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Leveraging QbD for API Scale Up

Earlier this month we wrote a piece on the Five Challenges Scaling Up an Active Pharmaceutical Ingredient (API). In the post’s conclusion, we discussed the role of QbD in aiding in scale-up:

Using QbD and a DoE approach, API manufacturers and CDMOs can reduce unanticipated challenges by developing deep process knowledge at lab scale – which aids in transfer to scale-up.

That isn’t just a random statement: QbD can radically transform the scale up process, and help companies avoid unforeseen complications.

Increased Regulatory Scrutiny and the Rise of QbD

Regulatory agencies are emphasizing the need for a more thorough understanding of products and processes prior to validating. This has led to widespread adoption of QbD (Quality by Design) approaches, which emphasize thorough process knowledge to avoid poorly-scaled processes.

QbD approaches typically use a Process Validation Lifecycle Approach, which is a holistic approach to development which supports and leverages:

  • Robust validation
  • Uni/multi variants
  • Use of modeling tools
  • Use of prior knowledge
  • Control strategy implementation
  • Proactive process monitoring, including PAT for trending, continuous verification and continuous proactive improvement.

Scale-up, which we previously discussed, is a critical link in this lifecycle. Without enough data, it can also be a ‘what if’ exercise. Bottom line: insufficient process knowledge can result in poorly scaled processes. This typically translates into more Out Of Specs (OOS), reduced process reliability, as well as higher production costs and a lower profit margin due to increased reprocessing.

With development and transfer to scale-up, the challenges which arise tend to be in a number of different categories: safety, environmental, health, quality and economics.

QbD Enables Robust Technology Development & Transfer to Manufacturing

With cause & effect analysis of Critical Process Parameters (CPP) on Critical Quality Attributes (CQA), QbD also aids in robust technology development & transfer at manufacturing plants. In order for QbD to aid in scale-up, an appropriate control strategy needs to be in place to ensure a focus on critical points.

Engineering API Scale-Up

The involvement of engineers in the laboratory during development and optimization of a process is the key to trouble-free scale up and technology transfer. The extent to which engineers need to address scale up of operations depends very strongly on the interaction between the chemist and the engineer, and the stage at which engineering became involved in the process.

While chemists tend to focus on process optimization, engineers focus on the scale- and hardware-dependent parameters, taking plant conditions under consideration. It is this dual approach that enables a ‘Right at First Time’ technology transfer. This can be achieved by adopting:

  • A scientific statistical approach (QbD-DOE, ICH Q8),
  • Simulation scale-up techniques (Dynochem / Visimix) for scale dependent parameters
  • Lab validation of process in conditions simulating the plant
  • The use of Quality Risk Assessment (ICH Q9) to evaluate each unit process and operations
  • Development and manufacturing (ICH Q10, 11) of robust/safe processes

Achieving successful technology transfer requires a robust manufacturing process, which – under QbD – is a collaborative effort, comprising Research and Development, Manufacturing Technical Operations, Quality and more.

The benefits of a QbD approach to scale-up are numerous:

  • Better manufacturing efficiencies
  • Higher yields
  • Superior quality
  • Enhanced process control
  • Higher design space, resulting in global regulatory flexibility
  • Fewer Deviations and a scientific rational for strong CAPA

QbD is an effective framework for bringing together a collaborative and inclusive team comprised of both chemists & engineers to ensure a successful API scale-up.

 

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