Health in a post-pandemic EU

KEEPING AN EYE ON AI-POWERED DRUGS
If these tests are successful, AI could
upend the drug discovery process. Researchers
typically spend years sifting
through troves of data and test results to
land on promising drug candidates in the
lab – only for many to fail during clinical
trials.
AI models could improve the odds by
helping researchers identify the right target
in the body for a particular disease,
then find or even create the right molecule
to interact with it, and lastly, predict
which patients it could help. Pharma
companies could then invest in only
the most favorable options, cutting out
much of the early trial and error.
It isn’t a surefire strategy: Exscientia’s
OCD trial, for example, shuttered in 2021
after failing to meet its targets. But ultimately,
the goal is to bring cheaper medicines
to patients faster, while bringing in
billions of dollars in profits.
“Just from drug discovery to clinical
development, that span is about five and
a half years,” said Aarti Chitale, a senior
industry analyst for health care and life
sciences at the advisory firm Frost & Sullivan.
“Some of the leading AI vendors
are able to bring that duration down to
only about 18 months.”
Money pours in
Investors have taken note of the opportunity,
pouring at least $10 billion into
startups targeting AI in early drug development
since 2019, while European
pharma giants have announced major
deals to expand their AI capabilities.
France’s Sanofi, for example, inked a
$1.2 billion pact with Atomwise to sort
through small molecules in 2022, while
the British-Swedish AstraZeneca expanded
its partnership with the United
Kingdom’s BenevolentAI to hunt for
treatments for systemic lupus erythematosus
and heart failure, in addition
We believe
that there is
huge promise
from artificial
intelligence
in terms of
medicines
development,”
Peter Arlett
to chronic kidney disease and idiopathic
pulmonary fibrosis.
As of 2022, there were nearly 270 companies
working on AI-powered drug discovery
around the world, with Western
Europe serving as a growing hub, according
to consultancy firm McKinsey & Co.
“We believe that there is huge promise
from artificial intelligence in terms of
medicines development,” said Peter Arlett,
head of data analytics and methods
for European Medicines Agency, which
oversees pharmaceutical products for the
European Union.
Notably, the use of AI for drug discovery
is generally considered low-risk because
if a potential medicine fails, it fails in a
simulation, not a patient. Instead, AI
likely poses a greater risk in later stages of
drug development given the potential for
ethical issues, risks of human biases to
work their way into algorithms or flawed
data analyses that are used in a drug’s application
for regulatory approval.
Regulating pharma AI
As pharmaceutical companies lean more
heavily on AI across the therapeutic
pipeline, regulators are catching up to
ensure these tools are used safely. The
EMA published a draft paper this summer
on the path forward for AI in drug
development, and will hold a workshop
in November to solicit feedback from the
pharma sector and other stakeholders.
“We see it as the start, the very start,
of [AI] guidance and regulation in the
pharmaceutical sector,” said Arlett, who
is also co-chair of the EMA’s Big Data
Steering Group.
The reflection paper is set to be finalized
by late 2024, but it will likely “change
significantly” before then based on external
feedback, Arlett said. While the
document won’t be binding, it will offer
a more concrete picture of the regulatory
guidance to come in 2025 or 2026, which
pharma companies will be expected to
follow.
Heading into the November workshop,
Arlett said regulators broadly agree that
they should categorize the risks of AI
for different purposes so that “we don’t
over-regulate where the use of AI may be
just a background process, and not impact
the benefit-risk balance for a medicine.”
Even so, he said regulators should have
at least some access to drugmakers’ algorithms
and the data used to train their
models during the discovery process, as
well as insight into how algorithms are
used to manage medicines after they’ve
been authorized – for example, if an algorithm
helps to determine a patient’s
insulin dosages, the EMA wants to know
how it works. The level of transparency
that will be required is still up for debate.
“Because the algorithm is learning, we
will need probably to think innovatively
as to how we oversee that,” Arlett said.
“The existing framework, which is rather
strict and very structured … may not be
optimal for something that’s as fast-moving
as a learning algorithm.”
The industry responds
The pharma industry is keeping tightlipped
ahead of the November workshop,
though executives from some
leading firms, including Exscientia, have
pushed back against proposals to establish
AI-specific drug discovery regulations.
In a statement, the Brussels-based trade
group European Federation of Pharmaceutical
Industries and Associations
said that new AI policies should “balance
benefits and risks of AI while supporting
and fostering innovation,” and that
“we already have a robust framework for
handling statistical and predictive models
and software that will apply to many
uses of AI in medicines development.”
Regardless of looming changes to the
regulatory landscape, drugmakers still
need to figure out how to bring AI-powered
medicines to market – and prove
that they’re more beneficial than existing
treatments. Ultimately, clinical success
will be the key determinant for how
widely AI is used for drug discovery, rather
than time or cost savings, as noted by
the Boston Consulting Group.
Transforming pharma
The industry faces some other challenges,
too. AI and machine learning models
need robust, high-quality datasets
to work well, and a central repository
for drugmakers doesn’t yet exist in Europe.
Further, most investment in the
past five years has been in high-income
countries and focused on the profitable
fields of oncology and neurology, leaving
infectious diseases – which carry a much
greater health burden globally – underinvested
in, excluding COVID-19.
Global economic uncertainty could also
slow progress for smaller firms and startups,
Chitale said. While venture capital
funding for AI-powered drug discovery
startups soared in 2021, reaching $4.7 billion,
that level was much lower in 2022
and 2023, in line with a broader investment
slowdown, according to analytics
firm CB Insights.
Even so, industry players, academics and
funders believe AI is poised to transform
the pharma sector. In a recent survey, 84%
of those currently using AI said they expect
it to play a significant role in drug discovery
over the next five years, compared with
70% among those not using AI.
In Europe, the use of AI isn’t limited to
the early stages of research into potential
blockbuster medicines. EFPIA, the drug
industry trade group, said major pharma
companies are “employing AI and ML
approaches across the entire lifecycle of
medicines development” – from drug
discovery and manufacturing to safety
monitoring and beyond. •
About
Gaby Galvin
Gaby Galvin is a freelance journalist
based in Amsterdam. Her work
has appeared in Euronews, U.S.
News & World Report and Healthcare
Brew, among others.
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