Formation Bio stood out a few weeks ago when it announced a partnership with the French manufacturer Sanofi and the leading player in generative artificial intelligence, OpenAI. The three companies plan to jointly identify use cases, define solutions and develop scalable and adapted AI tools. This week, the American biotech is making headlines again by raising $372 million in Series D.
The financing was led by the a16z fund with significant participation from Sanofi. Existing investors also participated in the round, including Sequoia, Thrive, Emerson Collective and Lachy Groom, which also attracted new investors, such as SV Angel Growth and FPV Ventures.
Advertisement
As part of this financing, Scott Kupor, managing partner at a16z and Alfred Lin, partner at Sequoia, will join the board of directors of Formation Bio. “Pharmaceuticals is one of the largest industries in the world, and there is immense potential to make the drug development process more efficient,” commented Scott Kupor.
Drug discovery accelerates with AI
Noting bitterly that advances in AI and drug discovery are creating a large number of drug candidates that the industry is unable to support due to the high cost and duration of clinical trials, Formation Bio – launched in 2016 under the name TrialSpark – has built a technology platform that “integrates into all essential aspects of the drug development life cycle.” In detail, the company leverages a data stack, LLMs, AI agents and other technologies to acquire pharmaceutical assets qualified as “promising”, develop development strategies and design and conduct clinical trials.
The company therefore partners with, acquires or licenses drugs from biotech and pharmaceutical companies and develops these programs to clinical proof of concept and beyond. This capital will therefore serve two objectives: the acquisition and licensing of drug candidates and the development of its capabilities in artificial intelligence. Bio Training is aimed at both small biotechnology companies and large pharmaceutical companies.
Advertisement
Improving automation and decision-making at the heart of the roadmap
In the future, the company hopes to develop fine-tuned and customized LLMs, which combined with human supervision and reinforcement, can automate many functions of drug development. These include: medical writing, protocol development, biostatistics, report generation, regulatory monitoring, etc.
In the short term, the company is focusing on workflow automation. Two examples it gives are AI-generated patient recruitment content tailored to specific cohorts and AI-generated adverse event reports that are created in minutes rather than hours.
In the medium term, the company is focusing on training AI to improve decision-making. In the long term, the company's goal is to build and train AI models that can better predict toxicity, tolerability and, ultimately, effectiveness.
Selected for you