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Continuous Crystallization of Cyclosporine: the Effect of Operating Conditions on Yield and Purity Jicong Li, Tsai-Ta C Lai, Bernhardt L Trout, and Allan S. Myerson Cryst. Growth Des., Just Accepted Manuscript • DOI: 10.1021/acs.cgd.6b01212 • Publication Date (Web): 11 Jan 2017 Downloaded from http://pubs.acs.org on January 12, 2017

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Crystal Growth & Design

Continuous Crystallization of Cyclosporine: the Effect of Operating Conditions on Yield and Purity Jicong Li †, Tsai-ta C. Lai †, Bernhardt L. Trout †, Allan S. Myerson *, † †

Novartis-MIT Center for Continuous Manufacturing and Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, E19-502, Cambridge, MA, 02139, USA

1 ACS Paragon Plus Environment


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ABSTRACT: Continuous crystallization process has potential advantages such as lower cost and improved flexibility in pharmaceutical production when compared to batch crystallization. In this work, multistage continuous cooling crystallization processes for cyclosporine were developed. The approach demonstrated that optimization of stage conditions can be used to improve yield and purity. For a multi-impurity system such as cyclosporine, the segregation of each impurity should be estimated separately due to their different behaviors. The effective distribution coefficients of the impurities were calculated and related to the steady state mother liquor concentrations. A population balance and mass balance model including distribution coefficients for impurities was used to estimate maximum yields and purities that could be obtained at various operating conditions. The results showed the limitation in yield and purity improvement using an MSMPR cascade. In addition, optimization along with economic analysis can aid in determining operating conditions for a high yield with acceptable equipment and operation cost.

Key words: yield, purity, continuous crystallization, distribution coefficient, process optimization 1. INTRODUCTION With 90 % of all small molecule drugs being delivered in crystalline form, crystallization is a crucial process in pharmaceutical manufacturing1. Currently, most crystallization steps in pharmaceutical industry are still conducted as a batch process. In recent years, continuous processes have gained interest in academia and industry, owing to a number of significant advantages2-5. For example, continuous manufacturing requires smaller equipment sizes, offering a reduction in capital expenditure by about 20 %. Crystallization conditions at steady state are constant over time, which leads to higher reproducibility and control of crystal properties.


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However, continuous processes achieve lower yields, as they are operated at steady state and cannot reach equilibrium3, 7. One approach for solving this issue is to concentrate (most commonly by evaporation) and recycle part of the continuous outlet stream7-10. However, recycle of the mother liquor is limited by buildup of impurity in the crystallizer, as also impurities are concentrated and retained in the recycle. Industrial crystallization processes often employ MSMPR cascades11-14. This is not true, however, in the pharmaceutical industry and thus the study of MSMPR cascades for pharmaceutical processes is of interest. Previous work in our laboratory on the continuous crystallization of cyclosporine showed that with 8h 50min of total residence time, a three stage MSMPR cascade only gave 71% yield without optimization10. The study also showed that the final stage temperature controlled the final yield10. In this work, we employed MSMPR cascades to study the crystallization of cyclosporine and varied the number of stages, residence time and temperature of each stage to optimize the yield and purity. We investigated the behaviors of individual impurities to better understand the effect of the crystallization on purification. 2. MATERIALS AND METHODS 2.1 Materials The compound chosen for this work is cyclosporine A (an immunosuppressant drug) which is supplied by Novartis in both crude and purified form with purity of 92.0% and 95.0% respectively. The chemical structure of cyclosporine A is shown in Figure 1. There are 20 impurities in the crude which are detected by HPLC. Acetone (99.5%) was purchased from Avantor Performance Materials and used as the solvent.



