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Statistical Design of Experiments Applied to

Organic Synthesis

Statistical Design of Experiments Applied to

Organic Synthesis

Luis SanchezMichigan State University

October 11th, 2006

Luis SanchezMichigan State University

October 11th, 2006

• Statistical Design of Experiments• Statistical Design of Experiments

Methodology developed in 1958 by the British statistician Ronald Fisher

Strategy• Appropriate statistical analysis before any

experimental data are obtained

Objective• To get as much information as possible

from a minimum number of experiments

Methodology developed in 1958 by the British statistician Ronald Fisher

Strategy• Appropriate statistical analysis before any

experimental data are obtained

Objective• To get as much information as possible

from a minimum number of experiments

Bayne, C. K.; Rubin, I. B., Practical experimental designs and optimization methods for chemists. VCH Publishers, USA, 1986.Tranter, R., Design and analysis in chemical research. Sheffield Academic; CRC Press: Sheffield, England, 2000.

DoEDoE

• Experimentation in Organic synthesis• Experimentation in Organic synthesis

In any synthetical procedure there are factorstemperature, time, pressure, reagents, rate of addition, catalyst, solvent, concentration, pH

that will have an influence on the resultyield, purity, selectivity

In any synthetical procedure there are factorstemperature, time, pressure, reagents, rate of addition, catalyst, solvent, concentration, pH

that will have an influence on the resultyield, purity, selectivity

Carlson, R., Design and optimization in organic synthesis. Elsevier: Amsterdam ; New York, 1992.

• Conventional approach to optimization• Conventional approach to optimization

• Analysis of the reaction conditions that affect the yield:• Analysis of the reaction conditions that affect the yield:

Tranter, R., Design and analysis in chemical research. Sheffield Academic; CRC Press: Sheffield, England, 2000.

Yield vs. Temperature (t=130 min)Yield vs. Temperature (t=130 min)

55

60

65

70

75

80

105 115 125 135 145 155

Temperature (°C)

Yiel

d (%

)

Yield vs. Reaction time (T=125°C)Yield vs. Reaction time (T=125°C)

55

60

65

70

75

80

40 70 100 130 160 190

Time (min)Yi

eld

(%)

T °C

t minutesZX + Y

• The maximum yield would be obtained at 125 °C in 130 min• The maximum yield would be obtained at 125 °C in 130 min?Are these really the optimum conditions?

?Are these really the optimum conditions?

Yield vs (Time and Temperature)Yield vs (Time and Temperature)

105

115

125

135

145

155

55 80 105 130 155 180Time (min)Time (min)

Tem

pera

ture

(°C

)Te

mpe

ratu

re (°

C)

• How yield actually behaves• How yield actually behaves

Carlson, R., Design and optimization in organic synthesis. Elsevier: Amsterdam ; New York, 1992.Tranter, R., Design and analysis in chemical research. Sheffield Academic; CRC Press: Sheffield, England, 2000.

maximumyield

70

8090

94

60

actual maximum

“response surface”

localmaximum

• The conventional approach• The conventional approach

Owen, M. R.; Luscombe, C.; Lai, L. W.; Godbert, S.; Crookes, D. L.; Emiabata-Smith, D.Org. Proc. Res. Dev. 2001, 5, 308-323.

• Analysis of the effect of one particular reaction condition by keeping all the other ones constant

• Analysis of the effect of one particular reaction condition by keeping all the other ones constant

The problem:• The optimum conditions obtained depend on the starting pointThe problem:• The optimum conditions obtained depend on the starting point

Concentration of substrate


Amount ofCatalyst

Amount ofCatalyst

TemperatureTemperature

catalyst

T °Ct minutes

CA + B

• The DoE approach• The DoE approach• To rationally choose points throughout the cube to fully

represent the entire space. • To rationally choose points throughout the cube to fully

represent the entire space.



