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Sequential Design – The Challenge Of Multiphase Systems Pd

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An example of how experimental design can be combined with process analytical technology

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Sequential Design – The Challenge Of Multiphase Systems Pd

  1. 1. GlaxoSmithKline Jim Ward, Bob Herrmann, Teo Ching-Lay and Ann Diederich Sequential Design – the challenge of multiphase systems
  2. 2. Outline <ul><li>Introduction </li></ul><ul><ul><li>Traditional approaches to problem solving </li></ul></ul><ul><li>Our problem </li></ul><ul><ul><li>Preparation of the correct crystalline form </li></ul></ul><ul><li>Our approach </li></ul><ul><ul><li>Mechanistic guided factor selection </li></ul></ul><ul><li>Results </li></ul><ul><li>Proposed problem solving approach </li></ul>
  3. 3. Conventional Modeling Approaches (1) <ul><li>Utility of Mathematical Model </li></ul><ul><ul><li>Prediction </li></ul></ul><ul><ul><li>Sensitivity Analysis </li></ul></ul><ul><li>Types and Issues </li></ul><ul><ul><li>Statistical </li></ul></ul><ul><ul><ul><li>Huge number of experiments </li></ul></ul></ul><ul><ul><ul><li>Little mechanistic insight </li></ul></ul></ul><ul><li>Mechanistic </li></ul><ul><ul><li>Requires a compete set of constitutive equations </li></ul></ul><ul><ul><li>May not be possible for multiphase systems (S/S/L) </li></ul></ul><ul><ul><li>May not fully understand mechanism </li></ul></ul>A blended approach may provide benefits of both statistical and mechanistic modeling Motivation and the Challenge of Various Approaches
  4. 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)
  5. 5. Conventional Approach: Factorial Burden <ul><li>Pareto Principle 2 </li></ul><ul><ul><li>80% of the effects come from 20% of the factors </li></ul></ul><ul><ul><li>For 20 experiments, 6 factors is roughly the maximum practical limit for study </li></ul></ul><ul><li>Need mechanistic data to limit factors </li></ul><ul><ul><li>DoE does not provide direct evidence of why something occurs </li></ul></ul>Even Optimized – Experimental Design can be Costly 2 http://en.wikipedia.org/wiki/Pareto_principle Realistically, we can only do about 20 pilot/kilo scale experiments for scale sensitive reactions, so factor selection is essential
  6. 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?
  7. 7. Our Approach: Dehydration Mechanism <ul><ul><li>Liquid Mediated (Lin and Lachman) </li></ul></ul><ul><ul><ul><li>Indomethacin </li></ul></ul></ul><ul><ul><ul><ul><li>Temperature and time control </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Mixing insensitive </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Solvent sensitive </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Effectively irreversible </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Scale insensitive </li></ul></ul></ul></ul><ul><ul><li>Solid State Transformation </li></ul></ul><ul><ul><ul><li>Thyminde, Caffeine and Cytosine </li></ul></ul></ul><ul><ul><ul><ul><li>Very difficult to control </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Mixing sensitive </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Reversible </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Heat transfer sensitive </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Scale sensitive </li></ul></ul></ul></ul>S. R. Byrn; Solid State Chemistry of Drugs, 2 nd Ed., Chapter 14 – Loss of Solvent of Crystallization Know the mechanism – Narrow the factor list
