1. Using Dynochem to determine a
suitable sampling endpoint in a
DOE
David W. Place, Ph. D.
401 N Middletown Rd
B222/2149
Pearl River, NY 10965
May 13-14, 2009

2. Outline
I. Comments on DoE Assessment Process
II. Case Study: Finkelstein activated alkylation
Establish control over impurity formation that carries through to API
III. Importance of sampling endpoint
Understand kinetics in order to remove time as a factor from the DoE
IV. Data Fitting: Establishing k’s and Ea’s
V. Simulating Alternate Design Points
Refine Factor/CPP ranges based on Dynochem solved kinetic model
VI. Summary
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3. I. DOE Investigation
Assessment process
Increasing Process Predictability
Fractional Response
Reproducibility Kinetic
Factorial Surface Model
Assessment Assessment
DOE DOE
Validation that Understanding of Establish/identify Generate predictive
Parameters NOT Factor ranges to Most important CPPs Equation for CQA
investigated are establish suitable And their rank order/ based on important
being controlled process endpoints interactions CPPs
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4. II. Case Study: Finkelstein activated alkylation
N y NaI
R N
R Cl + x
50-82 C N
N
H z parts Solvent
“substrate” “amine” “product”
Parts
Experiment Amine NaI Temp
Solvent
mol equiv mol equiv degC mL/g
A (low) 2 0 50 4
B (centerpoint) 3.5 0.5 66 5.5
C (repeat) 3.5 0.5 66 5.5
D (High) 5 1 82 7
Issue: Reaction Conditions lead to formation of a quaternary salt (0.1- 2%)
impurity that carries through into the API and is difficult to remove.
Approach: Use a Fractional Factorial Res V design to determine critical
process parameters (CPPs) to control Quat Salt formation
Problem: Reaction mixture is heterogeneous requiring sacrificial quench
of entire reaction mixture to determine impurity profile
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5. Dynochem Models used
n Model: Dynochem’s Yield loss from side reactions (batch)
n Data: HPLC assay data for substrate and product converted to
mmol via calibration curves
n Assumption: Use simplest mechanism to describe conversion
N
R N
k1 R Cl + x
N
+ HCl
N 50-82 C
H z parts Solvent
“substrate” “amine” “product”
k2 R Cl + y NaI
50-82 C
R I + NaCl
z parts Solvent “intermediate”
N
R N
k3 R I + x + HI
50-82 C N
N
H z parts Solvent
R N R N R
+
k4 R Cl
N 50-82 C N
z parts Solvent Cl-
“impurity”
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6. III. Importance of Sampling Endpoint
Removing time as a factor from the DoE
If an adequate Kinetic model of the mechanism can be elucidated:
Dynochem Simulator can be used to scope out suitable endpoints
DoE factor (CPP) ranges can be investigated prior to committing
time/resources
3.5 mol%
<1 mol% from Previous batch experience
9h 15 h
* Simulated using Dynochem’s Yield loss from side reactions (batch) Model
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7. IV. Data Fitting: Procedure to fit rate
constants and Ea
k1 substrate + amine > product + HCl
k2 substrate + NaI > intermediate + NaCl
Process sheet k3 intermediate + amine > product + HI
k4 product + substrate > impurity
Parts
Experiment Amine NaI Temp
Solvent
mol equiv mol equiv degC mL/g
A (low) 2 0 50 4
Scenario Sheet B (centerpoint) 3.5 0.5 66 5.5
C (repeat) 3.5 0.5 66 5.5
D (High) 5 1 82 7
1. Translate mechanistic proposal into process sheet
2. Translate design factors into the scenarios sheet
3. HPLC Area count data converted to mmol for substrate
and product using reference standard calibration curves
4. Use Dynochem fitting engine to solve 4 k’s and 4 Ea’s
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8. Yield loss from side reactions (batch)
Modified to model suspected reaction mechanism
Data Sheet
Scenario Sheet
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9. Dynochem Fits of Experimental Data
Low factors, 50C No NaI 2 equiv amine 4 parts
7.5
Centerpoint values, 66C 0.5 equiv NaI 3.5 equiv amine
Solution.product (Exp) (mmol)
5.5 parts
Solution.substrate (Exp) (mmol)
6.0
A 5.0
Solution.impurity (mmol)
Solution.product (mmol)
High factors, 82C 1 equiv NaI 5 equiv amine 7 parts
Solution.product (Exp) (mmol)
7.5
Solution.substrate (Exp) (mmol)
Solution.substrate (mmol) B&C Solution.impurity (mmol)
Solution.product (Exp) (mmol)
Solution.substrate (Exp) (mmol)
4.0 Solution.product (mmol) Solution.impurity (mmol) D
Process profile (see legend)
Solution.substrate (mmol) Solution.product (mmol)
6.0
4.5
Process profile (see legend) Solution.substrate (mmol)
3.0
Process profile (see legend)
4.5
3.0
2.0
3.0
1.5
1.0
1.5
0.0
0.0 361.2 722.4 1.084E+3 1.445E+3 1.806E+3
Time (min) 0.0
0.0 336.2 672.4 1.009E+3 1.345E+3 1.681E+3
Time (min)
0.0
0.0 336.2 672.4 1.009E+3 1.345E+3 1.681E+3
S/P R2 = 0.97/0.98 S/P R2 = 0.99/0.98 Time (min)
S/P R2 = 0.99/0.97
n Model fits substrate loss fairly well over the set of data
n Model overestimates impurity content – model refinement necessary
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10. Fitting Summary
SCENARIO 4
k1 substrate + amine > product + HCl
k2 substrate + NaI > intermediate + NaCl
k3 intermediate + amine > product + HI
k4 product + substrate > impurity
k 10-5 L/mol s Ea kJ/mol kcal/mol
k1 1.1 +/-0.2 Ea1 40 +/- 9 9 +/- 2
k2 34 +/- 6 Ea2 - -
k3 3.7 +/- 0.7 Ea3 100 +/- 30 24 +/- 6
k4 5.0 +/- 0.8 Ea4 90 +/- 10 23 +/- 3
n k values reported at T(ref) = 66 C
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11. V. Simulating Alternate Design Points
n Criteria for the reaction:
Reaction completed to <1% substrate
Reactions time <30h.
