Optimization of a Crystallization Process for Orantinib Active

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Organic Process Research & Development

Optimization of Crystallization Process for Orantinib Active Pharmaceutical Ingredient by Design of Experiment to Control Residual Solvent Amount and Particle Size Distribution

Hiroyasu Sato,*,†,‡, Shotaro Watanabe,§ Daisuke Takeda,ǁ Shingo Yano,† Norihito Doki,‡ Masaaki Yokota,‡ and Kenji Shimizu‡ †

Chemical Technology Laboratory, Taiho Pharmaceutical Co., Ltd., 200-22, Motohara, Kamikawa-machi Kodama-gun, Saitama 367-0241, Japan

‡

Department of Chemistry and Bioengineering, Faculty of Engineering, Iwate University, 4-3-5 Ueta, Morioka, Iwate 020-8551, Japan § CMC Division, Taiho Pharmaceutical Co., Ltd., 224-2, Ebisuno, Hiraishi, Kawauchi-cho, Tokushima 771-0194, Japan ǁ API Basic Research Laboratory, Taiho Pharmaceutical Co., Ltd., 3 Okubo, Tsukuba, Ibaraki 300-2611, Japan AUTHOR INFORMATION Corresponding Author *E-mail: [email protected]

ACS Paragon Plus Environment

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Table of Contents Graphic


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ABSTRACT Orantinib is obtained as a final active pharmaceutical ingredient (API) through crystallization by neutralizing the potassium salt of orantinib in a mixed solvent of isopropanol (IPA) and H2O. However, the amount of residual IPA in the orantinib API varies, and the neutralizing crystallization makes controlling the particle size distribution of the orantinib API difficult. We performed 36 experiments by using the design of experiment approach to screen and optimize process parameters for an orantinib API crystallization process. The screening clarified the strength and trends in the effects of various parameters on the amount of residual IPA and particle size, and the temperature and solvent ratio were critical process parameters. Next, we constructed a design space for temperature and solvent ratio by optimizing the process parameters, prepared a response surface model, and calculated optimal conditions under which both the amount of residual IPA and the particle size distribution could be controlled. Finally, we performed verification experiments under the optimal conditions, and obtained orantinib API with the desired amount of residual IPA and particle size distribution.

Keywords design of experiment, residual solvent amount, particle size distribution, crystallization, orantinib


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INTRODUCTION Orantinib (TSU-68) (Chart 1)1 is an angiogenesis inhibitor that inhibits platelet-derived growth factor receptor and vascular endothelial growth factor receptor-2, which are tyrosine kinase receptors. Phase II clinical trial studies targeting hepatocellular carcinoma treated by transarterial chemoembolization have been carried out2.

Chart 1. Chemical structure of orantinib Orantinib is produced by chemical synthesis3, and the orantinib active pharmaceutical ingredient (API) used in these clinical trials is obtained through crystallization by neutralizing the potassium salt of orantinib in a mixed solvent of isopropanol (IPA) and H2O (Scheme 1). However, with existing production methods, the residual amount of IPA in orantinib API varies and often exceeds the limit (5000 ppm) prescribed under option 1 in the International Conference on Harmonization guideline Q3C (R5)4. In addition, orantinib has been developed as a solid preparation, and the particle size distribution of the API in a solid preparation can affect its elution and bioavailability, production suitability, stability, and uniformity of content and properties. Therefore, where any of these properties are affected, controls are needed to achieve appropriate specifications5. Orantinib is classified as a class II drug (low solubility, high membrane permeability) in the Biopharmaceutics Classification System6,7. The elution of class II drugs from a preparation is the rate-determining process for gastrointestinal absorption, and the rate of elution tends to be more rapid as the particle size of the API decreases. Therefore,


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because the bioavailability will be affected if the particle size distribution of orantinib API varies greatly, it is necessary to produce orantinib API with a uniform particle size distribution. A pulverizing step is usually used to reduce the particle size and achieve a suitable particle size distribution. However, because orantinib is a colored compound, there are production controlrelated problems, such as pulverizers and rooms equipped with pulverizers becoming stained and difficult to clean. It is necessary to produce orantinib API with the desired particle size distribution by crystallization, avoiding pulverizing. A robust crystallization production process is required to obtain orantinib API with stable product quality by controlling the amount of residual IPA and the particle size distribution.

