Bioprocess Tech Transfer: Navigating The Data Dilemma

The transfer of institutional data and knowledge is critical to the development of biotechnologies, bioproducts, and biotherapeutics. At some point in the lifespan of a biotech company, they will need to perform bioprocess technology transfer to deliver information from one facility to another. It’s an unavoidable step for bringing a bioproduct to market.

Usually, this occurs when a company hires a contract development and manufacturing organization (CDMO) or contract manufacturing organization (CMO) for bioprocess development, scale-up, and at-scale biomanufacturing. The contract biomanufacturing market and capacity continue to grow, especially to accommodate the production needs of innovative and early-stage bioproduct companies that lack internal infrastructure for bioprocess development and scale-up.  Bioprocess tech transfer also is done internally among companies, where processes developed in research and development groups must be passed to their pilot and manufacturing counterparts.

However, transferring massive reserves of information is far from a trivial process, often presenting as a difficult and risky step along the path to commercialization. Through no fault of bioprocess teams, tech transfer struggles can add significant expense and delay time to market. Both can be devastating, particularly for early-stage innovators competing in a challenging market with limited funds.

Though there are different aspects that can make transferring bioprocesses demanding, sharing organized, detailed, and contextualized bioprocess data remains a core challenge. Made worse, as bioproduction approaches continue to diversify and get more complex, this challenge is multiplied. Despite this, there is a temptation to underestimate tech transfer. As many bioprocess experts would agree, it’s better to start planning for tech transfer early (or better yet, right now) than shortly before kick-off.

To encourage thinking ahead, this blog contextualizes the difficulty of bioprocess tech transfer, especially as it relates to managing bioprocess data and building coherent transferable institutional knowledge. After reviewing the key challenges, the blog will also discuss Invert’s framework for improving how companies share vital biomanufacturing information.

Understanding the Cost and Risks of Tech Transfer

First, it helps to acknowledge that tech transfer is just hard.

It requires many stakeholders to come together to share and understand complex biological data and bioprocess information, and every detail matters. Complicating the picture, everyone is time-limited and bioprocesses, including required upstream and downstream infrastructure, vary significantly. There is not a one-size-fits-all approach to biomanufacturing, even within common umbrella production formats (like fed-batch microbial fermentation, mammalian cell culture, cell-free systems, etc.). This means there are many differences across bioprocesses and bioproducts as you increase scale. In addition, and something that people don’t always expect to face, you also have to learn your CDMO/CMO team’s capabilities, work habits, preferred terminology, and idiosyncrasies to find effective communication mechanisms.

The Cost

Given wide market and product variability, the exact costs of tech transfer can be hard to pin down. But as an example, Seqens, a small molecule-focused pharmaceutical and specialty ingredient CDMO, once indicated that it’s common to expect to pay $6,000-$10,000 per week for tech transfer and familiarization (though some, like Seqens, charge a flat rate). Given that biomanufacturing tech transfer realistically can take up to 6 to 9 months with some going much longer (as opposed to 8 to 16 weeks for small molecules), it becomes pretty clear that making the tech transfer process more efficient can save a lot of expense.

The Risk

Though scale-up is routinely understood to be a hyper-critical juncture, the specific impact of bioprocess tech transfer gets baked into the larger step and is often lost in context. Simply put, a lot of things need to go right during bioprocess tech transfer for a successful scale-up outcome.

Large sums of money are spent on bioproduction runs, making this stage a risky and vulnerable time. To put this into clearer context, one 2019 study from Contract Pharma, collected 37 reported entries of CMO batch prices for 500-liter GMP biopharmaceutical mammalian cell culture. At this time, batch prices averaged at $726,000 ± $149,000. The report also indicated that these prices also likely excluded the costs of “engineering runs, change orders, raw materials, and consumables.”

While the cost of production runs can vary significantly, the Atos group published a blog earlier this year that shared their own estimates of average batch costs in biotechnology, reaching $2.5 million per batch including (both the tech and validation batches), though scale and bioproduct information were unclear.

Hitting snags in productivity or performance at scale, whether due to the bioprocess itself or its execution, can trigger costly rework. Digging through massive amounts of data to find a root cause, doubling back on R&D efforts, and ultimately booking more production runs, leads to increased spend and delayed timelines (especially if the CMO’s capacity is booked out for months).

Without a doubt, the need to spend (or raise) additional funds because of inefficiencies or challenges in sharing, deciphering, and utilizing complex bioprocess data–however legitimate–throws a pall over the whole operation. Thus, despite its challenge, the operability of bioprocess data holds a central importance.

The Status Quo of Bioprocess Data Management in Tech Transfer

Sharing process information, internal data, analysis workflows, and experimental results lie at the heart of bioprocess tech transfer. To get a new team up to speed for further bioprocess development and scale up they need to know what’s been done, what the outcomes were, where the existing bioprocess edges are, and more. Ideally, by the end of tech transfer, these teams are working off the same research knowledge as the original team, such that they can build off it efficiently.