Amount ofCatalyst

Amount ofCatalyst

TemperatureTemperature

catalyst

T °Ct minutes

CA + B


• Outline• Outline

Determining important reaction conditions• Fractional factorial design

Analysis of reaction condition effects• Factorial design

Estimation of the optimum conditions• Response surface analysis




• Factorial designs• Factorial designs• Two types of reaction conditions:

Numerictemperature, pH, rate of addition, concentrationCategoricsolvent, inert atmosphere, presence of molecular sieves, use of a particular reagent

• Each reaction condition will be screened over a defined set of values (numeric) or options (categoric)

• Experiments are run using all the possible combinations

• Two types of reaction conditions:Numerictemperature, pH, rate of addition, concentrationCategoricsolvent, inert atmosphere, presence of molecular sieves, use of a particular reagent

• Each reaction condition will be screened over a defined set of values (numeric) or options (categoric)

• Experiments are run using all the possible combinations

• mn Factorial designs• mn Factorial designs

• If we analyze 2 values (or options) for 3 reaction conditions, 23=8 experiments need to be run

• A mn factorial design requires mn experiments• The most used method is 2n design

• If we analyze 2 values (or options) for 3 reaction conditions, 23=8 experiments need to be run

• A mn factorial design requires mn experiments• The most used method is 2n design

mnmn

number of reaction

conditionsnumber of values for each reaction

condition

• 23 factorial design• 23 factorial design

• 2 values (or options) for 3 reaction conditions:• 2 values (or options) for 3 reaction conditions:

Box, G. E. P.; Hunter, W. G.; Hunter, J. S., Statistics for experimenters : an introduction to design, data analysis, and model building. Wiley: New York, 1978.

TTemperature

(°C)

CConcentration

(M)

KCatalyst

120 160 1.5 2.5 H3 PO4 H2 SO4

-1 +1 -1 +1 -1 +1

(1,1,1)(1,1,-1)

(-1,1,1)

(-1,1,-1)

(1,-1,1)

(1,-1,-1)

(-1,-1,1)

(-1,-1,-1)

acid catalyst(H2SO4/H3PO4)

OH

COOR

ROOCCOOR

COOR

ROOCCOOR

H2O

T °C

TT

CCKK

2323

number of conditionsnumber of

values

• 23 factorial design• 23 factorial design

• 8 experimental runs:• 8 experimental runs:


OH

COOR

ROOCCOOR

COOR

ROOCCOOR

H2O

T °C

run T C K label

1 - - - 12 + - - t3 - + - c4 + + - tc5 - - + k6 + - + tk7 - + + ck8 + + + tck

yield (%)

6072546852834580


k+--5tc-++4c-+-3t--+2

ck++-7tk+-+6

tck+++8

1---1

labelKCTrun

52685472

4583

80

60

yield (%)


• Measuring the effect: Temperature• Measuring the effect: Temperature

12

31

14

35

Effect of Trun T C K label

1 - - - 12 + - - t3 - + - c4 + + - tc5 - - + k6 + - + tk7 - + + ck8 + + + tck

One half of the average of the differences of each pair

5.1124

35311412

24

)cktck()ktk()ctc()1t(

=⎥⎦⎤

⎢⎣⎡ +++

=⎥⎦⎤

⎢⎣⎡ −+−+−+−

=

(1,1,1)(1,1,-1)

(-1,1,1)

(-1,1,-1)

(1,-1,1)

(1,-1,-1)

(-1,-1,1)

(-1,-1,-1)

yield (%)

6072546852834580


• Measuring the effect: Concentration• Measuring the effect: ConcentrationEffect of C

5.224

)3()7()4()6(

24

)tktck()kck()ttc()1c(

−=⎥⎦⎤

⎢⎣⎡ −+−+−+−

=⎥⎦⎤

⎢⎣⎡ −+−+−+−

=


-6

-4

-7

-3

run T C K label

1 - - - 12 + - - t3 - + - c4 + + - tc5 - - + k6 + - + tk7 - + + ck8 + + + tck

yield (%)

