox, C.; Idstein, K.; et al. Higher infliximab trough levels are associated with perianal fistula healing in patients with Crohn’s disease. Aliment. Pharmacol. Ther. 2017, 45, 933–940. [Google Scholar] [CrossRef] [Green Version]
  96. Gibson, D.J.; Heetun, Z.S.; Redmond, C.E.; Nanda, K.S.; Keegan, D.; Byrne, K.; Mulcahy, H.E.; Cullen, G.; Doherty, G.A. An Accelerated Infliximab Induction Regimen Reduces the Need for Early Colectomy in Patients with Acute Severe Ulcerative Colitis. Clin. Gastroenterol. Hepatol. 2015, 13, 330–335.e1. [Google Scholar] [CrossRef] [PubMed]
  97. Laharie, D.; Rivière, P. Letter: Should we intensify infliximab in acute severe ulcerative colitis? Aliment. Pharmacol. Ther. 2020, 51, 186–187. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  98. Papamichael, K.; Cheifetz, A.S.; Melmed, G.Y.; Irving, P.M.; Casteele, N.V.; Kozuch, P.L.; Raffals, L.E.; Baidoo, L.; Bressler, B.; Devlin, S.M.; et al. Appropriate Therapeutic Drug Monitoring of Biologic Agents for Patients with Inflammatory Bowel Diseases. Clin. Gastroenterol. Hepatol. 2019, 17, 1655–1668.e3. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  99. Papamichael, K.; Van Stappen, T.; Casteele, N.V.; Gils, A.; Billiet, T.; Tops, S.; Claes, K.; Van Assche, G.; Rutgeerts, P.; Vermeire, S.; et al. Infliximab Concentration Thresholds During Induction Therapy Are Associated with Short-term Mucosal Healing in Patients with Ulcerative Colitis. Clin. Gastroenterol. Hepatol. 2016, 14, 543–549. [Google Scholar] [CrossRef] [Green Version]
  100. Papamichael, K.; Baert, F.; Tops, S.; Van Assche, G.; Rutgeerts, P.; Vermeire, S.; Gils, A.; Ferrante, M. Post-Induction Adalimumab Concentration is Associated with Short-Term Mucosal Healing in Patients with Ulcerative Colitis. J. Crohn’s Colitis 2017, 11, 53–59. [Google Scholar] [CrossRef] [Green Version]
  101. Rosario, M.; French, J.L.; Dirks, N.L.; Sankoh, S.; Parikh, A.; Yang, H.; Danese, S.; Colombel, J.-F.; Smyth, M.; Sandborn, W.J.; et al. Exposure–efficacy Relationships for Vedolizumab Induction Therapy in Patients with Ulcerative Colitis or Crohn’s Disease. J. Crohn’s Colitis 2017, 11, 921–929. [Google Scholar] [CrossRef] [Green Version]
  102. Osterman, M.T.; Rosario, M.; Lasch, K.; Barocas, M.; Wilbur, J.D.; Dirks, N.L.; Gastonguay, M.R. Vedolizumab exposure levels and clinical outcomes in ulcerative colitis: Determining the potential for dose optimisation. Aliment. Pharmacol. Ther. 2019, 49, 408–418. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  103. Guidi, L.; Pugliese, D.; Tonucci, T.P.; Bertani, L.; Costa, F.; Privitera, G.; Tolusso, B.; Di Mario, C.; Albano, E.; Tapete, G.; et al. Early vedolizumab trough levels predict treatment persistence over the first year in inflammatory bowel disease. United Eur. Gastroenterol. J. 2019, 7, 1189–1197. [Google Scholar] [CrossRef] [Green Version]
  104. Yacoub, W.; Williet, N.; Pouillon, L.; Di-Bernado, T.; Bittencourt, M.D.C.; Nancey, S.; Lopez, A.; Paul, S.; Zallot, C.; Roblin, X.; et al. Early vedolizumab trough levels predict mucosal healing in inflammatory bowel disease: A multicentre prospective observational study. Aliment. Pharmacol. Ther. 2018, 47, 906–912. [Google Scholar] [CrossRef] [Green Version]
