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DOE Applications in Process Chemistry Presentation

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Design of Experiments talk given at University of Puerto Rico

Design of Experiments talk given at University of Puerto Rico

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  • mature categories: anti-hypertensives/anti-biotics/cholesterol unmet needs: cancer/alzheimers/obesity mature therapies = anti-hypertensive, antibiotics……. protein-based drugs and vaccines have greater opportunities
  • Develop infrastructure in those countries
  • Fosamax-coming off patent this week. Feb 6 Insurers/benefits managers/consumers
  • Fosamax-coming off patent this week. Feb 6
  • Automation: not necessarily always a robot.- can be any tool that speeds up a workflow or allows a chemist to perform more value added tasks.
  • interaction = dependance of one factor on the setting of another
  • Statistical methods are tools used to make sense out of numbers. This slide illustrates the combination of statistical methods with your core knowledge. Statistical methods (specifically, designed experiments in today’s course) are a catalyst to science, NOT a substitute for it.
  • lead in to next slide: How to run the rxns ????
  • Existing experience using HTS
  • Here is the question we are now asking ourselves at Merck…..
  • Isentress generically known as Raltegravir. 1 st commercial HIV integrase inhibitor. Over 3 dozen people from process res
  • Retro: oxadiazole/aza-lactone (oxo-pyrimidine)/fluoro-benzylamine
  • 10 step process
  • trimethyl sulfoxonium iodide
  • Note that the low setting for Pd still gave good results !! and thus was chosen
  • Octanol =NO EFFECT ( not included)
  • enol ether reduction last one: oxidative cleavage
  • enol ether reduction last one: oxidative cleavage
  • Transcript

    • 1. Design of Experiments (DOE): A “New” Approach to Reaction Optimization Dr. Steven Weissman Merck & Co. Feb 4, 2008/UPR
    • 2. Outline
      • Background: Big changes for Pharma
      • Basic Principles of Design of Experiments
      • Merck Case Studies
      • Take Home message/Questions
    • 3. Big Changes for Big Pharma
      • Costs/Risks of drug development are rising
        • low hanging fruit has been picked
        • small molecules no longer in vogue
          • protein-based and vaccines = more opportunities
        • ‘ Vioxx hangover’- more trials/more patients = $$
    • 4. Big Changes for Big Pharma
      • Costs/Risks of drug development are rising
      • Globalization of marketplace
        • US market sales is matured; slow growth
        • Emerging markets sales = high growth potential
        • Strategic portions of drug development outsourced to India/China (low-cost providers)
    • 5. Big Changes for Big Pharma
      • Costs/Risks of drug development are rising
      • Globalization of marketplace
      • Uncertain pipelines
        • ‘Batting average’ is unchanged despite huge investments
        • Is bureaucracy killing drug discovery?
          • Are smaller companies becoming better at this?
    • 6. Big Changes for Big Pharma
      • Costs/Risks of drug development are rising
      • Globalization of marketplace
      • Uncertain pipelines
      • Revenues/profits are being squeezed
        • patent expirations – Fosamax ($3 B)
        • tougher regulatory environment
        • payers demand value-added
        • lower cost structures: aggressively pursued
          • downsizing
          • plant closings-namely here in PR
    • 7. Big Changes for Big Pharma
      • Costs/Risks of drug development are rising
      • Globalization of marketplace
      • Uncertain pipelines
      • Revenues/profits are being squeezed
    • 8. New Approaches Needed
      • What can we do as chemists to change the way we do our jobs ?
      • Can we work smarter/faster ?
      • How ??
    • 9. New Approaches Needed
      • What can we do as chemists to change the way we do our jobs ?
      • Can we work smarter/faster ?
      • How ?? Automation/Technology
    • 10. Automated Synthesis Cycle Design Experiment Analysis Informatics
    • 11. Automated Synthesis Cycle Design Experiment Analysis Informatics Design of Experiments
    • 12. Current Approach to Optimization
      • Change One Factor at a time (OFAT)
        • Rarely leads to optimal conditions
        • Leads to different conclusions depending on starting point
        • Requires many expts/little information
        • Cannot separate “noise” from true variability
    • 13. Current Approach to Optimization
      • Change One Factor at a time (OFAT)
        • Rarely leads to optimal conditions
        • Leads to different conclusions depending on starting point
        • Requires many expts/little information
        • Cannot separate “noise” from true variability
        • Ignores interactions of variables
    • 14. Example of OFAT (11/07)
    • 15. 21 Reactions
    • 16. DOE vs OFAT
      • OFAT: 3 factors needed 21 reactions
        • No information on interactions of effects
      • DOE: 3 or 4 factors- 11 or 17 reactions
        • Better quality information
        • Learn about interactions of effects
        • Fewer reactions
    • 17. Notable Quote
      • “ If you test one factor at a time, there’s a low probability that you are going to hit the right one before everybody gets sick of it and quits”
      • Forbes magazine article on DOE in 1996
    • 18. What is DOE ?