6072546852834580


• Measuring the effect: Catalyst• Measuring the effect: CatalystEffect of K

75.024

12)9(11)8(

24

)tctck()cck()ttk()1k(

=⎥⎦⎤

⎢⎣⎡ +−++−

=⎥⎦⎤

⎢⎣⎡ −+−+−+−

=


-8

11

-9

12

run T C K label

1 - - - 12 + - - t3 - + - c4 + + - tc5 - - + k6 + - + tk7 - + + ck8 + + + tck

yield (%)

6072546852834580


• Concentration-temperature interaction• Concentration-temperature interactionEffect of C on the

effect of T

75.02

2231

235

212

214

2

22

)ktk(2

)cktck(2

)1t(2

)ctc(

on =⎭⎬⎫

⎩⎨⎧

⎥⎦⎤

⎢⎣⎡ −+⎥⎦

⎤⎢⎣⎡ −

=⎭⎬⎫

⎩⎨⎧

⎥⎦⎤

⎢⎣⎡ −

−−

+⎥⎦⎤

⎢⎣⎡ −

−−

=

12

31

14

35

1

2

6

15.5

7

17.5

run T C K label

1 - - - 12 + - - t3 - + - c4 + + - tc5 - - + k6 + - + tk7 - + + ck8 + + + tck

yield (%)

6072546852834580

One half of the average of the differences of each pair of effects


1

2

-3

-3.5

-2

-1.5

-6

-7

-4

-3

• Temperature-concentration interaction• Temperature-concentration interactionrun T C K label

1 - - - 12 + - - t3 - + - c4 + + - tc5 - - + k6 + - + tk7 - + + ck8 + + + tck

yield (%)

6072546852834580

Effect of T on the effect of C


75.02

22

)7(2

)3(2

)6(2

)4(

2

22

)kck(2

)tktck(2

)1c(2

)ttc(

on =⎭⎬⎫

⎩⎨⎧

⎥⎦⎤

⎢⎣⎡ −

−−

+⎥⎦⎤

⎢⎣⎡ −

−−

=⎭⎬⎫

⎩⎨⎧

⎥⎦⎤

⎢⎣⎡ −

−−

+⎥⎦⎤

⎢⎣⎡ −

−−

=


• Concentration-temperature interaction• Concentration-temperature interactionEffect of C on the

effect of T

75.02

2231

235

212

214

2

22

)ktk(2

)cktck(2

)1t(2

)ctc(

on =⎭⎬⎫

⎩⎨⎧

⎥⎦⎤

⎢⎣⎡ −+⎥⎦

⎤⎢⎣⎡ −

=⎭⎬⎫

⎩⎨⎧

⎥⎦⎤

⎢⎣⎡ −

−−

+⎥⎦⎤

⎢⎣⎡ −

−−

=

12

31

14

35

1

2

6

15.5

7

17.5

run T C K label

1 - - - 12 + - - t3 - + - c4 + + - tc5 - - + k6 + - + tk7 - + + ck8 + + + tck

yield (%)

6072546852834580



• Temperature-catalyst interaction• Temperature-catalyst interaction

9.5

10.5

run T C K label

1 - - - 12 + - - t3 - + - c4 + + - tc5 - - + k6 + - + tk7 - + + ck8 + + + tck

yield (%)

6072546852834580

12

14

31

35

6

7

15.5

17.5One half of the average

of the differences of each pair of effects

52

22

142

352

12231

2

22

)ctc(2

)cktck(2

)1t(2

)ktk(

on =⎭⎬⎫

⎩⎨⎧

⎥⎦⎤

⎢⎣⎡ −+⎥⎦

⎤⎢⎣⎡ −

=⎭⎬⎫

⎩⎨⎧

⎥⎦⎤

⎢⎣⎡ −

−−

+⎥⎦⎤

⎢⎣⎡ −

−−

=


• TCK interaction• TCK interaction

9.5

10.5

run T C K label

1 - - - 12 + - - t3 - + - c4 + + - tc5 - - + k6 + - + tk7 - + + ck8 + + + tck

yield (%)