  105. Dreesen, E.; Verstockt, B.; Bian, S.; De Bruyn, M.; Compernolle, G.; Tops, S.; Noman, M.; Van Assche, G.; Ferrante, M.; Gils, A.; et al. Evidence to Support Monitoring of Vedolizumab Trough Concentrations in Patients with Inflammatory Bowel Diseases. Clin. Gastroenterol. Hepatol. 2018, 16, 1937–1946.e8. [Google Scholar] [CrossRef]
  106. Pouillon, L.; Rousseau, H.; Busby-Venner, H.; Bittencourt, M.D.C.; Choukour, M.; Gauchotte, G.; Zallot, C.; Danese, S.; Baumann, C.; Peyrin-Biroulet, L. Vedolizumab Trough Levels and Histological Healing During Maintenance Therapy in Ulcerative Colitis. J. Crohn’s Colitis 2019, 13, 970–975. [Google Scholar] [CrossRef] [PubMed]
  107. Miller, C.; Morgan, H.; Steel, A.; Wahed, M. P467 Can induction and maintenance serum vedolizumab levels predict long-term clinical outcome in inflammatory bowel disease (IBD)? J. Crohn’s Colitis 2020, 14 (Suppl. S1), S413–S414. [Google Scholar] [CrossRef] [Green Version]
  108. Vaughn, B.P.; Yarur, A.J.; Graziano, E.; Campbell, J.P.; Bhattacharya, A.; Lee, J.Y.; Gheysens, K.; Papamichael, K.; Osterman, M.T.; Cheifetz, A.S.; et al. Vedolizumab Serum Trough Concentrations and Response to Dose Escalation in Inflammatory Bowel Disease. J. Clin. Med. 2020, 9, 3142. [Google Scholar] [CrossRef] [PubMed]
  109. Adedokun, O.J.; Xu, Z.; Gasink, C.; Jacobstein, D.; Szapary, P.; Johanns, J.; Gao, L.-L.; Davis, H.M.; Hanauer, S.B.; Feagan, B.G.; et al. Pharmacokinetics and Exposure Response Relationships of Ustekinumab in Patients with Crohn’s Disease. Gastroenterology 2018, 154, 1660–1671. [Google Scholar] [CrossRef] [Green Version]
  110. Adedokun, O.J.; Xu, Z.; Marano, C.; O’Brien, C.; Szapary, P.; Zhang, H.; Johanns, J.; Leong, R.W.; Hisamatsu, T.; Van Assche, G.; et al. Ustekinumab Pharmacokinetics and Exposure Response in a Phase 3 Randomized Trial of Patients with Ulcerative Colitis. Clin. Gastroenterol. Hepatol. 2020, 18, 2244–2255.e9. [Google Scholar] [CrossRef]
  111. Verstockt, B.; Dreesen, E.; Noman, M.; Outtier, A.; Berghe, N.V.D.; Aerden, I.; Compernolle, G.; Van Assche, G.; Gils, A.; Vermeire, S.; et al. Ustekinumab Exposure-outcome Analysis in Crohn’s Disease Only in Part Explains Limited Endoscopic Remission Rates. J. Crohn’s Colitis 2019, 13, 864–872. [Google Scholar] [CrossRef] [PubMed]
  112. Soufflet, N.; Boschetti, G.; Roblin, X.; Cuercq, C.; Williet, N.; Charlois, A.-L.; Duclaux-Loras, R.; Danion, P.; Mialon, A.; Faure, M.; et al. Concentrations of Ustekinumab During Induction Therapy Associate with Remission in Patients with Crohn’s Disease. Clin. Gastroenterol. Hepatol. 2019, 17, 2610–2612. [Google Scholar] [CrossRef] [PubMed]
  113. Battat, R.; Kopylov, U.; Bessissow, T.; Bitton, A.; Cohen, A.; Jain, A.; Martel, M.; Seidman, E.; Afif, W. Association between Ustekinumab Trough Concentrations and Clinical, Biomarker, and Endoscopic Outcomes in Patients with Crohn’s Disease. Clin. Gastroenterol. Hepatol. 2017, 15, 1427–1434.e2. [Google Scholar] [CrossRef]
  114. Liefferinckx, C.; Fassin, M.; Thomas, D.; Minsart, C.; Cremer, A.; Amininejad, L.; Tafciu, V.; Wambacq, V.; Van Gossum, A.; Franchimont, D. P346 Single-centre experience of ustekinumab: Therapeutic drug monitoring in Crohn’s disease patients. J. Crohn’s Colitis 2020, 14 (Suppl. S1), S331–S333. [Google Scholar] [CrossRef]
