AI medicine: The next step in drug research and development

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  Quantumrun Foresight
• April 21, 2022

Insight summary

Incorporating artificial intelligence (AI) into pharmaceutical research is transforming the way we approach healthcare, from discovering hidden molecules to speeding up the development of more targeted treatments. This collaboration is reshaping business models, labor demands, government regulations, and even environmental considerations within the pharmaceutical industry. While promising more efficient and personalized healthcare, it also calls for a thoughtful approach to ethical challenges.

AI medicine context

Pharmaceutical companies are increasingly using artificial intelligence (AI) systems to develop new treatments and medicines. British startup Exscientia and Japanese pharmaceutical firm Sumitomo Dainippon Pharma have used algorithms that sift through thousands of potential compounds to develop a medication that will soon be clinically tested on humans. (The drug is meant to treat obsessive-compulsive disorder.)

The partnership between Exscientia and Sumitomo Dainippon is one example of pharmaceutical companies and AI startups partnering to leverage machine learning to discover patterns that might be too subtle or complex for humans to recognize. Furthermore, AI can be used to predict the reactions and behaviors of tiny molecules within chemical compounds that could produce desirable properties for medicines. For instance, Iktos, a company that leverages AI to discover new drugs, is applying its technology to a number of small-molecule discovery programs in collaboration with pharmaceutical giant Pfizer.

Another example is the partnership between AstraZeneca and BenevolentAI. The two companies are collaborating to discover new drugs for chronic kidney disease and idiopathic pulmonary fibrosis—a serious lung disease. German biotechnology company, Evotec, has also formed a partnership with Exscientia. Evotec has announced a phase one clinical trial on a new anticancer molecule, created in partnership with Exscientia.

Disruptive impact 

The collaboration between pharmaceutical companies and AI startups is not only accelerating the discovery process but also unveiling molecules that were previously hidden from scientists. These new insights could open doors to understanding diseases at a deeper level and finding ways to treat or even cure them. For individuals, this means the possibility of more targeted and effective treatments, while for companies, it translates to more efficient research and development processes.

The acceleration in drug development is a significant aspect of AI's impact in this field. Algorithms have the ability to sift through enormous quantities of data on potential compounds, analyzing them against existing databases of parameters in a fraction of the time it would take a team of scientists. In one notable example, the Exscientia trial, a drug that typically would have taken about five years for approval was ready for trial in just 12 months. Governments can benefit from this increased speed by having more effective treatments available to the public more quickly, which can enhance overall public health.

However, the rapid development of drugs through AI also presents challenges that need to be addressed. Ethical considerations, such as the potential for bias in algorithms, may need to be carefully monitored. Additionally, the reliance on AI in drug development may lead to a shift in the skills and expertise required in the pharmaceutical industry. Educational institutions may need to adapt their curricula to prepare future scientists for a landscape where AI plays a central role.

Implications of AI-powered pharmaceutical research

Wider Implications of AI-powered pharmaceutical research may include:

• Enhanced collaboration between pharmaceutical and AI companies, leading to a more dynamic approach to treating intractable and previously incurable diseases.
• Accelerated drug discovery and manufacturing processes, resulting in quicker responses to emerging health crises and the ability to address rare or neglected diseases.
• The development of more effective and better-targeted drugs, allowing for personalized medicine that can cater to individual genetic profiles and specific health conditions.
• A shift in the pharmaceutical industry's business models, focusing on data-driven approaches and AI integration, which may lead to new investment opportunities and market dynamics.
• Changes in labor demands within the pharmaceutical sector, requiring new skill sets centered around data science and AI, and potentially affecting employment opportunities for traditional research roles.
• Governments adapting regulatory frameworks to ensure the ethical use of AI in drug development, balancing the need for rapid innovation with patient safety and data privacy.
• A potential increase in the success rate of medicines reaching the market, offering more treatment options and possibly reducing healthcare costs for consumers.
• More efficient research processes, reducing waste in trial-and-error approaches and leading to more sustainable pharmaceutical production.
• Potential challenges in global intellectual property rights and trade agreements, as the integration of AI in drug development may lead to complex legal and ethical considerations across international borders.

Questions to consider

• Would you take a drug discovered and developed with the application of AI systems?
• What do you believe to be potential risks in using drugs and treatments developed by AI systems for human conditions?