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Figure 1. Chemical structure of cyclosporine A (purity of commercial sample was 95%) 2.2 Experiment setup All continuous crystallization experiments were carried out with continuous MSMPR crystallizer with each stage being a 155 mL water jacketed reaction vessel with overhead mechanical stirring and independent temperature controlling (Thermo Scientific NESLAB RTE). In this system, peristaltic pumps (Masterflex, Cole-Parmer) with Chem-Durance Bio tubing (Cole-Parmer) were used for solution and slurry transfer. For all continuous experiments, feed solution, which is crude cyclosporine (27.3% w/w, API/solution, 90.8% purity) in acetone at 53 °C, was continuously pumped into the first crystallizer with flow rate of 0.29, 0.43 and 0.86 ml/min for single stage, three-stage and 5-stage MSMPR, respectively. So that the corresponding residence time of the solution in each stage was 9 hours, 6 hours and 3 hours. In the stages, slurry was removed intermittently so that every 1/10 of the stage residence time, 15.5 ml slurry (10% of the vessel volume) was removed rapidly with max pump flow rate. Once the solution level had dropped below the level of the outlet dip tube, air was pumped into the tube and removed the remaining slurry in the outlet tube. The pumps were set at “time distribution mode” to control the intermittent operation. 2.3 Procedure 2.3.1 Batch crystallization experiment 4 ACS Paragon Plus Environment

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A batch cooling crystallization experiment was carried out in the same 155 mL water jacketed reaction vessel with overhead mechanical stirring and independent temperature controlling (Thermo Scientific NESLAB RTE) in order to obtain a basis for comparison for the MSMPR experiments. A solution with identical composition to the MSMPR feed of 27.3% (w/w) of crude cyclosporine (90.8% purity) in acetone at 53 °C was cooled to 0 ˚C in 3 hours with a linear temperature decrease and maintained at 0 °C for 24 hours. The liquid and solid phase samples were taken at the end of the process. 2.3.2 Continuous MSMPR crystallization

(a)

(b)

(c) Figure 2. Schematic diagram of the (a) 1-stage, (b) 3-stage and (c) 5-stage MSMPR crystallization. Three sets of continuous MSMPR continuous crystallization were conduct. During the experiment, the feed was continuously pumped into the first stage. The slurry in stage(s) was pumped out intermittently. For 1-stage experiment, the stage temperature was 0 °C and residence time was 9 hours. For 3stage MSMPR, the temperatures were set at 15, 5, 0 °C from first to last stage. For 5-stage



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MSMPR, the temperatures were set at 15, 5, 5, 0 and 0 °C. The details of the process conditions were shown in Figure 2 and the actual experiment setup was shown in Figure 3 (5-stage). The design of the experiments was guided by the following considerations. First, the final stage temperatures of each process were set as the same so that the solubility at the end of the process was the same in all experiments. . Second, stage residence times were set as 9, 6 and 3 hours (corresponding to 1-stage, 3-stage and 5-stage MSMPR) to compare the effect of residence time of each stage. Third, longer stage residence time was used in processes with fewer stages in order to make the total residence time of the processes similar. Fourth, for the 5-stage MSMPR, the temperatures of last four stages were set at 5, 5, 0 and 0 °C so that the materials would experience 6 hours at 5 °C and 6 hours at 0 °C. The materials also experienced 6 hours at 6 hours at 5 °C and 6 hours at 0 °C in the last two stages of 3-stage MSMPR. Therefore, under this temperature design, we can directly compare the effect of using more stages at the same total residence time and temperature decrease. Focused beam reflectance measurement (FBRM) was used to determine when the steady state chord length distribution was reached. The liquid and solid phase samples were taken after the process reached steady state.


