Combined use of Design of Experiment (DoE) and Process
Automation for the Efficient Optimization of New Synthetic
Transformations
Federica Stazi Ph.D Thesis
Universita’ dell’Insubria-Dipartimento di Chimica
Via Valleggio no 11
22100 Como (Italy)
www.uninsubria.it
R&D Chemistry Research Centre
Via Lorenzini no 8
20139 Milano (Italy)
www.boehringer-ingelheim.it
Literature meeting May 2nd 2005
Reasons for DoE at the Chemistry Research Centre
Boehringer Ingelheim Pharma KG
Biberach, Germany
Drug Development
Drug Discovery
• Pre-dev. Candidates
• Intermediates
• Metabolites
• Process impurities…
• Building blocks
• Test compounds…
CRC
Boehringer Ingelheim Pharma KG
CRC
Milan, Italy
Target
Oriented
Synthesis
Diversity
Oriented
Synthesis
Target Oriented Synthesis (TOS) and DoE
NO2
NO2
H2N
F
F
F
N
+
O
H
N
CF3
N
S O
HO
N
NO2
HO
CF3
N
H
N
N
N
CF3
NaH, DMF
1
NO2
HO
H
N
MeO2C
N
CF3
N
O
Br
MeO2C
PivO
O
NO2 H
N
O
N
OPiv
OPiv
PivO
DoE-driven
search for optimal
conditions
CF3
N
OPiv
OPiv
1
Ag2O, CH3CN
NO2
MeO2C
O
O
O
H
N
N
N
N
PivO
OPiv
2
CF3
1. H2, Pd/C, THF
2. 1,1'Carbonyldi(1,2,4-triazole)
3. LiOH
HO2C
O
N
N
HO
OPiv
N
O
OH
OH
2
3
CF3
Target
Diversity-Oriented Synthesis (DOS) and DoE
CHO
CHO
DoE-driven
search for optimal
conditions
Same
starting material
and
rxn conditions
CHO
OH
OH
different RX
F
F
O
O
F
OH
O
CHO
CHO
Same
R’X and
rxn conditions
O
OH
different
starting material
CHO
O
O
O
CHO
O
OMe
O
OH
…
F
…
OMe
The DoE Concept: Basic Principles
controllable factors
x1
x2
xp
…
Inputs
Outputs
System
y
(products)
(starting materials)
…
z1
z2
zq
uncontrollable factors
OFAT (One Factor at A Time) Approach
OFAT results in a set of experiment in which only one factors is varied
P
SM
A B C
•
incomplete picture of the overall
process
•
factor interactions are not revealed
•
number of experiments not fixed
•
not possible to perform experiments in parallel
DoE (Design of Experiment) Approach
DOE results in a set of pre-planned experiments in which factors are varied at the same time
2-level Factorial Design
SM
P
7
experimental matrix
8
exp
3
4
A B C
5
1
6
2
1
2
3
4
5
6
7
8
Factor setting
A B
C
+
+
+
+
+
+
+
+
•
precise estimation of factors effect
•
factor interactions are revealed
•
mathematical model of the chemical process based on statistical analysis
•
possibility to perform experiment in parallel
+
+
+
+
Statistical Background and DoE Tools
Doe Simplified: Practical Tools for Effective Experimentation
Mark J. Anderson, Patrick J. Whitcomb
Productivity Press, 2000
Design and Optimization in Organic Synthesis
R. Carlson
Elsevier Science, 1997
Design and Analysis of Experiments, 5th Edition
D.C. Montgomery
Wiley, 2000
+ Chemical Journals
Statistical Background and DOE Tools: Examples
S.V. Ley et al. Organic Process Research Development , 2002, 6, 823
R
F
F
HCl H2N
COOtBu
H
N
F
R
COOtBu
NO2
NO2
DIEA
EtOH, reflux
Pre DoE: 40%
Post DoE: 91%
5 different R groups
Yields: 81-96%
S.V. Ley et al. Synlett , 2000, 11, 1603
COOH
NH2
O
MeO
R
DCC
N
H
R'
solvent
Post DoE: 97%
R: Et
4 F Res IV, 8 exps + 2 centres
A.A.
