This document discusses two case studies using kinetic modeling and DynoChem software to improve pharmaceutical synthesis processes. In the first case, three mechanisms were evaluated to predict an anti-bacterial reaction and reduce impurities. Mechanism 3 best fit the data and parameters from it improved yield. The second case developed a kinetic model for an API synthesis to minimize impurities and maximize yield through simulation and optimization. Process changes based on the mechanisms reduced reaction time and improved purity and yield. Overall, kinetic modeling with DynoChem helped analyze reaction mechanisms and improve two industrial synthesis processes.

1. Development of Kinetic Model
and Process Prediction
Date - 28/04/2011
Venue - DynoChem User Meeting, India
Name - Keerthi Pemula
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2. Outline
Introduction about Dr.Reddy’s Laboratories Ltd.
Case Study I - Mechanism Prediction
Introduction
Selection of Model
Data fitting using Dynochem
Prediction of kinetics parameters
Conclusions
 Case Study II - Kinetic Model & Simulation
Introduction
Mechanism and Kinetics prediction
Simulation and Optimization
Conclusions
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3. Dr.Reddy’s Laboratories Ltd.
Established in 1984, New York Stock Exchange Listed (NYSE: RDY) on April
11,2001
1.56 billion USD Company (2010), with an Employee strength of 13,000
Our purpose is to provide affordable and innovative medicines through our three core
businesses:
PSAI – CTO & CPS
Global Generics – Branded & Unbranded generics
Proprietary Products- NCEs, Differentiated Formulation and Generic
Biopharmaceuticals
200 plus strong Chemical Engineers
Work on areas like Process development, Technology development, Trouble
shooting, scale-up and platform technologies like Crystallization, Process Safety,
Reaction Engineering, Purification & Separations Technologies etc.,
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4. Case Study I – Mechanism Prediction
Brief Description
An anti bacterial drug synthesis having series-parallel reaction system
Issue
Excessive formation of Impurity1 (6%), which is difficult to remove and
results in yield loss ; Target Impurity1- 4%
Approach
Developing reaction mechanism and kinetics using DynoChem, to improve the
process
Believed Mechanism
A + B  Product + C
C + B  Impurity1 + D
C + Product  Impurity2 + D
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5. Process/Our system
Base Reactant (A)
Stir for 30 Reagent(B) +
min Acetonitrile Heated to 75 2 C,
maintain for 4 - 6 hrs
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6. Experiments
 Experiment conducted at 75 C
 Batch Size of B- 5gm ; A- 3gm
 Samples collected at different time intervals to generate
concentration vs time data
 Used HPLC by assay to track concentration changes and
subsequently converted to moles
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7. DynoChem Model selected and Criteria for it
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8. DynoChem Model Used
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9. Experimental Data
Concentration Profiles of the components
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10. Mechanism 1
1) A + B  Product +C k1 = 4.8E-04 L/mol s at 75 C
2) C + B  Impurity1 + D k2 = 1.7E-04 L/mol s at 75 C
3) C + Product  Impurity2 + D k3= 1.5E-05 L/mol s at 75 C
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11. Mechanism 2
1) A + B  Product + C k1=4.8E-04L/mol s at 75 C
2) C + Base Intermediate k2=1.0E+02L/mol s at 75 C
3) Intermediate + B  Impurity1 + Base + D k3=1.7E-04L/mol s at 75 C
4) Product + Intermediate  Impurity2+ Base + D k4 =1.5E-05L/mol s at 75 C
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12. Mechanism 3
1) A + B  Intermediate k1= 1.5E-04 L/mol s at 75 C
2) Base + B  Impurity1 + E k2 = 1.2E-05 L/mol s at 75 C
3) Intermediate  Product +C k3 = 1.7E-03 1/s at 75 C
4) Base + Intermediate  Impurity2+ E k4 = 7.2E-05 L/mol s at 75 C
5) E +C Base +D k5 = 1.0E+02 L/mol s at 75 C
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13. Statistical Parameter Comparison
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14. Conclusions
 Kinetic curve fitting with Mechanism 3 is perfect for all components
 Final SSQ and rSq are also good for Mechanism 3
 Therefore Mechanism 3 is accepted as a feasible mechanism with
the obtained kinetic parameters
Benefits from knowing mechanism
 Changed addition pattern to solvent, Base, A, stir for 30 min and