6072546852834580

12

14

31

35

6

7

15.5

17.5

4.75

5.25

0.5

25.02

22

12231

214

235

2

22

)1t(2

)ktk(2

)ctc(2

)cktck(

on =⎭⎬⎫

⎩⎨⎧

⎥⎦⎤

⎢⎣⎡ −−⎥⎦

⎤⎢⎣⎡ −

=⎭⎬⎫

⎩⎨⎧

⎥⎦⎤

⎢⎣⎡ −

−−

−⎥⎦⎤

⎢⎣⎡ −

−−

=


• Measuring the effect and interactions• Measuring the effect and interactions

(1)

132122

135

12512

1431

35

(2)

254260

2666-10

-10

2

4

(3)

51492

-2066

40

0

2

div

88

888

8

8

8

result

64.2511.5

-2.50.750.75

5.0

0

0.25

average

T

C

TCK

TK

CK

TCK

run T C K label

1 - - - 1

2 + - - t3 - + - c

4 + + - tc5 - - + k

6 + - + tk7 - + + ck

8 + + + tck

Yates’s algorithm: works for any 2n factorial designYates’s algorithm: works for any 2n factorial design

yield (%)

6072

5468

5283

4580


Factor effect plotFactor effect plot

-4-202468

101214

T C TC K TK CK TCK

FactorFactor

Effe

ctEf

fect

• What do those numbers mean?• What do those numbers mean?• First we need to evaluate if they are significant• First we need to evaluate if they are significant

3x

3x(when there is

no central point)

• If the effect of a factor is lower than the standard deviation, it’s likely to be due to experimental error

• If the effect of a factor is lower than the standard deviation, it’s likely to be due to experimental error

• What do those numbers mean?• What do those numbers mean?

±+−+= TK5C5.2T5.1125.64yieldresult

64.25

11.5

-2.5

0.75

0.75

5.0

0

0.25

average

T

C

TC

K

TK

CK

TCK

run T C K label

1 - - - 12 + - - t3 - + - c4 + + - tc5 - - + k6 + - + tk7 - + + ck8 + + + tck

yield (%)

6072546852834580

calculated

60.25 ± 273.25 ± 255.25 ± 268.25 ± 250.25 ± 283.25 ± 245.25 ± 278.25 ± 2

• The effects can be used to calculate a function that represents all the experimental runs

• The effects can be used to calculate a function that represents all the experimental runs

• The meaning of those numbers• The meaning of those numbers±+−+= TK5C5.2T5.1125.64yield

±−+= C5.2T5.1625.64yield

TTemperature

(°C)

CConcentration

(M)

KCatalyst

120 160 1.5 2.5 H3 PO4 H2 SO4

-1 +1 -1 +1 -1 +1

H2SO4(aq)OH

COOR

ROOCCOOR

COOR

ROOCCOOR

heat

• Categorical reaction conditions can be optimized• Categorical reaction conditions can be optimized

• It was possible to choose one catalyst because the interaction TK was identified

• Something important• Something important

yield (%)

6072546852834580

±+−+= TK5C5.2T5.1125.64yield

H3 PO4

H2 SO4

run T C K

1 - - -2 + - -3 - + -4 + + -5 - - +6 + - +7 - + +8 + + +

In order to get the maximum yield

(maximize the function), the catalyst has to be

H2 SO4

• The meaning of those numbers• The meaning of those numbers

• To find the optimum conditions, we need to make sure that this function represents the entire space

• To find the optimum conditions, we need to make sure that this function represents the entire space

±−+= C5.2T5.1625.64yield

H2SO4(aq)OH

COOR

ROOCCOOR

COOR

ROOCCOOR

heat

yield

TC

45.2583.25

• Other factorial designs• Other factorial designs

• Full factorial design• Central composite• Box-Benhken

• Full factorial design• Central composite• Box-Benhken

Tye, H. Drug Discovery Today 2004, 9, 485-491.

• Fractional Factorial designs• Fractional Factorial designs

• Factorial designs work perfectly for determining important factors …if you have 3 reaction conditions, as in the example

• If you had to analyze 7 reaction conditions at 2 values each, you would need to run 27=128 experiments!

• By virtue of statistics, it is possible to lower that number and get the same information

• Factorial designs work perfectly for determining important factors…if you have 3 reaction conditions, as in the example

• If you had to analyze 7 reaction conditions at 2 values each, you would need to run 27=128 experiments!

