Design of Experiments Design of Experiments (DOE): (DOE): A “New” Approach to A “New” Approach to Reaction Optimization Reaction Optimization Dr. Steven Weissman Dr. Steven Weissman Merck & Co. Merck & Co. Feb 4, 2008/UPR Feb 4, 2008/UPR
Jun 22, 2015
Design of Experiments (DOE): Design of Experiments (DOE): A “New” Approach to Reaction A “New” Approach to Reaction
OptimizationOptimization
Dr. Steven WeissmanDr. Steven WeissmanMerck & Co. Merck & Co.
Feb 4, 2008/UPR Feb 4, 2008/UPR
OutlineOutline
Background: Big changes for Pharma Background: Big changes for Pharma
Basic Principles of Basic Principles of Design of ExperimentsDesign of Experiments
Merck Case StudiesMerck Case Studies
Take Home message/QuestionsTake Home message/Questions
Big Changes for Big PharmaBig Changes for Big Pharma
Costs/Risks of drug development are risingCosts/Risks of drug development are rising– low hanging fruit has been pickedlow hanging fruit has been picked– small molecules no longer in voguesmall molecules no longer in vogue
protein-based and vaccines = more opportunitiesprotein-based and vaccines = more opportunities
– ‘‘Vioxx hangover’- more trials/more patients = $$Vioxx hangover’- more trials/more patients = $$
Big Changes for Big PharmaBig Changes for Big Pharma
Costs/Risks of drug development are risingCosts/Risks of drug development are rising
Globalization of marketplaceGlobalization of marketplace– US market sales is matured; slow growthUS market sales is matured; slow growth– Emerging markets sales = high growth potentialEmerging markets sales = high growth potential– Strategic portions of drug development Strategic portions of drug development
outsourced to India/China (low-cost providers)outsourced to India/China (low-cost providers)
Big Changes for Big PharmaBig Changes for Big Pharma
Costs/Risks of drug development are risingCosts/Risks of drug development are rising
Globalization of marketplaceGlobalization of marketplace
Uncertain pipelinesUncertain pipelines– ‘‘Batting average’ is unchanged despite huge Batting average’ is unchanged despite huge
investments investments – Is bureaucracy killing drug discovery?Is bureaucracy killing drug discovery?
Are smaller companies becoming better at this?Are smaller companies becoming better at this?
Big Changes for Big PharmaBig Changes for Big Pharma
Costs/Risks of drug development are risingCosts/Risks of drug development are risingGlobalization of marketplaceGlobalization of marketplaceUncertain pipelinesUncertain pipelinesRevenues/profits are being squeezedRevenues/profits are being squeezed– patent expirations – Fosamax ($3 B)patent expirations – Fosamax ($3 B)– tougher regulatory environmenttougher regulatory environment– payers demand value-addedpayers demand value-added– lower cost structures: aggressively pursuedlower cost structures: aggressively pursued
downsizingdownsizingplant closings-namely here in PRplant closings-namely here in PR
Big Changes for Big PharmaBig Changes for Big Pharma
Costs/Risks of drug development are risingCosts/Risks of drug development are rising
Globalization of marketplaceGlobalization of marketplace
Uncertain pipelinesUncertain pipelines
Revenues/profits are being squeezedRevenues/profits are being squeezed
New Approaches NeededNew Approaches Needed
What can we do as chemists to What can we do as chemists to change the way we do our jobs ?change the way we do our jobs ?
Can we work smarter/faster ?Can we work smarter/faster ?
How ?? How ??
New Approaches NeededNew Approaches Needed
What can we do as chemists to What can we do as chemists to change the way we do our jobs ?change the way we do our jobs ?
Can we work smarter/faster ?Can we work smarter/faster ?
