21 CFR Part 11: Meeting FDA compliance requirements without sacrificing speed

Life sciences teams are under pressure to accelerate AI and ML innovation while navigating increasingly strict regulatory requirements. Noncompliance is costly: abnormal quality events alone result in $7.5 to $9 billion in annual losses, and that figure does not include up to $2 billion in lost sales due to delays.

The FDA has increased scrutiny of data processes related to computational sciences and AI/ML, as noted in their guidance on software as a medical device. In particular, 21 CFR Part 11 Section 11.10 requires validated systems that ensure the authenticity, integrity, and confidentiality of electronic records.

Most data science tools weren’t built with FDA compliance in mind. Domino Enterprise AI Platform addresses this gap with built-in 21 CFR Part 11 controls that help teams maintain compliance without slowing down research.

Real risks of non-compliant workflows

When data science workflows lack proper controls, the consequences extend beyond regulatory headaches and face several consequences including:

• Financial penalties from FDA findings and remediation programs
• Delayed product approvals that lead to lost revenue and competitive disadvantages
• Reputational damage leading to heightened future FDA scrutiny and oversight

Many life sciences companies discover problems with reproducibility too late. When scientists can't recreate earlier results during regulatory review, months or years of work may need to be redone. The ad-hoc nature of traditional data science workflows makes these risks substantially worse, as they lack the automated controls needed for consistent compliance.

Common misconceptions that increase FDA compliance risk

Misunderstandings among data science and IT leaders often compound the risk of non-compliance. Here are some key misconceptions:

• Exploratory data science is not subject to Part 11: In reality, if analyses inform decisions for regulated products or processes, they likely fall under regulatory scope per Section 11.1(b).
• Standard notebooks with manual documentation meet compliance: The FDA expects robust controls like automatic audit trails and version control, per Section 11.10(e).
• FDA compliance is solely IT's responsibility: FDA guidance places responsibility on both system owners and users.
• FDA compliance can be retrofitted after work is complete: The FDA requires controls during the entire record lifecycle, not retrospectively.

How 21 CFR Part 11 maps to data science workflows

Understanding how Part 11 requirements apply across the data science lifecycle helps teams implement appropriate controls at each stage.

Data acquisition and preparation

During data acquisition and preparation, you need strong controls for data integrity during transmission. This includes validating that data remains unchanged during file transfers and transformations (per Section 11.10(a)). You also need proper access controls and authentication to ensure only authorized personnel can modify datasets (Section 11.10(d)). Comprehensive audit trails must document all data transformations, including who made changes, when, and why. Domino addresses this through secure data connections.

Model development and evaluation

For model development and validation, version control becomes essential for code, environments, and parameters. Every change must be tracked with appropriate documentation of decisions. Your systems should enable complete reproducibility of any result, allowing you to recreate the exact conditions and outputs from any point in time. Domino's Reproducibility Engine automatically versions all these components, creating traceable lineage from raw data to final outputs.

Model deployment and monitoring

When deploying and monitoring models, operational system checks should verify that processes execute in the correct sequence (Section 11.10(f)). Electronic signatures must authenticate approvals for production deployment (Sections 11.50-11.70). Most importantly, you need continuous validation to ensure models perform as expected and remain in a validated state. Domino's model monitoring capabilities track performance and automatically alert teams when models drift outside validated parameters.

FDA compliance readiness assessment: 4 key questions

How ready is your organization to meet these requirements? Ask yourself these four questions to gauge your current state:

1. Does your current environment automatically track all changes to data, code, and models? Manual tracking is error-prone and incomplete.
2. Can you reproduce any analysis from six months ago with exact fidelity? This includes recreating the exact software environment, code version, and dataset used.
3. Are your audit trails tamper-proof and comprehensive? They should capture not just what changed, but who made the change and their justification.
4. During an inspection, could you easily demonstrate who did what, when, and why? Scrambling to compile this information under pressure often leads to gaps that raise regulatory concerns.

Assessment guide

• 1 "no" answer: Your system has minor gaps that should be addressed. Action: Address these proactively to avoid future risks.
• 2-3 "no" answers: You face significant compliance risk that requires immediate attention. Action: Prioritize these fixes to avoid audit findings or delayed approvals.
• 4 "no" answers: Your current workflow is likely non-compliant with Part 11 requirements. Action: Immediate overhaul is necessary to avoid regulatory action or business disruption.

