18th September 2026
A summary of the full White Paper authored by NGT Biopharma Consultants Mark Buswell and Nicola Clear with Rob Innes and John Arthur of DTG.
“We generate data digitally, but we exchange it like it’s 1995’ Mark Buswell NGT BioPharma
Pharmaceutical organisations have invested extensively in digital systems across research, development and manufacturing. Laboratory platforms, manufacturing execution systems, enterprise systems and digital repositories are now embedded across much of the product lifecycle.
Yet technology transfer between R&D and commercial manufacturing continues to be affected by fragmented data, repeated manual handoffs and loss of scientific and process context.
This persistent divide is the focus of Bridging the Digital Divide in Pharmaceutical Tech Transfer, a joint white paper developed by NGT BioPharma BioPharma Consultants and Digital Technologies Group.
The paper brings together NGT BioPharma’s senior pharmaceutical expertise across Clinical Development, R&D, CMC, Regulatory, Quality and operations with DTG’s capabilities in Pharmaceutical Manufacturing transformation, digital architecture and data integration. The objective is to examine the gap as a connected industry , scientific and technological challenge, rather than as a series of isolated company and system issues.
Tech transfer is the point at which scientific and process knowledge developed within R&D must become a robust, scalable and compliant manufacturing process.
R&D and manufacturing, however, are designed around different operating requirements.
R&D needs flexibility alongside quality and regulatory compliance. Data structures, process parameters and scientific understanding constantly evolve as evidence is generated and development progresses.
Commercial manufacturing requires establishing control consistent execution, quality oversight and regulatory compliance to meet market authorisation requirements
Both environments may be digitally mature within their own boundaries. The divide appears when their systems, workflows and data models do not connect.
Information generated during development may need to be extracted, reviewed, reformatted and manually entered into manufacturing systems. Context is often weakened during the handoff, while previous decisions may need to be interpreted again by new teams.
What should be a progression of knowledge becomes a translation exercise.
This is the central distinction made in the white paper: the industry has digitised individual systems, but it has not created reliable continuity across the pharmaceutical value chain.
Many organisations still manage tech transfer through documents, meetings, trackers, email chains and sequential approvals.
“Tech transfer is effectively a knowledge workflow — but it’s not managed as one”. Mark Buswell
Certificates of analysis, specifications, batch records and process information may originate in digital systems, yet they are frequently exported into PDFs, transferred by email, reviewed manually and re-entered elsewhere.
This creates familiar inefficiencies:
The deeper issue is that the process is built around moving documents rather than preserving and applying knowledge.
The white paper therefore reframes tech transfer as an end-to-end knowledge workflow. In a more connected model, data, context, approvals and decisions would move through a structured process with clearer ownership and greater visibility across R&D, Manufacturing, Quality, Regulatory both internally and with external partners.
This represents more than a faster version of the current process. It changes the way tech transfer is designed and managed.
The growing use of specialist CDMOs has made the data continuity gap harder to ignore.
Each sponsor–CDMO relationship may involve different systems, quality procedures, data structures and exchange mechanisms. For organisations managing several external partners across a portfolio, this creates a many-to-many network of interfaces.
Most connections are still supported through bespoke integrations, manual processes or project-specific working practices. These approaches may solve an immediate problem, but they are difficult to scale.
Every new partner can require another combination of formats, controls and integrations.
The consequences include slower onboarding, reduced transparency and repeated loss of context at organisational boundaries. Digital capability therefore affects more than administrative efficiency. It can influence supply readiness, programme timelines and the strategic value of the sponsor–CDMO relationship.
The white paper explores a more scalable alternative: moving beyond repeated point-to-point integrations towards shared data exchange models based on common structures and standards.
The innovation proposed by NGT BioPharma and DTG is not based on adding another standalone tool or replacing every existing system.
It lies in connecting areas that are often addressed separately:
This combined perspective allows tech transfer to be approached as one connected transformation problem.
Pharmaceutical domain expertise is needed to identify where knowledge is created, where it is lost and which scientific, quality and regulatory requirements must be protected. Digital expertise is required to translate those needs into data structures, workflows, integrations and scalable platforms.
Bringing these capabilities together creates a practical route between strategic intent and implementation.
It also establishes the data foundation for more advanced possibilities. Trusted, structured data could support digital twins, predictive process optimisation, stronger supply-chain visibility and more meaningful use of analytics and AI.
The future state described in the paper is therefore broader than paperless tech transfer. It is an ecosystem in which data can carry knowledge across development, manufacturing and external partnerships without being repeatedly reconstructed.
A data-centric approach does not necessarily require organisations to replace all existing platforms.
It requires those systems to operate within a more coherent framework, supported by shared data structures, clear governance and workflows that preserve context as information moves between teams and organisations.
This also requires end-to-end ownership.
Tech transfer crosses R&D, Manufacturing, Quality, Regulatory, Supply chain, IT and external partners. Where responsibility is fragmented, digital initiatives tend to remain local. Functions improve their own systems, sites introduce isolated tools and project teams create temporary integrations.
The result is incremental improvement without structural change.
Closing the gap therefore depends on co-ordinated work across leadership, process, data, technology and operating model. It requires a clear understanding of the business and scientific outcomes first, followed by the architecture and execution needed to support them.
“Many companies are still accepting PDFs as the data exchange mechanism… even though the data originates in digital systems”. Rob Innes
The full white paper examines the data continuity gap in greater depth, including document-based exchange, validation burden, leadership and operating-model barriers, sustainability, evolving CDMO partnerships, shared platforms and a practical roadmap for transformation.
Read Bridging the Digital Divide in Pharmaceutical Tech Transfer to explore how NGT BioPharma BioPharma Consultants and Digital Technologies Group are combining pharmaceutical and digital expertise to help shape a more connected approach to tech transfer.