  115. Yanai, H.; Lichtenstein, L.; Assa, A.; Mazor, Y.; Weiss, B.; Levine, A.; Ron, Y.; Kopylov, U.; Bujanover, Y.; Rosenbach, Y.; et al. Levels of Drug and Antidrug Antibodies Are Associated with Outcome of Interventions After Loss of Response to Infliximab or Adalimumab. Clin. Gastroenterol. Hepatol. 2015, 13, 522–530.e2. [Google Scholar] [CrossRef]
  116. Roblin, X.; Verot, C.; Paul, S.; Duru, G.; Williet, N.; Boschetti, G.; Del Tedesco, E.; Peyrin-Biroulet, L.; Phelip, J.M.; Nancey, S.; et al. Is the Pharmacokinetic Profile of a First Anti-TNF Predictive of the Clinical Outcome and Pharmacokinetics of a Second Anti-TNF? Inflamm. Bowel Dis. 2018, 24, 2078–2085. [Google Scholar] [CrossRef]
  117. Roblin, X.; Williet, N.; Boschetti, G.; Phelip, J.-M.; Del Tedesco, E.; Berger, A.-E.; Vedrines, P.; Duru, G.; Peyrin-Biroulet, L.; Nancey, S.; et al. Addition of azathioprine to the switch of anti-TNF in patients with IBD in clinical relapse with undetectable anti-TNF trough levels and antidrug antibodies: A prospective randomised trial. Gut 2020, 69, 1206–1212. [Google Scholar] [CrossRef] [PubMed]
  118. Casteele, N.V.; Ferrante, M.; Van Assche, G.; Ballet, V.; Compernolle, G.; Van Steen, K.; Simoens, S.; Rutgeerts, P.; Gils, A.; Vermeire, S. Trough Concentrations of Infliximab Guide Dosing for Patients with Inflammatory Bowel Disease. Gastroenterology 2015, 148, 1320–1329.e3. [Google Scholar] [CrossRef]
  119. D’Haens, G.; Vermeire, S.; Lambrecht, G.; Baert, F.; Bossuyt, P.; Pariente, B.; Buisson, A.; Bouhnik, Y.; Filippi, J.; Woude, J.V.; et al. Increasing Infliximab Dose Based on Symptoms, Biomarkers, and Serum Drug Concentrations Does Not Increase Clinical, Endoscopic, and Corticosteroid-Free Remission in Patients with Active Luminal Crohn’s Disease. Gastroenterology 2018, 154, 1343–1351.e1. [Google Scholar] [CrossRef] [PubMed]
  120. Assa, A.; Matar, M.; Turner, D.; Broide, E.; Weiss, B.; Ledder, O.; Guz-Mark, A.; Rinawi, F.; Cohen, S.; Topf-Olivestone, C.; et al. Proactive Monitoring of Adalimumab Trough Concentration Associated with Increased Clinical Remission in Children with Crohn’s Disease Compared with Reactive Monitoring. Gastroenterology 2019, 157, 985–996.e2. [Google Scholar] [CrossRef]
  121. Sparrow, M.P.; Papamichael, K.; Ward, M.G.; Rivière, P.; Laharie, D.; Paul, S.; Roblin, X. Therapeutic Drug Monitoring of Biologics During Induction to Prevent Primary Non-Response. J. Crohn’s Colitis 2020, 14, 542–556. [Google Scholar] [CrossRef]
  122. Feagan, B.G.; Rutgeerts, P.; Sands, B.E.; Hanauer, S.; Colombel, J.-F.; Sandborn, W.J.; Van Assche, G.; Axler, J.; Kim, H.-J.; Danese, S.; et al. Vedolizumab as Induction and Maintenance Therapy for Ulcerative Colitis. N. Engl. J. Med. 2013, 369, 699–710. [Google Scholar] [CrossRef] [Green Version]
  123. Sands, B.E.; Feagan, B.G.; Rutgeerts, P.; Colombel, J.-F.; Sandborn, W.J.; Sy, R.; D’Haens, G.; Ben-Horin, S.; Xu, J.; Rosario, M.; et al. Effects of Vedolizumab Induction Therapy for Patients with Crohn’s Disease in Whom Tumor Necrosis Factor Antagonist Treatment Failed. Gastroenterol. 2014, 147, 618–627.e3. [Google Scholar] [CrossRef] [Green Version]
  124. Feagan, B.G.; Sandborn, W.J.; Gasink, C.; Jacobstein, D.; Lang, Y.; Friedman, J.R.; Blank, M.A.; Johanns, J.; Gao, L.-L.; Miao, Y.; et al. Ustekinumab as Induction and Maintenance Therapy for Crohn’s Disease. N. Engl. J. Med. 2016, 375, 1946–1960. [Google Scholar] [CrossRef] [PubMed]