      • Selected set of expts in which all relevant factors are varied simultaneously
      • ‘ Continuous’ factors are ideal (time, temp, equiv)-the ‘ How much’
      • Analysis reveals which factors influence the outcome and identifies optimal conditions
      • Systematic, organized approach to problem solving
      • Mathematical model of the design space
    • 19. DoE Introduction Core Knowledge (Engineering, Chemistry, Biology,…) Statistical Knowledge Develop Solutions DOE is NOT a replacement for process knowledge
    • 20. Questions to be Answered by DoE
      • How do we get the best synthetic yield ?
      • How much catalyst/ligand do I need ?
      • Can we minimize formation of an impurity?
      • Which experimental factors are (un) important?
      • How robust is my process ?
    • 21. DOE: Considerations
      • Can’t replace full screening of catalyst or solvent (HTS)- ‘ discreet ’ variables
      • Best suited for continuous variables
        • time, temp, stoichiometry
      • Not helpful for non-reproducible rxns
      • Best suited for ‘low maintenance’ rxns
        • Temp = 20 to 150 o C
        • All reactants added at once
    • 22. DOE: Experimental Objectives
      • Screening
        • Which factors are most influential ?
        • What are their appropriate values/ranges ?
      • Optimization
        • Extract information regarding how factors combine to influence response
        • Identify optimized reaction conditions
    • 23. DOE: Misconceptions
      • Requires in-depth statistics knowledge
        • User-friendly DOE software does this for you
          • MODDE (Umetrics)/ Design Expert (Stat-ease)
    • 24. DOE: Misconceptions
      • DoE requires in-depth statistics knowledge
        • Experimental design software does this for you
      • DoE requires a lot of experiments and time
        • Perhaps. but will always get better quality information
        • Typically 11-27 reactions per design
        • Automation/technology helps reduce the effort needed
    • 25. High Throughput Screening = 96 x Discreet variables- ‘The what” What is the best ligand/catalyst combination ? What is the best solvent ?
    • 26. High Throughput Screening = 96 x Can we do OPTIMIZATION this way too ??
    • 27. High Throughput Optimization ?? = 96 x If so,………………….. Which reactions do we run ? How do assess the data ?
    • 28. High Throughput Optimization ?? = 96 x Statistical Design of Experiments (DOE)
    • 29. HTS Reaction Vials
    • 30. DOE: Workflow
      • Define the Objective
        • screening, optimize, robustness
      • Definition of Factors
        • Prioritize: known, suspected, possibly, unlikely
        • Set HIGH/LOW values for factors (define design space )
      • Define the Response – how to measure ?
      • Select Experimental Design
      • Generate Worksheet
      • Run the Reactions
      • Perform Analysis with DOE software