How ?? How ?? Automation/TechnologyAutomation/Technology
Automated Synthesis CycleAutomated Synthesis Cycle
Design
Experiment Analysis
Informatics
Automated Synthesis CycleAutomated Synthesis Cycle
Designof Experiments
Design
Experiment Analysis
Informatics
Current Approach to OptimizationCurrent Approach to Optimization
Change One Factor at a time (OFAT)Change One Factor at a time (OFAT)
– Rarely leads to optimal conditionsRarely leads to optimal conditions– Leads to different conclusions depending on Leads to different conclusions depending on
starting pointstarting point– Requires many expts/little informationRequires many expts/little information– Cannot separate “noise” from true variabilityCannot separate “noise” from true variability
Current Approach to OptimizationCurrent Approach to Optimization
Change One Factor at a time (OFAT)Change One Factor at a time (OFAT)
– Rarely leads to optimal conditionsRarely leads to optimal conditions– Leads to different conclusions depending on Leads to different conclusions depending on
starting pointstarting point– Requires many expts/little informationRequires many expts/little information– Cannot separate “noise” from true variabilityCannot separate “noise” from true variability
– Ignores interactions of variablesIgnores interactions of variables
Example of OFAT (11/07)Example of OFAT (11/07)
21 Reactions21 Reactions
DOE vs OFATDOE vs OFAT
OFAT: 3 factors needed 21 reactionsOFAT: 3 factors needed 21 reactions– No information on interactions of effectsNo information on interactions of effects
DOE: 3 or 4 factors- 11 or 17 reactionsDOE: 3 or 4 factors- 11 or 17 reactions– Better quality informationBetter quality information– Learn about interactions of effectsLearn about interactions of effects– Fewer reactionsFewer reactions
Notable QuoteNotable Quote
““If you test one factor at a time, there’s a If you test one factor at a time, there’s a low probability that you are going to hit low probability that you are going to hit the right one before everybody gets sick the right one before everybody gets sick of it and quits”of it and quits”
Forbes magazine article on DOE in 1996Forbes magazine article on DOE in 1996
What is DOE ?What is DOE ?
Selected set of expts in which all relevant Selected set of expts in which all relevant factors are varied factors are varied simultaneouslysimultaneously
‘‘Continuous’ factors are ideal (time, temp, Continuous’ factors are ideal (time, temp, equiv)-the ‘equiv)-the ‘How much’How much’
Analysis reveals which factors influence the Analysis reveals which factors influence the outcome and identifies optimal conditionsoutcome and identifies optimal conditions
Systematic, organized approach to problem Systematic, organized approach to problem solvingsolving
Mathematical model of the design spaceMathematical model of the design space
DoE IntroductionDoE Introduction
Core Knowledge(Engineering, Chemistry, Biology,…)
Statistical Knowledge
Develop Solutions
DOE is NOT a replacement for process knowledge
Questions to be Answered by DoEQuestions to be Answered by DoE
How do we get the best synthetic yield ?How do we get the best synthetic yield ?
How much catalyst/ligand do I need ?How much catalyst/ligand do I need ?
Can we minimize formation of an impurity?Can we minimize formation of an impurity?
Which experimental factors are (un) Which experimental factors are (un) important?important?
How robust is my process ?How robust is my process ?
DOE: ConsiderationsDOE: Considerations
Can’t replace full screening of catalyst or Can’t replace full screening of catalyst or solvent (HTS)- ‘solvent (HTS)- ‘discreetdiscreet’ variables’ variables Best suited for Best suited for continuouscontinuous variables variables– time, temp, stoichiometrytime, temp, stoichiometry
Not helpful for non-reproducible rxnsNot helpful for non-reproducible rxnsBest suited for ‘low maintenance’ rxnsBest suited for ‘low maintenance’ rxns– Temp = 20 to 150 Temp = 20 to 150 ooCC– All reactants added at once All reactants added at once
DOE: Experimental ObjectivesDOE: Experimental Objectives
ScreeningScreening– Which factors are most influential ? Which factors are most influential ? – What are their appropriate values/ranges ?What are their appropriate values/ranges ?
