The development of cancer drugs is a costly, expensive, time-consuming process that has a high probability rate of failure. On average, it takes 24 to 48 months to find a suitable candidate and costs upwards of $100 million. And in the end, roughly 95% of all potential drugs fail in clinical trials. Because of this, scientists are understandably looking for a way to speed up the discovery process.
That’s where the anti-cancer drug known as BPM 31510 comes in play. Unlike most pharmaceuticals, it was developed by artificial intelligence instead of a group of researchers toiling away in a lab. Created by biotech company Berg (named after real estate billionaire Carl Berg) the company seeks to use artificial intelligence to design cancer drugs that are cheaper, have fewer side effects, and can be developed in half the time it normally takes.
Towards this end, they are looking to data-driven methods of drug discovery. Instead of generating cancer drugs based on chemical compounds identified in labs, the company compares tissue, urine, and blood samples from cancer patients and healthy patients, generating tens of trillions of data points that are fed into an artificial intelligence system. That system crunches all the data, looking for problems.
BPM 31510, which is the first of Berg’s drugs to get a real-world test, focuses on mitochondria – a framework within cells that’s responsible for programmed cell death. Normally, mitochondria triggers damaged cells to die. When cancer strikes, this process goes haywire, and the damaged cells spread. Berg’s drug, if successful, will be able to restore normal cell death processes by changing the metabolic environment within mitochondria.
BPM 31510 works by switching the fuel that cancer likes to operate on. Cancer cells prefer to operate in a less energy-efficient manner. Cancers with a high metabolic function, like triple negative breast cancer, glioblastoma, and colon cancer–that’s the sweet spot for this technology.
IBM is also leveraging artificial intelligence in the race to design better cancer treatments. In their case, this involves their much-heralded supercomputer Watson looking for better treatment options for patients. In a trial conducted with the New York Genome Center, Watson has been scanning mutations found in brain cancer patients, matching them with available treatments.
All of these efforts are still in early days, and even on its accelerated timeline, BPM 31510 is still years away from winning an FDA approval. But, as Narain points out, the current drug discovery system desperately needs rethinking. With a success rate of 1 out of 20, their is definitely room for improvement. And a process that seeks to address cancer in a way that is more targeted, and more personalized is certainly in keeping with the most modern approaches to medicine.