n Question: Which ranges of CPPs will fit the criteria?
Process sheet
Variables Yield %
molpctSM %
Calculate Yield:= solution.product / solution.substrate.Y0
molpctSM:= solution.substrate / solution.substrate.Y0
End time:= if(molpctSM<0.01,time,14400)
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12. Searching for New CPP Ranges
Use the Dynochem Simulator
Endpoint at 1 mol% substrate
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13. Reaction Endpoint Predictions
n Initial Design points predict Reaction endpoints between 8h and
136h @ <1% substrate
Parts Reaction
Experiment Amine NaI Temp
Solvent Endpoint
mol equiv mol equiv degC mL/g h
A (low) 2 0 50 4 136
B&C (centerpoint) 3.5 0.5 66 5.5 30
D (High) 5 1 82 7 8
A' (corner point) 2 0 50 7 261
D' (corner point) 5 1 82 4 4
n Simulation of alternate design points actually suggests that the
reaction endpoint will vary between 4 and 261 h within the design
space – the CPP ranges need to be altered to meet the criteria
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14. Influence of CPP “decrease” on Reaction Endpoint
Parts Reaction
Experiment Amine NaI Temp
Solvent Endpoint
mol equiv mol equiv degC mL/g h
B&C (centerpoint) 3.5 0.5 66 5.5 29
Scenario 1 2 0.5 66 5.5 47
Scenario 2 3.5 0 66 5.5 43
Scenario 3 3.5 0.5 50 5.5 70
Scenario 4 3.5 0.5 66 7 38
n Reaction Temperature is the most influential parameter governing
reaction endpoint.
n Rank order: Rxn Temp > Amine > NaI mol > Parts Solvent
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15. Identifying a new "All-factors-low" design point
Temperature effects
Parts Reaction
Experiment Amine NaI Temp
Solvent Endpoint
mol equiv mol equiv degC mL/g h
A (low) 2 0 50 4 136
Scenario 1 2 0 66 4 52
Scenario 2 2 0 66 7 99
Scenario 2A 2 0 68 7 89
Scenario 2B 2 0 70 7 81
Scenario 2C 2 0 72 7 73
Scenario 2D 2 0 82 7 44
n In order to preserve Amine CPP range between 2-5 mol equiv and
NaI CPP range to 0-1:
Parts solvent CPP would need to be set to <4 parts to meet criteria
-OR-
Reaction Temperature CPP would need to have its lowest value set to > 82 degC to
meet criteria
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16. Identifying a new "All-factors-low" design point
NaI effects
Parts Reaction
Experiment Amine NaI Temp
Solvent Endpoint
mol equiv mol equiv degC mL/g h
A (low) 2 0 50 4 136
Scenario 1 2 0 66 4 52
Scenario 2 2 0 66 7 99
Scenario 2E 2 0.1 66 7 91
Scenario 2F 2 0.2 66 7 86
Scenario 2G 2 0.3 66 7 78
Scenario 2H 2 0.5 66 7 61
Scenario 2I 2 0.9 66 7 28
n In order to preserve Amine CPP range between 2-5 mol equiv and
Reaction Temperature CPP range to 66-82 degC
NaI CPP would need to have its lowest value set to 0.9 mol equiv to meet
criteria
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17. Conclusion: The Trade-Off
Parts Reaction
Experiment Amine NaI Temp
Solvent Endpoint
mol equiv mol equiv degC mL/g h
A' (low) - Old Design Point 2 0 50 7 261
New "low" Design Point 2 0.5 72 6 29
New and Recommended CPP ranges for DoE
based on kinetic assessment
CPP Unit Low Centerpoint High
Amine mol equiv 2 3.5 5
NaI mol equiv 0.5 1.5 2.5
Temp degC 72 77 82
Parts Solvent ml/g 5 5.5 6
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18. VI. Summary
n A Kinetic assessment of the reaction prior to running a
DoE is essential to ensure proper choice of design
factor ranges.
n If an adequate Kinetic model of the mechanism can be
elucidated DoE factor (CPP) ranges can be
investigated prior to committing time & resources
n Dynochem is a powerful tool that enables the process
chemist to leverage data collected from 4 “shake-
down” runs.
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19. Acknowledgements
Jianxin Ren
Michael O’Brien
Marty Guinn
Peter Clark
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