  8. 8. Our Approach (1): Dehydration Mechanism <ul><ul><li>Liquid Mediated (Lin and Lachman) </li></ul></ul><ul><ul><ul><li>Indomethacin </li></ul></ul></ul><ul><ul><ul><ul><li>Temperature and time control </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Mixing insensitive </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Solvent sensitive </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Effectively irreversible </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Scale insensitive </li></ul></ul></ul></ul><ul><ul><li>Solid State Transformation </li></ul></ul><ul><ul><ul><li>Thyminde, Caffeine and Cytosine </li></ul></ul></ul><ul><ul><ul><ul><li>Very difficult to control </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Mixing sensitive </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Reversible </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Heat transfer sensitive </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Scale sensitive </li></ul></ul></ul></ul><ul><ul><ul><ul><li>PSD Sensitive / SSA </li></ul></ul></ul></ul>S. R. Byrn; Solid State Chemistry of Drugs, 2 nd Ed., Chapter 14 – Loss of Solvent of Crystallization Know the mechanism – Narrow the factor list
  9. 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. 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
  11. 11. Detailed Solubility Data <ul><li>Develop detailed solubility models to enhance mechanistic understanding </li></ul><ul><ul><li>Conversion Temperature </li></ul></ul><ul><ul><li>Conversion Composition </li></ul></ul><ul><li>Presence of water increases solubility, as does increasing temperature </li></ul><ul><li>Driving force for crystallization can be calculated across process conditions </li></ul><ul><li>Establishes water composition and temperature as key factors </li></ul>
  12. 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. 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
  14. 14. Our Approach: Desaturation Mechanism - Results <ul><li>RC/1 can be used to estimate desaturation rates </li></ul><ul><ul><li>Crystal nucleation and growth are exothermic processes </li></ul></ul><ul><ul><li>From the heat of crystallization a rate can be determined </li></ul></ul><ul><li>The conversion without solids present </li></ul><ul><ul><li>Autocatalytic- indicates nucleation </li></ul></ul><ul><ul><li>5 times slower in presence of solids – indicates affect of solids present (secondary nucleation) </li></ul></ul>2 Minutes RC1 – Thermal Conversion
  15. 15. Desaturation Mechanism - Results <ul><li>The conversion without solids present </li></ul><ul><ul><li>Autocatalytic </li></ul></ul><ul><ul><li>5 times slower </li></ul></ul><ul><li>The particles with solid present </li></ul><ul><ul><li>70% Larger- some growth </li></ul></ul><ul><ul><li>Faster conversion rate- previous slide </li></ul></ul>Unseeded X 90 - 34 Seeded X 90 - 20 Both the conversion rate and particle size supports a nucleation dominated mechanism, with minor crystal growth also occurs Important factors: amount of supersaturation (temperature, solvent comp., agitation rate)
  16. 16. Factor Selection <ul><li>Liquid Effects </li></ul><ul><li>Loss on Drying (LOD) The hydrate wet cake desolvated in a filter drier. Blowback of the hydrate wetcake prior to adding the dehydration solvent will decrease water content, lowering API solubility </li></ul><ul><li>Temperature As the conversion temperature is increased solubility rises perhaps effecting conversion. </li></ul><ul><li>Volumes of Solvent Larger solvent volumes mean higher dilution </li></ul><ul><li>Physical Effects </li></ul><ul><li>Agitation Both continuous and intermittent agitation investigated </li></ul>Dissolution Nucleation B ∽ Surface Area * Δ Cb
  17. 17. Parameter Investigation - Results <ul><li>Highly Sensitive </li></ul><ul><li>Water content of solvent – Highly LOD of wet cake (water) results in more water being present for the conversion, which raises solubility </li></ul><ul><li>Temperature – Higher temperature raises solubility </li></ul><ul><li>Interaction of solvent composition and Temp - The highest solubility, and liquid side effects, are seen at high LOD (water content) and high temperature </li></ul><ul><li>Less Sensitive </li></ul><ul><li>Agitation – Low agitation sensitivity increases confidence this product can be scaled with little physical effect </li></ul><ul><li>Volumes – Higher throughput can be obtained because the volumes of the desolvation solvent proves to be unimportant. </li></ul>Figure : D90 LOD/Temp Contour Plot Figure : D90 Agitation Contour Plot
  18. 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
