Context and Ambition

In the early 2020s, a fast-growing biotech set out a "digital-first" regulatory vision centered on moving from document-centric to data-centric submissions. Internal interviews highlighted largely manual authoring, duplicated effort, inconsistent data hand-offs, and growing complexity across global markets as key pain points shared with the majority of regulatory functions across the biotech and pharmaceuticals space globally.

SCA (Structured Content Authoring) was identified as one of the highest-priority capabilities, with the ambition to achieve growth on the order of multiples over the coming decade with a regulatory function powered by automation, advanced analytics, and structured, reusable content.

SCA Vision and Design

SCA is defined in the industry as a capability to standardize and modularize clinical and regulatory content into reusable components that could be assembled into complete documents across the lifecycle. The SCA content model distinguished four core object types: document templates, sections/sub-sections, components, and elements.^[1][2]

The target state assumed that the vast majority of dossier content could be reused through this model, enabled by standardized templates and robust reuse rules. SCA would be layered with automation and natural language generation (NLG) to auto-populate variables from source systems and autogenerate narratives for standard findings.^[3]

Early 2020s Expectations and Business Case

The original case anticipated significant benefits once SCA reached maturity. It was asserted that standardized, automated authoring could reduce overall submission timelines by several months via automated updates as new data arrived and through high content reuse across submissions. Industry benchmarks were cited to support potential cost savings of hundreds of thousands to several million dollars per day of accelerated approval, depending on product and trial size.^[4][5]

Expected qualitative benefits included reduced manual rework, improved consistency and quality, fewer health authority queries, and enhanced brand positioning as a digital leader. The vision assumed that dossier documents would effectively become "snapshots" generated from data and content systems, and that authors would work primarily through structured content management tools rather than directly in standard word processors.^[6][7][8]

Pilot Design and Scope

To validate the concept, the company executed a proof of concept (POC) focused on two use cases (two different submission types). These use cases were chosen due to high perceived reusability and relevance to near-term regulatory needs.

The POC involved:

The POC confirmed that, under controlled conditions and with intensive expert configuration, it was technically feasible to assemble key sections of a CSR and associated summary documents from reusable components and structured data.

Why the Initial Initiative Stalled

Despite technical feasibility in the POC, the initiative was assessed as too cumbersome and resource-intensive to scale beyond pilot scope at that time. Several structural issues emerged:

As a result, the initiative was perceived as an over-engineered solution whose operational and change-management demands exceeded the organization's readiness and the maturity of available AI and data foundations.

Would SCA Fare Differently Today?

By 2024-2025, the underlying technology landscape has shifted markedly, particularly in generative AI and LLM-based auto-tagging. Modern GenAI models support more flexible narrative generation, AI-assisted drafting, and semantic tagging at scale, reducing some of the manual work that burdened the original attempt. Regulators now explicitly acknowledge AI's role in regulatory documentation, with emphasis on transparency and human oversight, rather than blanket caution. In parallel, vendors have matured SCA-like offerings that combine structured content management with AI-assisted authoring and review workflows tailored for life sciences.^{[10][12][13][14][1][3][2]}

In this environment, a re-launched SCA program, if redesigned to use AI as a co-pilot, focused initially on narrow, high-value flows, would have a significantly greater chance of success. However, it would still not immediately match the full early 2020s expectations across all documents and indications.

Managing Expectations for Implementation

If the company implemented SCA today with a revised design, expectations should be calibrated around incremental, use-case-driven benefits rather than immediate end-to-end transformation.^[15][16]

Under these managed expectations, SCA can be seen as a core enabler of a broader data-centric regulatory strategy rather than a standalone, one-time project.

Requirements to Fully Realize the Early 2020s Vision

For an SCA system to deliver on all expectations articulated originally (high reuse, substantial timeline reductions, and data-driven, near real-time submissions), the company would need to execute a long-horizon, enterprise-level transformation across several dimensions.

Data and standards

Technology and AI

Operating model and processes

Governance, validation, and external alignment

Realistically, achieving this full end-state would require sustained investment and leadership sponsorship over a 5 to 10-year horizon, aligned with broader clinical data, RIM, and digital transformation roadmaps. The original SCA initiatives can thus be viewed as an important but early step along a longer journey toward truly data-centric regulatory submissions.^[17][24]

References