My name is Slavé Petrovski, I’m the Vice President and Head of
AstraZeneca's Centre for Genomics Research.
My background training is in human and statistical genetics, and I joined
AstraZeneca a couple of years ago to drive this group of genome analysts and
informaticians who are responsible for building the capabilities required
to analyze large scale human genetics data.
It's a ten-year initiative with the goal of analysing the sequence data
of up to 2 million human individuals.
Now we are averaging around 1,300 exomes per hour
for end-to-end informatics processing.
Big challenge there was building up those capabilities,
from the ground up to ensure that
we will be able to scale at two million genomes, which we're
talking petabytes of genomics data.
In addition to actually the infomatics part of turning
that raw data into information.
There's also the other arm of it, which is turning that information to knowledge.
And this is where the analytics team comes in.
To put into context, ten petabytes of genomics data is approximately equivalent
to over three billion iTunes songs.
When we set up with the genomics initiative we knew that our goal being
two million genomes would require petabytes of storage and informatics processing space.
It was clear based on current provisions locally
the storage capabilities and the kind of the horsepower
if you like, was insufficient to be able to cover this.
So we made the decision to go straight directly to the cloud, where we have an
opportunity to have a highly scalable infrastructure, which is also very elastic
in the sense that allows us to use and turn on and off as needed
throughout the life cycle of the project.
A big shift that's happening now that we are noticing is a migration from
generating exome to whole genome data.
Now to put this into context, an exome is about 2% of our genome,
an exome is 30 million data points. Whereas a whole genome, now we're going to
three billion data points per individual.
At AstraZeneca science is an important part of how we run R&D.
And a key part of that is asking the killer questions at every step of the pipeline.
We’ve embedded genomics, into our drug development pipeline,
to help us enrich for targets that are more likely to succeed
on the basis of having human genetics evidence.
And so as a company, how we are using genetics
Can be summarized into four pillars If you like.
First one being identifying novel targets, particularly novel targets
in patient populations with unmet clinical needs.
Another one is actually validating or invalidating existing targets
that may have come from other sources on the basis of human genetics support.
So can we determine whether a particular medicine is likely
or not to succeed later on,
If we have human genetics data earlier on.
Thirdly, it's about identifying what are the market opportunities
and repositioning opportunities.
So we can now use genetics to identify whether inhibition of a particular target,
is likely to be beneficial on the basis of identifying individuals in the population
who are born with natural inhibition of that target through a genetic mutation.
If those individuals share the phenotypes as expected, then that is encouraging.
If they don't it starts to raise additional questions so we can
do some additional research.
And finally, it's about really ensuring that we matched our medicines to the
patients that are most likely to benefit them based on their genetic profile,
their molecular cause of disease.
We have a great relationship collaborating with Illumina,
particularly the engineering team,
on the various DRAGEN pipelines that they are working on.
The one that we focus on primarily at the moment is the DRAGEN Germline Pipeline.
I'm proud to work for a company that pushes the boundaries of science to identify
new opportunities for medicines for patients with unmet clinical needs.
And we can use genomics to help us achieve this by identifying individuals
in the population who are most likely to benefit from particular medicines.