  125. Sands, B.E.; Sandborn, W.J.; Panaccione, R.; O’Brien, C.D.; Zhang, H.; Johanns, J.; Adedokun, O.J.; Li, K.; Peyrin-Biroulet, L.; Van Assche, G.; et al. Ustekinumab as Induction and Maintenance Therapy for Ulcerative Colitis. N. Engl. J. Med. 2019, 381, 1201–1214. [Google Scholar] [CrossRef] [PubMed]
  126. Verstockt, B.; Mertens, E.; Dreesen, E.; Outtier, A.; Noman, M.; Tops, S.; Schops, G.; Van Assche, G.; Vermeire, S.; Gils, A.; et al. Influence of Drug Exposure on Vedolizumab-Induced Endoscopic Remission in Anti-Tumour Necrosis Factor [TNF] Naïve and Anti-TNF Exposed IBD Patients. J. Crohn’s Colitis 2020, 14, 332–341. [Google Scholar] [CrossRef]
  127. Alsoud, D.; Vermeire, S.; Verstockt, B. Monitoring vedolizumab and ustekinumab drug levels in patients with inflammatory bowel disease: Hype or hope? Curr. Opin. Pharmacol. 2020, 55, 17–30. [Google Scholar] [CrossRef] [PubMed]
  128. Peyrin-Biroulet, L.; Sandborn, W.; Sands, B.E.; Reinisch, W.; Bemelman, W.; Bryant, R.V.; D’Haens, G.; Dotan, I.; Dubinsky, M.; Feagan, B.; et al. Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE): Determining Therapeutic Goals for Treat-to-Target. Am. J. Gastroenterol. 2015, 110, 1324–1338. [Google Scholar] [CrossRef]
  129. Kopylov, U.; Mantzaris, G.J.; Katsanos, K.H.; Reenaers, C.; Ellul, P.; Rahier, J.F.; Israeli, E.; Lakatos, P.L.; Fiorino, G.; Cesarini, M.; et al. The efficacy of shortening the dosing interval to once every six weeks in Crohn’s patients losing re-sponse to maintenance dose of infliximab. Aliment. Pharmacol. Ther. 2011, 33, 349–357. [Google Scholar] [CrossRef]
  130. Lee, M.; Chang, E.B. Inflammatory Bowel Diseases (IBD) and the Microbiome—Searching the Crime Scene for Clues. Gastroenterology 2021, 160, 524–537. [Google Scholar] [CrossRef] [PubMed]
  131. Lewis, J.D.; Chen, E.Z.; Baldassano, R.N.; Otley, A.R.; Griffiths, A.M.; Lee, D.; Bittinger, K.; Bailey, A.; Friedman, E.S.; Hoffmann, C.; et al. Inflammation, Antibiotics, and Diet as Environmental Stressors of the Gut Microbiome in Pediatric Crohn’s Disease. Cell Host Microbe 2015, 18, 489–500. [Google Scholar] [CrossRef] [Green Version]
  132. Estevinho, M.M.; Rocha, C.; Correia, L.; Lago, P.; Ministro, P.; Portela, F.; Trindade, E.; Afonso, J.; Peyrin-Biroulet, L.; Magro, F. Features of Fecal and Colon Microbiomes Associate with Responses to Biologic Therapies for Inflammatory Bowel Diseases: A Systematic Review. Clin. Gastroenterol. Hepatol. 2020, 18, 1054–1069. [Google Scholar] [CrossRef] [PubMed]
  133. Scaldaferri, F.; Gerardi, V.; Pecere, S.; Petito, V.; Lopetuso, L.R.; Zambrano, D.; Schiavoni, E.; D’Ambrosio, D.; Di Agostini, A.; Laterza, L.; et al. Tu1302 Anti-TNF-α Induction Regimen Modulates Gut Microbiota Molecular Composition While Inducing Clinical Response in Crohn’s Disease Patients: Toward a Personalized Medicine. Gastroenterology 2015, 148, S-852. [Google Scholar] [CrossRef]
  134. Shaw, K.A.; Bertha, M.; Hofmekler, T.; Chopra, P.; Vatanen, T.; Srivatsa, A.; Prince, J.; Kumar, A.; Sauer, C.; Zwick, M.E.; et al. Dysbiosis, inflammation, and response to treatment: A longitudinal study of pediatric subjects with newly diagnosed inflammatory bowel disease. Genome Med. 2016, 8, 1–13. [Google Scholar] [CrossRef] [PubMed]
