レガシーSAS臨床報告システムをAI創薬向けに非破壊で近代化する手法 A Non-Destructive Methodological Framework for Modernizing Legacy Clinical Reporting Systems for AI-Driven Pharmacoinformatics: A SAS Case Study

This paper introduces a non-destructive methodological framework for modernizing legacy SAS-based clinical reporting systems so they can participate in AI-driven pharmacoinformatics workflows. The central premise is that validated statistical and reporting assets, accumulated over decades in pharmaceutical organizations, should be preserved rather than rewritten when bridging to modern machine learning pipelines.

SAS has long been the de facto standard for producing regulatory submissions to agencies such as the FDA, including CDISC-aligned SDTM and ADaM datasets and the associated tables, listings, and figures. The code bases that generate these artifacts are subject to extensive validation under GxP regimes, which makes wholesale replacement both expensive and risky from a compliance standpoint. The authors appear to address this reality by proposing a wrapper-style architecture that exposes legacy SAS programs through interoperable interfaces, allowing Python- or R-based AI components to consume their outputs without altering the validated core.

The case study walks through an incremental modernization path. By separating data extraction, validation, and inference layers, the framework allows regulated reporting to continue running on SAS while exploratory and predictive analytics, such as safety signal detection, pharmacokinetic modeling, or adverse event prediction, are layered on top. This separation of concerns is intended to let organizations adopt AI capabilities without triggering full revalidation of their reporting stack, a recurring pain point in pharma IT modernization projects.

The broader ecosystem context is worth noting. CDISC continues to evolve standards for clinical data exchange, while Posit (formerly RStudio) has been pushing R as a complementary language for regulatory submissions, an effort backed by the R Consortium's Submissions Working Group that achieved successful FDA pilot submissions in recent years. At the same time, AI-for-drug-discovery platforms such as NVIDIA BioNeMo, DeepMind's AlphaFold, and a growing set of foundation models for molecular and clinical data have raised expectations for what pharmacoinformatics pipelines should deliver. SAS itself has responded with Viya, which adds cloud-native and open-language integration to its traditional analytics stack.

Against this backdrop, the paper's contribution is less about a specific implementation and more about offering a methodological blueprint for regulated organizations that cannot afford disruptive rewrites. The non-destructive principle aligns with established patterns in enterprise modernization, such as the strangler-fig approach popularized by Martin Fowler, applied here to a highly regulated analytics domain. Readers should treat specific performance or compliance claims with appropriate caution, as the generalizability of a single case study to diverse clinical reporting environments may be limited, and validation outcomes will ultimately depend on each organization's quality systems and regulatory interactions.

For practitioners, the takeaway is that bridging legacy SAS environments to AI workflows does not necessarily require choosing between preservation and innovation. A layered, interface-driven approach can let validated reporting coexist with experimental ML components, potentially shortening the path from retrospective reporting to prospective, model-assisted clinical insight.