Scheme 1. Scheme for synthesizing orantinib from the potassium salt of orantinib Orantinib API is crystallized by neutralizing crystallization (a type of reactive crystallization), through reaction of an acid and a base. In reactive crystallization, a compound is generally modified by a reaction, and the solubility of the compound changes dramatically as a result. Therefore, it is more difficult to control the particle size distribution in reactive crystallization than in methods such as cooling crystallization. Residual solvent amounts vary according to the properties of the compound, and are difficult to generalize. Because it is difficult to measure residual solvent amounts in suspended crystals, even by inline monitoring using process analytical technology8, the only suitable method is to evaluate the residual solvent amounts after isolating the crystals from the suspension. It is difficult to apply standard theories to this method


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and in cases where evaluation is possible only through input and output, it is necessary to ensure output quality by controlling process parameters. However, for conventional methods, a large number of experiments are required to evaluate all process parameters and interactions. Design of experiment (DoE) is an extremely useful tool to design efficient experimental methods and analyze the results appropriately. DoE was developed by R.A. Fischer in the 1920s from agricultural trials in the applied field of statistics. Introducing the concept of Quality by Design9,10,11,12 in regulations relating to medicinal product manufacturing has improved the understanding of production processes. Improving product quality is a priority and there has been an increase in the number of reports relating to optimizing API manufacturing processes through DoE13,14. The range of applications of DoE is broad, and extends to a variety of chemical reactions and the development of a wide range of crystallization and analytical methods15. In this work, we investigated the optimization of an orantinib API crystallization process with DoE to control the amount of residual solvent and the particle size distribution.

EXPERIMENTAL SECTION 1. Design of experiment and statistical analysis We used JMP statistical analysis software (Version 7.0.1) to prepare the experimental design and analyze the experimental data statistically. When screening the process parameters, we used the custom planning DoE function of the JMP software to create an experimental design for a primary model. We analyzed the experimental data statistically by eliminating statistically insignificant process parameters in a stepwise process and then applying a model by the least squares method. When optimizing the process parameters, we used the expansion plan DoE function of the JMP software to add experimental points to data obtained by the screening, and


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created an experimental design for a secondary model (a response surface model). We statistically analyzed the experimental data by applying a model by the least squares method. 2. Preparation of orantinib API The potassium salt of orantinib (6, 8, or 10 g) was dissolved in IPA/water at the crystallization temperature in a 300 mL brown round-bottom flask fitted with a temperature sensor, by heating in an oil bath. Brown glass flasks were used to protect the light-sensitive solution. The solution was subjected to suction filtration using two filter papers (pore size 4 µm) and the filtrate was placed in a 300 mL brown round-bottom flask, to which a temperature sensor, a pH sensor and a half-moon-shaped stirring blade were fitted. The pH was adjusted by adding aqueous HCl dropwise above the surface at an almost constant speed with a tube pump at a constant temperature in an oil bath, and the mixture was agitated at a constant speed with a motor. Agitation was continued at the same temperature and agitation speed, and the precipitated crystals were subjected to suction filtration using two filter papers (pore size 4 µm) at the same temperature, washed with 50 vol % IPA/H2O, and then with IPA. Orantinib API was obtained by vacuum drying the wet crystals at 50 °C. 3. Analysis of orantinib API 3.1. Residual IPA amount The amount of residual IPA in the orantinib API was measured by headspace gas chromatography (7890, Agilent Technologies), a hydrogen flame ionization detector, and a capillary column (5% diphenyl/95% dimethylpolysiloxane). 3.2. Particle size distribution