OptimizationOptimization– Extract information regarding how factors Extract information regarding how factors
combine to influence responsecombine to influence response– Identify optimized reaction conditionsIdentify optimized reaction conditions
DOE: MisconceptionsDOE: Misconceptions
Requires in-depth statistics knowledgeRequires in-depth statistics knowledge– User-friendly DOE software does this for youUser-friendly DOE software does this for you
MODDEMODDE (Umetrics)/ (Umetrics)/Design ExpertDesign Expert (Stat-ease) (Stat-ease)
DOE: MisconceptionsDOE: Misconceptions
DoE requires in-depth statistics knowledgeDoE requires in-depth statistics knowledge– Experimental design software does this for youExperimental design software does this for you
DoE requires a lot of experiments and timeDoE requires a lot of experiments and time– Perhaps. but will always get better quality informationPerhaps. but will always get better quality information– Typically 11-27 reactions per designTypically 11-27 reactions per design– Automation/technology helps reduce the effort neededAutomation/technology helps reduce the effort needed
High Throughput ScreeningHigh Throughput Screening
= 96 x
Discreet variables- ‘The what”
What is the best ligand/catalyst combination ?What is the best solvent ?
High Throughput ScreeningHigh Throughput Screening
= 96 x
Can we do OPTIMIZATION this way too ??
High Throughput Optimization ??High Throughput Optimization ??
= 96 x
If so,…………………..Which reactions do we run ?How do assess the data ?
High Throughput Optimization ??High Throughput Optimization ??
= 96 x
Statistical Design of Experiments (DOE)
HTS Reaction VialsHTS Reaction Vials
DOE: WorkflowDOE: WorkflowDefine the Define the Objective Objective – screening, optimize, robustnessscreening, optimize, robustnessDefinition of Definition of Factors Factors – Prioritize: known, suspected, possibly, unlikelyPrioritize: known, suspected, possibly, unlikely– Set HIGH/LOW values for factors (define Set HIGH/LOW values for factors (define design design
spacespace))Define the Define the Response Response – how to measure ?– how to measure ?Select Select Experimental DesignExperimental Design Generate Generate WorksheetWorksheet Run theRun the Reactions Reactions PerformPerform Analysis Analysis with DOE softwarewith DOE software
DOE Design (N=27)DOE Design (N=27)Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Response 1
Rxn # A:temp B:P/Pd C:Cu D:Boron/Br E:Conc Yield
1 85 2 0.25 1.2 0.4 45
2 115 2 0.25 1.2 0.1 49
3 85 4 0.25 1.2 0.1 43
4 115 4 0.25 1.2 0.4 55
5 85 2 1.5 1.2 0.1 45
6 115 2 1.5 1.2 0.4 55
7 85 4 1.5 1.2 0.4 39
8 115 4 1.5 1.2 0.1 54
9 85 2 0.25 2.5 0.1 48
10 115 2 0.25 2.5 0.4 63
11 85 4 0.25 2.5 0.4 88
12 115 4 0.25 2.5 0.1 76
13 85 2 1.5 2.5 0.4 79