  135. Zhuang, X.; Tian, Z.; Feng, R.; Li, M.; Li, T.; Zhou, G.; Qiu, Y.; Chen, B.; He, Y.; Chen, M.; et al. Fecal Microbiota Alterations Associated with Clinical and Endoscopic Response to Infliximab Therapy in Crohn’s Disease. Inflamm. Bowel Dis. 2020, 26, 1636–1647. [Google Scholar] [CrossRef] [PubMed]
  136. Aden, K.; Rehman, A.; Waschina, S.; Pan, W.-H.; Walker, A.; Lucio, M.; Nunez, A.M.; Bharti, R.; Zimmerman, J.; Bethge, J.; et al. Metabolic Functions of Gut Microbes Associate with Efficacy of Tumor Necrosis Factor Antagonists in Patients with Inflammatory Bowel Diseases. Gastroenterology 2019, 157, 1279–1292.e11. [Google Scholar] [CrossRef] [Green Version]
  137. Ding, N.S.; McDonald, J.A.K.; Perdones-Montero, A.; Rees, D.N.; Adegbola, S.O.; Misra, R.; Hendy, P.; Penez, L.; Marchesi, J.R.; Holmes, E.; et al. Metabonomics and the Gut Microbiome Associated with Primary Response to Anti-TNF Therapy in Crohn’s Disease. J. Crohn’s Colitis 2020, 14, 1090–1102. [Google Scholar] [CrossRef]
  138. Seong, G.; Kim, N.; Joung, J.-G.; Kim, E.R.; Chang, D.K.; Chun, J.; Hong, S.N.; Kim, Y.-H. Changes in the Intestinal Microbiota of Patients with Inflammatory Bowel Disease with Clinical Remission during an 8-Week Infliximab Infusion Cycle. Microorganisms 2020, 8, 874. [Google Scholar] [CrossRef] [PubMed]
  139. Zhou, Y.; Xu, Z.Z.; He, Y.; Yang, Y.; Liu, L.; Lin, Q.; Nie, Y.; Li, M.; Zhi, F.; Liu, S.; et al. Gut Microbiota Offers Universal Biomarkers across Ethnicity in Inflammatory Bowel Disease Diagnosis and Infliximab Response Prediction. mSystems 2018, 3, e00188-17. [Google Scholar] [CrossRef] [Green Version]
  140. Magnusson, M.K.; Strid, H.; Sapnara, M.; Lasson, A.; Bajor, A.; Ung, K.-A.; Öhman, L. Anti-TNF Therapy Response in Patients with Ulcerative Colitis Is Associated with Colonic Antimicrobial Peptide Expression and Microbiota Composition. J. Crohn’s Colitis 2016, 10, 943–952. [Google Scholar] [CrossRef]
  141. Wang, Y.; Gao, X.; Ghozlane, A.; Hu, H.; Li, X.; Xiao, Y.; Li, D.; Yu, G.; Zhang, T. Characteristics of Faecal Microbiota in Paediatric Crohn’s Disease and Their Dynamic Changes During Infliximab Therapy. J. Crohn’s Colitis 2018, 12, 337–346. [Google Scholar] [CrossRef] [PubMed]
  142. Ananthakrishnan, A.N.; Luo, C.; Yajnik, V.; Khalili, H.; Garber, J.J.; Stevens, B.W.; Cleland, T.; Xavier, R.J. Gut Microbiome Function Predicts Response to Anti-integrin Biologic Therapy in Inflammatory Bowel Diseases. Cell Host Microbe 2017, 21, 603–610.e3. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  143. Doherty, M.K.; Ding, T.; Koumpouras, C.; Telesco, S.E.; Monast, C.; Das, A.; Brodmerkel, C.; Schloss, P.D. Fecal Microbiota Signatures Are Associated with Response to Ustekinumab Therapy among Crohn’s Disease Patients. mBio 2018, 9, 1–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Figure 1. Different features that contribute to responsiveness to a certain drug and that needs to be included in multiparametric models for prediction of response in inflammatory bowel diseases. Created with
Figure 1. Different features that contribute to responsiveness to a certain drug and that needs to be included in multiparametric models for prediction of response in inflammatory bowel diseases. Created with
Jcm 10 00853 g001
Table 1. Genetic markers.
Table 1. Genetic markers.
