TL;DR
Drug Discovery using ML is working and holds great promise. Most cancer treatments are expensive and only extend life by months. Lack of diversity in approaches is a main bottleneck. Friction between researchers and funding. Chronotherapy deserves more attention.
About Sarah Constantin
Sarah Constantin is a mathematician with a PhD from Yale who worked at Recursion Pharmaceuticals. Read more on her blog: sarah-constantin.org
Using ML & Automation to Find Drugs
Recursion Pharmaceuticals focuses on rare genetic diseases, generating thousands of cells missing the gene causing malfunction. They screen how thousands of compounds interact with diseased cells using computer vision ML algorithms. Their cost structure is massively lower -- they screen thousands of compounds per week.
Recursion Pharmaceuticals Pipeline
Cancer Treatment History
Cancer treatment started after WWII with mustard gas. The first chemotherapy drug was Mustine. Targeted therapy helps but often "expensive drugs extend life only for several months." Cancer is heterogeneous, making it incredibly difficult to treat uniformly.
What's Broken
1. Lack of Diversity in Approaches
Certain promising pathways, such as the glycolysis pathway, are barely investigated. Immunotherapy is an exciting field, with CAR-T therapy showing particular promise for blood cancers.
CAR-T Immunotherapy - Sarah Constantin
2. Funding Friction
The median cost to get a drug approved is $648 million, creating enormous friction between researchers and the funding needed to bring treatments to patients.
JAMA Study on Drug Approval Costs
Solutions
- Regulatory reform for fast-track paths to get promising treatments to patients sooner.
- New funding models like the ChanZuckerberg Initiative that take different approaches to financing research.
- Chronotherapy -- timing drug administration could "render treatment twice as effective and 5 times less toxic." Read more about chronotherapy
Thanks to Anna Kornakiewicz for helping write this summary.
About Bio2040
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