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The particle size distribution of orantinib API was measured by laser diffraction/scattering (LA-950, Horiba) and a diluted McIlvaine buffer solution (pH 4.0) containing 0.01% Triton X100 as a dispersion medium. 3.3. Electron microscope observations Electron microscope observations of orantinib API were performed with a scanning electron microscope (VE-7800, Keyence). 3.4. Impurities Quantities of orantinib API impurities were measured by high-performance liquid chromatography (1100, Agilent Technologies) using an ultraviolet absorptiometer (254 nm), an octadecylsilylated silica gel column (4.6 × 150 mm, 5 µm) and a mobile phase of citric acid buffer solution (pH 4.7)/methanol/acetonitrile. 3.5. Crystal form The crystal form of orantinib API was identified by X-ray diffraction (XRD) (X’Pert PRO MPD, PANalytical).

RESULTS AND DISCUSSION 1. Screening of process parameters The amount of residual IPA in orantinib API could not be reduced beyond a certain level during the drying and crystal washing steps, suggesting that the amount of residual IPA is determined during crystallization. In addition, there were variations in the amount of residual IPA and particle size distribution from batch to batch, even though the batches were produced using the same production method. Therefore, we investigated the eight process parameters that affect product quality in the crystallization procedure (amount of solvent, solvent ratio,


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temperature, agitation speed, HCl concentration, duration of HCl addition, final pH, and additional agitation duration), that could affect the amount of residual IPA and particle size distribution, and carried out screening experiments. Current production conditions were used as central values, and low and high experimental values were set for the eight process parameters (amount of solvent: 16.4, 21.4, 26.4 v/w; solvent ratio: 31, 41, 51 vol % IPA/H2O; temperature: 40, 50, 60 °C; agitation speed: 80, 160, 240 rpm; HCl concentration: 7, 12, 17%; duration of HCl addition: 1, 2, 3 h; final pH: 1, 3, 5; additional agitation duration: 0.25, 1, 1.75 h). In addition, experimental designs for 18 experiments, including two experiments using these central values, were created by using the custom planning DoE function of the JMP software (Tables 1 and 2, Entries 1–18). Because it would have been necessary to carry out a large number of experiments (64) to deduce all possible interactions in this research, determining the main effect of each process parameter was essential; thus, we used plans (16 experiments) by which only possible interactions were deduced (28-4 fractional factorial design, Resolution IV).

Table 1. Experimental conditions in process parameter screening experiments (Entries 1–18), additional optimization experiments (creation of response surface models) (Entries 19–33), and verification experiments (Entries 34–36).

Entry

Solvent amount (v/w)

Solvent Agitation HCl ratio (vol Temp. speed conc. % (°C) (rpm) (%) IPA/H2O)

Duration of HCl Final addition pH (h)

Additional agitation duration (h)

