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19 Sep 2024

How AI is revolutionising the pharmaceutical industry

Dr Jimmy Muchechetere

Dr Jimmy Muchechetere | Equity Research Analyst, Investec UK

AI is revolutionising the pharmaceutical industry, offering big pharma companies a powerful tool to accelerate drug discovery, improve patient outcomes, and reduce costs.
 

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Imagine sifting through mountains of genetic data to pinpoint potential drug targets. For humans, it's a daunting task. But for artificial intelligence (AI), it's a breeze. This is just one way AI is revolutionising the pharmaceutical industry, offering big pharma companies a powerful tool to accelerate drug discovery, improve patient outcomes, and reduce costs.

Leading the charge are companies such as AstraZeneca. It is leveraging AI to unlock new treatment possibilities for inflammatory diseases. By analysing vast amounts of genomic data, it can identify genetic mutations linked to these conditions, leading them to promising drug targets with a higher chance of success.

This isn't just theoretical. Novartis is already prioritising drug candidates with this AI-powered approach. Similarly, Eli Lilly is heavily invested in machine learning algorithms that predict the efficacy and safety of potential drug molecules, significantly reducing the number of failed drug candidates and saving valuable time and resources – a win for both companies and patients.

 

Impact of AI on Drug Development Time

Source: Investec Investment Management, August 2024

 

Overall Drug Development Time with and without AI

Source: Investec Investment Management, August 2024

 

Beyond drug discovery

The impact of AI extends beyond drug discovery. Clinical trials, the crucial phase of testing new drugs on humans, are often plagued by slow enrolment and inefficiencies. Here too, AI steps in as a game changer.

 

AI Use Cases

Source: ‘AI in Drug Discovery’, Vijay Morampudi, October 2022  

Novo Nordisk, a pharmaceutical giant, is a prime example. It uses AI to optimise patient recruitment for clinical trials. By analysing patient demographics and medical histories, it identifies individuals most likely to benefit from a specific treatment. This targeted approach accelerates enrolment and yields more meaningful trial results, ultimately leading to faster development of new therapies.

Safety is paramount in clinical trials. Novartis, another industry leader, has adopted AI-powered platforms that enable real-time monitoring of patient data. This allows researchers to detect adverse events early and adjust the trial design as needed, improving patient safety and reducing overall costs.

But Novartis doesn't stop there. It is employing AI in over 150 projects to streamline drug discovery and boost efficiency across the board. Collaborations with technology giants Microsoft and Nvidia aim to further scale AI applications throughout the company.

 

Traditional vs. AI-Accelerated Drug Development Timeline

Source: Investec Investment Management, August 2024

Sanofi's "plai" AI platform, developed in collaboration with Aily Labs, aggregates internal data to support decision-making across the entire drug development process. This platform is already accelerating mRNA research and optimising clinical trial site selection, demonstrating the versatility of AI in big pharma.

Beyond discovery and trials, AI can breathe new life into existing drugs through a process called drug repurposing.  AstraZeneca is actively exploring this avenue, using AI algorithms to identify potential new uses for existing drugs based on their molecular structures and biological activities. This approach has the potential to significantly accelerate the development of new treatments for various diseases.

 

Not without challenges

While AI offers a multitude of benefits, it's not without its challenges. Implementing AI solutions requires substantial investments in hardware, software, and skilled data scientists. Additionally, data privacy and security are paramount concerns, as AI relies heavily on sensitive patient information.  

Integrating AI seamlessly into existing workflows also necessitates significant adjustments to organisational processes and culture.

The quality of data used to train AI models is critical. Biased or incomplete data can lead to inaccurate predictions and wasted resources. Furthermore, navigating the complex regulatory landscape of AI-driven drug development can delay the approval process.  

Finally, AI models themselves can be susceptible to bias if trained on unrepresentative data, potentially leading to misleading outcomes.

Despite these challenges, the future of AI in drug development is undeniably bright. By accelerating drug discovery, optimising clinical trials, and potentially repurposing existing drugs, AI has the power to revolutionise how we develop new treatments.  

Big pharmaceutical companies are actively addressing the challenges, and as AI continues to evolve, we can expect even more groundbreaking discoveries in the years to come. This has the potential to transform healthcare for all.

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