AI in Health Care: Innumerable Applications, Abundant Opportunity


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By Garvin Jabusch, CIO
This article was originally written as a contribution to a white paper that Pathstone is developing

From it’s uses in medical imaging, to the potential for using Artificial Intelligent (AI) to detect and diagnose diseases, to new drug discovery, to genomic analysis and therapeutic discovery, the existing and emerging uses of AI in health care are, in practical terms, limitless. In this article, I limit examples to just two from the realm of drug discovery, for sake of length.

One of the most promising applications of AI in drug discovery is in the identification of new drug targets (AI can be used to discover new drugs). The ability to analyze large amounts of molecular and genomic data quickly and efficiently is something that AI excels at. This can lead to the identification of new drug targets, which could potentially lead to new drugs that are more effective and have fewer side effects, such as Halicin, a new antibiotic compound that was discovered at MIT using AI. Halicin was discovered using a machine learning algorithm that screened a library of about 6,000 existing drugs to identify ones that could be effective against a range of bacteria. The algorithm found Halicin, which was originally developed as a treatment for diabetes, to be highly effective against a number of bacterial strains, including some that are resistant to multiple antibiotics.

AI-powered tools can analyze vast amounts of genomic data to identify genes that are associated with disease. This information can then be used to design new drugs that target these genes, or, to suggest gene-edited interventions to address the disease directly, such as CRISPR Therapeutics’ intervention for sickle cell anemia. The company’s therapy, called CTX001, uses CRISPR-Cas9 gene editing technology to modify a patient’s hematopoietic stem cells (HSCs) in the lab. HSCs are the cells in the bone marrow that give rise to all other blood cells. If approved, CTX001 could be a major breakthrough in the treatment of sickle cell disease. The therapy has the potential to cure the disease and improve the quality of life for millions of people around the world.

AI played a significant role in the development of CTX001. AI was used to:

  • Identify the HBB gene as a potential target for gene editing.
  • Design the CRISPR-Cas9 guide RNA that is used to edit the HBB gene.
  • Predict the efficacy and toxicity of CTX001 in preclinical studies.
  • Monitor the safety and efficacy of CTX001 in clinical trials.

AI has accelerated the development of CTX001 and has helped to ensure that the therapy is safe and effective. AI is likely to play an even greater role in the development of future gene therapies.

Here are some specific examples of how AI was used in the development of CTX001:

  • AI was used to analyze vast amounts of genomic data to identify genes that are associated with sickle cell disease. This information was then used to prioritize the HBB gene as a potential target for gene editing.
  • AI was used to design the CRISPR-Cas9 guide RNA that is used to edit the HBB gene. The guide RNA is a short piece of RNA that guides the CRISPR-Cas9 protein to the HBB gene. The AI-designed guide RNA was more efficient and precise than guide RNAs that were designed by traditional methods.
  • AI was used to predict the efficacy and toxicity of CTX001 in preclinical studies. AI-powered tools were used to simulate the interaction of CTX001 with the HBB gene and other proteins in the body. This information was used to predict the potential benefits and risks of CTX001.
  • AI is being used to monitor the safety and efficacy of CTX001 in clinical trials. AI-powered tools are being used to track the progress of patients who are receiving CTX001 and to identify any potential side effects. This information is being used to ensure that CTX001 is safe and effective.

The use of AI in the development of CTX001 is a testament to the power of this technology. AI is rapidly transforming the field of drug discovery and is helping to develop new and more effective treatments for a wide range of diseases.

AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way that new drugs are developed. AI-powered tools can help scientists to identify new drug targets, design new drug molecules, and predict the efficacy and toxicity of potential drugs. This could lead to the development of new and more effective treatments for a wide range of diseases.

For an investment manager like Green Alpha, the opportunity to gain market exposure to these revolutionary developments is as exciting as it is complicated. For research-intensive firms and analysts, I believe carefully researched and curated baskets of the world-leading firms at the intersection of AI and biotech may present generational growth opportunities. By staying informed about the latest advancements in AI and biotech, and investing in a diversified portfolio of companies at the forefront of this field, investors can potentially capitalize on the growth potential of this exciting sector.

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At the time this article was written and published, some Green Alpha client portfolios held long positions in CRISPR Therapeutics (ticker CRSP).These holdings do not represent all of the securities purchased, sold or recommended for advisory clients. You may request a list of all recommendations made by Green Alpha in the past year by emailing a request to any of us. It should not be assumed that the recommendations made in the past or future were or will be profitable or will equal the performance of the securities cited as examples in this article. Not all Green Alpha separate accounts or our sub-advised mutual fund held the stocks mentioned. To inquire whether a specific Green Alpha portfolio(s) holds stock in any particular company, please call or email us.