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DOE Applications in Process Chemistry Presentation

Jun 22, 2015

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saweissman

Design of Experiments talk given at University of Puerto Rico

  • 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 ofDesign 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, theres 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 variedsimultaneously
  • 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

  • Cant replace full screening of catalyst or solvent (HTS)- discreet variables
  • Best suited forcontinuousvariables
    • time, temp, stoichiometry
  • Not helpful for non-reproducible rxns
  • Best suited for low maintenance rxns
    • Temp = 20 to 150o C
    • All reactants added at once

22. DOE: Experimental Objectives

  • Screening
    • Which factors are most influential ?
    • What are their appropriate values/ranges ?
  • Optimization
    • Extractinformation 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 theObjective
    • screening, optimize, robustness
  • Definition ofFactors
    • Prioritize: known, suspected, possibly, unlikely
    • Set HIGH/LOW values for factors (definedesign space )
  • Define theResponse how to measure ?
  • SelectExperimental Design
  • GenerateWorksheet
  • Run theReactions
  • PerformAnalysiswith 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 ? screeningoptimaztion 29 9 11 9 5 27 10 7 10 4 17 5 7 5 3 35 16 3 16 519 8 3 8 4 11 4 3 4 3 factors Total rxns Lo MedHI 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 [email protected] M (68% isolated yield) 18

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