How Domino addresses FDA compliance challenges

Organizations consider several approaches to Part 11 compliance. For those who first attempt the do-it-yourself (DIY) approach, several challenges arise, including:

• Custom-built solutions are resource-intensive: They require significant development and maintenance, often leading to inconsistent controls across teams.
• Traditional MLOps platforms lack specific Part 11 controls: These platforms are generally designed for model management, not regulated environments.
• Legacy validated systems limit modern data science: While built for compliance, they often don't support the necessary tools and workflows for data scientists.
• Domino is purpose-built for life sciences with embedded compliance: The platform supports modern data science and maintains required regulatory controls.

Domino’s platform was designed specifically to address these challenges. It provides a centralized environment with strong access controls, creating a single source of truth while reducing security vulnerabilities.

Three unique Domino capabilities that set Domino apart:

1. Centralized and secure environment: Domino provides a centralized platform with strong access controls, a single source of truth, and integrated enterprise identity management, minimizing security vulnerabilities.
2. Automated versioning and reproducibility: Domino's platform automatically versions analyses and provides a Reproducibility Engine that captures all necessary details (code, data, comments, software environment, parameters, results), enabling easy recreation of results for regulatory review.
3. Built-in compliance and validation: Domino offers comprehensive audit trails, operational system checks for workflow enforcement (like approval signatures), built-in validation scripts, and model monitoring to detect drift, ensuring continuous compliance throughout the model lifecycle.

Part 11 compliance shouldn't be a barrier to innovation. When FDA compliance is baked into your platform rather than bolted on afterwards, it actually accelerates research by eliminating rework and enhancing collaboration.

Real-world impact: FDA compliance without compromise

A top 10 pharmaceutical company implemented an FDA-qualified research system using the Domino platform, overcoming initial challenges such as:

• Fragmented documentation across multiple tools.
• Inconsistent environments between teams.
• Difficulty reproducing results for regulatory review.

They adopted a phased approach:

• Started with a GxP-focused pilot group.
• Standardized environments and automated documentation.
• Enforced compliant workflows.
• Gradually expanded adoption across all teams.

Measurable outcomes included:

• Reduced inspection preparation time.
• Faster regulatory approvals due to complete documentation.
• Improved team collaboration.
• Scientists reported compliance no longer slowed research.

Top 10 pharmaceutical company results:

• Reduced inspection preparation time
• More efficient documentation processes
• Faster regulatory reviews

Why Domino over other approaches

Organizations typically consider several approaches to Part 11 compliance. Custom-built solutions require significant development resources and ongoing maintenance. They often lead to inconsistency across teams as groups implement controls differently.

Traditional MLOps platforms may offer some relevant capabilities, but typically lack specific Part 11 controls and life sciences focus. They're designed for general model management rather than regulated environments.

Legacy validated systems that were built for compliance often severely limit modern data science capabilities. They typically don't support the tools and workflows that data scientists need for effective work.

Domino's advantage comes from being purpose-built for life sciences with compliance embedded, not bolted on. The platform supports modern data science tools and techniques while maintaining the controls required by regulators, giving you the best of both worlds.

Three unique capabilities that set Domino apart:

1. Reproducibility Engine: Domino automatically captures the complete lineage of any result, including exact software environment, code version, data state, and parameters. This makes reproducing results for regulatory review effortless.
2. Compliance-aware workspace isolation: Domino uniquely provides containerized workspaces that isolate regulated work while enabling cross-team collaboration.
3. Built-in validation tools: Streamline validation with automated checks, approvals, and risk tracking to reduce manual documentation burdens.

Unlocking value: How Domino delivers for your team

Implementing Domino delivers key advantages across key stakeholder groups:

• For data science leaders, Domino enables faster research cycles with reproducible, inspection-ready results.
• For IT leaders, the platform lessens validation burden and simplifies security management through built-in controls and automation.
• For compliance teams, Domino ensures tamper-proof audit trails, documentation, and complete record traceability to reduce risk of findings.
• For executives, Domino helps shorten time-to-market and minimize regulatory exposure.

"Domino has some key capabilities around reproducibility, with its snapshotting features that capture everything needed to show traceability and enable full reproducibility later on. This is what we need for GxP. It's an absolute requirement when we do that final managed run of the artifacts from the statistical analysis plan."

- Eileen Ching, Senior Director, Biostatistics Technical Capability Delivery, GSK

Take the first step toward seamless FDA compliance

Implementing proper controls for 21 CFR Part 11 compliance doesn't have to slow innovation or create burdensome processes. With the right platform, compliance becomes an integrated part of the workflow rather than an obstacle to overcome.

Download the white paper, "Navigating 21 CFR Part 11: A practical guide for data science and IT leaders." Don’t let regulatory risk hold your team back. Build compliant workflows that drive innovation forward with confidence.