We talk with Justin Farlow, Co-Founder and CTO of Serotiny about his journey from UCSF to founding a company with his brother, Colin. In this conversation, Justin discusses his initial discovery of engineer-able biology from a physics lens to earning his PhD at UCSF under Zev Gartner while being in the epicenter of both synthetic biology and software startups. Then he goes into his journey as a founder, starting Serotiny initially as a SaaS company then pivoting toward building a wet-lab platform after the approval of the first CAR T therapies. Mammalian synthetic biology promised curative therapies in both new cell and gene therapies, and the rapid progress of these new modalities helped Serotiny build a unique business model exemplified by recent deals with both Janssen and Tessera Therapeutics. With more likely in the pipeline.

Serotiny is the market leader for designing therapeutic multi-domain proteins - from chimeric antigen receptors (CAR) to CRISPR gene editors, where the aim of the protein is to change the properties of a cell. Their platform relies on machine-guided variation to design in silico libraries of millions of protein designs and then test tens of thousands of them in vitro, and iterate to produce a high-value candidate. Versus the past state-of-the-art, Serotiny enables unbiased screening of large protein therapeutics in their native mammalian and therapeutic contexts. Unbiased screening for complex drugs has allowed the company to find new candidate combinations that are hard-if-not-impossible to discover with other approaches. By generating and intentionally structuring data that correlates primary amino acid with primary cell phenotype, the company's underlying platform is allowing Serotiny to move more quickly from idea to drug candidate.

At the end of the conversation, we discuss the long-term need for a common language in synthetic biology, building a world-class team, and the opportunities to standardize datasets in life sciences. Justin lays out a powerful framework for platform companies in drug development: going 0 to 1 to find a signal and invent a new candidate and then going from 1 to 100 and beyond by versioning the candidate to improve its therapeutic potential.