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Figure 3. Experiment setup for 5-stage MSMPR crystallizer 2.3.3 Experimental conditions Figure 4 shows the experimental conditions (i.e., residence time and temperature for each stage). 3-stage MSMPR

15 10

1

5 0 0

3

6

9

12

15

18

Temperature (°C)

Temperature (°C)

1-stage MSMPR

1

15 10

2

5

3

0 0

Residence Time (hr)

3

6

9

12

15

18

Residence Time (hr)

5-stage MSMPR Temperature (°C)

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1

15 10

2

5

3 4

5

0 0

3

6

9

12

15

18

Residence Time (hr)



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Figure 4. Process conditions of multistage MSMPR crystallizers. The Italic numbers are the stage numbers. For example, in the 5-stage MSMPR, the Italic 1 means the first stage of the process and its temperature and residence time are 15 °C and 3 hours, respectively. 2.3.4 Liquid- and solids-state characterization Mother liquor samples were prepared by filtrating the slurry in either stage. The concentration and purity of the mother liquor were measured using HPLC. Crystal size distributions (CSDs) were measured online using focused beam reflectance measurement (FBRM). After filtration, washing (with 0 °C acetone) and drying (in a 70 °C vacuum oven overnight ), crystal samples were characterized for purity and XRPD patterns using high performance liquid chromatography (HPLC) and X-ray powder diffraction (XRPD), respectively. The FBRM device was a Lasentec S400 probe from Mettler Toledo, with a measurement range from 785 nm to 1000 µm. Chord length data was collected by the FBRM probe, which is defined as the distance across a particle as observed by optics collecting backscattered light from a laser crossing the particle. The number of such chords measured in a specific time period yields a chord length distribution. In this work, the chord length distribution (cubic-weighted) was used as a characteristic crystal size distribution (CSD) of the particle. The HPLC system (Agilent Technologies 1260 Series) was equipped with a Zorbax Eclipse XDB-C18 column (4.6 × 250 mm, 5 µm) and DAD UV detector (detection wavelength 210 nm). X-ray powder diffraction patterns were obtained by a PANalytical X’Pert PRO Theta/Theta powder X-ray diffraction system using a monochromatic Cu Kα radiation source with nickel filter λ = 1.5418 Å) generated at 45 kV and 40 mA, with an X’Celerator high-speed detector. The data were collected from 5° to 40° with a step size of 0.0167° at a scan rate of 0.1078°/s.


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Aluminum sample holders with a zero background silicon plate were used to carry out the measurements. 3. EXPERIMENTAL RESULTS 3.1 Batch crystallization The final yield and purity for the batch crystallization is 86% and 97.0%, respectively. The yield is equal to the theoretical batch yield at equilibrium calculated by solubility. The XPRD patterns of the final crystals obtained from batch and continuous processes are consistent. Starting with an amorphous crude material (90.8% purity), the multistage MSMPR crystallizer with solids recycle has successfully produced purified crystalline products. 3.2 Continuous crystallization Table 1. Effect of stage number, stage temperature and residence time on process yield, purity and mean crystal size

Total Residence Time (hr)

Yield (%)

Crystal Purity (%)

Mean Crystal Size L4,3 (µm)

Batch at equilibrium

--

86.0

97.0 ± 0.2

1-stage MSMPR

9

76.3 ± 0.7

97.4 ± 0.1

83.9

3-stage MSMPR

18

80.4 ± 0.3

98.0 ± 0.3

88.2

5-stage MSMPR

15

80.8 ± 0.3

97.3 ± 0.2

96.1

Table 1 shows the final process yield, product purity and the mean crystal size of each experiment. 5-stage MSMPR provides the highest yield and slightly larger mean crystal size. Compared to the 3-stage MSMPR, 5-stage MSMPR obtain essentially the same yield with a shorter residence time. This is likely due to the very slow growth of cyclosporine at low



temperature. Figure 5 shows the yield progresses along the stage in each experiment. As noted above, cyclosporine grows very slowly at low temperature and thus the yield improvement in the later stages is small. The second stage of the 5-stage MSMPR crystallization showed a great increase in yield. It is the result of stage temperature and residence time that gave a good operating condition for crystal growth. Further optimization is needed so that high yield can be reached in each stage to minimize the total equipment and time cost. 90 85 79.5

