A new free checklist for early-stage biotherapeutics teams is aiming to change how go/no-go decisions are made in drug discovery. Released by Catherine Cifuentes-Rojas, a director at Ginkgo Bioworks with over 15 years of experience in moving biotherapeutic programs from target discovery to the clinic, the checklist distills lessons from dozens of go/no-go calls on RNA therapeutics, cell therapies, and biologics candidates. Its significance lies in addressing a critical gap: the tendency for momentum and confidence to override rigorous evidence when programs are advancing toward expensive clinical stages.
The checklist consists of five questions that teams must answer in writing before moving a program to the next stage of investment. These questions are designed to slow teams down at the exact moment momentum tends to speed them up. They ask whether primary assay results replicate in a second, independent system; whether teams have tried to kill their hypothesis rather than just confirm it; what in vivo data say versus in vitro data; who outside the generating team has reviewed the work; and what evidence would change the team's mind, and whether that evidence has been observed yet. Each question targets a common pitfall in early-stage research, such as overreliance on a single strong data set or celebrating before testing disconfirming evidence.
"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." This emphasis on protecting capital is particularly important in biotherapeutics, where advancing a flawed candidate to clinical trials can waste millions of dollars and expose patients to unnecessary risks. By providing a structured way to bring forward disconfirming data, the checklist also addresses a cultural issue: junior scientists often hesitate to raise concerns in front of senior colleagues. Cifuentes-Rojas noted that the idea came from watching this hesitation, not because the data was weak, but because no one had given them a structured way to present it. "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."
The checklist is designed to be used 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 requires no special software or platform; it works as a shared document filled in by the program owner, with dissenting views recorded rather than smoothed over. This simplicity makes it accessible to teams of any size, from academic labs to biotech startups, and could foster better communication across cross-functional teams. Cifuentes-Rojas pointed to her experience 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 implications of this release extend beyond individual teams. By promoting a culture of rigorous, evidence-based decision-making, the checklist could help reduce the high failure rate in biotherapeutic development and ensure that resources are directed toward the most promising programs. "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." In an industry where speed is often prioritized, this checklist offers a counterbalance that could ultimately save time and money by preventing costly late-stage failures.
