Lessons Learned in Tech Transferring a QbD/PAT enabled process

Transcription

1 Lessons Learned in Tech Transferring a QbD/PAT enabled process 1 Martin Warman, Analytical Development Joseph Medendorp, Technical Operations 26 th September 2012

2 Possible uses for PAT Identifying critical process steps The processing steps producing intermediates whose attributes impact final product quality Description of Product Design Space Define boundaries for acceptable product Defining Process Model Linking intermediate attributes to final product attributes Identifying Control Model Describing what process parameters to change to cause the change in the intermediate attribute Implementing and maintaining the Process and Control Models Carried out within the plant control and information technology systems 2

3 High-level description of Process Crystalline API production Reaction steps, crystallization, drying API spray dried dispersion Spray solution prep, spray drying, secondary drying Formulation Pre lube blend, post lube blend Compression Compression, film coat 3

4 First use of PAT Process Monitoring Gathering data to establish if a process step is critical But also, if the process is robust and has a common end point across the full range of process conditions. For example API drying Used through out development Helped establish drying conditions Not used on commercial process because a common end point is reached, no control action needed SDD secondary drying Used through out development Helped establish drying conditions Not used on commercial process because a common end point is reached, no control action needed Blend homogeneity Used for all DoE runs, formal stability batches, and engineering runs All showed a common end point (time) and expected composition Data submitted in the development section of the NDA Not used in commercial production Available for continuous improvement 4

5 Identifying critical process steps 5

6 Identifying Control Models - Regression Analysis, PS 6

7 Identifying Control Models - Regression Analysis, BD 7

8 Reduction to Practice Dashboard sits on plant control system showing Design Space ranges NOR Real-time indicator of CQA predictions from parametric data 8

9 9 Graphical Illustration of Design Space

10 Particle Size Application Why? DryPS is a Critical to Quality Attribute (CQA) WetPS predicts DryPS What? Prediction model correlating to off-line data Instantaneous WetPS is an inprocess control (IPC) Where? Within the process When? During production to allow parameters responsible for impacting particle How? Optimized commercial on line measurement 10

11 Ensuring Representative Measurement? Appropriate selection of sampling location Powder flow is linear (post vortex breaker) However this means it is the shadow of the vortex breaker Tried commercial sampling systems Poor correlation with off-line data The current sampling arrangement is the result of empirical testing of various restrictions and funnel configurations Has been proven to generate the equivalent results Covers 25% of the cross sectional area, i.e. 25% of production runs through the analyzer! 11

12 12 Correlation between on-line and off-line

13 13 What does this all look like together?

14 How do we know this works? Example of a process change Correlation of process parameters to intermediate attribute means we can change process parameters to cause desired change in attribute Make a change expected to move the Dv50 by 3um Dv50 responds immediately and stabilizes in 8mins (one block of data) 14

15 Process Life-Cycle Original risk assessments and criticality analysis were not equipment specific But could only include data from equipment we had run on Need to have a process in place that assesses changes in risk Process trending There has been on-going process improvements and revisions for the start of commercial production For example the PAT method for particle size has already been updated three times (driven by processing and equipment changes) Methods include acceptable ranges for model bias and we have used tools such as the ISO Guide for Measurement Uncertainty to calculate expected measurement performance 15

16 Process Changes Equipment modifications Commercial process runs on a new, dedicated equipment train Operation performance is always in revision Raw material attributes Didn t change them on purpose, but they do change Process Improvements Our overall Process Capability Index (CpK) was known coming out of development Kaizan () activities to help reduce variability of what is already a very good process. Already have systems in place to look for change, measure change and determine if change would impact performance 16

17 What was the biggest change we have made? Moving the critical process step! What changed Process equipment PAT installation How did we do it? Has the initial risk assessment changed NO Are the critical process steps the same Yes Do the existing Process Models hold YES Do the existing Control Models hold YES, with the addition of one more Critical Process Parameters (CPP) Therefore do the analytical methods change NO, they are transferred Has the PAT system changed? 17

18 PAT Changes The technology is the same Malvern Insitec, laser scattering Installation is totally different New Old 18

19 New PAT Method New installation requires a new method description New modeling parameters New validation protocol New validation report TO GET THE SAME DATA 19

20 What have we learned Following a systematic approach has allowed a systematic review/verification Lead us to be able to identify, and justify what had changed, and more importantly what had not Allowed us to focus on what had changed and therefore what needed to be updated Our existing Quality Systems (and those of our CMO s) are able to handle change And even if something has changed, it doesn t mean starting over 20

21 Acknowledgements Kelly Tolton, John Bric, Tom Gandek TechOps Geny Doss, John Goldwaite GMP Quality Patricia Hurter, Jeffrey Katstra - PharmDev 21