1 / 14

Debugging Finished Goods Manufacture For A Popularly Prescribed Pharmaceutical

Debugging Finished Goods Manufacture For A Popularly Prescribed Pharmaceutical. Jean-Marie Geoffroy, Xavier Castells Abbott Laboratories Robert H. McCafferty, Curvaceous Software Limited. Background. Reasonably Large Database 106 Variables in Total 212 Lots Spanning Full Year’s Manufacture

sivan
Download Presentation

Debugging Finished Goods Manufacture For A Popularly Prescribed Pharmaceutical

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Debugging Finished Goods Manufacture For A Popularly Prescribed Pharmaceutical Jean-Marie Geoffroy, Xavier Castells Abbott Laboratories Robert H. McCafferty, Curvaceous Software Limited

  2. Background • Reasonably Large Database • 106 Variables in Total • 212 Lots Spanning Full Year’s Manufacture • Raw Material, Logistical Information Also Tracked • History Of Issues • Granulation, Blending, Compression, And Coating Unit Processes Involved • Yield Loss And Associated Mechanisms Negligible • Variability Driving: • Test Cost • Idle Equipment Time • Dissolution Profile Anxiety

  3. Entire Spreadsheet in One Picture • Each Black Line An Observation • Missing Data Clustered At Bottom Of Axes

  4. Querying Against Variability And Yield • First Pass Approach Given Product Within Specifications • Considers All Standard Deviations Available In Dataset

  5. Variability Analysis (cont.) • Clear Temporal Patterns By Month, Possibly Day As Well

  6. Variability Analysis (cont.) • Adverse Results With Extended Hold Time • Different Gradients For Gran/Blend, Blend/Compress, Compress/Coat Delay • Also True Of Accumulated Time… Possible Hole In Blend/Compress Delay

  7. Cost Argument Analysis • Effectively Confirms All Items Discovered By Variability Analysis • No Relationship Between Yield And Tablets Tested (Different Mechanisms)

  8. Cost Analysis (cont.) • Adverse Signature In Raw Material 1 Characteristics • Map Directly To High Tablet Testing And Variability, Recent And Previous

  9. Dissolution Profile Analysis • Hardness Range Increases As Min Or Max Diverge From Spec. • Operators Taking Corrective Action (Too Hard/Soft) Only When Warranted • Standing Procedures Working

  10. Dissolution Profile Analysis (cont.) • Weight Range Increases As Min Or Max Diverge From Spec. • Operators Taking Corrective Action (Too Light/Heavy) Only When Warranted • Standing Procedures Working

  11. Dissolution Profile Analysis (cont.) • Clear Coating Process Sweet Spots • Nozzle To Bed Distance As Well As Pan Loading

  12. Analysis Results • Holistic View Taken… All Variables Considered Simultaneously • Clear Shift In Raw Material Constitution Driving High Variability • Problem Clearly Amplified By Unanticipated Hold Time Influence • Expected Temporal Patterns (Month and Campaign) Present, Mid- Month Hole In Daily Behavior Under Review • Manufacturing Procedures Confirmed Operating As Expected • High/Low Hardness Reaction • High/Low Weight Reaction

  13. Extending to Geometric Model • All Interactions Considered • Full Response Surface In Single View

  14. Money In The Bank • Improved Process Understanding Drives Reduced Variability • Immediate Manufacturing Savings Possible • Reduced Test Cost • Elimination Of Excess Idle Time • Unnecessary WIP/Inventory Cost Elimination • Much More Certain Planning and Scheduling Feasible • Entire Factory Throughput Can Now Be Optimized

More Related