80.8 80.3

77.3

80 Yield (%)

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74.5 75

76.4

Batch Equilibrium

76.3

1-Stage MSMPR

70 67.5 65

3-Stage MSMPR

60.4

5-Stage MSMPR

60 55 0

3

6

9

12

15

18

21


Figure 5. Steady-state yield for different stages The final purities of the product obtained in each experiment are very close. Final crystal purity increased about 5%. The purification effect in the later stages is not significant as shown in Figure 6. The reason for this observation will be discussed in section 3.3. The mean crystal size of the 5-stage MSMPR is the highest. The detailed chord length distributions for the final stages are shown in Figure 7. Further investigation for the chord length distribution in each stage explains the reason of the observation. First, the mean crystal size did not change very much in the later stages due to the low growth rate at low temperature. The crystal size distribution was largely decided by the first stage condition. For 5-stage MSMPR, the 10 ACS Paragon Plus Environment

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residence time of the first stage is short. Thus, the supersaturation in this stage was higher than in the three stage experiment (but not high enough to greatly increase nucleation), which led higher growth rate and larger mean crystal size. This indicates that larger mean crystal size can be achieved by designing a fast growth first stage. 100 99.5 99 Purity (%)

98.5 98

97.5 97.4

98.0

97.8

Batch Equilibrium

97.5 97

97.3 97.3 97.3

97.5

1-Stage MSMPR 97.3

3-Stage MSMPR

96.5

5-Stage MSMPR

96 95.5 95 0

3

6

9

12

15

18

21


Figure 6. Steady-state purity for different stages

0.06 0.05 Volume Fraction

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.04

1-Stage MSMPR (RT=1*9hr)

0.03


0.02


0.01 0 0

100

200

300

400

500

600

Chord Length (µm)

Figure 7. Steady-state chord length distribution of the final stage of each experiment



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3.3 Distribution coefficients Table 2 shows the content of the API and the impurities in the crude cyclosporine. There are 20 impurities detected in the crude cyclosporine. As discussed in section 3.2, after crystallization, the final purity could be improved by about 5% and changing operating conditions had no significant effect on further improving the product purity. Two possible explanations for the observation are (1) it reaches the purification limit of crystallization due to thermodynamic properties of the API, impurities and solvent; (2) there are other operating conditions that can affect the final purity. In order to verify the possible explanations and search for methods to improve purity using multistage MSMPR crystallization, we investigated the behaviour of each impurity during the crystallization. Figure 8, Figure 9, Figure 10 are the impurity level in each experiment. The impurities were named by the sequence it appeared during the HPLC measurement. 13 of the 20 impurities were successfully removed in all the experiments (e.g., impurity # 1 to 10). There were 5 impurities that were partially purified by the crystallization processes (i.e., impurity #11, 12, 13, 17, 18). In all the experiments, the contents of impurity #16 and 19 were found to increase after the crystallization, which means these two impurities accumulated in the crystal instead of being purified. Thus, it is necessary to divide the impurities into groups to quantify the separation efficiency. It is also observed that in the multistage MSMPR crystallization, the content of each impurity did not change significantly from stage to stage. The contents of impurities in each stage were the results of mass balance and chemical potential difference between liquid phase and solid phase. Therefore, a model relating the mass balance and the distribution of API and impurities in the two phases were built to quantify the separation efficiency of each impurity. 12 ACS Paragon Plus Environment

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1.60

Crude

1.40

Crystals from single stage MSMPR

Content (%)

1.20 1.00 0.80 0.60 0.40 0.20 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 Impurity #

Figure 8. Purification in 1-stage MSMPR 1.60

Content (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Crude

1.40

1st stage of 3-stage MSMPR

1.20

2nd stage of 3-stage MSMPR 3rd stage of 3-stage MSMPR

1.00 0.80 0.60 0.40 0.20 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 Impurity #

Figure 9. Purification in 3-stage MSMPR



1.60

Crude 1st stage of 5-stage MSMPR 2nd stage of 5-stage MSMPR 3rd stage of 5-stage MSMPR 4th stage of 5-stage MSMPR 5th stage of 5-stage MSMPR

1.40 1.20 Content (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1.00 0.80 0.60 0.40 0.20 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 Impurity #

