Anthony
Finbow explains how applying microbiome-based evidence to disease modelling
will enable researchers to devise more targeted treatments.
Life
sciences R&D has operated without reliable data science to support evidence
of correlations between a pharmaceutical product and the microbiome of the
recipient; which in turn has a bearing on immune responses, allergic
responses and more. Now, there is a growing acknowledgement of the role of the microbiome when modelling
disease, wellness and the impact of therapeutics.
Arguably,
the human microbiome and the wider planetary microbial ecosystem have taken a
hard hit over the last 50 years. Intensive farming, an overuse of chemicals in
the soil, antibiotics in the food chain
and a preference for convenience and processed foods have left even the richest
nations under-nourished, which is having a detrimental effect on public health.
The impact can be seen in the rise in diabetes, asthma and other long-term
health issues across large populations. Reversing these negative consequences
requires an appreciation of what has gone wrong, as well as an understanding of
how to put matters right. This demands an understanding of the workings of the
microbiome.
Stimulated
by COVID-19, stakeholders from across multiple sectors have recommitted their
focus to wellness and the notions of ‘food as medicine’ and ‘bugs as drugs’
(for example, pre- and pro-biotics).
Focus on genetics
While
influencing human genetics can take generations to have an impact, the
opportunity to continuously improve the human microbiome and produce positive
health outcomes is thought to be substantial. Particularly now with COVID-19,
there is an intense focus on microorganisms and how people can stay healthy and
combat the coronavirus.
There is growing thinking that the damage to cells from SARS-CoV-2
enables other bacteria to get a better foothold.
Unsurprisingly,
there is a substantial business opportunity attached to the field. A report suggested
that many trillions of dollars of business opportunity could be unlocked with a
greater understanding of the microbiome in life sciences alone.
However,
before any of this can happen, even the most ambitious organisations need to be
able to analyse and understand the microbiome – something which has been almost
impossible until now.
Microbiome
science has lagged behind other fields because there has been no real way to
combine all of the complex and disparate datasets and cross-compare them to
produce reliable conclusions about the interplay of different medicinal
ingredients or nutritional elements on a person’s microbiology.
Supporting
advances in immunopharmacology
This
advanced, multi-dimensional data science capability is critical to support
advances in immunopharmacology – ie, working with the microbiome or improving
its health, to produce better patient outcomes. Trailblazers such as Finch
Therapeutics, Microbiotica and Seres Therapeutics are among the innovative
therapeutics companies working to develop products in this area – for example
using microorganisms as the primary mode of addressing gut disorders. More
generally, there are numerous pharma companies that want to understand the role
of the microbiome in metabolising drugs and determining their efficacy.
To support
their endeavours and provide scientific evidence in support of their products,
R&D innovators need to apply microbiome science to their investigations and
developments. The ability to do this will enable more ‘stratified’ therapeutics
– the ability to target treatments more specifically at a certain category of
patient.
Technologically,
scientists are at a critical point now with microbiome discovery. Previously,
the infrastructure was not established to allow reliable signals to be detected
and move knowledge forward, but this situation is changing rapidly.
There is
growing agreement that network science (by which we mean understanding broader
biological interactions fully) will be the foundation for life sciences in the
future. This involves being able to explore data in a more connected way, which
is becoming possible now by using the latest multi-dimensional data analytics
and knowledge discovery, all powered in the cloud and advancing artificial
intelligence (AI) technologies.
Insight from big data
We have got
to a point where it is possible for companies to access the massive scale of
computing and apply sophisticated analytics to complex datasets and
constellations of data, to derive correlations and explore causal relationships
– employing AI to distil meaningful insights from this big data.
There are
still some practical hurdles to overcome, for example around data
standardisation. Yet, advances in so-called graph databases (which map
relationships between diverse datasets), AI-based analytics and the mainstream
accessibility of powerful computer processing are allowing life sciences
R&D organisations to distil new, credible scientific evidence about their
products’ interactions with the microbiome to help advance product innovation.
Within the
next decade, as more is done to standardise the metadata that describes
scientific data so it can be combined and cross-analysed reliably, we can
expect to achieve a much deeper understanding of the microbiome.
The good
news is that there is now a greater willingness among different organisations
to pool their data so it can drive step-changes in scientific discovery, which
will help accelerate progress. The urgency around COVID-19
has inspired global collaboration at an unprecedented level, which bodes well
for further life sciences breakthroughs once there is greater agreement around
how data is formatted and described, making it easier to cross-analyse.
An extended
digital ecosystem for microbiome research is key. Once life sciences innovators
can combine their own findings with external data from sources such as the
European Bioinformatics Institute, this could help their endeavours by
promoting a deeper understanding of the role of the microbiome in the performance
of their products. More distributed analysis and connective learning will
enable companies to make bigger strides forward.
Investment
in data standardisation will enable pharma researchers to query billions of
available data combinations to find specific information to support scientific
discovery. This will help life sciences leaders continue the momentum of
meeting the COVID-19
health challenge at speed and accelerate improved health outcomes for a wide
range of conditions in 2021 and beyond.
About the
author
Anthony
Finbow is Chief Executive Officer at Eagle Genomics, headquartered
in Cambridge, UK.
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