Replatforming Science: Software as a Strategic Lever in Modern Pharma R&D

The pharmaceutical industry stands at a pivotal moment. Organizations now find themselves constrained, not by the science, but by the software and systems supporting it. Here, I’ll outline five major shifts redefining how biopharma teams are scaling their research operations, and why these changes are shaping scientific performance, collaboration, and long-term agility.

Cloud-Native Foundations: Elastic Research at Production Scale

Historically, on-prem systems have served as the backbone of computation, offering control, predictability, and direct integration with local instruments. But they were built for localized compute needs. Today’s research workloads are highly variable, often unpredictable and frequently global. Teams need the flexibility to scale from analyzing a couple of samples to thousands. Collaboration often spans skill sets, time zones, and regulatory boundaries. And data generation is dynamic, requiring a setup that can flex with demand.

Modern Shift

Cloud-native systems deliver:

• Dynamic provisioning of compute and storage based on workload demands.
• Centralized control with distributed access for cross-functional collaboration.
• Infrastructure-as-code for automation, repeatability, and governance.

The Result

Scientists gain immediate access to the resources they need, when they need them, without infrastructure delays or overprovisioning. Cloud-native doesn’t mean abandoning existing systems; it means flexibility and the ability to extend them for a more agile, collaborative, and scalable research environment.

Workflow Standardization and Automation: Reducing Variability at Scale

Custom-built, legacy workflows are often developed for specific datasets, tools, or projects. While this flexibility enables innovation, it also creates inconsistencies, especially when manually executed or poorly documented. These issues undermine reproducibility and slow progress. Standardizing workflows across teams and projects is not about hindering creativity—it’s about enabling scalable science. This is where workflow engines like Nextflow have played a transformative role.

Modern Shift

Workflow engines that enable:

• Portability across environments, from laptops to HPC clusters to cloud instances; write once, run anywhere.
• Versioning and provenance tracking, ensuring that results can be revisited and validated at any time.
• Composability, allowing pipelines to evolve incrementally rather than being rewritten wholesale.

The Result

Research becomes more consistent, maintainable, and auditable—essential for both scaling and compliance. Organizations that adopt this model not only increase productivity with faster interaction cycles, but also significantly reduce onboarding time for new staff.

Operationalizing FAIR Principles: Turning Philosophy into Practice

FAIR principles are widely recognized, but difficult to implement in practice due to fragmented, siloed systems and diverse datasets. A more pragmatic approach is emerging, driven by systems that embed FAIR-aligned practices directly into pipelines.

Modern Shift

Workflow systems that enable FAIR by default:

• Captures metadata through workflows
• Tracks tools and data lineage automatically
• Connects to registries for discoverability

Result

This data stack is beneficial not only for data stewardship but is increasingly essential for regulatory readiness and downstream applications of AI and ML.

Flexible, Modular Infrastructure: Built for Complexity and Change

Today’s R&D involves a wide range of data types. From omics and imaging to electronic health records and real-world evidence, research now spans a wide range of structured and unstructured data modalities. Traditional tech stacks, often optimized for specific analysis types or fixed formats, struggles under this complexity. Rigid architectures can’t easily accommodate new tools, data types, or changing priorities. Modern R&D demands heterogeneous, modular computing.

Modern Shift

Technology built for modularity and extensibility:

• Abstraction of underlying compute, so that workflows can move fluidly between environments.
• Containerized toolchains, to encapsulate software dependencies and avoid configuration drift.
• Extensible data models that can accommodate new data types without extensive reengineering.

The Result

Technology and infrastructure that evolve with science, not against it. Teams can integrate new modalities without reinventing the wheel, adapt to surges in demand, and avoid vendor lock-in. These changes enable faster hypothesis generation and testing, better collaboration between experimental and computational teams, and greater resilience to scientific uncertainty.

From AI Prototype to Analysis at Scale

AI and ML are increasingly seen as catalysts for innovation in drug development. While ML drives data-centric tasks like model training and prediction, AI represents a broader transformation in how insights are generated, decisions are guided, and scientific processes are automated. But for many teams, the practical integration of AI/ML at enterprise-scale remains constrained by technical barriers. Now, pharma and biotech companies are making the shift from AI experimentation to full-scale production use.

Modern Shift

Integrating AI/ML into the core of scientific infrastructure by enabling:

• Reproducible pipelines with model training, inference, and monitoring
• Centralized data management, versioning, and annotation.
• Scalable compute environments with GPU support and resource scheduling.

The Result

AI and ML become a natural extension of R&D. Researchers can rapidly deploy new models, iterate on predictions, and bring AI from proof-of-concept to production. When the infrastructure supports the entire ML lifecycle—from data ingestion to model validation—then AI moves from promise to practice.

A Unified Infrastructure Vision Toward Scalable, Transparent, and Resilient Research Operations

Taken together, these shifts represent more than just trends. They reflect a broad movement toward scalable, collaborative, and resilient scientific operations, underpinned by infrastructure that is:

• Cloud-enabled but hybrid-ready
• Workflow-driven, but researcher-friendly
• Data-centric, but model-aware
• Composable, transparent, and secure

At Seqera, we’ve developed our platform with these principles in mind. Our goal is not just to help researchers run workflows more easily, but to provide a foundation on which entire research programs can operate more efficiently and collaboratively.

We believe that better science starts with better software—not in the abstract, but in the practical, reproducible, and scalable systems that underpin daily work.

Software Is Now a Scientific Strategy

Decisions on software and the underlying infrastructure have long been relegated to the background of R&D strategy. That era is over. Software and infrastructure are strategic levers that shape speed, quality, and real-world, sustainable impact.

Pharma leaders who embrace foundational data and technology shifts are not just modernizing IT. They’re future-proofing R&D, building solutions that support discovery, adaptability, and sustainable innovation for years to come. We’re committed to supporting that transition. If your team is looking to rethink infrastructure for the next decade of research, we’d welcome a conversation.