| Literature DB >> 35734396 |
Dan Pu1, Zhe Zhang1, Baisui Feng1.
Abstract
Inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis, is a chronic immune-mediated inflammatory disorder of the gastrointestinal tract that is closely associated with dysbiosis of the intestinal microbiota. Currently, biologic agents are the mainstream therapies for IBD. With the increasing incidence of IBD, limitations of biologic agents have gradually emerged during treatment. Recent studies have indicated that gut microbiota is highly correlated with the efficacy of biologic agents. This review focuses on alterations in both the components and metabolites of gut microbiota during biological therapy for IBD, systematically summarises the specific gut microbiota closely related to the clinical efficacy, and compares current predictive models for the efficacy of biologics, further highlighting the predictive value of intestinal microbiota. Based on the mechanistic analysis of faecal microbiota transplantation (FMT) and biologic agents, a new therapeutic strategy, comprising a combination of FMT and biologics, has been proposed as a promising treatment for IBD with improved efficacy.Entities:
Keywords: biologic agents; biomarkers; combination therapy; faecal microbiota transplantation; gut microbiota; inflammatory bowel disease
Year: 2022 PMID: 35734396 PMCID: PMC9207480 DOI: 10.3389/fphar.2022.906419
Source DB: PubMed Journal: Front Pharmacol ISSN: 1663-9812 Impact factor: 5.988
FIGURE 1Baseline levels of gut microbial taxa and intestinal metabolism in IBD patients associate with different responses to biologic agents (Kolho et al., 2015; Magnusson et al., 2016; Shaw et al., 2016; Ananthakrishnan et al., 2017; Doherty et al., 2018; Zhou et al., 2018; Aden et al., 2019; Ribaldone et al., 2019; Zhuang et al., 2020; Lee et al., 2021; Ventin-Holmberg et al., 2021). (A) Lineage diagram showing the baseline abundance of specific gut microbes positively (in green font) or negatively (in red font) correlated with the good response to biologic agents; (B) The left panel shows intestinal metabolites that are positively correlated with the good response to the biologic agents; the right panel shows intestinal metabolites that are negatively correlated with the good response to the biologic agents.
FIGURE 2Summary of previous predictors of response to biologic agents (Esters et al., 2002; Parsi et al., 2002; Arnott et al., 2003; Louis et al., 2004; Ferkolj et al., 2005; Hlavaty et al., 2007; Arijs et al., 2010; Schreiber et al., 2010; Kiss et al., 2011; Gazouli et al., 2013; Lee et al., 2013; Atreya et al., 2014; Billiet et al., 2015; Juillerat et al., 2015; Vande Casteele et al., 2015; Bek et al., 2016; Detrez et al., 2016; Ding et al., 2016; Zittan et al., 2016; Lopetuso et al., 2017; Nasuno et al., 2017; West et al., 2017; Boden et al., 2018; Gaujoux et al., 2019; Jung et al., 2019; Verstockt et al., 2019; Wilson et al., 2020; Agrawal et al., 2022; Bertani et al., 2022)
Comparison of predictive effects of different prediction models with or without gut microbiota.
