Where are the biggest gaps in current technologies to understand complex interactions?

As with any new field of research, there is a lot of excitement around the potential of human microbiome therapeutics, but also a lot of uncertainty. As the complexity of live microorganisms continues to baffle and inspire simultaneously, what roles could technology play to support our understanding?

In June 2019, we asked microbiome therapeutics professionals around the world where the biggest gaps are in technology to aid research and development within the field. The following is a selection of the varied responses to this question.

“A comprehensive enterprise solution to combine all researches and results together.”

“A lack of sampling capability in the site of interest - we analyse stool and consider it a useful indicator of everything from the stomach to the descending colon.”

“Addressing functionality of microbiome.”

“Identifying host-microbe mechanisms.”

“Improvement in machine learning algorithms that can be applied to the large data, to be able to find correlations in the complex interactive network between the host and their microbiome.”

“Better models of in vivo interactions that correlate to humans.”

“Biomarkers for disease susceptibility and treatment effect.”

“Compelling data on the efficacy or treatments.”

“Defined, standardized animal models.”

“Endogenous retroviral DNA influence, anthropological data exploration of adolescent immune exposure that determines microbiome landscape.”

“Getting results that are repeatable and specific down to species and bacterial metabolite.”

“How to employ extant knowledge to make patient clinical decisions regarding probiotics. The rheumatologist’s use of data to effect autoimmune/CTD patients.”

“PK of LBPs and how they can be manipulated despite inter-patient variability.”

“Standardization and communication.”

“Identification/precise microbiome mapping and connection with disease.”

“Interplay between microbial sub strains, bacteriophages and other microbiome with diet and its effect on metabolism. The technology more often gives a snapshot and it is difficult to assess how bacterial turnover really affects physiology. We need elegant high throughput compact data sets to work with. This is the biggest hurdle, it is unclear what tiny amounts of rare bacteria might do.”

“Lack of understanding of molecular pathways and sequencing expertise within R&D.”

“Complex data analysis for pleiotropic mechanisms, manufacturing capacity of anaerobes, restrictive regulatory design which doesn't correspond to systems approaches necessary for microbiome-based applications.”

“Connecting microbial strains and species to functional activity.”

“Defined communities to pinpoint the effects of specific microbe-associated metabolic pathways to host phenotypes.”

“Modulation of different microbiome populations and communities to systematically define their interactions with the host and computer simulations/modelling of these effects.”

“Strain level ID and phenotyping.”

“Translational research.”