14 115 2 1.5 2.5 0.1 65
15 85 4 1.5 2.5 0.1 80
16 115 4 1.5 2.5 0.4 76
17 85 3 0.875 1.85 0.25 64
18 115 3 0.875 1.85 0.25 66
19 100 2 0.875 1.85 0.25 58
20 100 4 0.875 1.85 0.25 64
DOE Creates a Design SpaceDOE Creates a Design Space
Design-Expert® Software
YieldX1 = A: tempX2 = B: P/PdX3 = C: Cu load
Actual FactorsD: Boron/Br = 2.50E: Conc = 0.40
CubeYield
A: temp
B:
P/P
d
C: Cu load
A-: 85.00 A+: 115.00B-: 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
DOE Expts: How Many ?DOE Expts: How Many ? rxnsrxns
factorsfactors
HIHI Med Med LoLo Total rxnsTotal rxns
3 factors3 factors 44 33 44 1111
44 88 33 88 1919
5 5 1616 33 1616 3535
33 55 77 55 1717
44 1010 77 1010 2727
55 99 1111 99 2929
scree
nin
g o
ptim
aztio
n
DOE Case StudiesDOE Case Studies
MK-0518MK-0518
N
N
O
OK
O
HN
HN
O
NN
O
F
First-in-Class Oral HIV-1 Integrase Inhibitor
Approved by FDA October-12-2007
MK-0518MK-0518
N
N
O
OH
O
HN
F
HN
O
NN
O
N
N
O
OH
O
H2N OMe
H2N
F
O
NN
OOK
MK-0518MK-0518
N
N
O
OH
O
HN
F
HN
O
NN
O
N
N
O
OH
O
H2N
F
O
NN
OOK
HN
Challenge: to reduce manufacture cost by 20%
MK-518: Problem stepMK-518: Problem step
Peter Maligres
HN
NCbzN
OH
O
H MeN
NCbzN
OH
O
H N
NCbzN
OH
OMe
H+
HN
O
F
O
HN
O
HN
F F
DMSO
58 C/5 h
Existing Conditions: 4 eq Mg(OMe)2/ 4 eq MeI @ 0.5 M (68% isolated yield)
18 solvents, 8 bases screened
78 22
MK-518: DOE OptimizationMK-518: DOE Optimization
Peter Maligres
HN
NCbzN
OH
O
H MeN
NCbzN
OH
O
H N
NCbzN
OH
OMe
H+
HN
O
F
O
HN
O
HN
F F
DMSO
DOE Optimzation Design Factors: Mg(OMe)2 equiv: 1.0 and 3.0MeI equiv: 2.5 and 5.0Conc: 0.25 and 1.0 MTemperature: 30 and 65 oC
19 reactions
Responses (4 and 20 h):Assay yieldSelectivity
MK-518 OptimizationMK-518 Optimization
Peter Maligres
HN
NCbzN
OH
O
H MeN
NCbzN
OH
O
H N
NCbzN
OH
OMe
H+
HN
O
F
O
HN
O
HN
F F
DMSO
20 h
> 90% AY
DOE Optimal SettingsBase equiv: 1.0 and 3.0 MeI equiv: 2.5 and 5.0Temperature: 30 and 65 oCConc: 0.25 and 1.0 MTime: 4 and 20 h
99 1
MK 518: Surface ModelMK 518: Surface ModelInvestigation: MK 518 Methylation 20 hr AB ratio (MLR)
Response Surface Plot
MODDE 8 - 10/26/2007 4:23:15 PM
Base Equiv = 3Temp = 65
Effect of Temp & ConcEffect of Temp & ConcInvestigation: MK 518 Methylation 20 hr AB ratio (MLR)
Contour Plot
MODDE 8 - 12/13/2007 11:25:45 AM
Base Equiv = 3MeI equiv = 5
Effect of Base and ConcEffect of Base and ConcInvestigation: MK 518 Methylation 20 hr AB ratio (MLR)
Contour Plot
MODDE 8 - 12/13/2007 11:38:40 AM
MeI equiv = 5Temp = 65
MK518-In Situ DemethylationMK518-In Situ Demethylation
HN
N
O
OH
O
CbzHNHN
N
N
O
OH
O
CbzHNHN
F F
Mg(OMe)2then MeIDMSO N
N
OMe
OH
O
CbzHNHN
F
+
Magnesium iodide salts
4 h4 h 20 h20 h
ConvConv 95%95% 99%99%
N vs ON vs O 80/2080/20 99/199/1
MK-518 ConcernsMK-518 Concerns
Peter Maligres
HN
NCbzN
OH
O
H MeN
NCbzN
OH