1

16.4

31

40

80

17

3

1

0.25

2

16.4

31

40

240

17

1

5

1.75

3

16.4

31

60

80

7

1

1

1.75


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4

16.4

31

60

240

7

3

5

0.25

5

16.4

51

40

80

7

1

5

0.25

6

16.4

51

40

240

7

3

1

1.75

7

16.4

51

60

80

17

3

5

1.75

8

16.4

51

60

240

17

1

1

0.25

9

21.4

41

50

160

12

2

3

1

10

21.4

41

50

160

12

2

3

1

11

26.4

31

40

80

7

3

5

1.75

12

26.4

31

40

240

7

1

1

0.25

13

26.4

31

60

80

17

1

5

0.25

14

26.4

31

60

240

17

3

1

1.75

15

26.4

51

40

80

17

1

1

1.75

16

26.4

51

40

240

17

3

5

0.25

17

26.4

51

60

80

7

3

1

0.25

18

26.4

51

60

240

7

1

5

1.75

Screening experiment range

16.4‒ 26.4

31‒51

40‒60

80‒240

7‒17

1‒3

1‒5

0.25‒1.75

19

36.4

11

40

280

12

3

3

1

20

26.4

11

60

160

12

1.625

3

1

21

36.4

51

40

160

12

0.25

3

1

22

36.4

11

80

40

12

3

3

1

23

16.4

11

80

40

12

1.625

3

1

24

16.4

31

80

160

12

0.25

3

1

25

16.4

11

80

280

12

3

3

1

26

26.4

51

80

40

12

0.25

3

1

27

36.4

11

80

280

12

0.25

3

1


10

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28

16.4

11

40

160

12

0.25

3

1

29

36.4

11

40

40

12

0.25

3

1

30

36.4

51

80

280

12

3

3

1

31

36.4

51

60

40

12

1.625

3

1

32

36.4

31

80

160

12

1.625

3

1

33

16.4

51

80

160

12

1.625

3

1

Additional optimization experiment range

16.4‒ 36.4

11‒51

40‒80

40‒280

12

0.25‒3

3

1

34

36

22

67

200

12

2

3

1

35

36

25

70

200

12

2

3

1

36

36

25

70

200

12

2

3

1


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Table 2. Responses in process parameter screening experiments (Entries 1–18), additional optimization experiments (creation of response surface models) (Entries 19–33), and verification experiments (Entries 34–36). Residual IPA Particle size (µm) amount D10 D50 (ppm)