• By virtue of statistics, it is possible to lower that number and get the same information


OH

COOR

ROOCCOOR

COOR

ROOCCOOR

H2O

T °C

• mn-p Fractional Factorial designs• mn-p Fractional Factorial designs

mn-pmn-pactual number

of reaction conditions

number of values for each reaction

condition

number of “ignored” reaction conditions

• A mn-p fractional factorial design requires mn-p experiments

• If we analyze 2 values or options for 4 reaction conditions (as if they were only 3), 24-1=8 experiments need to be run

• A mn-p fractional factorial design requires mn-p experiments

• If we analyze 2 values or options for 4 reaction conditions (as if they were only 3), 24-1=8 experiments need to be run


• Effects vs. interactions• Effects vs. interactions

Tranter, R., Design and analysis in chemical research. Sheffield Academic; CRC Press: Sheffield, England, 2000

result

64.25

11.5

-2.5

0.75

0.75

5.0

0

0.25

average

T

C

TC

K

TK

CK

TCK

• This is what we got before:

• This is what we got before: Important?

Very rarely4-factor interactions

If you get to here you have something very

unusual!

more-than-5-factor interactions

Sometimes3-factor interactions

Often2-factor interactions

Very oftenmain effects


(1)

##

#

##

##

#

(2)

##

###

#

#

#

(3)

##

###

#

#

#

div

88

888

8

8

8

result

##

###

#

#

#

run A B C D

1 - - - -

2 + - - +3 - + - +

4 + + - -5 - - + +

6 + - + -7 - + + -

8 + + + +

Yates’s algorithm:Yates’s algorithm:

yield (%)

##

##

##

##

• 24-1 Fractional factorial design• 24-1 Fractional factorial design

av + ABCD

A + BCD

B + ACDAB + CDC + ABDAC + BD

BC + AD

ABC + D

• Fractional factorial designs• Fractional factorial designs

Design Expert 7.0.3 (Stat-Ease Inc.) (http://www.statease.com)

Number of reaction conditionsNumber of reaction conditions

Num

ber o

f exp

erim

enta

l run

sN

umbe

r of e

xper

imen

tal r

uns

• How to compare the effects?• How to compare the effects?

Factor effect plotFactor effect plot

-4-202468

101214

T C TC K TK CK TCK

FactorFactor

Effe

ctEf

fect

In the case of 3 reaction conditions, a “Factor effect plot”is enoughIn the case of 3 reaction conditions, a “Factor effect plot”is enough

For a high number of reactions, a normal plot is neededFor a high number of reactions, a normal plot is needed

• Normal plots• Normal plots

Let’s assume that the experimental error follows a normal distributionLet’s assume that the experimental error follows a normal distribution


In a normal plot, reaction condition effects that are due to experimental error will appear forming a straight line

In a normal plot, reaction condition effects that are due to experimental error will appear forming a straight line

Normal plotNormal plot

% error

• Application example• Application example

Stazi, F.; Palmisano, G.; Turconi, M.; Clini, S.; Santagostino, M. J. Org. Chem. 2004, 69, 1097-1103.

HONO2 H

NN

N CF3

ONO2 H

NN

N CF3

O

OPivPivO OPiv

BrO

O

O

OPivPivO OPiv

O

O

Ag2Omol.sieves

18h

3%

1

2

3

(Koenigs-Knorrglucuronidation)

Chelation was identified as the reason for the bad yieldAddition of TMEDA (10 equiv.), increased the yield to 27%Chelation was identified as the reason for the bad yieldAddition of TMEDA (10 equiv.), increased the yield to 27%

N

NTMEDA =


DoE methods (32 factorial design) were applied to screen amine additives and silver sources giving: HMTTA and Ag2CO3 as best combination

DoE methods (32 factorial design) were applied to screen amine additives and silver sources giving: HMTTA and Ag2CO3 as best combination