  19. 19. Process surprises- a new chance to optimize <ul><li>While change to x90 on scale wasn’t a large project issue, the appearance of a new solvate was </li></ul><ul><li>As a result, workflow repeated with previous information to guide design, and incorporating seeding </li></ul>
  20. 20. New Process Design: Using solubility data to determine solvate stability regions
  21. 21. Detailed Thermo <ul><li>FBRM </li></ul><ul><ul><li>Indicates partial conversion to form A (2 minutes) </li></ul></ul><ul><ul><li>Conversion to form to solvate is 180 times slower (4 hours) </li></ul></ul><ul><li>Using detailed solubility models </li></ul><ul><ul><li>Conversion Temperature </li></ul></ul><ul><ul><li>Conversion Composition </li></ul></ul><ul><ul><li>Required Seed Load </li></ul></ul><ul><li>Previous DoE </li></ul><ul><ul><li>Minimize Water </li></ul></ul><ul><ul><li>Maximize Temperature </li></ul></ul>4 Hours
  22. 22. New Process Design: DOE Results
  23. 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. 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. 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. 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. 27. Scale-up of modified process Unmodified Modified Variability Source Both variance in particle size and form issue mitigated through guided experimental design
  28. 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
  29. 30. Alternative Workflow <ul><li>Portions can be done on small scale </li></ul><ul><ul><li>Thermodynamics </li></ul></ul><ul><ul><li>PAT Guided Mechanistic Studies </li></ul></ul><ul><li>Avoids investigating noise factors in DoE </li></ul><ul><ul><li>Fewer scale experiments </li></ul></ul><ul><li>Provides a mathematical model </li></ul><ul><ul><li>Predication </li></ul></ul><ul><ul><li>Control </li></ul></ul><ul><li>Provides direct scientific understanding </li></ul><ul><li>Provides </li></ul><ul><ul><li>Confidence in robustness </li></ul></ul><ul><ul><li>Estimate of process variance </li></ul></ul><ul><ul><li>Basis of measuring scale effects </li></ul></ul><ul><li>Consistent with FDA Guidance </li></ul>
  30. 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
  31. 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
  32. 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
  33. 34. Robustness Study <ul><li>Robustness Study </li></ul><ul><li>Investigated factors </li></ul><ul><ul><li>Set to the widest levels the plant can provide </li></ul></ul><ul><li>All Adjustable Factors </li></ul><ul><ul><li>Set outside the levels future plant modifications may be wanted </li></ul></ul><ul><li>Design </li></ul><ul><ul><li>Minimal 2 level DoE with no center points </li></ul></ul><ul><li>Results </li></ul><ul><ul><li>Proof of Robustness </li></ul></ul><ul><ul><li>Estimation of process variance </li></ul></ul>
  34. 35. The Scoping Study - Experimental <ul><li>For a good model the responses need to be </li></ul><ul><ul><li>Variable in region the factors are tested </li></ul></ul><ul><ul><li>Quantifiable </li></ul></ul><ul><ul><li>Distinguishable from noise </li></ul></ul><ul><ul><li>Ideally, controlled by the factors </li></ul></ul><ul><ul><li>Contain a passable region </li></ul></ul><ul><li>Scoping Study consists of </li></ul><ul><ul><li>1 Reaction at each extreme </li></ul></ul><ul><ul><li>2 Centre Points </li></ul></ul><ul><li>Scoping Study Should Result In </li></ul><ul><ul><li>Confidence in factor levels </li></ul></ul><ul><ul><li>Confidence in covering controlling factors </li></ul></ul><ul><ul><li>Estimate of pure error </li></ul></ul><ul><ul><li>Estimate of model curvature </li></ul></ul>
  35. 36. Robustness Study <ul><li>Robustness Study </li></ul><ul><li>Investigated Factors </li></ul><ul><ul><li>Set to the widest levels that will allow passing of critical process parameters </li></ul></ul><ul><li>All Adjustable Factors </li></ul><ul><ul><li>Set outside the levels future plant modifications may be wanted </li></ul></ul><ul><li>Design </li></ul><ul><ul><li>Minimal 2 level DoE with no center points </li></ul></ul><ul><li>Results </li></ul><ul><ul><li>Proof of Robustness </li></ul></ul><ul><ul><li>Estimation of process variance </li></ul></ul>

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