O
H N
NCbzN
OH
OMe
H+
HN
O
F
O
HN
O
HN
F F
DMSO (1M) 65 oC
3 eq Mg(OMe)25 eq MeI
Issues:
1. at this higher concentration, end of reaction difficult to stir2. Mg(OMe)2- long term issues with supply & cost3. MeI is mutagenic/carcinogen/toxic
99 1
MK-518 OptimizationMK-518 Optimization
Peter Maligres
HN
NCbzN
OH
O
H MeN
NCbzN
OH
O
H N
NCbzN
OH
OMe
H+
HN
O
F
O
HN
O
HN
F F
NMP (1.2 M)100 oC/6 h
2 eq Mg(OH)22 eq Me3SOI
Yield =90%Selectivity = >99.9 %Safer, more economical reagentsIncorporated best practices from DOE:
HI Temp/HI Concentration/Longer reaction times
MK-518 SummaryMK-518 Summary
HN
NCbzN
OH
O
H MeN
NCbzN
OH
O
H N
NCbzN
OH
OMe
H+
HN
O
F
O
HN
O
HN
F F
> 90% Yield
78 22
> 99 < 1
DOE
Goal of 20% reduction in drug inventory cost was achievedHigher Yield cascades back to allow fewer RM/solvents to be usedSubmitted for 2008 Presidential Green Chemistry Award
Case Study 2- SuzukiCase Study 2- Suzuki
Br R
R
(HO)2B
Tol/ THF, K2CO3
Pd(OAc)2/Ar-PCy2
95% AY92 A% pure
70 oC
Goal: 1. to reduce Pd(OAc)2 & ligand charges (0.4 mole%,0.8 mole%)2. Improve yield and/or purity
Case Study 2- SuzukiCase Study 2- Suzuki
DOE Factors:
Ligand/Pd ratio: 1.0 and 3.0Catalyst load: 0.1 and 0.5 mole%Molarity boronic acid: 0.5 and 1.5Temperature: 60 and 80 oC
27 Reactions in 96-well plate format, 2 days to plan/setup/execute/assay0.65 g material (24 mg/rxn) !!
Br R
R
(HO)2B
Tol/ THF, K2CO3
Pd(OAc)2/Ar-PCy2
95% AY92 A% pure
70 oC
Case Study 2- SuzukiCase Study 2- Suzuki
DOE Optimal Settings:Ligand/Pd ratio: 1.0 and 3.0Catalyst load: 0.1 and 0.5 mole%Molarity boronic acid : 0.5 and 1.5Temperature: 60 and 80 oC (65 oC)
Br R
R
(HO)2B
Tol/ THF, K2CO3
Pd(OAc)2/Ar-PCy2
Effect of Temp and Pd LoadingEffect of Temp and Pd Loading
Lig/ catalyst ratio fixed at 3:1; Triol M fixed at 1.5 M
Overall LCAP
MODDE 8 - 1/8/2008 5:20:14 PM
L i g / C a t = 3
OptimizedOptimized Conditions Conditions
Optimized Experiment:-increased LCAP by 1%-decreased DesBr impurity (50%)-decreased Pd by 75%-decreased Lig by 70%
Spencer Dreher
BrR
R
(HO)2B
Tol/ THF, K2CO3
0.1 %Pd(OAc)2
95% AY93 A% pure
70 oC
0.25 % Ar-PCy2
1.5 M
Case Study #3Case Study #3
N
OCl
N
OH
Cl
OHO
NADP+NADPH
Enzyme
2-octanol/buffer, pH 6, 30 oC
H2SO4
> 99% conv>98% ee
Dave Pollard
Goal: to reduce cost by increasing productivity
100 g/L
Screening DesignScreening Design
N
OCl
N
OH
Cl
OHO
NADP+NADPH
Enzyme
2-octanol/buffer, pH 6, 30 oC
H2SO4
Dave Pollard
FactorsOctanol: 40 and 60 %NADP equiv: 0.1 and 0.5 %Concentration: 50 and 150 g/LTemp: 25 and 35 oCEnzyme load: 0.3 to 1.0 g/L
19 experiments
DOE Factors PlotDOE Factors Plot
-1.0
-0.8
-0.6
-0.4
-0.2
-0.0
0.2
0.4
0.6
0.8
NA
D
Co
n
Te
mp
en
z
NA
D*C
on
Co
n*T
em
p
%
Investigation: -Ketone Bio-Reduction-Conv2 (MLR)
MODDE 8 - 2/1/2008 12:55:49 PM
