How Spreadsheet Chaos Derails Quality by Design in Pharma

Quality by Design in pharma is widely recognized as the modern framework for drug development. By systematically defining product and process parameters, conducting risk assessments, and applying experimental data, QbD ensures robust and consistent product quality (Kaur, 2024; ICH Q10, 2008).  

However, many organizations still depend heavily on spreadsheets for data management—generating scattered information, duplicated effort, and an elevated risk of errors.  

This blog post explores why spreadsheet overload undermines Quality by Design programs and how transitioning to a digital QbD platform can streamline workflows while reinforcing overall compliance.

Understanding the Data-Intensive Nature of QbD

• Define the QTPP (Quality Target Product Profile): Development starts by outlining the basis of design for the product—its intended use and the quality criteria appropriate for the intended market (ICH Q8(R2), 2009).
  
  
• Identify CQAs (Critical Quality Attributes): These are the characteristics derived from the QTPP that directly affect safety and efficacy throughout the process, such as impurity profiles, content uniformity, or glycosylation patterns for biologics.
  
  
• Conduct Risk Assessments: Methods like failure mode and effects analysis (FMEA) or cause-and-effect diagrams are used to pinpoint which process parameters or material attributes most strongly influence CQAs (ICH Q10, 2008).
  
  
• Define the Design Space: The multidimensional region of material attributes and process parameters, derived from multivariate analysis of inputs versus CQAs, within which quality is assured.
  
  
• Use DoE (Design of Experiments): If any knowledge gaps arise from the risk assessments, additional experiments may be required to establish the design space (ICH Q11, 2012; European Pharmaceutical Review, 2025).
  
  
• Establish a Control Strategy: Develop robust specifications, in-process controls, and monitoring procedures that ensure the desired quality is kept throughout the product lifecycle.
  
  
• Lifecycle Management: Under guidelines such as ICH Q10 and Q12, knowledge gained during development supports tech transfer, scale-up, and ongoing process verification to maintain a state of control—key components of CMC chemistry, manufacturing and control in drug development (Luciani et al., 2015).

Each of these stages generates large volumes of data—raw measurements, process parameter ranges, risk rankings, and data analyses. Managing this data effectively requires precise organization, transparent traceability, and fluid collaboration. Overreliance on spreadsheets can quickly destabilize this otherwise rigorous system.

The Pitfalls of Spreadsheet-Driven QbD

• Version Control Challenges: Multiple contributors often create or edit similar files, leading to confusion over which version is the latest.
  
  
• Error-Prone Data Entry: A single altered cell can disrupt calculations or references across an entire workbook. Yet most spreadsheets lack robust audit trails to trace how and when a value changed.
   
• Siloed Information: Spreadsheets stored on local drives or shared inconsistently create data silos. Retrieving historical experiments, cross-product comparisons, or trending analyses across sites becomes exceedingly time-consuming.
   
• Limited Collaboration: QbD depends on input from multidisciplinary teams of scientists, engineers, QA, and regulatory affairs. Spreadsheets do not support real-time, multiuser collaboration, inviting misalignment and duplicate effort.
  
  
• Insufficient Audit Trails: Regulatory agencies like the FDA and EMA expect data modifications to be documented thoroughly. Basic spreadsheet software often falls short of compliance with 21 CFR Part 11 or Annex 11.

These shortcomings undercut QbD’s data-driven foundation—and by extension, CMC in drug development—slowing product development, hindering regulatory submissions, and complicating tech transfers.

Real-World Consequences of Spreadsheet Chaos in QbD and CMC Manufacturing

Case A: Inconsistent Parameter Logging 
A cross-functional team investigating a new oral solid dosage form used separate spreadsheets to track process parameters: mixing speeds, granulation times, and drying temperatures. Only after analyzing scale-up data did they discover that one parameter was logged in different units (rpm vs. rad/s). The error skewed calculations for mixing efficiency and led to multiple failed batches and delayed project timelines (Kaur, 2024; European Pharmaceutical Review, 2025).

Case B: Unapproved Design Space 
A biopharmaceutical company attempted to justify a design space for its upstream cell culture process using a patchwork of spreadsheets. Regulators requested clarifications, but the team struggled to compile a cohesive narrative. As a result, their proposed design space was rejected, and they had to default to more restrictive parameter ranges (Luciani et al., 2015).

These scenarios show how spreadsheet-related issues can derail even the most thorough QbD efforts, creating inefficiency and undermining regulatory trust.

The Alternative: A Centralized, Digital QbD Solution

A more effective solution is to implement an integrated QbD system designed specifically for the complex data demands of CMC manufacturing and development (European Pharmaceutical Review, 2025). These systems solve key spreadsheet-related challenges:

• Single Source of Truth: All development and manufacturing data resides in one centralized repository. Teams can quickly locate essential data, compare historical experiments, and validate their findings with consistent references.
  
