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Quality by Design (QbD) in Action-2: Controlling Critical Material Attributes (CMA) during the synthesis of an API. Abdul Qayum Mohammed, Phani Kiran Sunkari, Amjad Basha Mohammed, P Srinivas, and Amrendra Kumar Roy Org. Process Res. Dev., Just Accepted Manuscript • Publication Date (Web): 21 Jan 2015 Downloaded from http://pubs.acs.org on January 23, 2015

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Organic Process Research & Development

Quality by Design (QbD) in Action-2: Controlling Critical Material Attributes (CMA) during the synthesis of an API. Abdul Qayum Mohammed†‡, Phani Kiran Sunkari†, Amjad Basha Mohammed§, Srinivas, P.*‡, Amrendra Kumar Roy*† †

CTO-III, Dr. Reddy’s Laboratories Ltd, Plot 116, 126C & Survey number 157, S.V. Co-operative

Industrial Estate, IDA Bollaram, Jinnaram Mandal, Medak District, Telangana State, India, PIN 502325. §

Research and Development, Integrated Product Development, Innovation Plaza, Dr. Reddy’s

Laboratories Ltd, Bachupally, Qutubullapur, R. R. Dist-500072, Telangana State, India ‡

Department of Chemistry, Osmania University, Hyderabad-5000007, Telangana State, India

*Corresponding Authors: Amrendra Kumar Roy and Srinivas, P. Telephone: +919701346355, Fax: + 91 08458 279619 Email: [email protected], [email protected]

ACS Paragon Plus Environment


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ABSTRACT QbD is of paramount importance not only for patient safety but also for the timely and uninterrupted supply of products at affordable price into the market. Both these objectives can be achieved only through a robust process and one of the major obstacles for developing a robust process is the quality of input materials and reagents used for the manufacturing of an Active Pharmaceutical Ingredient (API). This article demonstrates the use of QbD methodology for optimizing the quality of input materials and making the process more consistent so that the variation in the quality of API produced gets reduced. This article highlights the use of Failure Mode Effect Analysis (FMEA) for the unbiased identification of Critical Process Parameters (CPPs) and Critical Material Attributes (CMAs) associated with the manufacturing of Key Starting Material (KSM) which is later used as an input for the Design of Experiments (DoE) study that is used for the optimization.

KEYWORDS: QbD, DoE, FMEA, PP, MA, CMA, CQA, Design space


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Introduction: The main aim of any QbD is to address the variability in the Critical Quality Attributes (CQAs) of an API so that the risk to patient’s health is mitigated. QbD also helps in controlling the cost of medicines and ensuring uninterrupted supply of medicines into the market. There are many sources of variability and one of the major sources is the inconsistent quality of KSM and reagents used in the production process. If the quality of the KSMs if not studied and controlled properly, it could have far reaching consequences not only on the process robustness but also on the business as shown in Table 1. From Case-1 in Table 1, it is evident that a consistency in CQAs of an API is only possible if both the manufacturer and the supplier have a robust process for their API and KSM respectively. Any kind of reprocessing/rework of an unsuitable KSM at the manufacturer’s end is not a viable option, as it would increase the cost of production, which has to be borne either by the manufacturer or by the patients. Hence, it is important for a manufacturer to engage the suppliers in its QbD journey in order to eliminate at least one source of variation from the manufacturing process. Another analogous scenario is the multistep synthesis, where the quality of the penultimate stage (KSM manufactured in-house) becomes detrimental for the CQA of the final API. In QbD terms, the desired quality of KSM is described as Critical Material Attribute (CMA). This article demonstrates the use of QbD for optimizing the reaction parameters to achieve the desired quality of the KSM (the penultimate stage) which in turn results in minimizing the variability at the API stage.



