A new free checklist from a biotech industry veteran aims to help early-stage biotherapeutics teams decide when to halt a drug program, a decision that can save millions in development costs and protect patients from ineffective treatments. Catherine Cifuentes-Rojas, director and lead for genetic medicines and biologics at Ginkgo Bioworks, released the five-question go/no-go checklist this week, drawing on more than 15 years of experience moving programs from target discovery toward the clinic at Massachusetts General Hospital, Harvard Medical School, Sail Biomedicines, and Ginkgo.
The checklist is not a proprietary framework tied to any company’s pipeline. Instead, it is a set of plain questions meant to slow teams down at the moment momentum tends to speed them up. The five questions, to be answered in writing before a program advances to the next stage of investment, ask: Does the primary assay result replicate in a second, independent system? Have we tried to kill the hypothesis, not just confirm it? What does the in vivo data say, not the in vitro data? Who reviewed this outside the team that generated it? And what would change our mind, and has it happened yet?
According to Cifuentes-Rojas, a no-go decision made early and backed by real data is just as much a success as a program that clears every stage. “A no-go call made early, backed by real data, protects capital and protects people,” she 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 checklist’s importance lies in its potential to address a persistent problem in biotech: junior scientists often hesitate to raise disconfirming data in front of senior colleagues, not because the data is weak, but because no structured way exists to bring it forward. “If the only tool in the room is confidence, the most confident person wins the argument,” Cifuentes-Rojas said. “A checklist takes the argument out of it. Either the box is checked with evidence, or it isn’t.”
She 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, she said, gives those groups a common reference point instead of requiring every debate to start from scratch.
The checklist is designed 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 takes under an hour to complete as a group, with answers recorded and revisited at the next decision point. 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.”
