Sequential Design – The Challenge Of Multiphase Systems Pd
1. GlaxoSmithKline Jim Ward, Bob Herrmann, Teo Ching-Lay and Ann Diederich Sequential Design – the challenge of multiphase systems
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4. Conventional Modeling Approaches (2) Fractional screening and robustness are resource consuming. May have to do at a reasonable scale if equipment sensitive. Without mechanistic knowledge, number of factors is large. Route Selection Scoping Study (Scoping studies are used to narrow into the experimental region of interest) (4 Experiments) Fractional/ Screening (These designs are utilized to identify factors that affect the process) (16 Experiments) Foldover (Once the factors of interest are identified the foldover removes aliasing from the fractional design) (8 Experiments) RSM or Composite Design (utilized to determine curvature and to hone into an optimized process) Robustness Study (utilized to narrow or widen process parameters) (8 Experiments)
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6. Selected Process Isolate Hydrate via Filtration at 25 °C Agitate until Conversion Complete Charge 6 volumes Acetonitrile Heat to at least 60 C Charge Isolate Form A Anhydrate Vessel One Filter Drier Our process involves the formation of a hydrate and its subsequent desolvation to form an anhydrate (product) Greater than 20 unit operations- which factors to study?
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9. Our Approach: Dehydration Mechanism – Experimental ReactIR Filtered Saturated Solution Unstable Form charged Anhydrate Hydrate Solvate Seeded With Stable form Monitor Conversion PAT/Mechanism - ReactIR
10. Our Approach: Dehydration Mechanism – Results Theoretical Actual PAT/Mechanism - ReactIR The conversion is solvent mediated. Key factors are temperature and composition of the solvent affect solubility Hydrate Charged Concentration Time
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12. Our Approach: Desaturation Mechanism Determination Dissolution Growth Nucleation B Surface Area * Δ Cb G Δ Ca From solution to Solid A. G. Jones; Crystallization Process Systems, Pg 204 Eqs 7.36 & 7.38 simplified
13. Desaturation Mechanism - Experimental PAT / Mechanism – RC1 Monitor Thermal Conversion by RC1 Filtered Saturated Solution Unstable Form charged Unstable form charged while Seeded with Stable form Monitor Conversion Monitor Conversion
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18. Scale-Up of Selected Process DoE Robustness Kilo Pilot Plant Campaign I/II 1000x Scale Manufacturing Campaigns I/II 2000x Scale Results: Model worked well throguh kilo lab, 1000x DOE scale. Particle size changed when going to 2000x scale. Numbers acceptable, but unexplained variance
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20. New Process Design: Using solubility data to determine solvate stability regions
23. Selected Route Isolate Hydrate via Filtration at 25 °C Agitate until Conversion Complete Charge 6 volumes Acetonitrile Heat to at least 60 C Charge Isolate Form A Anhydrate Vessel One Filter Drier
24. Selected Route Isolate Hydrate via Filtration at 25 °C Agitate until Conversion Complete Charge 6 volumes Acetonitrile Heat to at least 60 C Charge Isolate Form A Anhydrate Vessel One Filter Drier
25. Selected Route Isolate Hydrate via Filtration at 25 °C Agitate until Conversion Complete Charge 6 volumes Acetonitrile Heat to at least 65 C Charge Isolate Form A Vessel One Filter Drier
26. Selected Route Isolate Hydrate via Filtration at 25 °C Agitate until Conversion Complete Charge 6 volumes Acetonitrile Heat to at least 65 C Charge Isolate Form A Vessel One Filter Drier Charge Water Charge Seeds Heat above Conversion Temp
27. Scale-up of modified process Unmodified Modified Variability Source Both variance in particle size and form issue mitigated through guided experimental design
28. Alternative Workflow Route Selection Thermodynamics (ensure the process is on stable thermodynamic footing) PAT guided mechanistic studies (kinetic model not required) Factor selection and scoping (using small scale results select factors and design space) 4 Experiments Factor investigation (DoE) 14 Experiments Robustness Study
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31. FDA Guidance A process is generally considered well understood when (1) all critical sources of variability are identified and explained; (2) variability is managed by the process; and, (3) product quality attributes can be accurately and reliably predicted over the design space established for materials used, process parameters, manufacturing, environmental, and other conditions. PAT – A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance 1 1 www.fda.gov/cder/guidance/6419fnl.pdf
32. Modeling – Statistical or Mechanistic 3 http://www.scale-up.com/usersarea/FDA/FDA_notes_28Feb08.pdf Question to the FDA “ the agency at the moment is much more tuned in to statistical models, in part due to the fact that drug product often requires statistical models in the absence of mechanistic detail” FDA Response Agreed. Statistics and DOEs should be integrated with mechanistic modeling. We do not want to see so many experiments “in the dark” as we are seeing now. Do fewer experiments. Show us that you have identified all the really critical parameters and understand the effects of all the CPPs . Notes of DynoChem presentation to FDA CDER, 28 February 2008 3
33. Mechanism – A word of caution We need a word of caution at this point. Just because the mechanism and the rate-limiting step may fit the rate data does not imply that the mechanism is correct. H. Scott Fogler Elements of Chemical Reaction Engineering, 3 RD Ed. Page 614