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Table 1: Effect of process inconsistency from supplier/manufacturer on API quality manufacturer's API process robust not robust supplier's KSM process

robust

case-1: robust process

case 2: variability due to process

not robust

case 3: variability due to KSM

case 4: disaster

In this regard, we reported a possible sequence of steps involved in the QbD implementation with a case study1, where the effect of critical process parameters for stage-5 (CPP5) and critical material attributes (CMA5)I on the CQAs of final API (compound 5, Scheme 1) was studied. The present article is an extension of the earlier article, where the QbD is used in a similar way to control the CMA5 in order to have a robust process at the API stage as shown in Figure 1 and Scheme 1. The various terminologies used in the present article are explained for the clarity of readers. As shown in Figure 1, CQA, CPP, and CMA associated with the final API or the stage 5 is denoted by CQA5, CPP5, and CMA5 respectively. The present article describes the optimization of CMA5 (i.e. desired specification of compound-4) by controlling CPP4 and CMA4. CMA5 itself is affected by two things: the critical reaction parameters related to stage 4 (denoted

by CPP4) and the material attributes of

compound 3 and methylamine solution which are together denoted by CMA4.

I

Desired specification of compound-4 and EtOAc/HCl used as an input for the manufacturing of final API (see scheme 1)


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O R1

a

(CH 2 )n N

R2

O O

OH

R1

1

(CH 2 )n N

R2

b R1

(CH 2)n O N R 2

Cl

O R1 5

R1

(CH 2 )n N

R2

NH 2

4 API freebase

(CH 2 )n N

R2

NH 3Cl

API Hydrochloride

R1 N R2

R2 R1 N

O

NH

R1 N R2

O

(CH 2)n

O O HN

O

O 3

2

d

c

N

(H 2C)n

O O HN

O

(CH 2)n NH

O

(CH 2 )n

HN Hydrolyzed impurity 6

Dimer impurity 7

Lactam impurity 8

Scheme 1: Synthetic route to API hydrochloride and impurities observed at final stage. (a) SOCl2, Toluene (b) Potassium phthalimide, DMF/H2O (c) 40% Aq. Methyl amine solution (d) EtOAc/HCl gas



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Figure 1: Various abbreviations used in the present article. Subscript represents the stage number. In the earlier article, the focus of the QbD is to investigate and identify the important process parameters (CPP4) along with important material attributes of input materials (compound 3 and methylamine solution), which together constitutes CMA4, involved in the deprotection of compound 3 to free compound 4. Application of QbD to control the CMA5: A stepwise QbD process described earlier1 was adopted for identifying the CPP4 and CMA4 required for controlling the CMA5. Step-1: Listing all material attributes (MA5) of compound 4 involved in the synthesis of final API The maximum numbers of CQAs pertaining to the final API (5) were originating from compound 4. Hence, all the CQAs (unreacted 3, residual toluene, impurity 6 and 7) of the API stage become the MA5 and need to be controlled by optimizing the conversion of compound 3 to compound 4 as shown in Table 1. In addition, the quality of EtOAc /HCl used at stage 5 is also included in MA5. Step-2: Risk Assessment-1: Identifying the CMA5 All MA5 of insitu manufactured compound 4 are captured in Table 2 and few of them are identified as CMA5 based on the criticality. Table 2: Screening of CMA5 specifications of compound 4 1

assay

2

residual Toluene

as per analysis

whether CMA5 or not? no no

remarks compound 4 is added to the next stage based on the assay of compound 4 in the crude reaction mass.

Impurities


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3 4

unreacted (3) hydrolyzed Imp. (7)

NMT 1%

yes

NMT 3%

yes yes

5

dimer Imp. (8)

NMT 3%

6

yield

> 80%

7

EtOAc/HCl

NLT 8% 8-12%

yes no

it was desired to keep these impurities at minimum level in order to have optimum yield. it was desired to have > 80% yield for optimum RMC. HCl concentration to be in range of 8-12%.

Step 3: Identification of CMA4 and CPP4 required for the synthesis of compound 4 After identifying the CMA5 associated with compound 4, it was important to identify the CMA4 (i.e. quality of compound 3) and CPP4 that are critical for obtaining the desired CMA5. Step-3.1: Identification of CMA4 The main inputs involved in the manufacturing of compound 4 were compound 3 and methylamine solution (Scheme 2). Hence, the material attributes of both the inputs that were critical to the quality of compound 4 was described as CMA4 and is captured in Table 3. Table 3: CMA4 for stage 4 MAs S. No.

raw Materials purity

1

compound 3

2

methylamine soln.