    • 31. DOE Design (N=27) 64 0.25 1.85 0.875 4 100 20 58 0.25 1.85 0.875 2 100 19 66 0.25 1.85 0.875 3 115 18 64 0.25 1.85 0.875 3 85 17 76 0.4 2.5 1.5 4 115 16 80 0.1 2.5 1.5 4 85 15 65 0.1 2.5 1.5 2 115 14 79 0.4 2.5 1.5 2 85 13 76 0.1 2.5 0.25 4 115 12 88 0.4 2.5 0.25 4 85 11 63 0.4 2.5 0.25 2 115 10 48 0.1 2.5 0.25 2 85 9 54 0.1 1.2 1.5 4 115 8 39 0.4 1.2 1.5 4 85 7 55 0.4 1.2 1.5 2 115 6 45 0.1 1.2 1.5 2 85 5 55 0.4 1.2 0.25 4 115 4 43 0.1 1.2 0.25 4 85 3 49 0.1 1.2 0.25 2 115 2 45 0.4 1.2 0.25 2 85 1 Yield E:Conc D:Boron/Br C:Cu B:P/Pd A:temp Rxn # Response 1 Factor 5 Factor 4 Factor 3 Factor 2 Factor 1
    • 32. DOE Creates a Design Space Design-Expert® Software Yield X1 = A: temp X2 = B: P/Pd X3 = C: Cu load Actual Factors D: Boron/Br = 2.50 E: Conc = 0.40 Cube Yield A: temp B: P/Pd C: Cu load A-: 85.00 A+: 115.00 B-: 2.00 B+: 4.00 C-: 0.25 C+: 1.50 63.7936 74.1825 86.3492 83.738 59.9047 70.2936 82.4603 79.8492
    • 33. DOE Expts: How Many ? screening optimaztion 29 9 11 9 5 27 10 7 10 4 17 5 7 5 3 35 16 3 16 5 19 8 3 8 4 11 4 3 4 3 factors Total rxns Lo Med HI rxns factors
    • 34. DOE Case Studies
    • 35. MK-0518 First-in-Class Oral HIV-1 Integrase Inhibitor Approved by FDA October-12-2007
    • 36. MK-0518
    • 37. MK-0518 Challenge: to reduce manufacture cost by 20%
    • 38. MK-518: Problem step Peter Maligres Existing Conditions : 4 eq Mg(OMe) 2 / 4 eq MeI @ 0.5 M (68% isolated yield) 18 solvents, 8 bases screened 78 22
    • 39. MK-518: DOE Optimization Peter Maligres DOE Optimzation Design Factors : Mg(OMe) 2 equiv: 1.0 and 3.0 MeI equiv: 2.5 and 5.0 Conc: 0.25 and 1.0 M Temperature: 30 and 65 o C 19 reactions Responses (4 and 20 h): Assay yield Selectivity
    • 40. MK-518 Optimization Peter Maligres DOE Optimal Settings Base equiv: 1.0 and 3.0 MeI equiv: 2.5 and 5.0 Temperature: 30 and 65 o C Conc: 0.25 and 1.0 M Time: 4 and 20 h 99 1
    • 41. MK 518: Surface Model
    • 42. Effect of Temp & Conc
    • 43. Effect of Base and Conc
    • 44. MK518-In Situ Demethylation 99/1 80/20 N vs O 99% 95% Conv 20 h 4 h
    • 45. MK-518 Concerns Peter Maligres
      • Issues:
      • 1. at this higher concentration, end of reaction difficult to stir
      • Mg(OMe) 2 - long term issues with supply & cost
      • MeI is mutagenic/carcinogen/toxic
      99 1
    • 46. MK-518 Optimization Peter Maligres Yield =90% Selectivity = >99.9 % Safer, more economical reagents Incorporated best practices from DOE: HI Temp/HI Concentration/Longer reaction times
    • 47. MK-518 Summary 78 22 > 99 < 1 DOE Goal of 20% reduction in drug inventory cost was achieved Higher Yield cascades back to allow fewer RM/solvents to be used Submitted for 2008 Presidential Green Chemistry Award
    • 48. Case Study 2- Suzuki
      • Goal:
      • to reduce Pd(OAc) 2 & ligand charges (0.4 mole%,0.8 mole%)
      • 2. Improve yield and/or purity
    • 49. Case Study 2- Suzuki DOE Factors : Ligand/Pd ratio: 1.0 and 3.0 Catalyst load: 0.1 and 0.5 mole% Molarity boronic acid: 0.5 and 1.5 Temperature: 60 and 80 o C 27 Reactions in 96-well plate format, 2 days to plan/setup/execute/assay 0.65 g material (24 mg/rxn) !!