StudyGenetic MarkersOutcomes
Bek et al. 2016 [34]Polymorphisms in TLR2, rs11938228, TLR4, TLR9, TNFRSF1A, IFNG, IL6 and IL1B (rs4848306) Clinical response to anti-TNF in IBD patients
Tong et al. 2013 [35]Polymorphisms in TNF-α promoter (-308 A/G and -857 C/T)Clinical response to anti-TNF in IBD e SpA patient
Bank et al. 2014 [36]Polymorphisms implicated in NF-kB pathway: TLR2, TLR4, TLR9, LY96 (MD-2), CD14, MAP3K14 (NIK), TNFA, TNFRSF1A, TNFAIP3(A20), IL1B, IL1RN, IL6, IL17A, IFNGClinical response to anti-TNF in IBD patients
Jürgens et al. 2010 [37]Polymorphisms in IL23R Early response to infliximab in UC patients
Sazonovs et al. 2020 [38]HLA-DQA1*05 Development of ADA against infliximab and adalimumab in CD patients
Billiet et al. 2015 [39]HLA-DRB1Development of ADA against infliximab in IBD patients
Louis et al. 2004 [40]Polymorphism in IgG Fc receptor IIIaDevelopment of ADA against infliximab in CD patients
Niess et al. 2012 [41] Polymorphisms in NOD2Clinical response to anti-TNF in CD patients
Juanola et al. 2015 [42]Polymorphisms in NOD2Loss of response to anti-TNF in CD patients
Schäffler et al. 2018 [43]Polymorphisms in NOD2Lower anti-TNF TLs in CD patients
Koder et al. 2015 [44]Polymorphisms in ATG16L1Clinical response to adalimumab in CD patients
Hlavaty et al. 2007 [45]Polymorphisms in Fas, Fas ligand and Caspase 9 (Apoptotic Pharmacogenetic Index)Clinical response to infliximab in CD patients
Barber et al. 2016 [46]Multiple polymorphisms (Combined clinical-genetic model)Short- and long-term to anti-TNF in CD patients
Burke et al. 2018 [47]Multiple polymorphisms (Combined clinical-genetic model)Short- and long-term response to anti-TNF in UC patients
Wang et al. 2019 [48]Polymorphisms in TNFSF4/18, PLIN2, rs762787, rs9572250, rs144256942, rs523781Clinical response to anti-TNF in IBD patients
ADA: anti-drug antibodies; ATG16L1: autophagy-related 16 like 1; CD: Crohn’s disease; HLA: human leukocyte antigens; IBD: inflammatory bowel disease; IL: interleukin; MAP3K14: mitogen-activated protein kinase kinase kinase 14; NF-kB: nuclear factor kappa-light-chain-enhancer of activated B cells; NOD2: nucleotide-binding oligomerization domain-containing protein 2; PLIN2: perilipin 2; TLR: toll-like receptor; TNF: tumor necrosis factor; TNFR: tumor necrosis factor receptor; UC: ulcerative colitis; TNFSF: tumor necrosis factor superfamily; IFNG: interferon gamma.
Table 2. Immunological markers.
Table 2. Immunological markers.
StudyImmunological MarkersOutcomes
Gaujoux et al. 2019 [58]Higher expression of TREM-1 and CCR2-CCL7 in intestinal biopsiesNonresponse to anti-TNF treatment
Verstockt et al. 2019 [53]Lower expression of TREM-1 in whole blood and intestinal biopsies, lower concentration in serumMucosal healing in patients treated with anti-TNF
Atreya et al. 2014 [60]Higher number of mTNF+ cells in intestinal biopsiesShort term (12 weeks) response to adalimumab
Jessen et al. 2020 [61]TNF production > 500 pg/mL by PBMC stimulated with LPSClinical response to infliximab at week 6
West et al. 2017 [62]Higher expression of OSM in intestinal biopsiesRefractoriness to infliximab (at weeks 8 and 30) and golimumab (at week 6)
Bertani et al. 2020 [63]Lower serum concentration of OSMMucosal healing at week 54 in infliximab-treated patients
Boden et al. 2018 [65]Higher expression of α4β7 on T effector memory cells and NK cellsResponse to vedolizumab
Rath et al. 2017 [66]Presence of α4β7+ cells in intestinal mucosaResponse to anti-integrin therapy
Allner et al. 2020 [67]Higher dynamic adhesion of peripheral blood CD4+ T cells to MAdCAM-1 and more pronounced reduction of adhesion following treatment Clinical response to vedolizumab in UC patients
Soendergaard et al. 2020 [68]Higher serum IL6
Higher serum CD40L
Higher serum osteocalcin
Nonresponse to vedolizumab in IBD patients
Nonresponse to vedolizumab in CD patients
Response to vedolizumab in UC patients
Bertani et al. 2020 [69]Higher serum IL6 and IL8, more pronounced decrease of IL6 after 10 weeksClinical response to vedolizumab after 12 months
Battat et al. 2019 [70]Increase of serum α4β7 and decrease of serum MAdCAM-1, VCAM-1, ICAM-1 and TNFClinical and endoscopic remission ate week 26 in vedolizumab-treated patients
Holmer et al. 2020 [71]Higher serum VCAM-1 and ICAM-1 and lower serum α4β7Endoscopic remission in vedolizumab-treated patients
Sands et al. 2017 [72]Higher serum IL22Clinical response to brazikumab
TREM-1: triggering receptor expressed on myeloid cells 1; CCR2-CCL7: chemokine receptor type 2–chemokine ligand 7; TNF: tumor necrosis factor; mTNF: membrane tumor necrosis factor; PBMC: peripheral blood mononuclear cells; LPS: lipopolysaccharide; OSM: oncostatin M; NK cells: natural killer cells; IL: interleukin; CD40L: ligand of cluster of differentiation 40; IBD: inflammatory bowel disease; CD: Crohn’s disease; UC: ulcerative colitis; MAdCAM-1: mucosal vascular addressin cell adhesion molecule 1; VCAM-1: vascular cell adhesion molecule 1; ICAM-1: intercellular adhesion molecule 1.