(equiv)
<1;2>
PS-DIEA (equiv)
<2;4>
Rnx time (hours)
<14;24>
Conc.
(volumes) <25;50>
8 different R groups
10 different R’ groups
80 cpds. Hit rate 95%
5 F ResIV, 16 exps + 4 centres
PS-DCC (equiv)
<1;3>
Conc.
(volumes) <40;160>
Rnx time (hours)
<0.5;4>
Solvent T1
<-1;+1>
Solvent T2
<-1;+1>
4 F ResIV, 8 exps + 1 centre
PS-DCC (equiv)
<1;3>
Conc.
(volumes) <40;160>
Amine
(equiv)
<1;2>
Statistical background and DoE Tools
?
Advantage Series 2050
(Argonaut)
SK233 React Array Workstation
(Anachem)
Design Expert 6.0.4
by Stat-Ease
Carousel
(Radley)
MODDE 7.0.0
by Umetrics
??
?
Statistical Background and DOE Tools
Syringes
Needle
HPLC
React.
rack
Reagent
Solvent
racks
UV/Vis
Detector
PC
Reaction
Control
HPLC control
The Sequential Workflow of DoE
1. Synthetic Problem
5. Interpretation and confirmations
?
2. Planning the experiment:
• State experimental objectives
6. Reiteration
4. Data analysis and modeling
• Choice of factors, levels and
response variable
• choice of experimental
design
3. Performing the experiment
Putting the Theory into Practice
Step 1. Defining the Synthetic Problem: a Problematic Glucuronidation
H
H
N
HO2C
O
N
O
H
N
O
N
HO
N
N
HO
CF3
O
N
OH
CF3
N
Flibanserin
1. cytochrome P450
2. UDPG transferases
MeOOC
O
RO
X
OR
HO
+
H
N
O
N
N
N
OR
BIMC-0576
CF3
Putting the Theory into Practice
Step 1. Defining the Synthetic Problem: O-Glucuronidation Background
O
O
HO
O
O
HO
UDPG transferases
O
O
O
UDP=
HO
OH
OUDP
N
P
P
O
O
HO
N
OH
acido UDP-glucuronic
CO2Me
O
RCO2
RCO2
+
RCO2
X
additive
R'OH
R'= alkyl, phenyl
R= Me, i-Pr, t-Bu
CO2Me
O
RCO2
RCO2
OR'
RCO2

X= leaving group
(Br, -tricloroacetammidate)
For a review, see: Stachulski, A. V.; Jenkins, N. J. Nat. Prod. Rep. 1998, 173.
O
Putting the Theory into Practice
Step 1. Defining the Synthetic Problem: O-Glucuronidation Background
transacylation
R'OCOR
CO2Me
O
RCO2
RCO2
+
RCO2
X
CO2Me
O
RCO2
RCO2
R'OH
O
R'= alkyl, phenyl
O
R= Me, i-Pr, t-Bu
R
X= leaving group
(Br,-tricloroacetammidate)
OR'
orthoester
MeO2C
O
RCO2
MeO2C
O
O2CR
O2CR
C1-elimination
X
O2CR
O2CR
C4-elimination
For a review, see: Stachulski, A. V.; Jenkins, N. J. Nat. Prod. Rep. 1998, 173.
