Startups, BioPharma, Artificial Intelligence

Personalized cancer treatment startup Notable Labs nabs $40M Series B

Notable CEO Matthew De Silva said the company has been able to successfully replicate its automated system, allowing for the export of the company’s technology and growth of its commercial use cases.

Silicon Valley startup Notable Labs, which uses AI to predict effective cancer treatments for patients, has raised a $40 million Series B round as it looks to to kick off a larger national and international expansion.

The financing round was co-led by new investor B Capital Group and previous investor LifeForce Capital and brings the company’s total funding haul to over $55 million. B Capital Group is receiving a board seat as part of the funding deal.

Notable’s automated lab testing technology uses patient cell samples to more quickly understand the effect of specific cancer treatments on individuals, thereby boosting the efficiency and speed of drug development efforts.

B Capital Group has made AI-based drug development an area of focus for their healthcare investment efforts. Aside from Notable, the firm has also invested in San Francisco-based Atomwise, which uses machine learning to help biotech and pharma companies screen for effective drug compounds more efficiently.

Notable CEO Matthew De Silva said the 40-person company’s work in building out the evidence base for its technology has helped it bring on new talent, sign new partnerships and broaden its investor support.

“The key progress we made from our Series A is the validation we’ve been able to achieve for our technology, which has played out in our clinical trial results,” De Silva said.

One trial done in partnership with Tempus and Stanford demonstrated the technology’s use in developing individualized treatment recommendations for MDS cancer patients, with around an 84 percent accuracy rate in predicting positive and negative responses to treatments.

According to De Silva, the new funding will be directed at helping the company scale past its initial lab location in Foster City, California. De Silva said the company has been able to successfully replicate its automated system, allowing for the growth of its commercial use cases.

Notable’s reliance on cancer cell samples means that locating their technology closer to where patients actually are or where trials are being run is of vital importance.

“We’re looking really seriously at locations beyond the U.S. and all our software is cloud-based for that reason,” De Silva said. “Global scale is really important because clinical trials and drugs are not developed in only one geography.”

De Silva said the new capital will also be invested in the company’s scientific development efforts to expand its indications past specific blood cancers.

B Capital Group Principal Adam Seabrook said the Notable’s ability to predict efficacy using a patient’s own cells made them an attractive investment target.

“In Notable’s particular focus of cancer, the diseases are incredibly complex and patients have multiple clones and variations. There’s no one thing to solve all those cases,” Seabrook said. “But by combining the power of software with interesting approaches in biology you can get closer to understanding how to treat diseases like blood cancers.”

Seabrook said he sees the company’s greatest value in the near-future as helping to accelerate clinical trials by predicting the effectiveness of potential therapies and understanding which drug candidates should be prioritized for further development.

When it comes to potential exit opportunities for companies like Notable and Atomwide, Seabrook pointed to big pharma as one potential acquirer, as well as tech firms like IBM and Google, who have made data driven research a stronger part of their growth strategy.

For De Silva, scaling the company up means more patient samples, more data and ultimately more insights into the drugs and diseases Notable is targeting. This could lead to new lucrative partnerships with drug development companies or the launch of its own development pipeline

“Over time as you’re mapping the drug to the patient subpopulation and sequencing those patients samples you can find patterns in the reasons why a certain drug is working for a certain population,” De Silva said. “At a thousand times the scale that picture becomes much more clear.”

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