FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2569418464> ?p ?o ?g. }

• W2569418464 endingPage "5" @default.
• W2569418464 startingPage "4" @default.
• W2569418464 abstract "Modulation of the human immune system has become the focus for several novel approaches to treat conditions related to immune dysregulation, chronic infections and oncology. The human gut microbiome is being recognized as a key factor associated with the innate immune response, and exploring it has resulted in the identification of leads for therapeutics to treat conditions related to immune dysregulation and chronic infections, such as asthma, allergic rhinitis, eczema, IBD, IBS, Crohn's disease, chronic intestinal infections and various forms of food allergies like allergies to peanuts, shellfish and dairy products. A number of companies are active in this area and developing therapeutic faecal microbiome transplants [FMT (Van Nood et al., 2013)] and defined microbial consortia to treat infections with Clostridium difficile, of which the products of Rebiotix and Seres Therapeutics are among the most advanced. Overall, synthetic microbiomes (designer formulations) for transplants are preferred over FMT to attain desired/demanded standardization and safety standards, mode of action understanding and acceptability by regulatory agencies (Bojanova and Bordenstein, 2016). However, recent clinical trials to treat chronic infections with C. difficile using FMT were successful (Van Nood et al., 2013), while a defined microbial consortium performed below expectation. Therefore, additional research is required to better understand the critical roles and interdependencies of keystone strains in the human gut microbiome to design successful therapeutics. The majority of designer formulations for modulating the immune response revolve around human-derived butyrate-producing bacterial species that belong to the Clostridia classes IV and XVIa to induce the accumulation of regulatory T cells that lead to the control of inflammation, a decrease in the secretion of a proinflammatory cytokine, or an enhanced secretion of an anti-inflammatory cytokine by a population of human peripheral blood mononuclear cells. The best-documented example of this approach is the work by Kenya Honda around a 17 species Clostridium strain consortium (Atarashi et al., 2011, 2013), VE202, which is currently being developed by companies like Vedanta Biosciences and Johnson & Johnson. Thus far, such probiotic formulations have proven useful in the treatment of immune disorders in only a subset of patients, further supporting the case for the need to better understand the interdependencies and interactions among keystone strains to improve engrafting and performance of probiotic formulations based on synthetic microbiomes. The complexity of the human gut microbiome has limited the development of microbiome-based therapeutics. This has prompted several efforts to develop predictive models to study the critical interdependencies of microbiome keystone species and the impact of host–microbiome interactions in specific diseases. Recent examples of such modelling systems include CASINO – Community and Systems-level Interactive Optimization (Shoaie et al., 2015) – and AGORA – Assembly of Gut Organisms through Reconstruction and Analysis (Magnúsdóttir et al., 2016). It is expected that in the near future, predictive modelling will change the way microbiome research and development is being carried out, not just for microbial therapeutics, but also in adjacent areas, such as immunotherapy drugs for cancer treatment. Examples of processes, which are constrained by costs and time for experimental validation and will benefit from predictive modelling, include mode of action understanding, finding new indications, add-on/adjunct therapies, root cause analysis, understanding of adverse events, optimized engraftment, effects of diet, secondary prevention (comorbidity), identification of predictive biomarkers, optimized trial design, detailed cohort studies and sample size extrapolation. An example of a start-up company that is at the forefront of using predictive modelling for every aspect of their R&D platform is Gusto Global: their modelling platform enables a significant (100-fold or more) in silico reduction of experimental permutations for hypothesis-driven experimental confirmation, mode of action understanding and product optimization. This provides a substantial opportunity for rapid optimization of existing microbial therapeutics through in silico modelling, as well novel therapeutic prediction. This is a rapidly developing area that requires a substantial focus on efficacy and reproducibility that will enable clinical application at scale." @default.
• W2569418464 created "2017-01-13" @default.
• W2569418464 creator A5017949757 @default.
• W2569418464 creator A5057060580 @default.
• W2569418464 creator A5068554577 @default.
• W2569418464 creator A5076146911 @default.
• W2569418464 date "2017-01-01" @default.
• W2569418464 modified "2023-10-14" @default.
• W2569418464 title "The microbiome as a source of new enterprises and job creation: Considering clinical faecal and synthetic microbiome transplants and therapeutic regulation" @default.
• W2569418464 cites W2110389012 @default.
• W2569418464 cites W2148171446 @default.
• W2569418464 cites W2164386968 @default.
• W2569418464 cites W2169527381 @default.
• W2569418464 cites W2461637653 @default.
• W2569418464 cites W2559597060 @default.