A Lack of FAIRness

However, biotechnology companies amass considerable expertise and institutional context as they research and develop their products. Data sets, analyses, or familiar terminology that appear clear to your team may not be as readily understood by an outside party (like, a CMO). Plus, the sheer scale of this data can be difficult to appropriately collect and share, let alone be understood. For example, an internal team member may know which spreadsheet tab has the answer they are looking for, but that information is lost in translation once removed from the immediate team. In addition, if your internal group spans multiple teams and sites, it makes the process all the more arduous.

A lack of FAIR data management principles (Findable, Accessible, Interoperable, Reusable) at a product company becomes a barrier to working efficiently with CDMOs and CMOs. Without implementing FAIR principles, it becomes exceedingly painful to parse through minimally curated data to bring a contract partner or internal production site up to speed and then transfer their data back out to the product company. Unfortunately, FAIR practices are not as common as you might think, due to the difficulty of manually implementing them.

Tech Transfer Packages

Data being accessible and well-structured is still not enough to have this process go smoothly. Bioprocess engineers must also properly communicate contextual information about things like process design, performance, and at-scale predictions based on well-characterized workflows. To accomplish this, product companies develop Tech Transfer Packages (TTPs) to consolidate vital bioprocess data and information in a collection of long documents, PDFs, and supporting data in spreadsheets. Manually generating and sharing these static TTPs can create complications, both during external transfer and data transfer back to the product company.

External Bioprocess Tech Transfer

Product companies first need to pull together their tech transfer package, including all their bioprocess information and data. The amount of time and effort this takes depends significantly on the level of FAIR principle adherence and site-wide standardization of analysis workflows. Even still, the most diligent teams that established these principles well in advance still need to neatly accumulate ALL of this information such that it can be easily understood by external teams.

In less ideal scenarios, teams can struggle with inaccessible data spread across many files and varied analysis workflows. As a result, teams can inadvertently cherry-pick their best runs or exclude critical details, creating mismatched expectations, a lack of awareness of failure modes, and beyond.

A bioprocess scientist or engineer should be able to run a model across their entire dataset to get a complete picture of their process. From there, they should be able to select representative runs and easily identify runs that performed differently due to known deviations. Though it should be easy to share process execution and determine the performance landscape, bioprocess engineers simply don’t have the resources, time, or tools to make this happen.

From there, TTPs are sent via email as static files that require careful version control. As additions and edits are made, new versions must be emailed back and forth. Naturally, this requires that all relevant stakeholders have the same access and maintain a keen eye on the versions. As you might expect, messages asking “What version is the latest one that I need to look at?” are all too common. This requires the most plugged-in stakeholders to re-familiarize themselves, confirm the details, and communicate back to the wider group.

Transfer Back to the Customer

Even after laboring through all that tedious bioprocess data management and TTP development, the customer still needs to ingest information from their external partner(s) and analyze the results against internal runs.

Something we hear A LOT at Invert is how manual and time-consuming it is to take data from the CMO, bring it into your existing analysis environment, and analyze the results against your internal, well-characterized process data sets. Since every CMO is different, data packages are all formatted and laid out differently. Plus, these are often very complex data sets: with a mix of measurements, observations, execution documentation, and final results. This makes tight timelines and turnaround times more stressful and less effective than they deserve to be.

According to an Invert customer, “We are forced to spend all of our time on data engineering, leaving little to no time on data science.”

Just processing the data package to find insights takes all of their bandwidth instead of actually leveraging the data.

Tech Transfer with Invert

Biotechnology companies need a more modernized tech transfer tool to make the process more straightforward. Instead of the status quo, product and contract teams need to be seamlessly speaking the same tech transfer “language.” That language requires managing, processing, and analyzing data in a common, accessible, and more automated environment.

Invert’s core focus is on operationalizing bioprocess data and information more effectively. Thus, we recognized that when designing our bioprocess data management software that it needed to streamline bioprocess tech transfer. Ultimately, a key goal was to transform tech transfer packages into “living” knowledge hubs. Put another way, Invert’s software can act as one central “source of truth” shared between both product and contract manufacturing companies.

Invert’s software emphasizes the sharing of information between companies with improved traceability and auto-contextualization with detailed record keeping: complete with raw data, metadata, and calculated results. As an added benefit of using Invert, CDMOs and CMOs have found that they are able to market a streamlined tech transfer process and provide those benefits to their customers.

Invert’s software drives tech transfer through secure data sharing, report generation, and improved planning and communication features.

On the data sharing side, customers can provide contextualized data instead of inconsistent and complex spreadsheets, documents, and PDFs while providing relevant layered data (like events, protocols, time-series data, and scale context). For reporting, customers get structured access to contextualized data they can then compare to secure historical data, with complete traceability. To support improved and more direct planning and communication, CDMOs and CMOs can share explicit details about the planned tech transfer experiments to confirm alignment and understanding of the appropriate campaigning strategy with the customer.

Looking ahead, Invert is also adding a number of additional features to further streamline tech transfer. Among those, we are building out automated tracking of acceptance criteria and deviation reports for quality tracking, as well as scaling calculators with predicted performance modeling capabilities.

If you’re interested in planning ahead for bioprocess tech transfer or want to see how our software can improve your next contract partnership, reach out to book a demo today!
Otherwise, if you are looking for a broader introduction to working with contract manufacturers, you can read our recent Inside Biomanufacturing newsletter co-written with Liberations Labs.

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