Process Design: Process Definition, Characterization and Control Strategy (Stages 1a and 1b)

Scale-down process characterization studies performed using a qualified scale-down model are a critical part of licensure applications for biologics. KBI has significant expertise in the design and execution of these studies to enable clients to define their process control strategy to take forward into manufacturing conformance/validation lots. The scale-down studies also create data that can justify process ranges and their classification through the creation of a design space for the process. This is in-line with current regulatory expectations in a post Quality by Design (QbD) guidance environment.

Scale-down validation studies are also employed to supplement data that will be generated from the manufacturing scale conformance lots. These studies can serve to explore regions in the process operating space that would not ordinarily be explored at large-scale. KBI has significant experience in regulatory expectations around validation and we design and execute appropriate studies to create a robust licensure package.

KBI performs these late-stage process characterization and scale-down validation studies as a stand-alone package to support late-stage process development and commercialization from a client’s manufacturing plant or from KBI’s manufacturing plant. KBI also supports PAI (pre-approval inspections) and other regulatory inspections needed for product approval. Specific offerings include:

Scale-Down Process Characterization Studies

  • Qualify scaled-down models for final seed and production bioreactor steps and for all downstream process steps and author scale-down qualification reports
  • Perform FMEA to identify important process parameters using process development and manufacturing experience to date
  • Perform single variable experiments to study process parameters identified as non-key during the FMEA exercise
  • For process parameters designated as important during the FMEA analysis, perform fractional factorial DOE’s followed by response surface DOE’s to create a process design space for each individual process step connecting process parameters to process performance and CQAs (critical quality attributes). This exercise also yields process models for each process step and enables classification of process parameters into key and non-key
  • Perform worst-case experiments to integrate process steps under worst-case conditions to help identify critical process parameters (CPPs) and their acceptable ranges
  • Define in-process control strategy (IPC) including definition of acceptance criteria, action and alert limits for process parameters
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