Getting clinical data to your SFTP endpoint is the easy part. How the file is handled from the moment it lands is what determines whether the audit trail emerges from the system itself or has to be produced after the fact.

SFTP isn't going anywhere in pharma. Vendors expect it, SOPs reference it, switching costs dwarf the benefits. The real work is making the infrastructure around it compliant, automated, and boring enough that nobody has to think about it.

Cloud-native is often discussed as a technical concept, but for pharma SCE leaders it has very practical implications. This article explains what cloud-native means in the context of a Statistical Computing Environment, how technologies like containers, Kubernetes, infrastructure as code, and CI/CD reduce manual work, and why they can make change management faster without weakening compliance. It also outlines key questions to ask when evaluating whether an SCE is built for reproducibility, auditability, and future modernization.

A practical guide for pharma leaders deciding whether to build their statistical computing environment (SCE) on-premise or in the cloud. The piece argues that while on-prem still suits stable, predictable workloads and large legacy datasets, modern SCEs are no longer stable workloads — they need elastic compute, multi-language support, and AI/ML-ready hardware that cloud delivers natively. It addresses the security objection head-on (citing Gartner's finding that 99% of cloud security failures are customer misconfigurations, not platform weaknesses), highlights how cloud democratizes access to the same infrastructure powering Novo Nordisk and Eli Lilly's NVIDIA partnerships, and lands on a pragmatic recommendation: build on cloud-native patterns (containers, IaC, automated deployments) regardless of where you deploy today, so a future migration is incremental rather than a rewrite. Hybrid is positioned as the enterprise default, with 90% of organizations expected to adopt it by 2027.

Where R, Python, and SAS each fit in a modern pharma SCE, and why the AI era turns this from a language choice into an architecture decision.

If you work in clinical development, you probably hear “SCE” in every other meeting about infrastructure, compliance, or moving off SAS. The acronym gets used loosely. Sometimes it means one specific tool. Sometimes it means an entire ecosystem. Sometimes it’s shorthand for “the thing our statisticians open every morning.”

In this article, we'll cover why R package validation is harder than most teams expect, the two main ways pharma organizations go about it today, and what we built at Appsilon to solve it.

Most statistical computing environments in pharma were designed when SAS was the default language, studies were simpler, timelines were longer, and "open source in regulated settings" was still a controversial idea.