NLT 98% 40 % aqueous

assay range > 98% 35-40%

is it a CMA4?

remarks

yes

starting material

yes

reagent for reaction

Step-3.2: FMEA-1 for the identification of CPPs After defining CQA5 and CMA5 (Table 2 and 3), it was required to determine CPP4 that were critical for obtaining the desired CMA5. As described before, a risk-based analysis of the process was used for the identification of CPP4 and this risk assessment was done using Failure Mode Effect Analysis (FMEA).



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But, before starting FMEA on any process, it is important to have a process description, as it is the main input for the FMEA. A brief process involved in the manufacturing of compound 4 is described below.

Scheme 2: Synthetic scheme for stage 4 “Toluene and compound 3 were charged into an RBF and stirred for 10-15 minutes and then heated to 55 ± 5 °C. This was followed by the addition of 40% aqueous methylamine solution at 55 ± 5 °C and was further maintained at 55 ± 5 °C for 4-6 hours for completion of the reaction. The reaction mass was then cooled to 50 ± 2 °C followed by the separation of toluene layer. Aqueous layer was once again extracted with toluene and the combined toluene layer containing the free API base (4) was concentrated under vacuum below 50 °C. After the entire toluene layer was distilled, the reaction mass was cooled to 30 ± 5 °C and was send for assay analysis. This crude mass was then directly taken for final stage based on assay, where it was converted to its hydrochloride form (5).” Each unit operation described above was then subjected to extensive FMEA procedure by a crossfunctional team (R&D, AR&D, PE and Production) as captured in Table 4. This FMEA helped in filtering out the three CPP4 (Reaction time, Reaction temperature, and Methylamine equivalents) based on high risk priority number (RPN), which was then taken as the main output of any FMEA procedure.


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Table 4: FMEA for identifying CPPs for stage 4

1

↕

less quantity of toluene charging of toluene at high temperature charging of toluene at low temperature

↕

↕

↕

↕

↕

↕

↕

↕

↕

no impact between 912 volumes

↕

↕

↕

no effect

↕ no Impact 25- 40 °C

↕

API (5)

error in manual charging

2

more quantity of compound 3

2

2

20

not required

5

2

2

20

not required

charging by flow meter

ensure steam valve is closed

5

7

2

70

replace steam line with hot water line

temperature fluctuation in RT water

no action

5

2

5

50

not required

no effect

↕

↓ ↓ ↑ ↑ ↑ incomplete reaction

5

unreacted compound 3 carried to API stage

proposed control

failure of steam inlet valve

weighing error charge of compound 3 into the reactor at 30±5°C

RPN number

↕

(lack of) detection2

↕

present control

failure mode stage (4)

severity

↕

potential effect(s) of failure on

occurrence

dimer impurity 7

↕

potential failure mode

more quantity of toluene charge toluene 11 volumes into the reactor at 30±5°C

hydrolyzed impurity 6

unit operations or process parameters (PPs)

unreacted 3

S. N o

Purity

effect on CQAs of stage 5

yield of 4

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


error in methyl amine assay escape of methyl amine from container

calibration of weighing balance on daily basis in ware house qualifying methyl amine based on vendor COA


remarks

keep it constant between 912 volumes

charging temperature to be held constant at 30±5°C

restricting the batch size in multiples of 50Kg 2

7

5

70

suppliers to give compound 3 in 50Kg bags reanalysis of methylamine just before

batch size to be held constant


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

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use

less quantity of compound 3

3

4

5

stir the reaction mass for 1015 minutes at 30±5°C

heat the reaction mass to 55±5°C

addition of 40% methyl amine solution (CPP-1)

