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Artificial intelligence (AI) is one of the biggest buzzwords of
2023. With new generative AI platforms hitting the internet for
free use, it’s no wonder that the technology has quickly become
a mainstream phenomenon. It’s also picked up steam in the life
sciences industry as a way to expedite the drug discovery and
development process.
Current processes for drug discovery and development are
time-intensive and expensive, taking between 10 to 15 years and
$1.5 to $2 billion to bring a new drug to market. But with the
power of AI and machine learning (ML) to analyze and manage large
bodies of data, it has been estimated that life sciences companies
can save nearly $54 billion in research and development costs each
year by using the technology.
AI can sift through data from clinical trials, electronic health
records and medical publications to find patterns and even predict
outcomes at much faster and more efficient rates than humans. It
can help R&D teams understand biological mechanisms and
underlying diseases of populations and discover novel targets to
counteract those diseases. It also has the potential to identify
the right therapeutic dose, optimize the selection of suitable
subjects for clinical trials, find new avenues for repurposing
existing therapies, and can enable life sciences companies to
explore de novo drug design.
Advancements in AI-enabled Drug Discovery
Since 2020, investment in intelligent drug discovery and
development has grown exponentially. Third-party investment in AI-enabled drug
discovery has more than doubled annually for the last five years,
jumping from $2.4 billion in 2020 to $5.2 billion in 2021. Life
sciences companies are also increasingly investing internally to
build their own AI infrastructure to pursue intelligent drug
research and development. According to the Food and Drug
Administration (FDA), more than 100 drug and biologic application
submissions in 2021 had AI/ML components.
Indeed, while AI-enabled drug R&D is still relatively new,
there have been a number of key milestones in recent years that
indicate intelligent drug development is real and is gaining
momentum.
- 2020: First-ever AI-designed drug molecule
created by start-up Exscientia entered human clinical trials to
treat patients with obsessive-compulsive disorder. - 2021: AI system called AlphaFold from DeepMind
predicted the protein structures for 330,000 proteins, including
all proteins in the human genome. - 2022: Insilico Medicine started Phase I
clinical trials for the first-ever AI-discovered molecule based on
an AI-discovered novel target. - 2023: The FDA granted the first-ever Orphan
Drug Designation to a drug discovered and designed using AI from
Insilico Medicine.
Notably, this past May the FDA published an Initial Discussion Paper seeking feedback from
stakeholders on the use of AI and ML in drug discovery and
development to inform the regulatory landscape. Recognizing the
increased use of AI and ML throughout the drug development life
cycle across a range of therapeutic areas and acknowledging that
this technology is sure to play a critical role in drug development
moving forward. The FDA has stated that it “plans to develop
and adopt a flexible risk-based regulatory framework that promotes
innovation and protects patient safety.”
Criteria for AI/ML in Drug Development
While the FDA appears to be cautiously optimistic about AI/ML
drug development, it has outlined key regulatory areas of interest
including human-led governance, accountability and transparency;
quality, reliability and representativeness of data; model
development, performance, monitoring and validation. With this
framework in mind, there are a few considerations for AI/ML-led
drug discovery and development from FDA’s discussion paper that
life sciences companies interested in this area should keep in
mind.
- Human involvement – The FDA believes
that human-led governance, accountability and transparency will be
critical to AI-enabled drug discovery to ensure adherence to legal
and ethical values. The FDA suggests that risk management plans be
used as a form of governance to guide the level of documentation,
transparency and explainability of AI/ML models being used and
their outputs. Essentially, life sciences companies will need to be
able to provide critical insight into how algorithms function and
be able to explain and interpret outputs. - High-quality, reliable, and representative
data – A common criticism of AI today is that it can
amplify existing bias in data. Life sciences companies using AI/ML
models will want to ensure the data they are using is strong and
representative of the entire population that the intended therapy
is targeting. The FDA is also focused on data privacy and security,
ensuring that appropriate measures are in place. - Criteria for development and assessing models
– While the FDA acknowledges the potential of AI/ML to
accelerate drug development and make clinical trials more
efficient, it is wary of the technology introducing specific risks
and harms. Life sciences companies interested in using AI/ML should
look to establish criteria for developing AI/ML models that are
trustworthy and for assessing models on risk, credibility and
complexity. - Evaluation of model over time – The FDA
emphasizes the importance of regularly monitoring AI/ML models and
documenting results to ensure the models are reliable, relevant and
consistent over time. - External validation – Finally, the FDA
recommends that AI/ML models and algorithms used for drug discovery
and development be externally validated using independent
data.
Your Roadmap to Success
As AI and ML models continue to advance and mature, their use in
drug discovery and development will only continue to grow.
According to Boston Consulting Group, biotech companies
using an “AI-first” approach had more than 150
small-molecule drugs in discovery and more than 15 in clinical
trials as of March 2022. For life sciences companies interested in
tapping into the world of AI/ML-enabled drug discovery and
development, here are a few steps you can follow to get
started.
- Identify the business goals you hope to achieve by
integrating AI. Whether you want to use AI/ML to reduce
research costs or are interested in encouraging the market, your
business goals will impact how you integrate AI/ML into your
R&D processes. - Align AI use cases with business objectives.
As outlined previously, AI and ML have many use cases in drug
discovery and development such as clinical trial participant
selection or de novo drug design. Determine what use case aligns
best with the needs of your organization. - Identify project requirements and assess whether you
have the team and technology to meet them. If you’re
investing in building an AI/ML model, you want to ensure you have
the right people and resources to maintain and monitor it. For
example, do you have the in-house capabilities to build an
algorithm or will you need to engage a third-party partner? Knowing
the answers to these questions will help you develop the proper
strategy and determine what your investment needs to be. - Determine how “success” will be objectively
measured. Finally, it’s always important to outline
key performance indicators to guide your AI initiatives. For
example, is there a specific ROI you want to achieve? What is the
breaking point of an AI investment?
Intelligent drug discovery is a burgeoning area of the market
that will only continue to see increased investment. Life sciences
companies looking to expedite drug discovery and development while
managing costs should consider the benefits – and the
drawbacks – of AI and machine learning. Buchanan’s life sciences attorneys are
attuned to advances in AI-led drug discovery and are equipped to
help life sciences companies navigate the changing requirements and
regulations of this new market.
The content of this article is intended to provide a general
guide to the subject matter. Specialist advice should be sought
about your specific circumstances.
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