A drug developed by combining artificial intelligence with traditional drug development methods has shown early promise in the treatment of some cancer patients in trials.
Berg, a US biotech company, presented the findings at the American Society of Clinical Oncology conference in Chicago this week.
The results, which suggest the new drug (BPM 31510) does indeed slow the growth rate of cancerous cells, were obtained by training AI to spot and suggest ways the transformation of a healthy cell into a cancer cell could be reversed. Essentially, it was looking for a way to cut off the cancer cell's 'energy supply'.
In trials on 85 cancer patients, the drug led to a 25 per cent reduction in the size of a tumour in one patient.
Berg hopes that by removing the long-established, and often inaccurate, hit-and-miss approach that currently dominates the industry, leading to excruciatingly long development processes.
One of Berg's founders, Dr Niven Narain said "we're at a very explosive stage" in merging computing and technology with biological processes, but that the potential of supercomputing to help advances in medicine was undeniable.
BPM 31510, a compound made with an enzyme that plays a key role in cellular metabolism, was the first treatment suggested by Berg's AI. The development of the drug effectively began back in 2009, when the startup bought cancer tissue samples from more than 1,000 cancer patients at various medical schools from across the US. These samples included over 40 types of cancer cells - multiple types of breast cancer, prostate cancer, liver cancer, kidney cancer, lung cancer, brain cancer - along with healthy tissue extracted from the same patients.
Results from the initial treatments are then fed back into the AI, so it can further refine the data and make (potentially) even more accurate suggestions for treatments.
Speaking to WIRED in March, Dr Narain said he didn't want to narrow down his study to just one cancer. Instead, he wanted to build a model that looked at the commonalities between the different cancers. From these samples, Berg researchers created in-vitro cell cultures that they could subject to further experimentation.
To mimic the micro-environments that cells experience inside the human body, they subjected the cells in Petri dishes to different levels of sugar and oxygen and tracked their evolution by continuously identifying and measuring the lipids, metabolites, proteins and enzymes they produced. "We were measuring how the data was changing with the environment," Narain said. "Besides the genomic information, we had 14 trillion data points coming out
from one single tissue sample."
The startup fighting cancer with AI
For now, Berg's concentrating on making sure the drug is used for treatments that are most likely to be beneficial, in this case, focusing on high-energy diseases like pancreatic cancer.
Berg is by no means alone in espousing the virtues of technology and AI for helping people diagnose and treat medical conditions and diseases. For example, Microsoft researchers also think that by combining search data from the web, it's possible to identify people who may have pancreatic cancer before they've even been diagnosed.
What's particularly key, in that instance, was being able to weed out the genuine symptom search queries for symptoms from people who were 'casually' searching or those who tend towards hypochondriac behaviour as standard.
It's clearly very early days, but with the ability to crunch data more efficiently, and more data than ever before, it looks like AI could be a beacon of hope for medical care.