Interaction: Conc and NADInteraction: Conc and NAD
-1.4
-1.2
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
50 60 70 80 90 100 110 120 130 140 150
Co
nv
ers
ion
Concentration
NAD (low )NAD (high)
NAD (low)
NAD (low)
NAD (high)
NAD (high)
MODDE 8 - 2/1/2008 12:53:16 PM
Screening ResultScreening Result
N
OCl
N
OH
Cl
OHO
NADP+NADPH
Enzyme
2-octanol/buffer, pH 6, 30 oC
H2SO4
Dave Pollard
Factor Preferred SettingOctanol: 40 and 60 % No impactNADP equiv: 0.1 and 0.5 % No impact- increase more ? Concentration: 50 and 150 g/L 50 g/L- undesirable settingTemp: 25 and 35 oC minimal effect- set at 30 oCEnzyme load: 0.3 to 1.0 g/L 1.0 g/L-increase more
Optimization DesignOptimization Design
N
OCl
N
OH
Cl
OHO
NADP+NADPH
Enzyme
2-octanol/buffer, pH 6, 30 oC
H2SO4
Dave Pollard
FactorNADP equiv: 0.5 and 1.5 % Concentration: 100 and 200 g/LEnzyme load: 0.5 to 3.0 g/L
19 experiments
Optimization DesignOptimization Design
N
OCl
N
OH
Cl
OHO
NADP+NADPH
Enzyme
2-octanol/buffer, pH 6, 30 oC
H2SO4
Factor Preferred settingNADP equiv: 0.5 and 1.5 % No effect Concentration: 100 and 200 g/L 200Enzyme load: 0.5 to 3.0 g/L 3.0
Optimization DesignOptimization Design
N
OCl
N
OH
Cl
OHO
NADP+NADPH
Enzyme
2-octanol/buffer, pH 6, 30 oC
H2SO4
Factor Preferred settingNADP equiv: 0.5 and 1.5 % No effect Concentration: 100 and 200 g/L 200Enzyme 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
Case Study #4-SonogashiraCase Study #4-Sonogashira
NH
O
I
15 mol% CuI2 equiv TEA
DMF25 oC/1 h
~100% conversion33% isolated yield
10 mol% Pd(TPP)4
1.2 eq
NH
O NH
O
H+ + bis-addition
impurity
S. Krska/A. Northrup
Medicinal Chemistry conditions
HTS ResultHTS Result
NH
O
I
NH
O
3
40 mol% CuI3 equiv iPr2NH
MeCN45 oC/18 h
90 A%
10 mol% [(allyl)PdCl]240 mol% (2-furyl)3P
+
10 A%
bis additionimpurity
Screened: ligands and Pd sources32 reactions (HTS-96 well plate format) – 1.5 days- 125 mg of substrate
DOE OptimizationDOE Optimization
3 Factor RSM design – 19 reactionsFixed factors: L/Pd ratio (2:1); temperature (45 oC); base equiv (3)
Varied Factors:• Pd loading (2 and 10 mole%)• Cu/Pd ratio (0.5 and 2.0)• Alkyne equiv (1 and 5)
NH
O
I
NH
O
3
40 mol% CuI3 equiv iPr2NH
MeCN45 oC/18 h
90 A%
10 mol% [(allyl)PdCl]240 mol% (2-furyl)3P
+
10 A%
bis additionimpurity
DOE OptimizationDOE Optimization
3 Factor RSM design – 19 reactionsFixed factors: L/Pd ratio (2:1); temperature (45 oC); 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
NH
O
I
NH
O
3
40 mol% CuI3 equiv iPr2NH
MeCN45 oC/18 h
90 A%
10 mol% [(allyl)PdCl]240 mol% (2-furyl)3P
+
10 A%
bis additionimpurity
DOE OptimizationDOE OptimizationInvestigation: PDK-1Assay yield (MLR)
Response Surface Plot
MODDE 8 - 11/15/2007 1:14:36 PM
Cu/Pd ratio = 2
Cu/Pd = 2
DOE ConfirmationDOE 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
NH
O