  
• Automated Version Control: Changes are captured in real time with user identification, timestamps, and detailed audit logs, so teams always know which version is current.
  
  
• Standardized Risk Assessments: Many platforms incorporate templates for FMEA or cause-and-effect analyses. Updates can be tracked to ensure they remain accurate as new data emerges.
  
  
• Real-Time Collaboration: All stakeholders—quality, engineering, R&D, and regulatory affairs—can access documents and comment on data sets in a shared workspace, eliminating the inefficiency of emailing multiple spreadsheets.
  
  
• Regulatory Alignment: High-quality QbD solutions support data integrity requirements from the FDA and ICH, incorporating electronic signatures, encryption, and robust user authentication.

These systems do more than eliminate spreadsheet chaos—they strengthen CMC chemistry manufacturing and control processes, enable rigorous science, and support stronger regulatory submissions.

Essential Features to Seek in a QbD Solution

If you’re ready to move beyond spreadsheets, especially for managing CMC in drug development, look for software with these features:

• User-Friendly Interface: A system with a steep learning curve may remain underutilized. Software that offers intuitive dashboards and clear navigation promotes user adoption (Kaur, 2024).
  
  
• Configurable Risk Management: Choose an application that supports your organization’s preferred risk assessment methods—FMEA, Ishikawa, etc.—across product lines and sites.
  
  
• Regulatory-Ready Documentation: The software should facilitate the generation of reports for new drug applications (NDAs), biologics license applications (BLAs), or marketing authorization applications (MAAs). Consistent definitions of CQAs, CPPs, and CMAs must align with ICH guidelines.
  
  
• Scalability and Security: As your portfolio grows or you transfer processes to multiple facilities, the software must accommodate higher data volumes and remain compliant with 21 CFR Part 11 or Annex 11. In addition, standardization of templates and workflows across sites and products is essential for effective knowledge management.
  
  
• Advanced Analytics and PAT Integration: When implementing process analytical technology (PAT) or advanced machine learning in manufacturing, a centralized, validated data system is often essential for real-time process monitoring control (Kaur, 2024; European Pharmaceutical Review, 2025).

Tangible Gains from Going Beyond Spreadsheets

Companies that adopt digital QbD platforms report multiple advantages:

• More Accurate Data: With a single source of truth and robust audit trails, errors introduced by inconsistent spreadsheets drop significantly.
  
  
• Faster Regulatory Approvals: Well-organized data and a coherent presentation and justification of design space often yield fewer agency queries, accelerating reviews.
  
  
• Streamlined Tech Transfers: When transferring processes to new facilities or contract manufacturers, accessible data and documented controls help avoid costly delays or misunderstandings (Luciani et al., 2015).
  
  
• Continuous Improvement: QbD is not a one-time initiative. Modern platforms enable ongoing process monitoring and trend analysis, as envisioned in ICH Q10 and Q12.
  
  
• Pathway to Real-Time Release: With advanced analytics, validated data pipelines, and consistent control strategies in place, organizations can work toward real-time release testing (RTRT) or more advanced manufacturing models, including continuous processing (European Pharmaceutical Review, 2025).

Ready to Reinforce Your QbD Strategy?

Quality by design in pharma hinges on scientific rigor, detailed knowledge capture, and cross-functional collaboration. Spreadsheets undermine these foundations, inviting errors, confusion, and oversight gaps. In contrast, purpose-built digital QbD platforms centralize and standardize data, enhance traceability, and simplify regulatory compliance. 

While a digital shift requires upfront investment in technology and change management, the long-term gains, such as improved process understanding, faster time to market, and proactive lifecycle management, are well worth the effort. As regulatory agencies increasingly favor thorough, data-driven submissions, moving beyond spreadsheet chaos can significantly elevate your organization’s QbD and CMC chemistry manufacturing and control practices, ensuring safer products and smoother approvals.

References

European Pharmaceutical Review. (2025). Considerations for tech transfer of a biologics upstream process. https://www.europeanpharmaceuticalreview.com 

International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). (2008). ICH Q10: Pharmaceutical quality system. https://www.ich.org/page/quality-guidelines 

International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). (2019). ICH Q12: Technical and regulatory considerations for pharmaceutical product lifecycle management. https://www.ich.org/page/quality-guidelines 

Kaur, N. (2024). A guide to QbD for small molecule drug product manufacturing excellence. Pharmaceutical Online. https://www.pharmaceuticalonline.com 

Luciani, F., Wenzel, J., & Schneider, C. K. (2015). EMA’s experience with the first QbD monoclonal antibody. mAbs, 7(2), 362–367. [Link no longer active]