HONO2 H

NN

N CF3

ONO2 H

NN

N CF3

O

OPivPivO OPiv

BrO

O

O

OPivPivO OPiv

O

O

Ag2O

mol.sieves18h

27 %

1

2

310 equiv. TMEDA

N

N

HMTTA = N

N


HONO2 H

NN

N CF3

ONO2 H

NN

N CF3

O

OPivPivO OPiv

BrO

O

O

OPivPivO OPiv

O

O

Ag2CO3

mol.sieves18h

42 %

1

2

310 equiv. HMTTA

A 27-4 fractional factorial (8 experiments) design was used:A 27-4 fractional factorial (8 experiments) design was used:

Reaction condition -1 +1A pre-complex time (min) 0 60 B reaction time (h) 2 6 C Ag2 CO3 (equiv) 1.5 3.8 D HMTTA (equiv) 1.5 12.6 E sugar derivative (equiv) 1.5 3 F 4 Å mol sieves (mg) 0 100 G solvent (mL) 0.5 1.5


run A B C D E F G yield (%)

1 - - - + + + - 14.7 2 + - - - - + + 19.5 3 - + - - + - + 24.4 4 + + - + - - - 11.2 5 - - + + - - + 34.2 6 + - + - + - - 83.2 7 - + + - - + - 56.5 8 + + + + + + + 55.4

• 27-4 factorial design results:• 27-4 factorial design results:A pre-complex time (min)

B reaction time (h)

C Ag2 CO3 (equiv)

D HMTTA (equiv)

E sugar derivative (equiv)

F 4 Å mol sieves (mg)

G solvent (mL)



HONO2 H

NN

N CF3

ONO2 H

NN

N CF3

O

OPivPivO OPiv

BrO

O

O

OPivPivO OPiv

O

O

Ag2CO3 (3.7 eq)HMTTA (0.7 eq)

30 min86%

(2.4 eq.)

• Finally, a 23 factorial design and response surface analysis gave the optimum conditions

• Finally, a 23 factorial design and response surface analysis gave the optimum conditions


• Response surface analysis• Response surface analysis

Carlson, R., Design and optimization in organic synthesis. Elsevier: Amsterdam; New York, 1992.

• The problem of optimizing a synthetic reaction corresponds to locate the maximum value of a function from a mathematical point of view

• The problem of optimizing a synthetic reaction corresponds to locate the maximum value of a function from a mathematical point of view

yield yield

• Response surface analysis• Response surface analysisH2SO4(aq) 1.0M

OH

COOR

ROOCCOOR

COOR

ROOCCOOR

T °Ct min

ttime(min)

TTemperature

(°C)

70 80 127.5 132.5

-1 +1 -1 +1

run t T

1 - -2 + -3 - + 4 + + 5 0 06 0 0 7 0 0

Central point: three times to calculate the

experimental error



run t T yield (%)

1 - - 54.32 + - 60.3 3 - + 64.6 4 + + 68.0 5 0 0 60.3 6 0 0 64.3 8 0 0 62.3

3 central points

Yield vs. (Time and Temperature)Yield vs. (Time and Temperature)

125

127

129

131

133

135

65 70 75 80 85time (min)time (min)

Tem

pera

ture

(°C

)Te

mpe

ratu

re (°

C)

62.3

68.0 64.6

60.3 54.3e = 2

±++= T5.4t35.201.62yield

H2SO4(aq) 1.0MOH

COOR

ROOCCOOR

COOR

ROOCCOOR

T °Ct min





120

130

140

150

160

11060 70 80 90 100

time (min)time (min)

Tem

pera

ture

(°C

)Te

mpe

ratu

re (°

C)

69.1

58.2

87.4


135

140

145

150

155

70 80 90 100


Tem

pera

ture

(°C

)Te

mpe

ratu

re (°

C)

110



87.4

±+−= T97.6t69.209.82yield

77.2 73.01

85.991.9

71.2

91.1

86.8 79.3

• Equation for the 22 factorial design:

• Equation for the 22 factorial design:

• Calculated equation for the surface:

• Calculated equation for the surface:

