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BioPharma Dive· Gwendolyn Wu·· 2 小时前AI 评分31

Flagship 合并三家生物科技初创公司成立 Etcelera Therapeutics

Flagship blends three biotech startups to form Etcelera

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Flagship Pioneering 将 Etiome、Cellarity 和 Empress Therapeutics 三家投资组合公司合并为新公司 Etcelera Therapeutics,由前 Adaptimmune CEO Adrian Rawcliffe 领导,结合 AI 与药物发现工具预测候选药物能否在临床中成功。

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The biotechnology startup creator Flagship Pioneering is rolling three of its fledgling companies into a new venture called Etcelera Therapeutics, which is combining artificial intelligence with an array of drug discovery tools to try to predict which experimental medicines might succeed in the clinic.

Flagship announced Thursday that the new startup has been formed from the parts of three former portfolio companies: Etiome, Cellarity and Empress Therapeutics. It’s led by Adrian “Ad” Rawcliffe, the former CEO of Adaptimmune and a veteran of GSK.

“Each had a piece of this puzzle, and if you bring them together, then we have the opportunity to go beyond what each of them was doing and redefine cause-and-effect in biology,” Rawcliffe said in an interview.

Etcelera claims that combining the technologies of these three companies with Flagship’s in-house AI capabilities should help its scientists pinpoint novel disease drivers and uncover small molecule medicines to target them. It also believes these drugs will have better odds than those discovered with traditional approaches. Rawcliffe said the datasets and technology developed by its three forebears could be useful in gauging which drugs might fail in human testing.

“If you feed appropriate machine learning enough biological language, then maybe it can infer what biology looks like, where meaning lies in biology and can predict what will happen in biological systems,” Rawcliffe said.

Biotech companies have long spoken enthusiastically about the ability of high-powered computing tools to upend drug discovery and cure many diseases. The hype has sparked blowback that companies using AI to design medicines may be overstating their potential and underestimating the complexities of human biology. Multiple biotechs built on AI technology have struggled in the public markets. 

Yet private investment in AI drug discovery companies has accelerated in 2026. Several have raised large funding rounds, and one company, Iambic Therapeutics, recently went public. (AI giant Anthropic, which has invested heavily in life sciences research, could soon follow.) 

Etcelera is combining AI tools with a series of other strategies. And one edge Rawcliffe believes the company has is its use of “perturbation data,” a way of seeing how a cell would respond to disruption, such as by silencing a gene or introducing a chemical.

The company intends to develop medicines for hematological conditions, immune diseases and metabolic-fibrotic disorders. It’s starting out with a handful of programs for conditions such as myelofibrosis and essential thrombocythemia.

“Our vision is to build a compounding AI system that becomes a definitive source of novel medicines that are more likely to succeed in clinical development than any prior technology,” said Alec Reynolds, a senior partner at Flagship and chair of Etcelera’s board, in a statement.

Flagship will shuttle an oral sickle cell disease drug that Cellarity discovered into a different, standalone company, the firm said.

No new funding was announced alongside Etcelera’s public launch. The three biotechs it’s built on had banked $374 million in venture capital backing prior to the merger, which Rawcliffe said will help carry the company further.

The new biotech will retain a 2022 partnership between Novo and Cellarity formed to find a small molecule treatment for metabolic dysfunction-associated steatohepatitis. A second alliance with an unnamed large pharmaceutical firm is also in the works, Rawcliffe said.

“We’ll be looking for partners who bring substantial disease or therapy area expertise to pair with this platform,” he said.

Etcelera’s name is a kind of portmanteau of “et cetera” and “accelerate,” an idea Rawcliffe said was born out of the theory that simulating early drug development on a computer could speed up the iterative process of finding new drugs.

来源:BioPharma Dive · biopharmadive.com

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