Putting the Theory into Practice
Step 1. Defining the Synthetic Problem: A New Strategy
Typical Koenigs-Knorr cond.: 3% yield (Ag2O, mol sieves, 18 h CH3CN, R=Ac or R=Piv)
NO2
HO
H
N
N
CF3
N
MeO2C
O
Br
Koenigs-Knorr
conditions
NO2
MeOOC
O
O
H
N
N
+
CF3
N
RO
OR
RO
OR
OR
OR
1 h 45 °C, then
1. H2, Pd/C
2. 1,1'Carbonyldi(1,2,4-triazole)
3. LiOH
MeSO2(CH2)2OH + NaH
1 h 0 °C; DMSO
O
NO2
F
N
F
+
CF3
N
NH2
HN
HOOC
O
O
N
N
HO
OH
N
OH
Modified Koenigs-Knorr cond.: 25% yield (Ag2O, mol sieves, 18 h CH3CN + TMEDA 10 eq , R=Piv)
CF3
Step 2. Planning the Experiment
Find the best starting point: small-scale parallel reagent screening (10 mg scale). Amine vs. “Ag”
45
40
Ag2O
40.6
38.6
Ag2CO3
influence of
35
29.3
30
26.1
24.8
• amine complexing abilitya
• amine basicity
• silver source
25
20
15.2
15
10
5
0
0
0
0
0
DIPEA
TMEDA
DMEDA
DIPEDA
HMTTA
• HMTTA works best.
N
N
<
N
<
N
HN

NH
HN
N
<
NH
N
N
pKa :
11.0
9.1
10.3
10.4
9.2
a. Meyerstein and al. J. Am. Chem.Soc. 1995, 117, 8353-8361
• The silver source does not
significantly influence yields.
Step 2. Planning the Experiment: Statement of the Problem
State experimental objectives: which type of design?
• Process screening
which variables are most influential?
• Process optimization
how variables are relevant?
• Process robustness testing
Do small changes in uncontrolled variables
influence the response?
Step 2. Planning the Experiment: Selection of Factors
Choice of factors and factor levels: use of process knowledge + team work
Ag2CO3
Ag2O
“Ag”
Br-sugar
...
NO2
MeOOC
O
PivO
O
H
N
OPiv
N
N
OPiv
HMTTA
• Define design factors, held constant factors, allowed-to-vary factors
• Factors can be either quantitative (time, stoichiometry) or qualitative (“Ag” type)
CF3
Step 2. Planning the Experiment
7 factors to be investigated in a screening factorial design
Variables Considered and Levels Used in the Factorial Design
name
factor
units
(-)
0
(+)
A
B
C
D
E
F
G
pre-complexation time
reaction time
amount of Ag2CO3
amount of HMTTA
amount of Br-sugar
4Å molecular sieves
amount of solvent
min
h
equiv
equiv
equiv
mg
mL
0
2
1.5
1.5
1.5
0
0.5
30
4
2.6
7.1
2.2
50
1
60
6
3.8
12.6
3
100
1.5
FI relative importance:
2-FI > 3-FI >> 4/7-FI
•
A complete investigation of 7 factors over 2 levels requires: 27 = 128 exps
•
128 parameters are estimable: 1 constant term, 7 linear terms, 21 2-FI, 35 3-FI, 64 4/7-FI
Step 2. Planning the Experiment: Full vs. Fractional Factorial Designs
Fractional Factorials exploit the redundancy of Full Factorials to reduce the no of exps
no of factors
2
3
4
5
6
7
8
9
Full
4
no of experiments
8
16
32
64
Fractional
128
256
7 factors can also be studied in only a fraction of the original full factorial design.
Step 2. Planning the Experiment: Final Output of Pre-Experimental Plan
7 factors to be investigated in a 27-4 Resolution III design: 8 exps + 3 center points (50mg scale)
Experimental matrix:
exp
A
factor settings
B C D E F
1
2
3
4
5
6
7
8
9
10
11
+
+
+
+
0
0
0
+
+
+
+
0
0
0
+
+
+
+
0
0
0
+
+
+
+
0
0
0
+
+
+
+
0
0
0
+
+
+
+
0
0
0
G
+
+
+
+
0
0
0
name
factor
(-)
A
B
C
D
E
F
G
pre-complexation time
reaction time
amount of Ag2CO3
amount of HMTTA
amount of Br-sugar
4Å molecular sieves
amount of solvent
0
2
1.5
1.5
1.5
0
0.5
0
(+)
30 60
4
6
2.6 3.8
7.1 12.6
2.2
3
50 100
1
1.5
center points for curvature detection
for calculation of pure error
Step 3. Performing the Experiment
run
1
2
3
4
5
6
7
8
9
10
11
A
0
60
0
60
0
60
0
60
30
30
30
factor settings
B C D
E
F
2
2
6
6
2
2
6
6
4
4
4
1.5
1.5
1.5
1.5
3.8
3.8
3.8
3.8
2.6
2.6
2.6
12.6
1.5
1.5
12.6
12.6
1.5
1.5
12.6
7.1
7.1
7.1
3
1.5
3
1.5
1.5
3
1.5
3
2.2
2.2
2.2
100
100
0
0
0
0
100
100
50
50
50
G
0.5
1.5
1.5
0.5
1.5
0.5
0.5
1.5
1
1
1
Prod.