Table 3. Therapeutic drug monitoring.
Table 3. Therapeutic drug monitoring.
Anti-TNF Agents
Adedokun et al. 2014 [82]Infliximab TLs ≥41 μg/mL at week 8
Infliximab TLs ≥3.7 μg/mL during maintenance
Clinical response, clinical remission, mucosa healing in UC patients
Clinical response, clinical remission, mucosa healing in UC patients
Yarur et al. 2015 [83]Infliximab TLs ≥8.3 μg/mL during maintenanceMucosal healing in IBD patients
Roblin et al. 2014 [84]Adalimumab TLs ≥6 μg/mL during maintenance
Adalimumab TLs ≥6.5 μg/mL during maintenance
Clinical remission in IBD patients
Mucosal healing in IBD patients
Bodini et al. 2016 [85]Adalimumab TLs ≥10.1 μg/mL at week 48Clinical remission in CD patients
Paul et al. 2013 [86]Increase of infliximab TLs >0.5 μg/mL after dose-escalationMucosal healing in IBD patients
Papamichael et al. 2018 [78]Infliximab TLs ≥7.5 μg/mL during maintenance
Infliximab TLs ≥7.5 μg/mL during maintenance
Endoscopic healing in CD patients
Histologic healing in CD patients
Kennedy et al. 2019 [87]Infliximab TLs >7 μg/mL at week 14
Adalimumab TLs >12 μg/mL at week 14
Clinical remission at weeks 14 and 54 in CD patients
Clinical remission at weeks 14 and 54 in CD patients
Adedokun et al. 2017 [88]Golimumab TLs ≥2.5 μg/mL at week 6
Golimumab levels (steady-state) ≥1.4 μg/mL at week 44
Clinical response at week 6 in UC patients
Clinical remission at week 54 in UC patients
Samaan et al. 2020 [89]Golimumab TLs ≥3.8 μg/mL at week 6
Golimumab TLs ≥2.4 μg/mL during maintenance
Combined clinical and biochemical remission at week 6 in UC patients
Combined clinical and biochemical remission during maintenance in UC patients
Magro et al. 2019 [90]Golimumab TLs ≥2.9 μg/mL at week 6Higher rates of clinical response, lower rates of endoscopic and histologic activity at week 6 in UC patients
Boland et al. 2019 [91]Golimumab TLs ≥8.0 μg/mL during maintenanceMucosal healing in CD patients during maintenance
Dreesen et al. 2019 [92]Golimumab TLs ≥7.4 and 3.4 μg/mL at weeks 6 and 14Endoscopic remission at week 14 in UC patients
Rosario et al. 2017 [101]Median vedolizumab TLs 26.8 μg/mL at week 6
Median vedolizumab TLs 34.7 μg/mL at week 6
Clinical remission at week 52 in CD patients
Clinical remission at week 52 in UC patients
Osterman et al. 2019 [102]Vedolizumab TLs ≥37.1 μg/mL at week 6, ≥18.4 μg/mL at week 14 and ≥12.7 μg/mL during maintenanceClinical remission at week 52 in UC patients
Guidi et al. 2019 [103]Vedolizumab TLs ≥16.55 μg/mL at week 14Vedolizumab persistence in IBD patients
Yacoub et al. 2018 [104]Vedolizumab TLs ≥18 μg/mL at week 6Mucosal healing within the first year in IBD patients
Dreesen et al. 2018 [105]Vedolizumab TLs ≥28.9 μg/mL at week 2
Vedolizumab TLs ≥23.4 μg/mL at week 6
Vedolizumab TLs ≥13.9 μg/mL at week 14
Vedolizumab TLs ≥13.5 μg/mL at week 22
Mucosal healing at week 14 in UC patients
Biochemical remission at week 6 in CD patients
Mucosal healing at week 14 in UC patients
Mucosal healing at week 22 in CD patients
Vedolizumab TLs ≥20.9 μg/mL at week 6 and ≥10.1 μg/mL at week 14
Vedolizumab TLs ≥26.2 μg/mL at week 6 and ≥30.1 μg/mL at week 14
Endoscopic improvement at week 14
Endoscopic remission at 6 months
Pouillon et al. 2019 [106]Vedolizumab TLs ≥25 μg/mL during maintenanceEndoscopic and histological healing in UC patients
Miller et al. 2020 [107]Vedolizumab TLs ≥27 μg/mL during maintenanceClinical remission at week 52 in IBD patients
Vaughn et al. 2020 [108]Vedolizumab TLs <7.4 μg/mL before dose-escalationResponse to dose-escalation in IBD patients
Adedokun et al. 2018 [109]Ustekinumab TLs ≥3.3 μg/mL at week 8
Ustekinumab TLs 0.8–1.4 μg/mL during maintenance