I
NH
O
2
12 mol% CuI3 equiv iPr2NH
MeCN45 oC/ 1 h
99 A%
3 mol% [(allyl)PdCl]212 mol% (2-furyl)3P
+
1 A%
bis additionimpurity
iChem ExploreriChem ExplorerHardware: heating (to 150 Hardware: heating (to 150 ooC) C) and stirring block for HP and stirring block for HP 1100/1200 systems1100/1200 systems
Software: to visualize dataSoftware: to visualize data
up to 100- 1 mL reactions in LC up to 100- 1 mL reactions in LC vialsvials– monitor by direct injectionmonitor by direct injection
DOE SummaryDOE Summary
33% isolated yield
78% isolated yield
HTS/DOE (19 rxns)
NH
O
I
NH
O
2
12 mol% CuI3 equiv iPr2NH
MeCN45 oC/ 1 h
99 A%
3 mol% [(allyl)PdCl]212 mol% (2-furyl)3P
Case Study # 5Case Study # 5Cl
CO2H
OMe
Cl
CO2H
OMe
CO2H
OMe
Cl
CO2H
Cl
CO2H
CHO
+ + +
H2
Mark Weisel
10% loading Pearlman’s catalyst25 oC/45 psi/EtOAc
88 A%
Goal: to minimize formation of impurities/maximize desired product
12 A%
Case Study # 5Case Study # 5Cl
CO2H
OMe
Cl
CO2H
OMe
CO2H
OMe
Cl
CO2H
Cl
CO2H
CHO
+ + +
H2
Mark Weisel
88 A%
12 A%
DOE design: 4 Factors (19 reactions)
Temp (25 and 55 oC)Pressure (30 and 60 psi)Pd(OH)2 loading (5 and 15 wt%)Volume EtOAc (6 and 10 ml/g)
FactorsFactors
-6
-4
-2
0
2
4
6
8
10
12
14
16
18T
em
p
Pd
Vo
l
Te
mp
*Pd
Pd
*Vo
l
A%
MODDE 8 - 1/7/2008 4:30:11 PM
Effect of Pd and TempEffect of Pd and Temp
MODDE 8 - 2/1/2008 10:52:20 AM
v o l u m e = 1 0
Optimal SettingsOptimal Settings
Cl
CO2H
OMe
Cl
CO2H
OMe
CO2H
OMe
Cl
CO2H
Cl
CO2H
CHO
+ + +
H2
Pd(OH)2EtOAc
Mark Weisel
Relevant Factors-ranked
1. Pd loading ( 15 wt%)2. Temp (25 oC)3. Volume (10 ml/g)4. Pressure- no effect- run at 30 psi
Selectivity improved from 88 A% to > 99 A%
DOE BenefitsDOE Benefits
Increase your process knowledgeIncrease your process knowledge
Discover the effects of changing factorsDiscover the effects of changing factors
Understand the effects of interactionsUnderstand the effects of interactions
Learn what is and what is NOT importantLearn what is and what is NOT important
Save time, materials, and Save time, materials, and moneymoney
Take Home MessageTake Home Message
DOE is a powerful tool for optimization of DOE is a powerful tool for optimization of reactionsreactions
Automated tools minimize the effort of Automated tools minimize the effort of running multiple rxnsrunning multiple rxns
HTS & DOE in 96-well format represents HTS & DOE in 96-well format represents leading-edge scienceleading-edge science
Academics embracing HTS approachAcademics embracing HTS approach– Professor D. MacMillan (Professor D. MacMillan (Princeton)
AcknowledgmentsAcknowledgments
Peter MaligresPeter Maligres
Danny GauvreauDanny Gauvreau
Spenser DreherSpenser Dreher
Dave PollardDave Pollard
Shane KrskaShane Krska
Mark WeiselMark Weisel
Dave TellersDave Tellers
QUESTIONS ?QUESTIONS ?