Revolutionary Machine Learning Company Identifies Two Possible Treatments for Intractable Breast Cancer Type
REDMOND, Washington. — March 21, 2019 — Pattern Computer®, Inc. has received confirmation from Lawrence Berkeley National Laboratory that drug therapy combinations it identified using its proprietary machine learning pattern discovery system are effective in killing malignant breast cancer tumors. Notably, the Pattern Computer team chose to go after basal-like or “triple negative” breast cancer because it represents 15-20% of breast cancer patients, is aggressive, and has no effective drugs available. For further technical detail, go to https://www.patterncomputer.com/news/lbnl/.
“We’re very excited by the two-tier bio-validation results we’ve just received for our combination therapy candidates, which we filtered for drugs that were already individually FDA-approved or in process. This was our first project in biotech, and we are hopeful for many future successes along the path to developing a treatment for this and other cancers and diseases,” said Mark R. Anderson, CEO of Pattern Computer.
The two therapy treatments successfully completed final in vitro organoid testing at Lawrence Berkeley National Laboratory, demonstrating “significant synergistic interaction” in killing malignant tumor cells, with statistically “low-to-no adverse effects” on healthy cells. With these results, Pattern Computer will proceed to the next phase of preclinical testing.
“We appreciate our results are still relatively early; we’re therefore approaching these next steps with due care and caution,” Anderson said, “but we’re encouraged by the biological validation we are seeing to date of our computational models in this area. We are delighted to be working with Lawrence Berkeley National Labs on confirming these findings.”
About Pattern Computer
Pattern Computer, a Seattle-area startup, uses its proprietary Pattern Discovery Engine™ to solve the most important and intractable problems in business and medicine. The company’s proprietary mathematical techniques can find complex patterns in very high-order data which have eluded detection by much larger systems.
While the company is currently applying its computational platform to the challenging field of drug discovery, it is also making pattern discoveries for partners in several other sectors, including additional biomedical research, materials science, aerospace manufacturing, veterinary medicine, air traffic operations, and finance.
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