Clinical remission at week 8 in CD patients
Clinical remission during maintenance in CD patients
Adedokun et al. 2020 [110]Ustekinumab TLs ≥3.7 μg/mL at week 8
Ustekinumab TLs 1.1–1.3 μg/mL during maintenance
Clinical remission at week 8 in UC patients
Clinical remission at week 44 in UC patients
Verstockt et al. 2019 [111]Ustekinumab TLs ≥21.8 μg/mL at week 4 and ≥6.6 μg/mL at week 8
Ustekinumab TLs ≥2.7 μg/mL at week 16 and ≥1.9 μg/mL at week 24
50% decrease of faecal calprotectin in CD patients
Endoscopic response at week 24
Soufflet et al. 2019 [112]Ustekinumab TLs ≥2 μg/mL at week 8Steroid-free clinical remission and biochemical remission at week 16
Battat et al. 2017 [113]Ustekinumab TLs ≥4.5 μg/mL at week 26 Endoscopic response from week 26
Liefferinckx et al. 2020 [114]Median ustekinumab TLs 2.45 μg/mL at week 16Need for optimization during maintenance
CD: Crohn’s disease; IBD: inflammatory bowel disease; TLs: trough levels; TNF: tumor necrosis factor; UC: ulcerative colitis.
Table 4. Microbiological markers.
Table 4. Microbiological markers.
StudyMicrobial MarkersOutcomes
Magnusson et al. 2016 [131]Lower dysbiosis indexes and higher abundance of F. prausnitiziiClinical response to anti-TNF in IBD patients Clinical response to anti-TNF in IBD patients
Shaw et al. 2016 [132]Difference in abundance of specific genera (Akkermansia, Coprococcus, Fusobacterium, Veillonella, Faecalibacterium, and Adlercreutzia)Clinical response to anti-TNF in IBD patients Clinical response to anti-TNF in IBD patients
Zhuang et al. 2020 [133]Increased proportions of Lachnospiraceae and Blautia taxa at week 6Clinical and endoscopic response to infliximab in CD patients
Wang et al. 2018 [134]Higher abundance of SCFA-producing bacteriaSustained response to infliximab in CD patients
Aden et al. 2019 [135]Reduced metabolic interactions
Higher levels of SCAFs after anti-TNF initiation
Non-response to anti-TNF in IBD patients
Clinical remission in IBD patients
Seong et al. 2020 [136]Increased bacterial diversity, richness and relative abundance of F. prausnitziiInfliximab TLs >5 μg/mL at week 8 and mucosal healing within 3 months in IBD patients
Ananthakrishnan et al. 2017 [137]Higher α-diversity, higher abundance of Roseburia inulinivorans and of a Burkholderiales species
Metabolic pathways associated with microbial functions
Clinical remission in IBD patients treated with vedolizumab
Clinical remission in IBD patients treated with vedolizumab
Doherty et al. 2018 [138]Higher diversity and higher abundance of 2 OTUs affiliated with Faecalibacterium and BacteroidesClinical remission at week 6 in CD patients treated with ustekinumab
CD: Crohn’s disease; IBD: inflammatory bowel disease; OTU: operational taxonomic unit; SCFAs: short-chain fatty acids; TLs: trough levels; TNF: tumor necrosis factor; UC: ulcerative colitis.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Privitera, G.; Pugliese, D.; Rapaccini, G.L.; Gasbarrini, A.; Armuzzi, A.; Guidi, L. Predictors and Early Markers of Response to Biological Therapies in Inflammatory Bowel Diseases. J. Clin. Med. 2021, 10, 853.

AMA Style

Privitera G, Pugliese D, Rapaccini GL, Gasbarrini A, Armuzzi A, Guidi L. Predictors and Early Markers of Response to Biological Therapies in Inflammatory Bowel Diseases. Journal of Clinical Medicine. 2021; 10(4):853.

Chicago/Turabian Style

Privitera, Giuseppe, Daniela Pugliese, Gian Ludovico Rapaccini, Antonio Gasbarrini, Alessandro Armuzzi, and Luisa Guidi. 2021. "Predictors and Early Markers of Response to Biological Therapies in Inflammatory Bowel Diseases" Journal of Clinical Medicine 10, no. 4: 853.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop