National Library of Medicine – The Microbiome As A Source of New Enterprise and Job Creation

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January 4, 2017

The Microbiome as a source of new enterprises and job creation: Considering clinical faecal and synthetic microbiome transplants and therapeutic regulation.

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Modulation of the human immune system has become the focus for several novel approaches to treat condi- tions related to immune dysregulation, chronic infections and oncology. The human gut microbiome is being rec- ognized 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 dif- ficile, of which the products of Rebiotix and Seres Thera- peutics are among the most advanced. Overall, synthetic microbiomes (designer formulations) for transplants are preferred over FMT to attain desired/demanded stan- dardization and safety standards, mode of action under- standing and acceptability by regulatory agencies (Bojanova and Bordenstein, 2016). However, recent clin- ical trials to treat chronic infections with C. difficile using FMT were successful (Van Nood et al., 2013), while a defined microbial consortium performed below expecta- tion. 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 theClostridia classes IV and XVIa to induce the accumula- tion of regulatory T cells that lead to the control of inflammation, a decrease in the secretion of a proinflam- matory cytokine, or an enhanced secretion of an anti- inflammatory cytokine by a population of human periph- eral blood mononuclear cells. The best-documented example of this approach is the work by Kenya Honda around a 17 species Clostridium strain consortium (Atar- ashi et al., 2011, 2013), VE202, which is currently being

developed by companies like Vedanta Biosciences and Johnson & Johnson. Thus far, such probiotic formula- tions have proven useful in the treatment of immune dis- orders in only a subset of patients, further supporting the case for the need to better understand the interdepen- dencies and interactions among keystone strains to improve engrafting and performance of probiotic formula- tions 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 interde- pendencies of microbiome keystone species and the impact of host–microbiome interactions in specific dis- eases. Recent examples of such modelling systems include CASINO – Community and Systems-level Inter- active Optimization (Shoaie et al., 2015) – and AGORA – Assembly of Gut Organisms through Reconstruction and Analysis (Magnusdottir 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 experimen- tal validation and will benefit from predictive modelling, include mode of action understanding, finding new indi- cations, add-on/adjunct therapies, root cause analysis, understanding of adverse events, optimized engraft- ment, effects of diet, secondary prevention (comorbid- ity), 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 fore- front of using predictive modelling for every aspect of their R&D platform is Gusto Global: their modelling plat- form enables a significant (100-fold or more) in silico reduction of experimental permutations for hypothesis- driven experimental confirmation, mode of action under- standing 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.

Daniel van der Lelie1, Safiyh Taghavi1, Christopher Henry1,2,3 and Jack A. Gilbert1,4,5 1Gusto Global LLC, 5960 Fairview Road, Suite 400, Charlotte, NC 28210, USA; 2Argonne National Labora- tory, Mathematical and Computer Sciences, 9700 S Cass Avenue, Lemont, IL 60439, USA; 3Computation Institute, University of Chicago, Chicago, IL 60637, USA; 4The Microbiome Center, Bioscience Division, Argonne National Laboratory, 9700 S Cass Ave., Lemont, IL 60439, USA and 5The Microbiome Center, Department of Surgery, University of Chicago, Chicago, IL 60637, USA


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