D90

1

9307

2.3

3.9

5.9

2

8767

1.5

2.4

3.5

3

7025

2.2

3.4

5.0

4

4696

3.7

5.7

8.4

5

8921

2.8

4.7

7.2

6

7347

2.9

4.3

6.0

7

5229

5.5

8.4

12.4

8

4959

4.3

6.4

9.2

9

6010

4.1

6.1

8.7

10

5563

3.4

4.9

6.9

11

7403

1.6

2.8

4.5

12

7317

1.5

2.5

4.0

13

4734

2.3

3.5

5.0

14

3023

3.2

4.8

6.8

15

7755

2.8

4.4

6.5

16

5879

3.3

4.8

6.8

17

3868

6.2

9.5

14.0

18

3292

3.7

5.3

7.5

Screening experiment range

3023‒9307

1.5‒6.2

2.4‒9.5

3.5‒14.0

Entry


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19

1134

0.9

1.6

4.6

20

3072

1.4

2.4

3.8

21

7659

1.8

3.0

4.7

22

2847

1.5

2.4

3.7

23

3570

1.8

3.2

5.1

24

5563

3.6

5.6

8.5

25

2169

2.5

4.9

8.4

26

2018

7.1

10.8

15.9

27

2127

1.2

2.2

3.9

28

3555

1.0

2.1

6.9

29

3850

0.7

1.0

1.8

30

413

6.9

10.6

15.5

31

3370

6.1

9.4

14.0

32

1310

6.5

9.9

14.8

33

2380

7.1

11.2

17.1

Additional optimization experiment range

413‒7659

0.7‒7.1

1.0‒11.2

1.8‒17.1

Predicted values for 2223 ± 1210 verification experiment 1

3.2 ± 0.8

4.9 ± 1.2

7.4 ± 2.0

34 a

1.6

2.7

4.2

Predicted values for 2319 ± 933 verification experiment 2

3.3 ± 0.8

5.0 ± 1.1

7.5 ± 1.8

35 b

2622

3.5

5.6

8.6

36 b

2297

3.4

6.0

9.7

2138


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a

Verification experiment 1

b

Verification experiment 2

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Based on the experimental designs, we prepared orantinib API and analyzed the amount of residual IPA and particle size distribution, and found that the amount of residual IPA and particle size distribution of orantinib API varied significantly according to the experimental conditions. The amount of residual IPA was between 3023 and 9307 ppm, and the particle size distribution had D10 values between 1.5 and 6.2 µm, D50 values between 2.4 and 9.5 µm, and D90 values between 3.5 and 14.0 µm (Tables 1 and 2, Entries 1–18). Electron microscope observations of the orantinib API crystals with the largest particle size (Tables 1 and 2, Entry 2) and those with the smallest particle size (Tables 1 and 2, Entry 17), showed that there were clear differences in crystal size (Figure 1). Orantinib API was obtained as needle-like crystals, and the data obtained through particle size distribution measurements by laser diffraction/scattering was confirmed as an indicator of actual crystal size. Moreover, the XRD data for the orantinib APIs obtained showed the same patterns. This indicates that the solvate was not formed. Therefore, we speculate that the residual IPA was trapped during crystal growth owing to occlusion effects. In addition, no significant difference was found for impurities and for the yield (92.2%–97.7%) of orantinib API obtained from the screening experiments (Tables 1 and 2, Entries 1–18).


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Figure 1. Electron microscope images of orantinib API obtained under different conditions. (a) Tables 1 and 2, Entry 2; (b) Tables 1 and 2, Entry 17 The results of the statistical analysis of the amounts of residual IPA (Tables 1 and 2, Entries 1– 18) are shown in Figure 2. The size of the histogram in Figure 2(a) indicates the strength of the process parameter, and the temperature had the strongest effect on the amount of residual IPA. After temperature, the parameters with the next strongest effects were in the order amount of solvent, agitation speed, duration of HCl addition, and solvent ratio. However, the effect of temperature was approximately twice that of amount of solvent. In addition, the line graphs in Figure 2(b) show the trends in the effects of process parameters on the amount of residual IPA. The amount of residual IPA decreases as the temperature increases, the amount of solvent increases, the agitation speed increases, the duration of HCl addition increases, and the proportion of IPA in the solvent increases. We speculate that increase in these parameters relieves the rapid supersaturation, and accordingly, the amount of IPA occlusion decreases because the crystal growth kinetics improve compared with uncontrolled crystal growth and nucleation.


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Figure 2. Results of statistical analysis of data from screening of process parameters for the amount of residual IPA. (a) Strength of each process parameter with respect to the amount of residual IPA (blue lines show 95% confidence intervals). (b) Trends in the effect of each process parameter on the amount of residual IPA (predicted values are shown to the left of the graph for when the process parameters are set to the values shown below the graph, and blue dotted lines show 95% confidence intervals). The statistical analysis of particle size D50 (Tables 1 and 2, Entries 1–18) is shown in Figure 3. The histograms in Figure 3(a) show that the solvent ratio and temperature have an approximately equal effect on particle size D50, and that the duration of HCl addition has the next strongest effect. In addition, the line graphs in Figure 3(b) indicate that the particle size D50 increases as the proportion of IPA in the solvent increases, the temperature increases, or the duration of HCl addition increases. We speculate that increase in these parameters relieves the rapid supersaturation, producing larger crystals were obtained because crystal growth increases rather than nucleation. Moreover, because statistical analysis of the D10 and D90 particle sizes gives similar results to the D50 values, D50 values are used in the discussion in this report. The results


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above show that temperature and solvent ratio are critical process parameters that affect the amount of residual IPA and particle size distribution. Meanwhile, because HCl concentration, final pH, and additional agitation duration had no effect on the amount of residual IPA or particle size distribution, these parameters were omitted when optimizing the process parameters.

Figure 3. Results of statistical analysis of data from screening of process parameters for particle size D50. (a) Strength of each process parameter with respect to particle size D50 (blue lines show 95% confidence intervals). (b) Trends in the effect of each process parameter on particle size D50 (predicted values are shown to the left of the graph for when the process parameters are set to the values shown below the graph, and blue dotted lines show 95% confidence intervals).