    • 50. Case Study 2- Suzuki DOE Optimal Settings : Ligand/Pd ratio: 1.0 and 3.0 Catalyst load: 0.1 and 0.5 mole% Molarity boronic acid : 0.5 and 1.5 Temperature: 60 and 80 o C ( 65 o C )
    • 51. Effect of Temp and Pd Loading Lig/ catalyst ratio fixed at 3:1; Triol M fixed at 1.5 M Overall LCAP
    • 52. Optimized Conditions Optimized Experiment: -increased LCAP by 1% -decreased DesBr impurity (50%) -decreased Pd by 75% -decreased Lig by 70% Spencer Dreher
    • 53. Case Study #3 Dave Pollard Goal: to reduce cost by increasing productivity 100 g/L
    • 54. Screening Design Dave Pollard Factors Octanol: 40 and 60 % NADP equiv: 0.1 and 0.5 % Concentration: 50 and 150 g/L Temp: 25 and 35 o C Enzyme load: 0.3 to 1.0 g/L 19 experiments
    • 55. DOE Factors Plot
    • 56. Interaction: Conc and NAD
    • 57. Screening Result Dave Pollard Factor Preferred Setting Octanol: 40 and 60 % No impact NADP equiv: 0.1 and 0.5 % No impact- increase more ? Concentration: 50 and 150 g/L 50 g/L- undesirable setting Temp: 25 and 35 o C minimal effect- set at 30 o C Enzyme load: 0.3 to 1.0 g/L 1.0 g/L- increase more
    • 58. Optimization Design Dave Pollard Factor NADP equiv: 0.5 and 1.5 % Concentration: 100 and 200 g/L Enzyme load: 0.5 to 3.0 g/L 19 experiments
    • 59. Optimization Design Factor Preferred setting NADP equiv: 0.5 and 1.5 % No effect Concentration: 100 and 200 g/L 200 Enzyme load: 0.5 to 3.0 g/L 3.0
    • 60. Optimization Design Factor Preferred setting NADP equiv: 0.5 and 1.5 % No effect Concentration: 100 and 200 g/L 200 Enzyme load: 0.5 to 3.0 g/L 3.0 Confirming experiment at 200 g/L NADP= 0.5 g/L and enzyme at 3 g/L gave 100% conversion Goal achieved
    • 61. Case Study #4-Sonogashira S. Krska/A. Northrup Medicinal Chemistry conditions
    • 62. HTS Result Screened: ligands and Pd sources 32 reactions (HTS-96 well plate format) – 1.5 days- 125 mg of substrate
    • 63. DOE Optimization
      • 3 Factor RSM design – 19 reactions
      • Fixed factors: L/Pd ratio (2:1); temperature (45 o C); base equiv (3)
      • Varied Factors:
      • Pd loading (2 and 10 mole%)
      • Cu/Pd ratio (0.5 and 2.0)
      • Alkyne equiv (1 and 5)
    • 64. DOE Optimization
      • 3 Factor RSM design – 19 reactions
      • Fixed factors: L/Pd ratio (2:1); temperature (45 o C); base equiv (3)
      • Varied Factors:
      • Pd loading (2 and 10 mole%)- little effect- set to 3 mole%
      • Cu/Pd ratio (0.5 and 2.0 )- most important therefore 12 mole% CuI
      • Alkyne equiv (1 and 5) – 2 equiv
    • 65. DOE Optimization Cu/Pd = 2
    • 66. DOE Confirmation
      • Confirming reaction run using iChem Explorer to monitor reaction
      • DOE/Automation Improvements over HTS Result:
      • 70% reduction in Pd charge
      • 70% reduction in ligand charge
      • 63% reduction in Cu charge
      • 94% reduction in time cycle
      • Improved selectivity from 9:1 to 100:1-mostly due to time aspect
    • 67. iChem Explorer
      • Hardware: heating (to 150 o C) and stirring block for HP 1100/1200 systems
      • Software: to visualize data
      • up to 100- 1 mL reactions in LC vials
        • monitor by direct injection
    • 68. DOE Summary
    • 69. Case Study # 5 Mark Weisel 10% loading Pearlman’s catalyst 25 o C/45 psi/EtOAc 88 A% Goal: to minimize formation of impurities/maximize desired product 12 A%
    • 70. Case Study # 5 Mark Weisel 88 A% 12 A% DOE design: 4 Factors (19 reactions) Temp (25 and 55 o C) Pressure (30 and 60 psi) Pd(OH) 2 loading (5 and 15 wt%) Volume EtOAc (6 and 10 ml/g)
    • 71. Factors
    • 72. Effect of Pd and Temp
    • 73. Optimal Settings Mark Weisel
      • Relevant Factors-ranked
      • Pd loading ( 15 wt% )
      • Temp ( 25 o C )
      • Volume ( 10 ml/g )
      • Pressure- no effect- run at 30 psi
      Selectivity improved from 88 A% to > 99 A%
    • 74. DOE Benefits
      • Increase your process knowledge
      • Discover the effects of changing factors
      • Understand the effects of interactions
      • Learn what is and what is NOT important
      • Save time, materials, and money
    • 75. Take Home Message
      • DOE is a powerful tool for optimization of reactions
      • Automated tools minimize the effort of running multiple rxns
      • HTS & DOE in 96-well format represents leading-edge science
      • Academics embracing HTS approach
        • Professor D. MacMillan ( Princeton)
    • 76. Acknowledgments
      • Peter Maligres
      • Danny Gauvreau
      • Spenser Dreher
      • Dave Pollard
      • Shane Krska
      • Mark Weisel
      • Dave Tellers
    • 77. QUESTIONS ?

    ×