Why Pharmaceutical Companies are Turning to Artificial Intelligence for Drug Discovery

There is a lot of excitement surrounding artificial intelligence (AI) in the healthcare industry. AI is predicted to bring $50 billion of opportunity by facilitating the transfer of over 50 new novel therapies to the market over the next ten years.

AI-first biologics companies generated $4.5 billion in deals in 2022 despite the overall cooling observed in the biotechnology industry that year. We expect this number to rise as the introduction of the Inflation Reduction Act in America spares biologics from price negotiations four years longer than small molecules; which could stunt investment in small molecules moving forward in favour of biologics.

For small molecules, the sum of all the deals made between 2019 and 2024 between pharmaceutical companies and AI biotechnology companies totals $16.4 billion.

Behind the statistics are panels of board members and business development professionals making decisions on strategic partnerships with AI companies. What is motivating them to invest in AI?

1. Unsuccessful programmes

The Economist reporters highlight the argument that drug developers have addressed many of the 'low-hanging fruits' in drug discovery. These include targets with simpler pathologies and favourable properties for drug design. This has left researchers to grapple with a panel of more complex diseases needing treatments. In many cases, drug targets exist with known pathologies; however, they show unfavourable properties for drug design. These targets are often referred to as "undruggable".

Whilst traditional discovery methods offer complex and powerful tools, 'undruggable' targets have remained elusive. In these unique cases, artificial intelligence offers exciting new tools to tackle these targets, stepping in where previous methods have failed.

Despite our growing understanding of disease, drug development is becoming more expensive. This may reflect the struggles existing methods face from more complex disease indications, calling for the development of new technologies.?

2. Fear of missing out

Being first to market is essential in the pharmaceutical industry, so R&D teams continuously brainstorm new ways to accelerate their development pipelines and bring more drugs to market before their competitors.?

This often means staying ahead of the curve with new technologies. Most pharmaceutical companies have departments, typically under the names of 'Search and Evaluation' or 'External Innovation' and related titles, dedicated to identifying promising technology to bring into their workflows.?

Computational frameworks that lay the foundations for AI take time to establish correctly. This is especially true for pharmaceutical companies which rely on complex R&D frameworks to synchronise R&D efforts across thousands of employees and research campuses. Consequently, the AI wave has started a race for who can scale AI to address the industry's unique challenges the fastest.?

3. Efficiency

The most frequent benefit circulated about AI is its ability to accelerate the drug development process. Faster development cycles help treat more diseases more rapidly, ultimately allowing pharmaceutical companies to push more drugs to market. In addition, the saved resources can be funnelled into underrepresented areas, such as rare diseases, bringing more opportunities to both pharmaceutical companies and patients.

According to McKinsey research, the average time frame for a drug to reach 50% of its value has fallen by over 18 months, reducing the amount of capturable profitability per drug. Biopharmaceutical companies commonly follow industry trends, fostering a fiercely competitive environment, as companies focus on targets or target families that have had previous success in the market.

Additionally, standards of care are continuously improving as newer drugs replace old ones and treatment guidelines change. Biosimilars also consistently pose a threat.?

As discussed at the beginning, The Inflation Reduction Act (IRA) will further reduce the value that can be extracted from a drug. The Centres for Medicare and Medicaid Services (CMS) will negotiate drug prices after 9 years for small molecules and 13 years for biologics in a scheme designed to make drugs more affordable in the United States.?

All of these factors are constricting drug profitability. Overall, pharmaceutical companies will need to manufacture more drugs to sustain profitability. Artificial intelligence may become a primary tool in the pharmaceutical arsenal to combat asset compression.

??4. New opportunities for patients

New technologies open new possibilities. While we have primarily focused on the financial benefits thus far, we cannot overlook the fantastic work pharmaceutical companies, biotechnology companies and researchers do for patients.

There are many rapidly expanding use cases for how AI is already benefiting patients. For example, integrating multimodal data with AI algorithms (among other techniques) is currently being used to predict how individuals will respond to different treatment plans with some degree of accuracy. This is paving the way for personalised medicines, which will almost certainly direct the future of healthcare, particularly in multifactorial diseases like cancer.?

As we mentioned above, staying ahead of the curve is essential. If the trajectory of healthcare points towards personalised medicine, pharmaceutical companies are incentivised to invest in it.??

5. Mitigate risk

Artificial intelligence can support drug development across the entire development pipeline. The full scope of the benefits AI can bring to drug discovery is outside the reach of this article.

When you total the benefits of AI to the drug development process, you find that it can mitigate some of the risks involved.

For example, artificial intelligence can predict which target will likely involve the least side effects or suggest structural changes to avoid toxicity-related motifs. Generative AI can also extend to designing corporate strategies, such as which diseases to research. It can also strategically select patients to recruit for a study (Source).

McKinsey predicts that generative AI could increase the probability of success by around 10% in clinical trials alone. This is a small improvement, but it could be game-changing given the immense costs involved in drug development.

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