Science is currently grappling with a reproducibility crisis, particularly in preclinical research, where it is estimated that over 50% of efforts to reproduce experiments fail. This issue costs the industry more than $50 billion annually. To tackle this challenge, techbio startup Briefly Bio has launched with innovative software designed to make lab work more reproducible by helping scientists capture and share their work clearly and consistently.
Briefly Bio has successfully secured a $1.2 million pre-seed funding round, led by Compound VC, with participation from NP Hard, Tiny VC, and various angel investors from the tech and biotech sectors.
As biological experiments become increasingly complex, critical details often go undocumented and are lost, making scientific collaboration inefficient. Lab scientists find it difficult to reproduce and build on each other's experiments, data scientists lack the necessary context to analyze data, and automation teams struggle to build robotic labs due to missing details. Briefly Bio aims to solve these issues by creating a shared language for experiments that is consistent and clear for any collaborator to understand.
The software developed by Briefly Bio leverages AI to convert existing experiment descriptions into a standardized format, automatically filling in gaps and identifying errors. This not only captures the value of every experiment but also enables scientists to learn from each other's work.
Founded by Dr. Katya Putintseva, Harry Rickerby, and Staffan Piledahl, Briefly Bio brings together a wealth of experience in academia, tech, biotech, and robotics. The founders previously worked together at drug discovery startup LabGenius, where they contributed to building an ML-driven antibody discovery platform.
Harry Rickerby, CEO and co-founder of Briefly Bio, likened scientific methods to software code, noting that most of this 'code' is incomplete because documenting each experiment thoroughly is highly labor-intensive. Rickerby emphasized that new technologies like Large Language Models (LLMs) offer a way to make these methods consistent without disrupting scientists' workflows. He compared Briefly Bio's potential impact to that of Github for software engineers, enabling scientists and engineers to collaborate and build on each other's work more effectively.
With AI and high-throughput experimentation, there is a significant opportunity for improving the efficiency of scientific discovery. Hundreds of billions of dollars are being invested in this area by startups and big pharma. However, achieving this potential requires greater consistency and transparency in how datasets are generated, as the value of any model depends on the data it has been trained on. Briefly Bio aims to provide the necessary infrastructure to accelerate scientific discovery in biology.
Dr. Gena Nikitin, Founder of Miphic, hailed the development as a revolution in documenting lab experiments and the future of foolproof knowledge-sharing among scientists. Dr. Maria Anastasina, Wet Lab Head at the Evolutionary and Synthetic Biology Unit, OIST, noted that Briefly Bio has become a core part of her lab's knowledge base, aiding in training researchers and lab management. Suparna Kumar, a PhD student at Weill Cornell, commented that Briefly Bio has become indispensable in her lab routine, saving her a significant amount of time.
Rob Harkness, CTO of Biosero, emphasized the importance of digitalizing and automating laboratory operations to address issues of inconsistent and incomplete data, which can compromise research. He praised Briefly Bio for converting scientific protocols written in natural language into a consistent structured format, thereby enhancing workflow integration, efficiency, and data quality.
Shelby Newsad, an investor at Compound, highlighted that the success of science hinges on consistent and executable methods. Unlike most bio software companies that focus on data analysis, Briefly Bio addresses the core problem of reproducibility via protocols, incentivizing scientists to share more of their knowledge. This creates unique potential for network effects from their software.
Briefly Bio envisions a future where scientists can stop reinventing the wheel, focusing instead on producing reproducible datasets that will rapidly expand our understanding of biology.
