{"id":108626,"date":"2026-03-02T14:27:01","date_gmt":"2026-03-02T17:27:01","guid":{"rendered":"https:\/\/tech.einnews.com\/article\/896913073"},"modified":"2026-03-02T14:27:01","modified_gmt":"2026-03-02T17:27:01","slug":"best-practices-for-tech-transfers-amid-a-revolution-in-digitalization","status":"publish","type":"post","link":"https:\/\/new7.shop\/zerocostfreehost\/index.php\/2026\/03\/02\/best-practices-for-tech-transfers-amid-a-revolution-in-digitalization\/","title":{"rendered":"Best Practices for Tech Transfers Amid a Revolution in Digitalization"},"content":{"rendered":"
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For contract development and manufacturing organizations (CDMOs<\/a>) that partner with companies on a technology transfer, the process may be familiar but nevertheless daunting. And while electronic records management in some form has been a long-accepted element of this process, the explosion of artificial intelligence (AI<\/a>) platforms in the last three years has added both the potential for new and game-changing efficiencies as well as crippling pitfalls.<\/p>\n

As tech transfer partners consider their relationships within this increasingly digital environment early in 2026, they are examining all stages and components of the process. Accelerating timelines as much as possible, while continuing to prioritize quality, is a constant concern. AI and digital tools figure to provide a big boost to this goal\u2014but CDMOs, while enthusiastic, are treading lightly for now.<\/p>\n

Current Challenges in Tech Transfer Initiation<\/h2>\n

In polling numerous experts on the most common issues partners need to watch for, almost all cited discrepancies in equipment.<\/p>\n

\u201cOne of the most common challenges we see is the inherent difference between the sending and receiving sites\u2014whether differences in equipment, production scale or consumables,\u201d says Patrick Cushing, Vice President, Operations at Rentschler Biopharma<\/a>. \u201cThese differences almost always require some degree of process adaptation. Determining the scope of these adaptations early and a risk mitigation strategy are critical to de-risking the transfer.\u201d<\/p>\n

Russell Miller, Vice President, Global Sales and Marketing at Enzene<\/a>, agrees.<\/p>\n

\u201cBoth technical teams must be fully aligned on the process, equipment, and supply chain,\u201d Miller says. \u201cIf any of these elements are insufficiently addressed, delays can arise as issues are identified and corrected during the transfer.\u201d<\/p>\n

Equipment requirements tend to surface early in the process. That\u2019s according to Jackie Klecker, Executive Vice President, Quality & Development Services at Lifecore Injectables CDMO<\/a>.<\/p>\n

\u201cProduct-contact parts often require dedicated components, and long equipment lead times can slow even the best-planned transfers,\u201d Klecker says.<\/p>\n

\u201cDetailed mapping and advance planning are essential to ensure a smooth transition,\u201d Miller adds.<\/p>\n

According to Samsung Biologics<\/a>\u2019 Sangkyu Hwang, Senior Director of MSAT Technical Excellence, and Bumjoon Cha, Associate Director of MSAT Digitalization, one danger is a \u201ctransparency gap\u201d in process information.<\/p>\n

\u201cPartners are often hesitant to share the full critical information of their processes, such as detailed historical data or minor deviations, if they do not fully trust the CDMO\u2019s scale-up capabilities,\u201d Hwang and Cha contend. \u201cIf a partner perceives that the CDMO lacks the technical maturity to handle complex scaling, partners will likely want to lead the tech transfer process themselves without disclosing information, rather than trusting the CDMO to handle the tech transfer.\u201d<\/p>\n

Klecker concurs, saying that \u201cit can be difficult to filter which historical decisions, assumptions, and development learnings are critical for a CDMO to understand.\u201d<\/p>\n

\u201cDifferences between development and commercial manufacturing equipment can affect critical quality attributes if not properly assessed,\u201d says Dipak Gordhan, Associate Director of Manufacturing Operations at Upperton<\/a>. \u201cThese issues are compounded by compressed timelines and the heightened regulatory and commercial risk typical of late-stage programs, where there is little room for error.\u201d<\/p>\n

That calls into question another top challenge: timing.<\/p>\n

\u201cIt is critical to clearly understand the timeline requirements from both the client and the CDMO in order to develop a well-thought-out plan that aligns with project objectives,\u201d says Miller.<\/p>\n

This is relevant at all points of the process, according to Hwang and Cha. It dovetails with their transparency gap by creating another chasm\u2014in reliability.<\/p>\n

\u201cIf the partner discovers hidden issues or late-stage failures that weren\u2019t proactively disclosed, the foundation of trust collapses,\u201d they say. \u201cThis lack of transparency leads to intensified oversight, project delays, and a breakdown in the collaborative spirit needed to solve complex biological hurdles.\u201d<\/p>\n

