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Design Space Between Dissolution and Rate Controlling Polymer


Design space under ICH Q8(R2) is powerful, but in modified-release products it is often treated as statistics instead of science. Here is a practical, regulator-aligned method.

1. Define the objective linked to QTPP.

  • Set a clear goal such as establishing a polymer operating range that delivers consistent dissolution across the lifecycle.

2. Define dissolution as time-specific CQAs.

  • Use multiple time points (early, middle, final) such as percent release at 1 to 2 hours, 6 to 8 hours, and 12 to 24 hours.

3.Define polymer variables as CMAs.

  • Include polymer level, viscosity grade, and blend ratio because they control gel strength, diffusion path length, and erosion.

4. Perform risk assessment.

  • Use Ishikawa and FMEA to justify polymer concentration and viscosity as high-risk drivers of dissolution.

5. Select the DOE.

  • Use factorial, Box–Behnken, or Central Composite Design and ensure it can detect interactions and curvature.

6. Execute DOE batches with controls.

  • Fix non-critical parameters and vary DOE factors only, then test dissolution at predefined time points.

7. Build the model and interpret scientifically.

  • Use regression and ANOVA, but focus on meaning of main effects and interactions, not only p-values.

8. Generate response surfaces and contour plots.

  • Show how polymer level and viscosity shift dissolution and where control is robust.

9. Set dissolution acceptance limits.

  • Define upper and lower boundaries at each time point based on RLD behavior and regulatory expectations.

10. Create overlay plots to define design space.

  • The common region meeting all dissolution limits simultaneously is the design space.

11. Verify the design space.

  • Make confirmation batches at low edge, high edge, and center and confirm dissolution robustness.

12. State regulatory significance.

  • Per ICH Q8(R2), movement within an approved design space is not a regulatory change if quality is maintained.
  • Design space is not a statistical exercise. It is a scientific commitment to true product understanding.
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    Resource Person: Moinuddin Syed. Ph.D, PMP®
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