T97.6t69.236.87yield +−=

±−−− Tt58.0T12.3t15.2 22


140

145

150

155

160

70 80 90 100


Tem

pera

ture

(°C

)Te

mpe

ratu

re (°

C)

110



T97.6t69.236.87yield +−= ±−−− Tt58.0T12.3t15.2 22

88

8590

93

Optimum conditions:T = 157 °Ct = 73 minyield: 93%

Optimum conditions:T = 157 °Ct = 73 minyield: 93%

• Sequential nature of experimentation• Sequential nature of experimentation

PlanPlan

Fractional factorial design

Fractional factorial design

Hypercube design in n dimensions

Hypercube design in n dimensions

Design in 2,3,4 dimensions

Design in 2,3,4 dimensions

Full factorial design

Full factorial design


Central composite

Central composite

Response surface analysis

Response surface analysis

• Application of response surface analysis• Application of response surface analysis


N

O

HTBSO

R

HO

R''

O

OR'

N

O

HHO

R

HO

R''

O

OR'

N

O

H

O

R H

O

OR'

+TEA•3HF

NMP

1 2 3

reaction condition range units

temperature 10 30 °C

time 19 31 hours

volume of NMP 3 7 mL/g of substrate

equivalents of TEA.3HF 1 1.67 Equivalents

24 central composite

Monitored results:• % yield of alcohol• % lactone• % remaining silyl ether

24 central composite

Monitored results:• % yield of alcohol• % lactone• % remaining silyl ether

• Application of response surface analysis• Application of response surface analysis


N

O

HTBSO

R

HO

R''

O

OR'

N

O

HHO

R

HO

R''

O

OR'

N

O

H

O

R H

O

OR'

+TEA•3HF

NMP

1 2 3

• Application• Application


Predicted conditions product yield (%) impurity (%)

target/constraints T (°C) Time (h) solvent Et3N·3HF predicted actual predicted actual

max yield 19 31 3.6 1.42 95.3 95.8 3.3 3.3

lactone < 2% 17 31 4.8 1.50 94.2 94.0 1.9 1.7

lactone < 1.1% 16 29 5.3 1.68 92.4 93.1 1.1 1.1

lactone < 2%, solvent < 3.5 mL/g 14 31 3.45 1.58 93.9 94.2 1.8 2.0

lactone < 2% Et3 N.3HF < 1.18eq. 28 19.5 7 1.17 93.7 93.4 1.9 2.0

lactone < 2%, time < 23 h 24 23 6.3 1.41 94.2 94.2 2.0 1.9

N

O

HTBSO

R

HO

R''

O

OR'

N

O

HHO

R

HO

R''

O

OR'

N

O

H

O

R H

O

OR'

+TEA•3HF

NMP

1 2 3

• When DoE “fails”• When DoE “fails”

Larkin, J. P.; Wehrey, C.; Boffelli, P.; Lagraulet, H.; Lemaitre, G.; Nedelec, A.Org. Proc. Res. Dev. 2002, 6, 20-27.

entry Ac2 O (equiv)

AcBr (equiv) T (°C)

yield (%)(20 g)

yield(%)(20 kg)

comments

1 3 3.8 23-27 77.3 < 70 original conditions23

1.51

42.5

13-1721-24

75.882.7

–74

optimum of DoEnew conditions

Conditions: t = 4-5h; yield of 2 after crystallization

O

O

ONH

H

O

HO

ONH

H

H1) AcBr, Ac2O, CH2Cl2

2) KOH, MeOH3) HCl, CH2Cl2

1 2





Recent advances• Software• Automation




Recent advances• Software• Automation

• “DoE involves a lot of math, it’s rather complicated”

• “DoE involves a lot of math, it’s rather complicated”

• People tend not to utilize DoE because of the tedious mathematical manipulations.

• People tend not to utilize DoE because of the tedious mathematical manipulations.

Lendrem, D.; Owen, M.; Godbert, S. Org. Proc. Res. Dev. 2001, 5, 324-327.

