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Confidential
Design of Experiments (DoE): A “New” Approach to Reaction
OptimizationSteven A. Weissman
NEACS Symposium: Oct 22,2010
OutlineBasic Principles of Design of Experiments (DoE)Case StudiesTake Home message/Questions
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Current Approach to OptimizationChange One Factor at a time (OFAT)
o Rarely uncovers the optimal conditions• Local vs global maxima
o Different conclusions depending on starting pointo Requires many experiments/little informationo Cannot separate “noise” from true variability
• Is a 2% yield gain real or just run-to-run variation
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Current Approach to OptimizationChange One Factor at a time (OFAT)
o Rarely leads to optimal conditionso Leads to different conclusions depending on starting pointo Requires many expts/little informationo Cannot separate “noise” from true variability
o Ignores interactions of variables
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OFAT Example: 2007
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OFAT: 21 Reactions
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DOE vs OFAT
OFAT: 3 factors needed 21 reactionso No information on interactions of effectso No information on robustness; near ‘edge of failure’
DOE: 3 factors: 11 reactionso Better quality informationo Learn about interactions of effectso 10 Fewer reactions
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Notable Quote
“If you test one factor at a time (OFAT), 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 (1996)
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What is DOE ?• Statistically-based set of expts in which all chosen factors are
varied simultaneously• ‘Continuous’ factors are ideal (time, temp, equiv)
• the ‘How much/many’
• Analysis reveals which factors influence the outcome and identifies optimal conditions
• Systematic, organized approach to problem solving• Generates a mathematical model of the design space• Integral component in the QbD movement
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DoE IntroductionCore Knowledge(Engineering, Chemistry,…)
Statistical Knowledge
Develop Solutions
DOE is NOT a replacement for process knowledge
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Questions Answered by DoE
How do we get the best reaction yield ?How much costly catalyst or reagent do we really need ?Can we minimize formation of an impurity?Which experimental factors are relevant?How robust is my process or assay ?
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DOE: ConsiderationsCan’t replace screening of discreet variables (catalyst, solvent)
Best suited for continuous variableso time, temp, stoichiometry
Not helpful for non-reproducible reactions
Best suited for ‘low maintenance’ reactionso Temp = RT to 150 oCo All reactants added at once
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DOE: Experimental Objectives
Screeningo Which factors are most influential ? o What are their appropriate values/ranges ?
Optimizationo Extract information on how factors combine to influence
responseo Identify optimized reaction conditions
Robustnesso To assess if small changes in continuous factors have an effect on outcome
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DOE: Misconceptions
Requires in-depth statistics knowledgeo User-friendly DOE software does this for you
• MODDE (Umetrics)/Design Expert (Stat-Ease)
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DOE: Misconceptions
Requires in-depth statistics knowledgeo Experimental design software does this for you
Requires lots of experiments and timeo Perhaps. but will always get better quality informationo Typically 11-27 reactions per designo Automation/technology can help reduce the effort
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DOE: WorkflowDefine an Objective
What issue(s) do you want to resolve?
Define the Factors Prioritize: known, suspected, possibly, unlikelySet HIGH/LOW values for factors (define design space)
Define the Response(s) – how to measure ?Select Experimental Design Generate WorksheetRun the ReactionsPerform Analysis with DOE softwareRun Confirming reaction
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DOE Design: 24 Full factorial Factor 1 Factor 2 Factor 3 Factor 4
Std A:water B:temp C:time D:acid
% C h equiv
1 0 55 2 3
2 10 55 2 3
3 0 75 2 3
4 10 75 2 3
5 0 55 8 3
6 10 55 8 3
7 0 75 8 3
8 10 75 8 3
9 0 55 2 15
10 10 55 2 15
11 0 75 2 15
12 10 75 2 15
13 0 55 8 15
14 10 55 8 15
15 0 75 8 15
16 10 75 8 15
17 5 65 5 9
18 5 65 5 9
19 5 65 5 9
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DOE Design: 24 Full FactorialFactor 1 Factor 2 Factor 3 Factor 4
Std A:water B:temp C:time D:acid
% C h equiv
1 0 55 2 3
2 10 55 2 3
3 0 75 2 3
4 10 75 2 3
5 0 55 8 3
6 10 55 8 3
7 0 75 8 3
8 10 75 8 3
9 0 55 2 15
10 10 55 2 15
11 0 75 2 15
12 10 75 2 15
13 0 55 8 15
14 10 55 8 15
15 0 75 8 15
16 10 75 8 15
17 5 65 5 918 5 65 5 919 5 65 5 9
3 Center points
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DOE Design: 24 Full FactorialFactor 1 Factor 2 Factor 3 Factor 4 Response 1
Std A:water B:temp C:time D:acid yield
% C h equiv %
1 0 55 2 3 44.6
2 10 55 2 3 16.5
3 0 75 2 3 66.9
4 10 75 2 3 65.7
5 0 55 8 3 73.5
6 10 55 8 3 38.9
7 0 75 8 3 64.7
8 10 75 8 3 66.6
9 0 55 2 15 54.3
10 10 55 2 15 82.6
11 0 75 2 15 11.1
12 10 75 2 15 75.9
13 0 55 8 15 1
14 10 55 8 15 80.6
15 0 75 8 15 1
16 10 75 8 15 77.8
17 5 65 5 9 92
18 5 65 5 9 92
19 5 65 5 9 89
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DOE Creates a Design Space
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DOE Expts: How Many ?rxns
factors
HI Med Lo Total expts
3 4 3 4 114 8 3 8 195 16 3 16 353 5 7 5 174 10 7 10 275 9 11 9 29
screeningoptim
aztion
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DOE Publications Trend
22
Total # of OPRD DOE Publications
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Why is DOE Usage Trending Upwards?