\u201cEven understanding the intended commercial regions can impact CDMO planning,\u201d Klecker says. \u201cWhen these details are incomplete or unspecified, the CDMO must spend additional time gathering them\u2014time that directly affects the sponsor\u2019s desired timeline.\u201d<\/p>\n

And the documentation itself must be as complete as possible. Fragmented or inconsistent documentation, Gordhan warns, slows progress and increases risk.<\/p>\n

CDMOs Can Smooth Rough Spots and Ensure Quality<\/h2>\n

The universal theme that emerged in considering how CDMOs can be most useful was risk management. For Hwang and Cha, this plays into the trust issues they identified as a challenge in initiating the process.<\/p>\n

\u201cTrust is not just a \u2018soft skill,\u2019\u201d they say, \u201cit is a technical requirement for success. To overcome the challenges of trust and comparability, the partnership must be built on implementing a \u2018joint risk management\u2019 framework.\u201d<\/p>\n

What this essentially involves is having all partner scientists sit down together during the initial facility-fit analysis. This can be in person or virtual.<\/p>\n

\u201cBy acknowledging technical limitations or equipment differences early and openly, we eliminate the \u2018fear of the unknown,\u2019\u201d say Hwang and Cha.<\/p>\n

Klecker agrees that getting everyone in the same room is the most expeditious path to success.<\/p>\n

\u201cEarly face\u2011to\u2011face dialogue helps both sides align on product maturity, equipment expectations, method readiness, and potential risks before work begins,\u201d Klecker says.<\/p>\n

Cushing says Rentschler offers a risk assessment that first identifies potential roadblocks in the process transfer, then outlines mitigation measures.<\/p>\n

\u201cWhen multiple viable options exist, these can be tested during the transfer phase of the project to ensure the resulting GMP [good manufacturing practice] batch is successful,\u201d Cushing says.<\/p>\n

According to Gordhan, experienced CDMOs can undertake such tasks as documentation audits early in the process. This ensures elements like development data, batch records, analytical methods, and material specifications are not only complete, but also standardized.<\/p>\n

\u201cTogether with formal risk assessments, these practices help build quality into the process from the outset,\u201d says Gordhan.<\/p>\n

And it is quality that remains fundamental to any CDMO engagement, argues Miller.<\/p>\n

\u201cThe preparatory work done before the transfer begins is one of the most effective ways to minimize challenges,\u201d Miller says. \u201cWhile it is rare for equipment and standard operating procedures to align perfectly, a well-outlined and well-reviewed transfer plan, supported by deep technical expertise, can significantly reduce risks. This proactive approach helps ensure that quality is maintained throughout the transfer and that potential issues are identified and addressed early.\u201d<\/p>\n

\u201cA CDMO can significantly streamline the transfer by approaching the early proposal phase as a structured, collaborative exchange,\u201d Klecker concurs. \u201cMaintaining quality during the transfer hinges on readiness, experience, and robust systems.\u201d<\/p>\n

But that\u2019s not to suggest that transparent communication isn\u2019t absolutely necessary later in the process as well.<\/p>\n

\u201cWe commit to a policy of immediate disclosure,\u201d Hwang and Cha state. \u201cIf a deviation occurs, the partner is notified of the event alongside a preliminary impact assessment and a proposed mitigation plan. By being transparent about failures, we actually strengthen the partnership.\u201d<\/p>\n

Ensuring and Enhancing Timeliness While Maintaining Quality<\/h2>\n

While quality is critical, maximizing tech transfer efficiency in terms of time committed by partners is also a priority.<\/p>\n

There are opportunities to optimize timeliness at all points of the process. Yet the consensus among the experts is the earlier, the better.<\/p>\n

\u201cTimeliness often depends on early identification and communication of potential constraints, particularly within the supply chain,\u201d Miller says. \u201cIf a process relies on unique or high-demand raw materials, those requirements must be clearly communicated to the CDMO at the outset to avoid delays, especially when timelines are tight.\u201d<\/p>\n

\u201cAccelerating timelines requires a commitment from both organizations to support the common goal, from project resourcing [to] making prompt decisions [to] timely review of documents,\u201d Cushing adds. \u201cAll help contribute to incremental gains to produce the first batch as soon as possible.\u201d<\/p>\n

According to Gordhan, getting things right early on accelerates a timeline at that nascent stage. But it also saves the timeline from slowing down at a future point.<\/p>\n

\u201cTimelines can be ensured and even improved through early planning, clear communication, and effective project management,\u201d Gordhan says. \u201cAligning validation strategies and regulatory expectations early helps avoid late-stage delays, while clearly defined timelines, deliverables, and points of contact keep both parties aligned.\u201d<\/p>\n