Figure 10. Purification in 5-stage MSMPR The segregation of an impurity between a liquid phase and crystalline phase at equilibrium has been previously studied22-24. Since the content of each impurity in the solution was low, we assumed that there were no interactions among impurities and the distribution of a certain impurity in the two phases were affected only by the API, solvent and the impurity itself. And since only acetone is considered in this work, the distribution is then just a function of properties of the API and the specified impurity. When impurities incorporate at a small level into the crystal, the incorporation can be characterized by an equilibrium distribution coefficient, DC, numerically equal to the ratio of impurity concentration to the host compound concentration in the solid phase divided by that ratio in the liquid phase, as defined by Equation (1). Givand et al. have previously measured impurity distribution coefficients for model amino acid systems and used them to predict purity data in isomorphic systems in a common solvent25, 26. , = ( , / , ) ( / ,

)

,

(1)

where i is the ith stage, j is the jth impurity and Cyc means cyclosporine A.


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This equation can also be used in the segregation of an impurity at non-equilibrium conditions and the DC in this case means effective distribution coefficient27. Effective DC is commonly used in crystallization process where the crystal growth rate is finite (i.e., the process is in nonequilibrium condition). Using the concentration data of API and impurity in solid and liquid phase, we obtained the effective DC for each impurity in each stage. The calculation result of effective DC in 1-stage MSMPR crystallization experiment is listed in Table 3. The effective DC values indicate the separation efficiency: if effective DC>1, the impurity accumulates in the crystal; if effective DC=1, there’s no purification in the separation process; if effective DC200

17

0.54

0.21

1.39

0.13

18

0.46

0.47

0.44

0.88

19

0.47

0.62

-

>200

20

1.07

-

3.73

-

As discussed above, the effective distribution coefficient is related to the properties of API and impurity in the solid and liquid phase. A common linear relationship between effective DC and the mother liquor concentrations of API and impurity is shown in Equation (2)8-10, 14. , =

∙ "

" , #

, $ , #

+ &

(2)

where again, i is the ith stage, j is the jth impurity and Cyc means cyclosporine A.


0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 0.000

Distribution Coefficient

Impurity 11

y = -47.05x + 0.58 R² = 0.96 0.005

0.010

0.60

Impurity 12

0.50 0.40 0.30 0.20

y = -24.08x + 0.80 R² = 0.98

0.10 0.00 0.000

0.010

0.020

0.030

Cj/(Cj+CA)

Cj/(Cj+CA)

(b) 0.25

Impurity 13



(a)

0.20 0.15 0.10

y = -6.66x + 0.35 R² = 0.96

0.05 0.00 0.000

0.010

0.020

0.030

0.15

Impurity 17

0.10 y = -5.59x + 0.20 R² = 0.70

0.05

0.00

0.040

0.00

Cj/(Cj+CA)

0.01

0.01

0.02

0.02

0.03

Cj/(Cj+CA)

(c)

(d) Distribution Coefficient

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60



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1.00

Impurity 18

0.80 0.60 0.40

y = -135.37x + 1.58 R² = 0.80

0.20 0.0050

0.0055

0.0060

0.0065

Cj/(Cj+CA)

(e)

Figure 11. Estimated parameters for distribution coefficient of impurity # (a) 11, (b) 12, (c) 13, (d) 17, (e) 18. 17 ACS Paragon Plus Environment


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For the thirteen impurities that have effective DC close to zero for all stages, we assumed the a

and b close to zero. For the five impurities that have finite DC (i.e., 0 < DC < 1), the linear

relationship between DC and the concentrations of API and impurity in the mother liquor were shown in Figure 11. For the two impurities that have large DC value, we assumed the a close to zero while b is much larger than one (based on the detection limit of HPLC, the b value should be larger than 200). All the parameters estimated for effective DC are summarized in Table 4. Table 4. Estimated parameters for distribution coefficient Impurity #