2. Optimization of process parameters Response surface models were created to determine the optimal conditions for the process parameters identified in the screening (amount of solvent, solvent ratio, temperature, agitation speed, and duration of HCl addition). The intention was to reduce the amount of residual IPA and obtain a particle size distribution similar to that of orantinib API produced previously (D10: 1.7–4.1 µm; D50: 2.6–7.1 µm; D90: 3.8–12.2 µm). Experimental ranges were expanded as shown


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below in the optimization experiments to reduce the amount of residual IPA and obtain the desired particle size distribution. Temperature: The particle size tends to increase as the temperature increases, but reducing amount of residual IPA is a priority, and the higher temperature range was expanded (40–80 °C). Ratio of solvent: To reduce the particle size (the particle size increases with the temperature), the lower solvent ratio range was expanded (11–51 vol % IPA/H2O). Amount of solvent: To reduce amount of residual IPA, the higher range of the amount of solvent was expanded (16.4–36.4 v/w). Duration of HCl addition: To reduce the particle size, the lower range of the duration of HCl addition was expanded (0.25–3 h). Agitation speed: Because no significant effect was observed in the screening experiments, both the low and high ranges of agitation speed were expanded (40–280 rpm). Next, experimental designs for optimization tests (response surface models) were created by adding 15 experiments to the screening experiments (18 experiments) using the expansion plan DoE function in the JMP software (Tables 1 and 2, Entries 19–33). Based on the experimental designs, we prepared and analyzed orantinib API. The amount of residual IPA and particle size distribution of the orantinib API were significantly different from those in the screening experiments. The amount of residual IPA was between 413 and 7659 ppm, and the particle size distribution had D10 values between 0.7 and 7.1 µm, D50 values between 1.0 and 11.2 µm, and D90 values between 1.8 and 17.1 µm (Tables 1 and 2, Entries 19–33). Meanwhile, when the impurities in orantinib API were measured, specific impurities were detected only when the solvent ratio was 11 vol % IPA/H2O or the temperature was 80 °C. When the solvent ratio was 11 vol % IPA/H2O, the quantity of impurity A was 0.00–0.09%, the


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quantity of impurity B was 0.00–0.02%, the quantity of impurity C was 0.00–0.04%, the quantity of impurity D was 0.00–0.03%, and the quantity of impurity E was 0.00–0.04%. When the temperature was 80 °C, the quantity of impurity F was 0.00–0.04%. The optimal conditions based on these results had a lower limit for the solvent ratio of 21 vol % IPA/H2O, an upper limit for the temperature of 70 °C, and an upper limit for the agitation speed of 200 rpm in anticipation of the scale-up to production equipment. The yield of the orantinib API obtained in the additional optimization experiments was between 85.2% and 96.6% (Tables 1 and 2, Entries 19–33). For these process parameters, statistical response surface models were created with the JMP software by using the data from a total of 33 experiments, namely the screening experiments (18 experiments) and the additional optimization experiments (15 experiments). The optimal conditions that satisfy both the amount of residual IPA and the particle size D50 were derived through calculations based on the desirability of the predicted profiles shown in Figure 4 (amount of solvent: 36 v/w; solvent ratio: 22 vol % IPA/H2O; temperature: 67 °C; agitation speed: 200 rpm; duration of HCl addition: 2 h). The predicted values under the derived optimal conditions were as follows: amount of residual IPA, 2223 ± 1210 ppm; particle size D10, 3.2 ± 0.8 µm; D50, 4.9 ± 1.2 µm; and D90, 7.4 ± 2.0 µm.


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Figure 4. Predicted profiles and desirability for optimal conditions derived from process parameter optimization experiments (response surface models). Predicted values are shown to the left of the graph for when the process parameters are set to the numerical values shown below the graph, and the blue dotted lines show 95% confidence intervals. Figure 5 shows a contour line profile (design space) for the amount of residual IPA and particle size D50 in relation to the temperature and solvent ratio, which are critical process parameters. The boundaries in the design space were set to be more stringent because of the errors in predicted values (amount of residual IPA: < 3000 ppm; D50: 4–6 µm). It was confirmed that the derived optimal conditions were located approximately in the center of the design space and are conditions (space) under which both the amount of residual IPA and particle size D50 fall within the desired ranges.


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Figure 5. Contour line profile for amount of residual IPA and particle size D50 in relation to temperature and solvent ratio (design space is shown in white; amount of residual IPA:

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