Why QbD Breaks Down After Approval (and What Leaders Miss)

Across pharma and biopharma, teams often assume QbD initiatives stall during development. While that does happen, the real breakdown often appears after approval, when the consequences of a weak QbD approach become evident. 

This is not due to increased scientific or technical complexity. It fails because decisions are made under pressure without clear traceability — ownership, rationale, and supporting data are not consistently preserved. 

After approval, programs transition into tech transfer, scale-up, site onboarding, and commercial readiness, where decisions multiply and become harder to justify. New teams inherit the process, knowledge becomes fragmented, and earlier development decisions must be applied in routine operations.

At this stage, familiar questions arise:  

• Why was a specific critical quality attribute (CQA) prioritized over others? 

• What was the original rationale for a specification definition?  

• Which data supports the proven acceptable range (PAR)?  

• How can a post-approval change be justified without reconstructing the full scientific and regulatory rationale?  

The challenge is that these answers are often unclear and fragmented across different systems and folders. This slows decision-making and prevents QbD from functioning as a lifecycle approach. 

That’s not a scientific complexity issue—it reflects gaps in system integration and decision continuity.

The Uncomfortable Truth About Post-Approval QbD

QbD methodologies haven’t failed; they’ve been neglected by systems and teams working under tight deadlines and regulatory pressure.  

Many pharma and biopharma companies still treat QbD as a tick-the-box methodology used to obtain approval. This includes defining the QTPP, CQAs, and CPPs; running risk assessments; building a control strategy; and ultimately preparing the required documentation for regulators.

But what happens after approval? Project teams tend to shift operating modes. Development teams move on, tech transfer becomes a handoff, and QbD continues without the necessary infrastructure.  

QbD delivers value after approval only if the organization can retain the logic behind its processes and controls, align on risk and acceptable uncertainty, retrieve evidence quickly for investigations and changes, and make improvement decisions without revisiting first principles each time. This requires treating the control strategy as a living framework that evolves as process knowledge grows. 

When these elements are not addressed early on, QbD does not scale into commercial stages. As a result, post-approval work often feels like starting over, with significant costs when issues arise.

The Real Culprit: Poor Decision Traceability in CMC Development

The reality is that CMC development involves many decisions that are frequently made under pressure, such as: 

• Which attributes matter most — and why? 

• Which assays are necessary at this stage? 

• Which parameter ranges support scale-up? 

• Where should controls be applied (in-process vs. release vs. monitoring)? 

• How should specifications be set with limited data? 

• How should variability in early lots be interpreted? 

The issue is how those decisions are captured and carried forward. Most pharma organizations capture what was decided, but not the underlying justifications, supporting data, assumptions, or downstream constraints. 

After approval, when new stakeholders (regulators, new sites, new leaders, and new partners) challenge those decisions, organizations can’t defend them efficiently. This also makes them difficult to change safely, causing QbD to stall as continuous improvement becomes risk-heavy and slow-paced.

What Pharma and Biopharma Leaders Might Be Missing

To ensure QbD survives approval, companies must fix early CMC decision-making processes. For example: 

• Define what constitutes knowledge beyond reports that exist somewhere in the system. 

• Ensure every critical decision is supported by documented evidence and justifications (e.g., link evidence to specifications and control strategy definition). Without this, teams tend to rely on additional testing rather than improving process design.  

• Run risk assessments based on the criticality of quality attributes, process parameters, and material attributes, using cause-and-effect analysis. 

• Standardize how to record decisions, as documentation is not the same as decision traceability (i.e., options considered, rationale, and supporting data, risk implications, level of uncertainty, ownership, and timestamps). 

• Clarify decision rights early to avoid inconsistency and weak knowledge transfer across stage transitions. QbD might involve trade-offs, such as more process understanding vs. faster timelines, tighter control limits vs. manufacturing flexibility, extensive method development vs. acceptable uncertainty, and monitoring vs. testing vs. capability improvement. 

• Turn control strategies into dynamic operating models rather than compliance objects. To achieve this, define a clear intent, monitoring and action loops, links to deviations and CAPAs, and a plan to update based on process performance over time.

Reframing QbD for Post-Approval Success

If QbD is treated as a development initiative, it will eventually lose sponsorship as teams rotate. It becomes static rather than a living system and is eventually replaced by the strongest post-approval force in the organization: risk aversion.

The good news is that QbD breakdown after approval is preventable. This requires establishing a few early non-negotiables to ensure continuity: 

• Decision records for key CMC choices (traceable, searchable, owner-defined, and timestamped) 

• Evidence linkage from data through risk assessments, control strategy,  specifications, and risk review 

• Infrastructure that ensures data and knowledge continuity across phase transitions (development, PPQ, commercial, and post-approval changes) 

• Stable governance with consistent decision rights and escalation paths 

• Operational KPIs that measure whether process knowledge is preserved and used effectively across the product lifecycle. For example: how long it takes to assess, justify, approve, and implement a change; how often decisions are revisited due to unclear rationale, evidence, or ownership; and how long changes take after approval.

QbD success is more than a scientific achievement; it’s an organizational one. More important than generating knowledge throughout the lifecycle is how organizations collect and reuse that knowledge when the process is running after approval.  

Watch the video below to learn how automation is helping organizations build unified, compliant, and insight-driven QbD processes.