aj

bj

Impurity #

aj

bj

11

-47.05

0.58

1-10, 14, 15, 20

0

0

12

-24.08

0.80

16, 19

0

> 200

13

-6.66

0.35

17

-5.59

0.20

18

-135.37

1.58

Based on the observation, we can categorize the impurities into three groups. Most impurities have very small distribution coefficient. These are in the first group for parameters equal zero and they are easy to remove. The ones have finite value are in second group. Their level will decrease after crystallization. The ones that have very large distribution coefficient (impurity # 16 and 19), are difficult to remove and their level will increase after crystallization. 4. STEADY STATE SIMULATION The process in this work was developed for obtaining crystalline solids of the API. Therefore, all crystal properties including yield, purity, morphology, and particle size are important. A model relating nucleation and crystal growth kinetics to operating variables is necessary to evaluate the effect of operating conditions on all of these properties. A model for the continuous 18 ACS Paragon Plus Environment

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crystallization process has been developed based on population balance equation, mass balance equation and effective distribution coefficient. The purpose of the model is to predict the yield, purity and crystal size distribution in the multistage continuous MSMPR crystallization system. And the optimization algorithm based on this model is developed to investigate the limit of yield and purity improvement in such a crystallization system. 4.1 Governing equations and analytic solution

Figure 12. Schematic diagram of the multistage MSMPR The flow chart for n-stage MSMPR crystallization is shown in Figure 12. In this schematic diagram, the stage number, stage temperature and stage volume can be set at any value. A one dimension population balance model was introduced to describe the crystallization of crystallization in each stage. Assuming a clear feed stream into the first stage, the governing equations at steady state for stage i is: , - , -

./ 0

. 0

= 12 3

(3)

= 24 34 1 2 3 , 5 = 2,3, ⋯ , 9

where , is the growth rate and 3 is crystal number density distribution, 5 is the i-th stage.

(4)

Equation (3) and (4) form a homogeneous ordinary differential equation system: : 0

= ;:

(5)

and the homogeneous solution is : = ∑> ? = @ ABC(D E), where D is the eigenvalues of matrix A, @ is the corresponding eigenvectors, = is constant that satisfy the boundary condition: 3 = F ⁄,

(6) 19

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where B is the nucleation rate. The population balance equations are coupled by the mass balance of the API at each stage. At steady state, there is no accumulation of the mass in each stage. Thus, the mass flowing into the stage equals the mass flowing out. Since the suspension density in this system is very high, the volume of the crystals in the solution is taken into consideration. 2H H 1 2 I1 1 24 I1 1

J N/ K

J/ K

L 1 2 M = 0

L 4 + 24 M4 1 2 I1 1

The crystallization kinetics is: , = OP,H exp I1

F = O\,H exp I1

L Y WX

TU,V

UZ,

L Y WX

TU,]

UZ,

1 1[

(7) J K

L 1 2 M = 0, 5 = 2,3, ⋯ , 9

(8)

P

(9)

\

(10)

1 1[ M^ _

where OP,H and O\,H are the pre-exponential factors, à,P and à,\ are the energy barrier for growth and nucleation, ab, is the solubility at the stage temperature, M is the suspension density and ω is the stir rate. Several sets of MSMPR crystallization experiments under different temperatures were performed to obtain the parameters in Equation (9) and (10). To predict the purity of the final product, first, guess the impurity concentrations in mother

liquor , , where i is the i-th stage and j is the j-th impurity. Second, calculate the effect

distribution coefficients , by Equation (2) based on the mother liquor concentrations. Third,

estimate the impurity amounts, M, , in the crystals by Equation (1) using the calculated effective

, ; Finally, the , , and M, should satisfy the mass balance below. If not, modify the guess of , .