1. Changing R&D Landscapeo Need to do more, with less: efficiency is paramounto Shorter timelines
2. Technology advances enable parallel experimentationo Easier to set up 24 reactions today than 10 years ago
3. Quality by Design (QbD) Movement is here to stayo DOE can create the requisite design space
4. Rising popularity of Green Chemistry Initiatives
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DOE Case Studies
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Case Study #1: MK-518
First-in-Class Oral HIV-1 Integrase InhibitorApproved by FDA October-12-2007 $ 1.1 B sales in first two years
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MK-518
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MK-518
N
N
OOH
O
HN
F
HN
O
NN
O
N
N
OOH
O
H2N OMe
H2N
F
O
NN
OCl
13 step routeChallenge: to reduce manufacture cost by 20%
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MK-518: Problem step
18 solvents, 8 bases screened (Discreet variables)
Existing Conditions: 4 eq Mg(OMe)2/ 4 eq MeI @ 0.5 M (68% isolated yield)
78 22
Peter Maligres
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MK-518: DOE Screening
DOE Screening 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):ConversionSelectivity (N vs O)
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MK-518: DOE Screening
DOE Preferred SettingsBase equiv: 1.0 and 3.0MeI equiv: 2.5 and 5.0Temperature: 30 and 65 oCConc: 0.25 and 1.0 MTime: 4 and 20 h
All factors were relevant
HN
NCbzN
OHO
H MeN
NCbzN
OHO
H N
NCbzN
OHOMe
H
HN
O
F
O
HN
O
HN
F F
DMSO
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MK-518: DOE Success
Preferred DoE SettingsBase equiv: 1.0 and 3.0MeI equiv: 2.5 and 5.0Temperature: 30 and 65 oCConc: 0.25 and 1.0 MTime: 4 and 20 h
99 1
HN
NCbzN
OHO
H MeN
NCbzN
OHO
H N
NCbzN
OHOMe
H
HN
O
F
O
HN
O
HN
F F
DMSO
20 h
> 90% Assay yield
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MK 518: Effect of Conc and MeI
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Effect of Temp & Conc
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Effect of Base and Conc
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MK-518: Time Cycle
4 hConv 95%
N vs O 4/1
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MK-518: In Situ Demethylation
4 h 20 hConv 95% 99%
N vs O 4/1 >200/1
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MK-518 Concerns
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
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MK-518:Commerical Process
Peter Maligres
Yield = 90%Selectivity = >99.9 %Safer, more economical reagentsIncorporated best practices from DOE:
HI Temp/HI Concentration/Longer reaction times
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MK-518 Summary
78 22
> 99 < 1
DOE>20% reduction in drug inventory cost achievedHigher Yield cascades back to allow fewer RM/solvents to be usedRunner-up: 2008 Presidential Green Chemistry Award
HN
NCbzN
OHO
H MeN
NCbzN
OHO
H N
NCbzN
OHOMe
H
HN
O
F
O
HN
O
HN
F F
> 90% Yield
Humphrey, Pye et al. OPRD, 2010, in press
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Case Study #2: NNRTI
Jeff Kuethe
Double deprotection in one-pot
Last step of process; modest, variable yield
Goal: Improve yield; single acid
Approach: HTS, followed by DoE
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HTS Summary @ 70 oC