yield
(%)
14.7
19.5
24.4
11.2
34.2
83.2
56.5
55.4
50.2
43.2
50.5
•
Monitor and record values of
uncontrolled factors
•
Use randomization to reduce the
influence of nuisance factors
•
If possible, operate in parallel
since we rely on a previous
experimental plan
•
Perform a scoping study: check --
- vs. +++ and reproducibility.
Step 4. Data Analysis and Modeling: ANOVA Testing (Analysis of Variance)
Ag2CO3
Br-sugar
HMTTA
Source
of changing variable
Sum of
Squares Df
Model
A
B
C
D
E
F
G
3,32
0,051
0,005369
2,54
0,38
0,33
0,001572
0,020
Curvature
Pure Error
Cor Total
0,53
0,016
3,87
Mean
Square
F
Value
P
Value
0,47
0,051
0,005369
2,54
0,38
0,33
0,001572
0,020
60,30
6,46
0,68
322,50
47,91
41,76
0,20
2,56
0.0164
0.062
0.4957
0.0031
0.0202
0.0231
0.6986
0.2505
1
0,53
2 0,007869
10
67,21
0.0146
7
1
1
1
1
1
1
1
Step 5. Interpretation and Confirmation
After stepwise modifying the insignificant terms we obtain the definitive linear model
Source
Sum of
Squares Df
Mean
Square
F
Value
P
Value
96.10
5.93
296.13
43.99
38.35
< 0.0001
0.0059
<0.0001
0.0012
0.0016
1.15
0.4967
61.71
0.0005
Model
A
C
D
E
3.29
0.051
2.54
0.38
0.33
4
1
1
1
1
0.82
0.051
2.54
0.38
0.33
Residual
Lack of fit
Pure Error
Curvature
Cor Total
0.0043
0.0027
0.016
0,53
3,87
5
3
2
1
10
0.0085
0.0090
0.0078
0,53
Is this linear model adequately modeling the response?
y = 0 + 1* A+ 2* C - 3*D + 4* E + e
Step 6. Reiteration: Altering Factors Ranges
The contour plot directs us outside the investigated region
modify factors ranges to explore a better experimental region
name
factor
(-)
(+)
A
B
C
D
E
F
G
pre-complexation time
reaction time
amount of Ag2CO3
amount of HMTTA
amount of Br-sugar
4Å molecular sieves
amount of solvent
0
2
1.5
1.5
1.5
0
0.5
60
6
3.8
12.6
3
100
1.5
(-)
(+)
60
2
3.3
0.7
2.0
5.5
2.5
2.5
0
0.5
Response Surface Modelling (RSM): an Overview
Different options when the linear model is not adequate.
