The Distribution Cost of Science

Handoffs are ubiquitous in science, from the graduating postdoc handing a protocol to the new RA to a multinational pharmaceutical company handing off a drug’s manufacturing process to a new CDMO partner. Unfortunately these handoffs are plagued by hidden details, tacit knowledge, and contextual dependencies taken for granted. The stakes are high. A recent Accenture analysis of FDA complete response letters (CRLs) found that 64% of drug launch delays stemmed from CMC issues, of which tech transfer to CDMOs is a major component. These delays can cost drug companies hundreds of millions if not billions of dollars.

At the most fundamental level, tech transfer represents the distribution cost of science. In other industries - distribution is straightforward: we talk about “packaging” and “shipping” code or widgets. But science can’t be packed up and shipped, it’s taught, transferred, and debugged. This time and dollar-intensive distribution process is, we believe, one of the most critical bottlenecks in science. It evaporates demand as acquirors / licensors determine that the distraction and integration costs required are simply too high to evaluate a risky asset. More fundamentally, it caps supply, because expert operators are required to directly hand off protocols and materials at sites of transfer, and the people with “magic hands” can only be in so many places at once.

Why? Written procedures compress physical judgment. Phrases such as “mix gently,” “inspect the sample,” or “transfer carefully” can represent many small decisions. At an originating site, experts accumulate preferences, workarounds, failure knowledge, and recovery strategies. Some details feel too ordinary to document. Others are difficult to describe in words or appear only during an unusual event. Furthermore, the receiving site changes the operating context. Equipment, layouts, vendors, materials, room conditions, scale, staffing, and local practices introduce new variables. Processes that worked reliably in the hands of its developers can become fragile in the new setting.

Organizations solve this problem with manual observability in high-stakes science. As an industry, we know implicitly that success lives in the execution details, often flying scientists between labs to do on-the-ground training and auditing. When a billion dollar a year drug is on the line, the cost of putting 100 people on a plane to a manufacturing site is a drop in the bucket. The time lost to the delays is worth orders of magnitude more. We know we need to maintain and improve visibility, but our reliance on human availability and travel to get it is a high-cost, low-bandwidth bottleneck. Science needs a new way to scale scientific observability.

The Transfyr Agent

At Transfyr, we envision a world where these handoffs are seamless, where science literally can be “packed and shipped” in the same way as code. To do this, however, we need a system that can understand the nuance and contextual dependencies of a given process. This requires a level of observability and documentation that simply does not exist in the lab today, and it requires technology that can distill the essence of which variables matter for a given process. When the system can understand what matters for successful execution, it can ensure sufficient attention is paid at those critical moments in tech transfer, recording the details that would otherwise be missed.

Handoffs involve a change not only in technician, but also often in context: equipment, location, scale, and environment might all change. Transfyr addresses these gaps from both sides. At the originating lab, Transfyr’s platform assembles a detailed understanding of the protocols and assesses variance in both process and results, allowing researchers to assess which variables seem to matter most and where the existing SOPs compress or omit key steps (we call these “ghost steps”). At the receiving site, the same platform identifies contextual and procedural differences between the facilities and helps the teams identify differences worth testing. In a world where observability is built into both sites, many of those differences can be analyzed in advance and the process optimized before the handoff occurs.

At the minimum, Transfyr’s platform can create a much more complete SOP — in forms readable by humans as well as machines. But as observability infrastructure becomes more widespread, Transfyr’s agents can manage the tech transfer directly, identifying key contextual differences between sites in advance of a handoff, suggesting process optimizations to make protocols more robust and transferable, and coaching the receiving site when key deviations are noticed.

A new technology’s real-world impact is limited if it only works in the hands of its developers. Transfyr is focused on making science more robust, reproducible, and ultimately, transferable.