Initial screen: 17 acids @ 2 levels/7 co-acids/2 solvents = 180 rxns
Follow-up # 1: 6 acids/3 co-acids = 18 reactions
Follow-up # 2: H2SO4 (3 and 7 eq) in 22 solvents
H2SO4 in MeCN
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Case Study: NNRTI
Sequential removal gave higher yieldBy-product rejection was important
N NN
NH
O
O
Cl
Cl
NC
N NN
NH
O
O
Cl
Cl
NCH2SO4 (2.2 eq)
MeCN/20 oC95% Yield
O (H2SO4)2
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Case Study: NNRTI
DOE to optimize 2nd reaction conditions
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Case Study: NNRTI
Considerations:
1. Water accelerates rate, but too much water precipitates impurities
2. More acid accelerates rate, but too much acid generates impurities
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Case Study: NNRTI DoE
N NN
NH
O
O
Cl
Cl
NC
N NN
H2N
O
O
Cl
Cl
NC
H2SO4
MeCN
Factors Settings% water 0-10 vol%Temp 55-75 ºCTime 2-8 hAcid 3-15 eq
Response: Assay Yield Screening design (24 full factorial) = 19 reactions
Chosenbased onobservations
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DOE Design (N=19)
Factor 1 Factor 2 Factor 3 Factor 4 Response 1
Std A:water B:temp C:time D:acid yield
% C h equiv %
1 0 55 2 3 44.6
2 10 55 2 3 16.5
3 0 75 2 3 66.9
4 10 75 2 3 65.7
5 0 55 8 3 73.5
6 10 55 8 3 38.9
7 0 75 8 3 64.7
8 10 75 8 3 66.6
9 0 55 2 15 54.3
10 10 55 2 15 82.6
11 0 75 2 15 11.1
12 10 75 2 15 75.9
13 0 55 8 15 1
14 10 55 8 15 80.6
15 0 75 8 15 1
16 10 75 8 15 77.8
17 5 65 5 9 9218 5 65 5 9 9219 5 65 5 9 89
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DOE Design (N=19)
Factor 1 Factor 2 Factor 3 Factor 4 Response 1
Std A:water B:temp C:time D:acid yield
% C h equiv %
1 0 55 2 3 44.6
2 10 55 2 3 16.5
3 0 75 2 3 66.9
4 10 75 2 3 65.7
5 0 55 8 3 73.5
6 10 55 8 3 38.9
7 0 75 8 3 64.7
8 10 75 8 3 66.6
9 0 55 2 15 54.3
10 10 55 2 15 82.6
11 0 75 2 15 11.1
12 10 75 2 15 75.9
13 0 55 8 15 1
14 10 55 8 15 80.6
15 0 75 8 15 1
16 10 75 8 15 77.8
17 5 65 5 9 9218 5 65 5 9 9219 5 65 5 9 89
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Case Study: NNRTI
C (time) has no bearing on outcome
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Case Study: NNRTIDesign-Expert® SoftwareFactor Coding: Actualyield
Design Points
X1 = A: waterX2 = D: acid
Actual FactorsB: temp = 65.00C: time = 5.00
D- 3.000D+ 15.000
D: acid
0.00 2.00 4.00 6.00 8.00 10.00
Interaction
A: water
yiel
d
0
20
40
60
80
100
120
More water is better than less water
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Case Study: NNRTI
Factors Settings% water 2-7 %Acid 5-9 eq
11 reactions-Optimiztion designResponses: Conversion and Assay Yield
IncludesCenter point settings
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Case Study: Conversion
Factor 1 Factor 2 Response 2Std Run A:water B:acid conversion
% equiv %1 2 2 5 992 9 7 5 953 7 2 9 1004 6 7 9 1005 11 2 7 1006 10 7 7 967 3 4.5 5 988 4 4.5 9 1009 5 4.5 7 10010 1 4.5 7 9911 8 4.5 7 95
No relevant model terms
∴Conversion is robust over design space
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Case Study: Yield
Only water was relevant
Selected:4% water7 equiv acid
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Case Study: Confirmation
Assay yield = 92% (DoE Prediction: 88.5%)Isolated yield = 90%50 g scale
Kuethe, Weissman et. al. OPRD, 2009, 471.