Hwang and Cha argue the biggest potential for a slowdown lies outside the earliest stages of the tech transfer process. They agree, though, that precision at the start is the greatest accelerator.<\/p>\n

They say the tech transfer is a \u201clive, evolving event.\u201d<\/p>\n

\u201cThe most common cause of timeline slippage is discovering a fundamental process gap during the mid-to-late stages of manufacturing,\u201d opine Hwang and Cha. \u201cBy establishing mutual trust and transparency from day one, we can perform an incredibly granular gap analysis.\u201d<\/p>\n

Klecker echoes Hwang and Cha\u2019s point about trust as an effective accelerant.<\/p>\n

\u201cTimelines can be accelerated when sponsors provide dedicated equipment or transferable tools early in the process,\u201d Klecker says. \u201cSponsor flexibility and trust in the CDMO\u2019s experience enable more efficient planning and execution.\u201d<\/p>\n

But also, Klecker advises, what a sponsor knows\u2014and what it doesn\u2019t\u2014can have an impact on speed. A bit of creativity comes into play.<\/p>\n

\u201cTools such as bracketing\u2014validating only the smallest and largest fill sizes\u2014or rationalizing raw material lots can reduce required batches,\u201d Klecker says. \u201cSponsors may not know these FDA\u2011acceptable options exist, and early alignment can save months.\u201d<\/p>\n

Advantages and Pitfalls of Digitalization and AI<\/h2>\n

As with seemingly all other aspects of life in 2026, AI is working its way into the tech transfer process. However, the use of automation and digital tools have been commonplace for a while.<\/p>\n

Cushing says that while these models are powerful, real data is still their foundation.<\/p>\n

\u201cIn today\u2019s digital age, AI is here to stay,\u201d Cushing says. \u201cDigital tools and AI are increasingly helpful in modeling process behavior. We are increasingly seeing instances of modeling bioreactor conditions to better understand performance within an available dataset and better predict optimal performance versus scale. This helps anticipate how a process may perform across a variety of scales and across different equipment platforms.\u201d<\/p>\n

According to Klecker, CDMOs are increasingly developing standards for experience-based digital schedules to serve as a baseline for all transfers. A major advantage AI may provide is in real-time impact assessment.<\/p>\n

\u201cAI currently supports administrative efficiencies such as drafting and office\u2011related tasks, but major benefits for tech transfer arise from automation and digital project\u2011management tools,\u201d Klecker says.<\/p>\n

The technology, however, is not yet perfect, cautions Miller.<\/p>\n

\u201cWhile digitalization is not new, the integration of AI, automation, and advanced digital tools is still evolving within tech transfer processes,\u201d Miller says. \u201cTools such as electronic notebooks and electronic data transfer have improved information sharing, but seamless interoperability between different organizations\u2019 systems is not yet universal.\u201d<\/p>\n

Tying this together, Hwang and Cha say speed is something the increased digitalization and adoption of AI will naturally promote.<\/p>\n

\u201cEven with strong planning, management, and relationships, the initial tech transfer and kick-off to the first at-scale batch usually takes six months or more,\u201d they say. \u201cCDMOs are investing in expanding and refining tech transfer models. Digitalized workflows, automated GMP documentation, and standardized templates are integrated to further accelerate tech transfer while maintaining high-quality standards.\u201d<\/p>\n

Ultimately, Hwang and Cha predict, digitalization can cut report-generation time from days to minutes. However, correct deployment of available tools is essential. With digitally connected operations, real-time integration of process\u2011level, analytical, and equipment data streams into a secure data lake is possible. Examples include electronic manufacturing batch record systems and electronic lab notebooks. Ideally, establishment of data summary and visualization functionalities comes first. From there, a report-generation system can produce charts.<\/p>\n

\u201cWhile digitalization brings undeniable efficiencies, a careless rollout can create new vulnerabilities,\u201d they warn. System design, validation, and monitoring can all ensure GMP compliance. This requires additional data governance. And new systems can conflict with legacy systems or procedures. For example, an automated report generation system should receive data from the manufacturing execution system. Although requirements include data interface frequency and unique ID, gaps between two systems can exist. This is especially true when implementing commercial solutions.<\/p>\n

Conclusion<\/h2>\n

The emergence of AI has not changed the primary challenges in initiating tech transfers. Namely, these include timing and differences in equipment. But it has added new challenges. The tantalizing enhancement in efficiency runs the risk of costing quality.<\/p>\n

However, whether regarded as inevitable or organically adopted, CDMOs are accepting AI as a new part of the process. They have added it to elements of digitalization that have long been in place. Checking in with partners at every stage, ensuring the human element remains, seems to be the way forward.<\/p>\n

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