Catherine Cifuentes-Rojas, Director and Lead for Genetic Medicines and Biologics at Ginkgo Bioworks, has released a free early-stage go/no-go checklist for biotherapeutics teams. The checklist, built from her more than 15 years of experience moving biotherapeutic programs from target discovery toward the clinic, is designed to help discovery teams make more disciplined decisions about whether to advance programs.
Cifuentes-Rojas has worked in academic labs at Massachusetts General Hospital and Harvard Medical School, at Sail Biomedicines, and now at Ginkgo Bioworks. She has participated in dozens of go/no-go calls on RNA therapeutics, cell therapies, and biologics candidates. The checklist is not a proprietary framework tied to any company's pipeline; it is a set of plain questions meant to slow teams down at the moment momentum tends to speed them up.
The checklist consists of five questions that teams should answer in writing before a program moves to the next stage of investment. First, does the primary assay result replicate in a second, independent system? A single strong data set is not evidence; a repeated one is. Second, have we tried to kill the hypothesis, not just confirm it? Teams are asked to name the experiment most likely to disprove their favored mechanism and run it before celebrating. Third, what does the in vivo data say, not the in vitro data? Candidates that perform well in primary cell assays can lose stability entirely in a living system, so in vivo results should be weighed above earlier, easier signals. Fourth, who reviewed this outside the team that generated it? A second set of eyes, ideally from a different technical background, is required before a program clears the checklist. Fifth, what would change our mind, and has it happened yet? Teams write down their own disconfirming evidence in advance so they cannot quietly redefine success after the fact.
"A no-go call made early, backed by real data, protects capital and protects people," Cifuentes-Rojas said. "It is just as much a success as a program that clears every stage. The checklist exists so a team does not have to rely on gut feel or seniority in the room to make that call."
The idea for the checklist came from watching junior scientists hesitate to raise disconfirming data in front of more senior colleagues, not because the data was weak, but because no one had given them a structured way to bring it forward. "If the only tool in the room is confidence, the most confident person wins the argument," she said. "A checklist takes the argument out of it. Either the box is checked with evidence, or it isn't." She also pointed to her experience directing cross-functional teams at Ginkgo Bioworks, where discovery biologists, computational scientists, and automation engineers each see a different slice of a program's data. A shared checklist gives those groups a common reference point.
The checklist is meant to sit at defined decision points: after initial target validation, before committing to in vivo studies, and before a program moves toward IND-enabling pre-clinical work. It is designed to take under an hour to complete as a group, with answers recorded and revisited at the next decision point. Teams do not need any particular software or platform; it works as a shared document, filled in by whoever owns the program, with dissenting views recorded rather than smoothed over.
"The goal isn't to slow science down," Cifuentes-Rojas said. "It's to make sure the fast decisions are the right ones, and the slow ones are worth the time they take."
The checklist's release matters because early-stage biotherapeutic development is capital-intensive and risky. Poorly validated candidates can consume millions of dollars and years of research before failing in the clinic. By providing a free, structured tool, Cifuentes-Rojas aims to help teams catch flawed hypotheses early, protect investors' capital, and ultimately speed the delivery of effective therapies to patients. The checklist could become a standard reference for discovery teams across academia and industry, fostering a culture of rigorous, evidence-based decision-making.