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Case Study #2: NNRTI Summary
Acid YieldOriginal 30 eq 65%Improved 9.2 eq 87.5%
70% reduction in acid eq used>35% increase productivity
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Case Study #3- Suzuki
Goal:1. to reduce Pd(OAc)2 & ligand charges (0.4 mole%,0.8 mole%)2. Improve yield and/or purity
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Case Study- Suzuki
DOE Factors (Optimization design):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 RR
(HO)2B
Tol/ THF, K2CO3
Pd(OAc)2/Ar-PCy2
70 oC
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High Throughput Optimization
= 96 x
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HTS Reaction Vials
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Case Study- Suzuki
DOE Optimal Settings:Ligand/Pd ratio: 1.0 and 3.0Catalyst load: 0.1 and 0.5 mole% - small effectMolarity boronic acid : 0.5 and 1.5Temperature: 60 and 80 oC (65 oC)
Br RR
(HO)2B
Tol/ THF, K2CO3
Pd(OAc)2/Ar-PCy2
70 oC
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Effect of Temp and Pd Loading
Lig/ catalyst ratio fixed at 3:1; Triol M fixed at 1.5 M
Overall LCAP
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Optimized Conditions
Optimized Experiment:-increased LCAP by 1%-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
65 oC0.25 % Ar-PCy2
1.5 M
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Case Study #4-Sonogashira
S. Krska/A. Northrup
Medicinal Chemistry conditionsGoal: improve yield via HTS and DoE
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HTS 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 two discreet factors: ligand and Pd source32 reactions (HTS-96 well plate format) 1.5 days- 125 mg of substrate
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DOE Optimization
Fixed factors: P/Pd ratio (2:1); temperature (45 oC); base equiv (3)DoE Factors: Optimization Design (17 reactions)• Pd loading (2 and 10 mole%)• Cu/Pd ratio (0.5 and 2.0)
• Alkyne equiv (1 and 5)
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DOE Optimization
Fixed factors: P/Pd ratio (2:1); temperature (45 oC); base equiv (3)DoE Factors: Optimization Design (17 reactions)• Pd loading (2, 10 mole%)- little effect- set to 3 mole % Pd dimer (6% Pd)• Cu/Pd ratio (0.5, 2.0)- most important; therefore 12 mole% CuI
• Alkyne equiv (1, 5) –2 equiv; less is more
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DOE Optimization
Cu/Pd = 2
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DOE Confirming Reaction
DOE Improvements over HTS Result:• 70% reduction in Pd charge• 70% reduction in phosphine charge• 63% reduction in Cu charge• 94% reduction in time cycle (18 h in HTS)• Improved selectivity from 9:1 to 100:1
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Case Study # 5: Hydrogenation
10% loading Pd(OH)2
25 oC/45 psi/EtOAc (8 vol)
88 A%
Goal: to minimize formation of impurities/maximize desired product
12 A%
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Case Study # 5
Mark Weisel
88 A%
12 A%DOE Screening design: 4 Factors (19 reactions)1. Temp (25 and 55 oC)2. Pressure (30 and 60 psi)3. Pd(OH)2 loading (5 and 15 wt%)4. Volume EtOAc (6 and 10 ml/g)
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Relevant Factors: Pd andTemp
-6
-4
-2
0
2
4
6
8
10
12
14
16
18Te
mp Pd Vol
Tem
p*Pd
Pd*V
ol
A%
MODDE 8 - 1/7/2008 4:30:11 PM
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Effect of Pd, Temp on A% Desired Product
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Optimal Settings: Confirmation
Cl
CO2H
OMe
Cl
CO2H
OMe
CO2H
OMe
Cl
CO2H
Cl
CO2H
CHO
+ + +H2
Pd(OH)2EtOAc
Mark Weisel
Factors-ranked vs Original Conditions 1. Pd loading (15 wt%) vs 10% 2. Temp (25 oC) vs 25 oC3. Concentration – little effect (10 volumes)4. Pressure –no effect; therefore lowered from 45 to 30 psi
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Optimal Settings: Confirmation
Cl
CO2H
OMe
Cl
CO2H
OMe
H2 (30 psi)
Pd(OH)2 (15 wt%)EtOAc (10 volumes)25 oC
Mark Weisel
Selectivity improved from 88 A% to >99 A%
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DOE Benefits
• Increase your process knowledge; more complete picture
• Discover the effects of changing factors
• Understand the relevant interactions
• Can facilitate route selection; define upside
• Save time, materials, and money
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Take Home Message
DOE is a powerful, efficient approach for optimization of continuous variables at all stages of process developmentThe decision to implement DoE depends on effort required and payoff
High barrier requires big payoff : commercial API processLow barrier requires smaller payoff: Med Chem program
Technology can minimize the effort of running, assaying multiple reactions
Parallel reactors, liquid dispensers, solids dispensing robotics, etc.