Many are extensions of the 2-level factorial design
2-level FD
Box-Behnken
CCD
CCF
3-level FD
3
5
3
3
12+3
14+3
14+3
27+3
cubic
cubic
Characteristics:
•
•
•
Factor levels
Number of Experiments
Geometries of the Explored Space
spherical
spherical
Optimizing Glucuronidation Yield using CCD: Performing the Experiment
Point
type
exp
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
fact
fact
fact
fact
fact
fact
fact
fact
axial
axial
axial
axial
axial
axial
center
center
center
center
center
center
A
B
C
equiv of equiv of equiv of Product Residual
HMTTA Ag2CO3
Bryield
SM
sugar
0.7
2.1
0.7
2.1
0.7
2.1
0.7
2.1
0.2
2.5
1.4
1.4
1.4
1.4
1.4
1.4
1.4
1.4
1.4
1.4
3.7
3.7
5.1
5.1
3.7
3.7
5.1
5.1
4.4
4.4
3.3
5.5
4.4
4.4
4.4
4.4
4.4
4.4
4.4
4.4
2.1
2.1
2.1
2.1
2.4
2.4
2.4
2.4
2.25
2.25
2.25
2.25
2.0
2.5
2.25
2.25
2.25
2.25
2.25
2.25
82.8
63.5
81.6
69.9
87.5
72.8
85.1
74.3
62.1
70.2
73.9
77.0
71.9
79.6
71.8
79.8
77.6
77.2
76.6
78.4
11.4
11.2
5
15.9
12.4
20.8
4.8
15.4
3.6
19.0
21.6
9.1
14.4
12.1
12.0
14.0
13.9
14.2
12.8
13.3
axial
center
factorial
20 exps on (100mg scale)
Optimizing Glucuronidation Yield using CCD: Data Analysis and Model Building
source
SS
Df
MS
F
P
model
A
B
C
A2
AB
A3
residual
lack-of- fit
pure error
cor total
686,640
293,242
6,64122
117,042
18,6096
16,5025
121,418
78,559
41,219
37,340
765,202
6
1
1
1
1
1
1
18
13
5
24
114,441
293,242
6,641
117,042
18,610
16,502
121,418
4,364
3,171
7,468
26,222
67,190
1,522
26,818
4,264
3,782
27,820
0.0001
0.0001
0.2330
0.0001
0.0540
0.0680
0.0001
0,424
0.901
S= 2.0891 R2=0.897 R2adj=0.863 PRESS=146,590
Maximum
Definitive coded model
yield = 76.91 - 9.58 A + 0.70 B + 2.57 C- 0.75 A2 + 1.44 A B + 2.51 A3
Optimizing Glucuronidation Yield using CCD: Empirical Model Interrogation
Program optimization tools indicate
the best conditions found
and
the confidence intervals
Factor
Name
Level
Low Level
High Level
A
HMTTA
0.70
0.2
2.5
B
Ag2CO3
3.76
3.3
5.5
Qty phenol
C
Br-sugar
2.42
2.0
2.5
1 gr
86.0
80.6
Prediction
SE Mean
95% CI low 95% CI high
1 gr
87.2
81.0
86.5
1.34
83.71
3.5 gr
85.7
80.0
Model validation
P yield
89.33
in situ yield
isolated yield
Optimizing Glucuronidation Yield Using CCD: Conclusion
NO2
HO
H
N
NO2
N
N
CF3
MeO2C
O
Br
MeOOC
O
O
H
N
N
+
N
PivO
OPiv
PivO
OPiv
OPiv
OPiv
Initial conditions:
Optimized conditions:
Ag2O 2.7 eq
Reagents Screening
10 exp
Ag2CO3 3.76 eq
Br-sugar 1 eq
DoE Factorial Screening 11 exp
Br-sugar 2.4 eq
mol sieves
DoE CCD Optimization
HMTTA 0.7 eq
20 exp
18 h CH3CN
1h CH3CN
isolated yield 3%
in situ yield
86.0%
isolated yield 80.5%
CF3
Mechanistic Modelling: the Manifold Actions of HMTTA
Ag2O >> Ag2CO3
Ag+
NO2
HO
N
N
Ag+
H
N
N
N
N
CF3
N
active !
120,00
% complex (SM-Ag+) at 2h
(%) complexation
100
90
80
70
3 + Ag2O + HMTTA
60
3 + Ag2CO3 + HMTTA
50
3 + Ag2O
40
3 + Ag2CO3
30
20
100,00
Ag+
dissolution /
activation
•Ag2CO3
•no Br-Sugar
80,00
60,00
40,00
Ag+ competitive
complexation
20,00
10
0,00
0
0
5
10
15
time (h)
20
25
0,00
0,20
0,40
0,60
0,80
1,00
equiv of HMTTA
1,20
1,40
1,60
Mechanistic Modelling: the Manifold Actions of HMTTA
Negative effect of HMTTA :
Positive effects of HMTTA :
Excess favours the formation of unwanted side product
•
competitive ligand for SM complexation
Base (pKa=9.23, 8.47, 5.36, 1.68) on the Br-sugar (-HBr)
•
activator of Ag+
MeO2C
O
PivO
Br
OPiv
OPiv
MeO2C
O
PivO
Consistent
depletion of Brsugar
OPiv
OPiv
The postulated irreversible binding of starting
material (SM) to Ag+ ions is really operative.
