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Investing in Pharma and the Game Changing Impact of AI

The pharmaceutical industry is fundamentally driven by the creation and approval of new drugs, which has a direct impact on the stock prices of drug companies. Innovative drugs that successfully pass through rigorous clinical trials and receive regulatory approval can significantly boost the financial performance of pharmaceutical firms.

Investors closely monitor the pipeline of new drugs, understanding that a breakthrough medication can open up substantial revenue streams and market opportunities. Conversely, any setbacks in the drug development process, such as adverse side effects discovered during trials or delays in regulatory approval, can lead to a decline in stock prices.

The potential of a new drug to address unmet medical needs or to offer significant improvements over existing treatments is a critical factor in evaluating the investment potential of pharmaceutical stocks. This is best done during the final phase of drug development, before it starts to generate revenue, by answering questions such as how much demand it will generate and whether it will change the revenue growth profile of the company.

AI will improve innovation rather than reduce prices for consumers

Artificial intelligence (AI) is poised to revolutionize the pharmaceutical industry, particularly in the realm of drug discovery and development. The traditional process of bringing a new drug to market is lengthy, complex and expensive. However, AI has the potential to streamline this process by analyzing vast amounts of biological data, predicting how new compounds will behave and identifying promising drug candidates more efficiently. This capability not only accelerates the discovery of new drugs but also enhances the precision and effectiveness of treatments.

While AI is expected to drive significant innovation in drug development and better patient outcomes, it is unlikely to lead to a decrease in drug prices for consumers in the near term. The cost savings achieved through AI-driven efficiencies are often reinvested into further research and development, rather than being passed on to consumers in the form of lower prices. Additionally, the high costs associated with regulatory compliance, clinical trials and marketing continue to contribute to the overall expense of bringing new drugs to market.

Nevertheless, the ability of AI to enhance the innovation process holds immense promise for the pharmaceutical industry. By enabling the development of more effective and targeted therapies, AI has the potential to improve patient outcomes and address complex medical challenges in ways that were previously unimaginable.

Source: AI in Drug Discovery and Repurposing: Benefits, Approaches, as at 27 Aug, 2022.
*This remains uncertain; timelines will vary.
** Estimates are based on these sources: Why Artificial Intelligence Could Speed Drug Discovery by Morgan Stanley, Generative AI in the pharmaceutical industry: Moving from hype to reality by McKinsey & Company, From volume to value: Indian pharma’s transformation with data and AI by EY and At conference, drugmakers tout AI efforts as US tariffs cast shadow by Reuters.

Datasets will help to determine winners and losers resulting from AI

The integration of AI into the pharmaceutical industry is expected to create clear winners and losers. Companies that successfully leverage AI to enhance their drug discovery and development processes are likely to gain a competitive edge as they benefit from faster and more efficient drug discovery, reduced development costs and the ability to bring novel therapies to market more rapidly. Having regular contact with management and R&D leaders and visiting their premises are both crucial when identifying which companies are most likely to benefit from using AI in drug innovation.

The winners in this new era of pharmaceutical innovation will be those companies that not only invest in AI but also integrate it seamlessly into their existing R&D frameworks. In this respect, datasets matter - those with the deepest datasets will be able to extract the most value from AI going forward. For example, a company that specializes in one therapeutic area and has the most widely used drug will have the most patient and clinical data. They can use this data to potentially create new versions of that drug or deploy it in new ways to treat other illnesses. This incumbency advantage may help bigger companies to continue winning, as it is more difficult for others with a lack of data to compete.

Beyond pharmaceutical companies, the use of AI in drug development will also have downstream impacts that present opportunities for investors, such as tools companies that provide services and facilitate R&D. Having an integrated investment platform with crosssector collaboration is key to understanding the downstream impacts and identifying which companies are set to benefit.

As investors navigate this complex and ever-changing sector, understanding the dynamic interplay between new drug development, AI innovation and the evolving pharmaceutical landscape will be essential for making informed decisions and identifying long-term winners.

The views expressed are those of the speaker and are subject to change at any time. These views are for informational purposes only and should not be relied on as a recommendation to purchase any security or as an offer of securities or investment advice. No forecast can be guaranteed. Past performance is no guarantee of future results.

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