IDBS bloggperson interacting with digital technology technology transfer

IDBS Blog | 28th September 2023

A new blueprint for technology transfer

person interacting with digital technology technology transfer

By Stuart Ward, Director, Platform & Solutions, IDBS

A technology transfer can take between 18 and 30 months and cost millions of dollars. But life science experts are optimistic that time can be reduced to just 8 to 11 months, if best-in-class practices are implemented.1

Transferring product, process and equipment knowledge is the necessary evil that exists between läkemedelsutveckling and manufacturing. Unfortunately, in many organizations, the technology transfer handoff is a disjointed and intermittent sharing of information, writes William Scott-Dunn, PhD, product team manager at IDBS, in a recent Bio-IT World article.2

“To address the tech transfer challenges that can delay or inhibit successful process scale-up, there is a need to rethink how the process description is controlled from the very start of process development, through process characterization and optimization and ultimately on to manufacturing,” agrees Marc Smith, Director of Strategic Solutions at IDBS.3

Both concur that sophisticated technology exists today that can help draw “a new blueprint” for technology transfer built around data integrity, and that technology transfer as we know it will become a relic.

Myriad data storage muddies technology transfer

When scaling up from clinical trial supply to commercial manufacturing processes and supply chain activities, it is imperative that technology transfer be meticulous, ensuring that only “safe, effective therapies reach patients.” But even the best laid plans can go awry because of lost or incomplete data, resulting in repeated process development steps and ultimately progression to market.

Dunn points to a common example to illustrate the power of faulty data: A fed-batch bioreactor process has been developed in the UK to manufacture therapeutic proteins at multiple sites around the world. Those involved considered the necessary documentation to be in order. However, as manufacturing began, the team identified “consistently lower performance” at one of the manufacturing sites. While bioreactor culture growth and final product titer were well within limits at several of the sites, this particular facility’s were barely within acceptable limits.

In an effort to replicate and investigate these differences, the team invested time creating scale-down models in both lab and pilot reactor cultures. Dunn explains that investigations focused on one particular cause — a failure to follow processes correctly on the manufacturing side. Deeper examination using a fishbone diagram ascertained that raw material could be the cause. “Fed-batch bioreactor cultures require many raw materials over the duration of the production run, and differences in these raw materials can contribute to significant variability,” states Dunn.

Indeed, after many costly scale-down runs using materials sourced directly from the problem site, material variability was revealed to be the problem. While the exact raw material was eventually identified, manufacturing had already begun to ship all raw materials from a well-performing location.

Dunn points out that while technology transfer was performed properly with all data being carefully entered – using spreadsheets, an electronic lab notebook (ELN) and a laboratory information management system (LIMS) – and development results were captured and verified, the data was, however, missing “context.” In this particular case, actions and records were combined and synthesized to create a scientific understanding and varied databases were united to create new SQL queries to achieve a rudimentary material and culture genealogy. In the example that Dunn provides, understanding of the root cause was costly and time-consuming because the data were poorly labeled and out of context, genealogy tracing was harder than it needed to be, says Dunn.

Continuous data sharing isolates technology transfer errors

Could and should this raw material issue have been identified earlier? Simply speaking, yes! Rather than R&D just handing off the data maintained in myriad places, i.e., spreadsheet, ELN and LIMS, the data and context could and should have been stored and shared on a digital backbone acting as a “single source of truth between development and manufacturing.” And, done correctly, this shared digital data backbone can create a blueprint for continuous knowledge sharing between development and manufacturing to more easily identify and solve problems like the one described above.

In that particular situation, the development and manufacturing teams could work together to “leverage native genealogies nested within visual analysis tools to trace the history of all the cultures and raw materials used at each site,” suggests Dunn. Using contextualized data from the backbone, changes in raw materials and correlations with process shifts would be quickly detected. He goes on to explain that shared data could have helped the teams identify how raw materials were being combined and consumed as well as isolate the reasons for decreasing growth and product titer. This information could be correlated to changes in specific raw material lots at specific sites.

A digital backbone curates technology transfer data

McKinsey points out that digital processes that contextualize data can facilitate technology transfer while ensuring data integrity, data completeness and a two-way communication between development and manufacturing about process and product knowledge.4

One such digital backbone is IDBS Polar, a BioPharma Lifecycle Management platform (BPLM) that aims to accelerate time to market by addressing the common challenges in technology transfer. By leveraging contextual data on how process and product interact, it helps efficiently execute your processes while curating the data you need. IDBS Polar also removes biases towards “success” data to confidently leverage AI algorithms, streamlining the transfer of the process description to manufacturing.

Dunn believes that shared data between R&D and manufacturing and the use of analytics tools, such as multi-variate analysis, can speed analysis and save millions in lost revenue by speeding up time to market. He says: “This new blueprint radically reinvents the concept of technology transfer built on shared resources accessible to all.”


Om författaren

Stuart ward, Director of Platform and Solutions, IDBSStuart is the Director of Platform and Solutions and is responsible for ensuring that IDBS products meet the needs of customers. He has grown the IDBS Platform team, which includes Product Owners, User Experience Designers and Technical Authors, so that it can provide the necessary business and domain experience required to create software and solutions to enable BioPharma and other industries achieve faster scientific breakthroughs. In addition, he led the creation and launch of The E-WorkBook GxP Cloud, which was IDBS’ first SaaS product for use in regulated (21 CFR Part 11, GxP) environments.
Before starting this role in January 2014, he was Product Manager for E-WorkBook for four years and worked in IDBS Global Professional Services for five years, responsible for deploying IDBS’ products both from a technical and project management perspective.
Prior to working at IDBS, Stuart completed a post-doctoral fellowship at the NIH and then worked for Ionix Pharmaceuticals. He obtained his PhD in Pharmacology from the MRC National Institute for Medical Research (University of London).


  1. O’Sullivan, C., Rutten, P., Schatz, C. (2020). Why tech transfer may be critical to beating COVID-19. McKinsey & Company. Retrieved from []
  2. Scott-Dunn, W. (2023). Why tech transfer needs a new blueprint. Bio-IT World. Retrieved from []
  3. IDBS. (2023). Next-generation bioprocessing strategies to improve speed, cost and quality of tech transfer. Retrieved from []
  4. Fontanillo, M., Paulick, K., Poda, P., & Silberzahn, T. (2022). Out of the shadows: A brighter future for pharma technical development. Retrieved from [


Further reading

Infosheet: Tech transfer and the need for digital transformation

Blog: Process digitization for tech transfer in pharma helps reduce risks in data availability and persistence

Blog: Ensuring product, process and patient data integrity and traceability throughout the BioPharmaceutical lifecycle in the era of personalized medicine

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