At steady state, the mass balance for the impurity at each stage can be expressed as follows:


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2H H, 1 2 I1 1

24 I1 1

J N/ K

J/ K

L , 1 2 M, = 0

(11)

L 4, + 24 M4, 1 2 I1 1 K L , 1 2 M, = 0, 5 = 2,3, ⋯ , 9 J

(12)

The homogenous ordinary equation system coupled with mass balance and kinetics were solved numerically. We can solve the steady-state conditions for each stage. The parameters are set as the same as those in previous work7, as listed in Table 5. We incorporate the purity estimation module and use the fitted value for parameters

coefficient as shown in Table 4.

and & to estimate distribution

Table 5. Kinetic parameters for nucleation and growth OP,H à,P /f O\,H à,\ /f n & o p

1.13 × 10e 9.06 × 10i 4.80 × 10lH 7.03 × 10i 1.33 1.50 2/3 0

parameter

value

units m/min K #/(m3 min) K dimensionless dimensionless dimensionless dimensionless

4.2 Optimization for the final yield and purity

The optimization conditions for the final yield are: (1) choose a stage number 9; (2) fixing

total residence time, ∑> ? q = qbba ; (3) treat stage temperature r and stage residence time q

as variables; (4) objective function is the final yield.

Since in real case, the temperature of ith stage is always higher than (i+1)th stage. Thus the optimization problem can be defined as:

max X ,u v5Awx (y53Az5= C { oAzA{p, \b , H , r| , r0 , qbba , 9baP} ) r| ≥ r ≥ rl ≥ ⋯ r> ≥ r0

p. z. ~

>

q = qbba ?



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Page 22 of 30

The optimization of the final purity is similar. In real case, we cannot sacrifice too much yield to achieve a very high purity. Thus, when optimizing purity, we usually add one more constraint: the lower boundary for the final yield. Thus the optimization problem can be defined as: max X ,u {5z (y53Az5= C { oAzA{p, \b , H , r| , r0 , qbba , 9baP} )

p. z.

r| ≥ r ≥ rl ≥ ⋯ r> ≥ r0 >

q = qbba ? v5Awx > 90%v5Awx ab ab T\^

4.3 Steady state simulation

Simulation results for purity at the experimental conditions were shown in Figure 13. The model accurately predicted the impurity level in crystal and mother liquor at steady state. Previous work regarded all the impurities as one compound5 and its simulation results showed a negative correlation between yield and purity in cyclosporine system, which was not consistent with experimental results. The accuracy of the model in this work indicates that it is necessary to categorize the impurities by their behavior and simulate the performance of the each impurity separately in this system. The final purities for different operating conditions are quite close. The values are all within 96.9% ~ 97.4%. This is due to impurities that are hard to remove according as shown in the simulation results. The results demonstrate that it reaches the purification limit of crystallization due to properties of the API, impurities and solvent. Moreover, it indicates that in order to affect the final purity, other approaches are needed (e.g., changing solvent and adding a third compound to interact with API or the impurity). This result also shows that there is limitation of purification only using multistage MSMPR crystallization.


Page 23 of 30

Since the product purity of the process is not significantly related to the process condition, we then focus on the steady state optimization of the final yield using the algorithm discussed in section 4.2. Crystal Impurity Level

ML Impurity Level

1.0

20.0

0.8

Prediction (%)

Prediction (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


y = 0.9088x

0.6 0.4 0.2 0.0

y = 1.0835x

15.0 10.0 5.0 0.0

0.0

0.5 Measurement (%)

(a)

1.0

0.0

5.0 10.0 15.0 Measurement (%)

20.0

(b)

Figure 13. Model validation: (a) crystal impurity level, (b) mother liquor impurity level 4.4 Steady state optimization Figure 14 shows the optimization results for the final yield obtained at steady state. Each data point means the maximum yield the process can be achieved at a given stage number and total residence time. The results show two obvious trends, longer residence times and more stages result in higher yield. The yield vs. residence time curves become flatter as the total residence time increases. This indicates that after a certain point, the cost of extra total residence time will not provide enough yield improvement. For instance, for a 3-stage MSMPR, the final yield only increases 3% as the total residence time changes from 15 hours to 20 hours. The yield vs residence time curves move closer together as the number of stages is increased thus showing that adding many additional stages will not significantly improve yield.