then add B, where A has more selectivity to react with B
 Reduced the quantity of Base from 2 equivalents of B to 0.8
equivalent of B => concentration increased, rate increases and
reaction time reduced by 2 hrs
 Impurity1 got reduced by 3%
 Yield improved by 14%
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15. Case Study II – Kinetic Model & Simulation
Brief Description
An API synthesis having series-parallel reaction system
Issue
Reducing the formation of Impurities and increasing yield
Approach
Developing kinetic model and optimizing the process using DynoChem
Believed Mechanism
A + B Product
Product + A  Impurity1
Impurity1 + B Impurity2
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16. Process/Our System
B
A at 25 C
Heated to 32 C,
maintain for 18 2 hrs
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17. Experiments
 Two temperature experiments at 32 C and 38 C were
conducted (to study the extremes) and get complete kinetic
data
 Cylindrical 2L vessel
 310rpm with 10cm Anchor impeller
 Samples collected at different time intervals to generate
concentration vs time data
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18. Mechanism and kinetic parameters
1) A +B  Intermediate1 Reaction K Ea(KJ/mol)
2) Intermediate1  Intermediate2+ H2O Rxn 1 4.98E-03L/mol s 43
3) Intermediate2  Product Rxn 2 1.27E+02 1/s 58
4) Product +A  Impurity1 Rxn 3 1.18E+02 1/s 118
5) Intermediate2 + Product  Impurity2 Rxn 4 1.74E-06 L/mol s 104
Rxn 5 2.19E+00 L/mol s 120
T = 32oC
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19. Mechanism: Fit at higher temperature
T = 38oC
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20. Simulation at 33 C
Temp Time Volume B Product Impurity1 Impurity2
S.No.
C min cc % % % %
1 33 960 500 + 0 2.5 96.4 0.16 0.96
2 33 960 500 + 500 11.9 87.7 0.08 0.41
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21. Simulation at 33 C
Temp Time Volume B Product Impurity1 Impurity2
S.No.
C min cc % % % %
1 33 960 500 + 0 2.5 96.4 0.16 0.96
2 33 960 500 + 500 11.9 87.7 0.08 0.41
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22. Optimization
 Effects of Time, Temp and Concentration
Temp Time Volume B Product Impurity1 Impurity2
S.No.
C min cc % % % %
1 33 120 500 + 0 54.36 45.43 0.01 0.2 High Unreacted B
2 33 960 500 + 0 2.06 96.89 0.12 0.93 High impurity2
3 33 480 500 + 500 27.79 71.92 0.02 0.26 High unreacted B
4 33 960 500 + 500 8.93 90.59 0.06 0.42 High unreacted B
5 38.7 1200 500 + 1330 3.02 96.54 0.17 0.28 Best Solution,
Impurity1 is good
Best Solution.
6 40.5 960 500 + 1340 3.02 96.51 0.19 0.28
Impurity1 is high
7 47 480 500 + 1360 3.02 96.4 0.3 0.28 Impurity1 is high
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23. Simulation: More sophisticated
 Temperature ramp effects
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24. Simulation: More sophisticated
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25. Conclusions
 Simulation shows that dilution slows down the reaction
 Optimization shows a dilution with additional 2.5V of solvent at
38.7 C and end time of 20 hrs gives minimum impurities
Actual benefits obtained from knowing the mechanism
 Impurity1, which was difficult to remove is reduced from 0.2% to
0.09% , by reducing reaction time
 Reaction time is reduced by 8 hrs
 Once we came to know that Impurity 2 doesn’t form from
Impurity1,we tried different ways to isolate it and succeeded in
removing it completely
 Since impurities are reduced, by reducing the volumes of solvents in
workup, yield was improved
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26. How Dynochem helped us
 We could get the feasible mechanisms for 2 API molecules
 We could also get kinetic parameters for them
 This helped in improving our process by adopting certain
changes in the process
 Yet to explore more and learn for different nature of reaction
systems and other unit operations
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27. Contd…
Few Limitations
 Sensitivity of the kinetic parameter values to the initial guess
 Doesn’t give Order of complete reaction right away- assumes
Stoichiometry orders
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28. Acknowledgements
 Process R&D team, PSAI
 My Team-
Mrs. Puja Jain
Mr. B. S. Chakravarthy
Ms. Anchal Jain
 Dr.Reddy’s Laboratories Ltd.,
 Dynochem, Indiasoft Technologies (P) Ltd.,
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29. THANK YOU
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