Frequently Asked Questions (FAQs) about Computer System Validation (CSV) in Life Sciences

General Questions

1. What is Computer System Validation (CSV)?
   CSV is a documented process used in the life sciences industry to ensure that computer-based systems meet their intended purpose, consistently produce accurate and reliable results, and comply with regulatory requirements.

2. Why is CSV important in life sciences?
   CSV is critical because it ensures data integrity, regulatory compliance, patient safety, and product quality in pharmaceutical, biotechnology, and medical device companies.

3. Which regulations govern CSV in life sciences?
   Key regulations include:

   • FDA 21 CFR Part 11 (Electronic Records; Electronic Signatures)
   • EU Annex 11 (Computerized Systems)
   • Good Automated Manufacturing Practice (GAMP 5)
   • ICH Q7, Q9, and Q10
   • ISO 13485 (Medical Devices)

4. What is the difference between Computer System Validation (CSV) and Software Validation?
   CSV applies to the validation of software and hardware systems in regulated industries, ensuring compliance with industry regulations, while software validation typically refers to testing software functionality to meet user requirements.

Process & Implementation

5. What are the key steps in the CSV process?
   The CSV process typically includes:

   • User Requirements Specification (URS)
   • Functional and Design Specifications (FS/DS)
   • Risk Assessment
   • Installation Qualification (IQ)
   • Operational Qualification (OQ)
   • Performance Qualification (PQ)
   • Validation Summary Report

6. What is the GAMP 5 approach to CSV?
   GAMP 5 (Good Automated Manufacturing Practice) is an industry standard that provides a risk-based framework for validating computerized systems. It categorizes software into different risk levels and tailors validation efforts accordingly.

7. How often should computer systems be validated?
   Validation is an ongoing process. Systems should be revalidated when:

   • There are major updates or modifications.
   • Regulatory requirements change.
   • System failures or deviations occur.

8. What is a Validation Master Plan (VMP)?
   A VMP is a high-level document that outlines an organization’s approach to validation, including policies, responsibilities, processes, and timelines.

Compliance & Data Integrity

9. How does CSV ensure compliance with FDA 21 CFR Part 11?
   CSV helps establish controls over electronic records and signatures by ensuring:

   • System access controls and authentication
   • Secure audit trails
   • Data integrity and backup
   • Electronic signature compliance

10. What is the role of risk assessment in CSV?
    Risk assessment determines the criticality of a system and guides validation efforts, focusing on systems that impact patient safety, product quality, and regulatory compliance.

11. What is Data Integrity in CSV?
    Data integrity ensures that data is complete, accurate, and consistent over its lifecycle. It follows the ALCOA+ principles:

• Attributable
• Legible
• Contemporaneous
• Original
• Accurate
• Plus: Complete, Consistent, Enduring, and Available

12. What are common data integrity issues in CSV?

• Unauthorized system access
• Lack of audit trails
• Manual data manipulation
• Poor change control management

CSV for Cloud & SaaS Systems

13. How does CSV apply to cloud-based systems and SaaS?

• Cloud-based systems must follow the same validation principles as on-premise solutions.
• Companies must ensure vendor compliance with regulatory requirements.
• Validation includes supplier audits, risk-based approaches, and defining roles in shared responsibilities.

14. Who is responsible for validating SaaS applications?

• The vendor ensures that the system is designed and maintained to meet compliance standards.
• The life sciences company is responsible for validating the system within their specific regulatory and operational framework.

15. How does CSV differ for AI and Machine Learning (ML) applications?

• AI/ML systems require continuous monitoring and validation due to their dynamic nature.
• Companies should validate AI algorithms against predefined acceptance criteria.
• Data inputs, outputs, and learning models should be well-documented and auditable.

Best Practices & Common Challenges

16. What are best practices for successful CSV implementation?

• Follow a risk-based approach.
• Maintain thorough documentation.
• Ensure strong collaboration between IT, Quality Assurance (QA), and business users.
• Regularly train employees on validation requirements.
• Leverage automation tools for validation and testing.

17. What are common challenges in CSV?

• Keeping up with regulatory changes.
• Managing validation for multiple systems.
• Ensuring vendor compliance for cloud-based solutions.
• Balancing validation efforts with business agility.

18. How can automation improve the CSV process?

• Automated testing tools streamline validation efforts.
• Continuous monitoring reduces revalidation time.
• AI-driven documentation tools improve compliance tracking.

CSV and Cloudbyz eClinical Solutions

19. How does Cloudbyz eClinical ensure compliance with CSV requirements?
    Cloudbyz eClinical solutions are built on Salesforce, offering:

• End-to-end validation support aligned with GAMP 5.
• Audit trails, electronic signatures, and data integrity controls for compliance with 21 CFR Part 11.
• Automated workflows and change control management to support continuous compliance.