• W2569418464 doi "https://doi.org/10.1111/1751-7915.12597" @default.
• W2569418464 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5270749" @default.
• W2569418464 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28052560" @default.
• W2569418464 hasPublicationYear "2017" @default.
• W2569418464 type Work @default.
• W2569418464 sameAs 2569418464 @default.
• W2569418464 citedByCount "9" @default.
• W2569418464 countsByYear W25694184642017 @default.
• W2569418464 countsByYear W25694184642018 @default.
• W2569418464 countsByYear W25694184642020 @default.
• W2569418464 countsByYear W25694184642022 @default.
• W2569418464 countsByYear W25694184642023 @default.
• W2569418464 crossrefType "journal-article" @default.
• W2569418464 hasAuthorship W2569418464A5017949757 @default.
• W2569418464 hasAuthorship W2569418464A5057060580 @default.
• W2569418464 hasAuthorship W2569418464A5068554577 @default.
• W2569418464 hasAuthorship W2569418464A5076146911 @default.
• W2569418464 hasBestOaLocation W25694184641 @default.
• W2569418464 hasConcept C104317684 @default.
• W2569418464 hasConcept C143121216 @default.
• W2569418464 hasConcept C15151743 @default.
• W2569418464 hasConcept C177713679 @default.
• W2569418464 hasConcept C190944805 @default.
• W2569418464 hasConcept C203014093 @default.
• W2569418464 hasConcept C207480886 @default.
• W2569418464 hasConcept C2777165150 @default.
• W2569418464 hasConcept C2780194698 @default.
• W2569418464 hasConcept C2994496256 @default.
• W2569418464 hasConcept C501593827 @default.
• W2569418464 hasConcept C55493867 @default.
• W2569418464 hasConcept C60644358 @default.
• W2569418464 hasConcept C71924100 @default.
• W2569418464 hasConcept C86803240 @default.
• W2569418464 hasConcept C8891405 @default.
• W2569418464 hasConcept C89423630 @default.
• W2569418464 hasConcept C91478284 @default.
• W2569418464 hasConceptScore W2569418464C104317684 @default.
• W2569418464 hasConceptScore W2569418464C143121216 @default.
• W2569418464 hasConceptScore W2569418464C15151743 @default.
• W2569418464 hasConceptScore W2569418464C177713679 @default.
• W2569418464 hasConceptScore W2569418464C190944805 @default.
• W2569418464 hasConceptScore W2569418464C203014093 @default.
• W2569418464 hasConceptScore W2569418464C207480886 @default.
• W2569418464 hasConceptScore W2569418464C2777165150 @default.
• W2569418464 hasConceptScore W2569418464C2780194698 @default.
• W2569418464 hasConceptScore W2569418464C2994496256 @default.
• W2569418464 hasConceptScore W2569418464C501593827 @default.
• W2569418464 hasConceptScore W2569418464C55493867 @default.
• W2569418464 hasConceptScore W2569418464C60644358 @default.
• W2569418464 hasConceptScore W2569418464C71924100 @default.
• W2569418464 hasConceptScore W2569418464C86803240 @default.
• W2569418464 hasConceptScore W2569418464C8891405 @default.
• W2569418464 hasConceptScore W2569418464C89423630 @default.
• W2569418464 hasConceptScore W2569418464C91478284 @default.
• W2569418464 hasIssue "1" @default.
• W2569418464 hasLocation W25694184641 @default.
• W2569418464 hasLocation W25694184642 @default.
• W2569418464 hasLocation W25694184643 @default.
• W2569418464 hasLocation W25694184644 @default.
• W2569418464 hasOpenAccess W2569418464 @default.
• W2569418464 hasPrimaryLocation W25694184641 @default.
• W2569418464 hasRelatedWork W1222064643 @default.
• W2569418464 hasRelatedWork W1577826155 @default.
• W2569418464 hasRelatedWork W1968369492 @default.
• W2569418464 hasRelatedWork W2791249885 @default.
• W2569418464 hasRelatedWork W2896009274 @default.
• W2569418464 hasRelatedWork W2909919296 @default.
• W2569418464 hasRelatedWork W2943085386 @default.
• W2569418464 hasRelatedWork W2972695893 @default.
• W2569418464 hasRelatedWork W3149998985 @default.
• W2569418464 hasRelatedWork W4226166138 @default.
• W2569418464 hasVolume "10" @default.
• W2569418464 isParatext "false" @default.
• W2569418464 isRetracted "false" @default.
• W2569418464 magId "2569418464" @default.
• W2569418464 workType "article" @default.