• Software• Software

Most commonly used:

• Stat-Ease Design Expert ®

(http://www.statease.com)

• Umetrics MODDE ®

(http://www.umetrics.com)

• S-matrix Fusion Pro ®

(http://www.smatrix.com)

Most commonly used:

• Stat-Ease Design Expert ®

(http://www.statease.com)

• Umetrics MODDE ®

(http://www.umetrics.com)

• S-matrix Fusion Pro ®

(http://www.smatrix.com)

• What if I need to run >24 experiments?• What if I need to run >24 experiments?

Harre, M.; Tilstam, U.; Weinmann, H. Org. Proc. Res. Dev. 1999, 3, 304-318.

The answer is to use automation

• Some features of automated systems, commercially available:Up to 100 simultaneous reactionsAutomated liquid handler

Vessel volume: 100 μL 250 mLTemperatures: -100 °C 350 °C

Reflux, N2 blanketing, automated N2/vacuum manifold

On-line HPLC

The answer is to use automation

• Some features of automated systems, commercially available:Up to 100 simultaneous reactionsAutomated liquid handler

Vessel volume: 100 μL 250 mLTemperatures: -100 °C 350 °C

Reflux, N2 blanketing, automated N2/vacuum manifold

On-line HPLC

• Example of the use of automation• Example of the use of automationAr

OH

HO

R

O

R

ArPPh3, DIAD

toluene50 - 70 %

Important factors: ratio DIAD/alcohol, alcohol, temperature Important factors: ratio DIAD/alcohol, alcohol, temperature

• System:Automated liquid handlerOn-line HPLC

• Reaction conditions:A equivalents of alcoholB equivalents of DIADC volume of tolueneD temperatureE addition rate of DIAD

• 20 experimental runs• Total research time: 5 days

• System:Automated liquid handlerOn-line HPLC

• Reaction conditions:A equivalents of alcoholB equivalents of DIADC volume of tolueneD temperatureE addition rate of DIAD

• 20 experimental runs• Total research time: 5 days

Emiabata-Smith, D. F.; Crookes, D. L.; Owen, M. R. Org. Proc. Res. Dev. 1999, 3, 281-288.

• Why DoE methods are ideal for us• Why DoE methods are ideal for us

Further exploration would lead us to obtain > 94% yield

Further exploration would lead us to obtain > 94% yield

1.1 equivalents of DIAD and 1.1 equivalents of alcohol89% yield, almost pure product after workup

1.1 equivalents of DIAD and 1.1 equivalents of alcohol89% yield, almost pure product after workup

Emiabata-Smith, D. F.; Crookes, D. L.; Owen, M. R. Org. Proc. Res. Dev. 1999, 3, 281-288.

• Some final comments• Some final comments

• DoE offers powerful mathematical models that are applicable to the behavior of organic reactions

• DoE methods are a daily practice in industrial chemistry. Current applications and results are not being published

• DoE is not a substitute for creative chemistry, but it can be a great supplement

• DoE offers powerful mathematical models that are applicable to the behavior of organic reactions

• DoE methods are a daily practice in industrial chemistry. Current applications and results are not being published

• DoE is not a substitute for creative chemistry, but it can be a great supplement

• DoE is a tool• DoE is a tool

• A tool… like a hammer

• The only way to know how it works is to use it

• If you don’t try it, you will never know that it actually works

• When you get used to the hammer, you wouldn’t use a rock again

• A tool… like a hammer

• The only way to know how it works is to use it

• If you don’t try it, you will never know that it actually works

• When you get used to the hammer, you wouldn’t use a rock again

Lendrem, D.; Owen, M.; Godbert, S. Org. Proc. Res. Dev. 2001, 5, 324-327.

• Acknowledgements• Acknowledgements

Prof. Maleczka

Prof. Walker

The Maleczka groupNicki, Jill, Monica, Feng, Soong-Hyun (Kim),

Il Hwan, Bani, and Kyoungsoo

Aman, Aman, Toyin, Calvin

Prof. Maleczka

Prof. Walker

The Maleczka groupNicki, Jill, Monica, Feng, Soong-Hyun (Kim),

Il Hwan, Bani, and Kyoungsoo

Aman, Aman, Toyin, Calvin

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