| Study author | Biologics | Disease | Specimen | Design | Research technique | Population (cohort size) | Predictive markers | Prediction accuracy/AUC | Prediction time | |
|---|---|---|---|---|---|---|---|---|---|---|
| Prediction model with gut microbiota |
| IFX | UC | Mucosal biopsy | cross-sectional | microarray and qPCR | cohort A: adult (24) | top five genes from cohort A | accuracy:A to A (83%) A to B (59.1%) | week 4–6 |
| cohort B: adult (22) | top five genes from cohort B | accuracy:B to B (90.9%) B to A (70.8%) | Week 8 | |||||||
|
| IFX | CD | clinical data | retrospective | Matrix model | adult CD (201) | age at first IFX, BMI, and previous surgery | 0.78 < AUC <0.80 | week 14 | |
|
| IFX | IBD | serum | prospective | ELISA | adult CD (184) | Infliximab trough level | AUCTLweek2 = 0.72 | week 14 | |
| adult UC (107) | Infliximab trough level | AUC TLweek2 = 0.81 | week 14 | |||||||
|
| IFX | IBD | serum | retrospective | ELISA | adult (140) | Infliximab levels <6.8 μg/ml ATI > 4.3 μg/mL-eq | AUC = 0.68 | week 14 | |
| AUC = 0.78 | ||||||||||
|
| VDZ | CD | GEMINI 2 and VICTORY Dataset | cross-sectional | Model derivation | discovery cohort: GEMINI 2 (814) Validation cohort: VICTORY (336) | Individual multi-variable logistic regression prediction models | AUC = 0.67 | week 26 | |
|
| IFX | CD | serum, clinical data | prospective | ELISA | discovery cohort: adult (16) Validation cohort: RISK(668), PRISM(155) | CDAI | accuracy: CD (58.7%) | week 30 | |
| Fecal calprotectin | accuracy: CD (62.5%) | |||||||||
|
| IFX | IBD | feces, serum | cross-sectional | ELISA and near-infrared particle immunoassay | adult (CD: 76 | Fecal calprotectin >221 μg/g | AUC = 0.71 | week 12 | |
| CRP > 2.1 mg/L | AUC = 0.58 | |||||||||
|
| IFX/ADA | IBD | Mucosal biopsy | cross-sectional | RNA-seq and microarray | GEO and SRA databases | GIMATS module | AUC = 0.720–0.853 | week 4–6 | |
| VDZ | AUC = 0.661–0.728 | |||||||||
|
| IFX/UST/VDZ | IBD | feces, serum | prospective | Random forest classifiers | adult (CD: 108 UC: 77) | clinical features | AUC = 0.624 | week 14 | |
| Prediction model with gut microbiota |
| IFX,VDA | IBD | feces | prospective | 16srRNA | adult (CD: 42 UC: 43) | Gut microbiota | AUC 0.872 | week 14 |
|
| IFX | IBD | feces | prospective | 16srRNA | Discovery cohart :adult (16) Validati Cohort: RISK (668) PRISM(155) | Gut microbiota | Accuracy.CD (87.5%) UC (79.1%) | week 30 | |
|
| IFX | IBD | feces, serum,Clinical, data | prospective | 16srRNA ELISA | Discovery cohart :adult (16) Validati Cohort: RISK PRISM(155) | Gut microbiota+FC+CDAI | Accuracy.CD (93.8%) | week 30 | |
|
| UST | CD | feces, serum,Clinical, data | prospective | 16srRNA | Adult(306) | Gut microbiota | AUC = 0.844 | week 6 | |
|
| IFX | CD | feces | prospective | 16srRNA | Adult(49) | Gut microbiota | Clinical response (83.4%) Clinical response (83.4%) endoscopic response(89.1%) | week 30 | |
|
| IFX | IBD | feces | prospective | 16srRNA | adult (CD: 25 UC: 47) | Gut microbiota | CD AUC = 0.933 UC AUC=0.818 | week 12 | |
|
| IFX/UST/VDZ | IBD | feces, serum | prospective | Metagenomic Sequencing | adult (CD: 108 UC: 77) | Gut microbiota+Clinical Features | AUC = 0.849 | week 14 |
FIGURE 3Common targets of FMT and biologic agents in the regulation of intestinal inflammatory responses. (A) FMT and biologics co-inhibit the activation and proliferation of pathogenic Th17 and Th1 cells; (B) FMT and biologics co-target the crosstalk between neutrophils and pathogenic Th17 cells; (C) FMT and biologics jointly promote the proliferation of Foxp3+ Treg cells and the formation of the anti-inflammatory cytokine IL-10; (D) FMT and biologics downregulate the function of helper T cells through inhibiting antigen presentation by APCs, including macrophages and dendritic cells, thereby suppressing the expansion of the inflammatory response; Meanwhile, FMT and biologics jointly promote the production of the anti-inflammatory cytokine IL-10 by APC.