90

Maximum Yield (%)

85 Batch at Equilibrium 80

8 Stage 7 Stage

75

6 Stage 5 Stage

70

4 Stage 65

3 Stage 2 Stage

60 5.0

10.0 15.0 Total Residence Time (hr)

20.0

Figure 14. Maximum yield at a chosen stage number and total residence time After the total residence time and stage number were determined, the model can provide the detailed operating conditions for each stage. For example, 5-stage MSMPR with 15 hours total residence has a maximum yield 83.74%. The optimum operating conditions to obtain this yield are shown in Figure 15. The first stage has higher temperature and shorter residence time for faster growth. The last stage has lowest temperature and longest residence time. Since the supersaturation in the last stage is low and the crystal growth rate is low, long residence time is

8.0 6.0 4.0 2.0 0.0 0

1

2 3 4 Stage Number

5

Residence Time (min)

needed to allow more crystal deposition.

Temperature (°C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 30

300 250 200 150 100 50 0

1

2

3

4

5

Stage Number


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(a)

(b)

Figure 15. Maximum yield at a chosen stage number (5 stages) and total residence time (900 min): (a) temperature of each stage, (b) residence time of each stage. 5. CONCLUSION In continuous steady state processes, it can be difficult to obtain the same yield-purity relationship as in a well-designed batch process. In this work, three sets of lab scale multistage MSMPR cascade were built and successfully run at steady state. It was observed that the yield of 5-stage MSMPR was the same as that of a three stage MSMPR even though its total residence time was shorter. This result indicates that a good process optimization is needed to maximize the yield of the process for a reasonable stage number and total residence time. The purities of the final crystal products were close for all three processes. The investigation of the incorporation of each impurity reveals the reason for the observation. The final purity was highly dependent on the impurities that were hard to remove. Since the feed contained the same amount of such impurities, the final product purities were similar. The comparison of the three processes also shows the possibility to control the crystal size distribution using MSMPR cascade and this conclusion is consistent with the previous computational studies. A population balance model was built to optimize the yield and by adjusting stage residence time and stage temperature at a given stage number and total residence time. This model can simulate the content level of each compound in any stage by using the mass balance and distribution coefficients. The simulation results indicate that with more stages and longer residence time, the MSMPR cascade can boost the yield close to the theoretical maximum. But the improvement of yield decreases as the stage number and total residence time increase.



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Page 26 of 30

Economically, an optimum stage number and total residence time can be found using this model and the model will also give the detailed stage residence time and stage temperature information at the given optimum conditions. The methodology outlined in this work can also be applied to other systems to achieve acceptable yield, purity and crystal size in a continuous process. With the development of continuous crystallization processes in pharmaceutical industry, the performance difference between MSMPR cascade and batch process is noticed, especially for process yield and purity. The methodology developed in this work demonstrates a way to optimize the yield and purity of multistage MSMPR cascade at steady state. With the optimization model, operating conditions will be determined for an economically efficient continuous crystallization process. This work will significantly help the transition from batch to continuous manufacturing in pharmaceutical industry. ASSOCIATED CONTENT Supporting Information. Protocols of FBRM, HPLC and XRPD, XRPD data. This material is available free of charge via the Internet at http://pubs.acs.org. AUTHOR INFORMATION Corresponding Author *Phone: 617-452-3790. Fax: 617-253-2072. E-mail: [email protected]

ACKNOWLEDGMENTS We acknowledge the Novartis-MIT Center for Continuous Manufacturing for funding and technical guidance. REFERENCES: [1]

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Page 30 of 30

For Table of Contents Use Only:

Synopsis:

The design of n-stage continuous MSMPR crystallization provided an effective way to transfer batch to continuous manufacturing in pharmaceutical industry. By optimizing the operating conditions, process yields were improved. Giving a total residence time and stage number, the maximum yield was calculated by a population balance model, which helped design an economically efficient continuous crystallization process.


Continuous Crystallization of Cyclosporine: Effect ... - ACS Publications

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