The presence of the tetramine additive
(HMTTA) influences the complexation equilibria.
SM Complexation
> Ag+ activation
The relationship between complexation of SM
and concentration of HMTTA is non-linear.
F.Stazi, G. Palmisano, M. Turconi, S. Clini, and M. Santagostino, J. Org. Chem, 2004, 69, 1097-1103.
Max Ag+
activation
Max competitive
binding to Ag+
Scope and Limitation of the Methodology
% isolated yield : optimized conditions % isolated yield: classical Koenigs-Knorr conditions
NO2
GlucO
NO2
H
N
H
N
GlucO
N
71%
CHO
O
OGluc
R'
OMe
20%
+ HMTTA 0.2-0.7 eq
HO
0%
80%
3%
MeO2C
79%
CHO
OMe
OGluc
OMe
CF3
N
0%
CHO
O
Br
MeO2C
88%
O
RO
OR
54%
0%
CHO
O
OR
RO
OGluc
0%
R'
86%
MeO
OR
OMe
OGluc
OR
0%
CHO
85%
74%
mix
15%
80%
CHO
OGluc
GlucO
O
OGluc
65%
30%
O
O
O
O
Br
Br
OGluc
Other Applications
Pd-Catalysed Cyanation of aryl bromide at room temperature
F.Stazi, G.Palmisano, M.Turconi, M.Santagostino Tetrahedron Letters, 46 (2005) 1815-1818.
Br
CN
R
R
Initial conditions: 1 % Pd2(dba)3.CHCl3 2 % [(tBu)3PH]BF4 Zn(CN)2 1.2 eq,wet DMF, 50 oC
30 % yield
Modified conditions: 0.5 % Pd2(dba)3.CHCl3 1.4 % [(tBu)3PH]BF4 Zn(CN)2 1.1 eq, NMP (0.1 % water content), 5% Zn poweder, RT
75-98 % yield
Regioseletive Alkylation of 3,4-dihydroxybenzaldehyde
Unpublished Results
Br
CHO
CHO
CHO
CHO
R=
+
+
O
OH
OH
OH
Initial conditions: RX 1 eq, NaH 2.7 eq, DMF, 0-5 oC
Modified conditions: RX 1.5 eq, NaH 2.5 eq, KI 0.05 eq,
OH
R
O R
O
R
O
30 %
15 %
5%
65%
15 %
0%
optimized work-up
TBAI 0.05 eq, DMF, 25 oC
Modified conditions: different RX
R
40-80 %
Summary and Conclusions
DOE results in a set of experiments in which factors are varied at the
same time in an organized and systematic approach
A mathematical regression model is generated.
This model is empirical and valid only within the studied
factor range.
A better understanding and control of the process are gained
by interacting with the model.
Use of non-statistical knowledge of the problem
for choosing factors and their levels, interpreting the results ...
“Using
statistics is no substitute for thinking about the problem.”
Design and analysis of Experiments
D.C. Montgomery
Suggestion
If you find DoE applied to boring chemistry problem …..
•
Using DoE to Spend Less Time in The Traffic
•
Screening Ingredients (for Homemade Bread) Most Efficiently with Two-
Level Design of Experiment
•
Applied DoE to Microwave Popcorn
and more and more….
By Mark J. Anderson, consultant, Stat-Ease, Inc., Minneapolis, MN
Acknowledgment
Prof. Giovanni Palmisano
Universita’ dell’Insubria-Dipartimento di Chimica
Dr. Marco Santagostino
Boehringer-Ingelheim R&D Chemistry Research Centre