charging of compound 3 at high temperature charging of compound 3 at low temperature stirring for reaction mass more than required time heating of reaction mass more than required heating of reaction mass less than required slow heating of reaction mass fast heating of reaction mass low concentratio n of methyl amine

weighing error

↓ ↕

↕

↕

↕

less yield

↕

↕

↕

↕

↕

no impact between 25- 40 °C

↕

↕

↕

↕

↕

no impact between 25- 40 °C

↕

↕

↕

↕

↕

↕

↕

↕

↕

↕

↕

↕

↕

↕

↕

↕

↕

↕

↕

↕

↕

↕

↕

no Impact

no impact as methyl amine is not added

no impact

3

error in methyl amine assay

7

5

105

Suppliers to give compound 4 in 50Kg bags for

no impact

failure of steam inlet valve

ensure steam valve is closed

5

7

3

105


no impact

temperature fluctuation in RT water

no action

5

2

3

30

not required

no Impact

manual error

no action

ensuring RT water in condenser to stop toluene loss

3

1

3

9

5

3

3

45

2

1

3

6

↕

5

1

3

15

↕

5

1

3

15

incomplete

↓ ↓ ↑ ↑ ↑ reaction

no impact

may give raise to SMUI

manual error

error in methyl amine assay

qualifying methyl amine based on vendor COA

escape of


5

7

3

105

charging temperature to be held constant at 30±5°C

not required

stirring time to be held constant between 1520 minutes


to be held constant between 55±5°C and heating time to be constant between 3045 minutes

reanalysis of methylamine just before use

specification of assay to be constant between 3540%

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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


high concentratio n of methyl amine more eq. of methyl amine

6

7

add methylamin e solution at 55±5°C (CPP-2)

maintain the reaction mass at 55±5°C for 5 Hrs (CPP-3)

↕

↕

↕

↕

↕

maximum available concentrati on is 40%

?

?

?

?

?

to be investigated

no impact

manual error

less eq. of methyl amine

give rise to impurities ↓ ↓ ↑ ↑ ↑ with less yield

addition at high temperature

↓ ↓ ↑ ↑ ↑

addition at low temperature more maintenance time than the required time low maintenance time than the required time maintenance at high temperature

maintenance at low temperature

methyl amine from container none, as assay cannot be more than 40%

↓ ↓ ↑ ↑ ↑

to be investigated, as it can escape before it reacts

manual error

cap sealed after sampling

QC analysis

issue only required no. of carboys

use of steam and temperature indicator

2

3

3

18

?

?

?

?

not required

impact to be studied using DoE 5

9

5

225

9

9

3

243

5

8

3

120

5

8

5

200


impact need to be studied by DoE along with reaction temperature

to be investigated, as it

↓ ↓ ↑ ↑ ↑ can escape before it

manual error

reacts impact need to be studied by DoE

log book for recording time to be investigated.

↓ ↓ ↑ ↑ ↑ May give raise to SMUI to be investigated, ↓ ↓ ↑ ↑ ↑ as it can escape before it reacts incomplete

↓ ↓ ↑ ↑ ↑ reaction

may give raise to SMUI

manual error

manual error

hourly record of temperature and adjusting the temperature accordingly


5

9

5

225

7

8

5

280 impact need to be studied by DoE

5

8

5

200


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

8

separate the organic layer

less settling time

↓ ↕

↕

↕

↕

yield loss

less yield

9

concentrate the organic layer to remove toluene

residual toluene

↕

↕

↕

↕

improper assay

OVI

calculate the yield of 2a

residual toluene giving wrong weight

10

↕

↕

↕

↕

↕

↕

residual toluene not included while reporting yield

↑ ↓ ↑

increase in desired CQA decrease in undesired CQA increase in undesired CQA

Good Bad

↓

decrease in desired CQA

Bad

↕

no effect of CPPs on CQA

manual error

failure of hot water and vacuum pump

settling time of 15 minutes

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3

5

3

45

5

5

3

75

no control

OVI

Good


5

5

3

75

settling time to be 30 minutes

IPC for residual toluene and correction factor to be included while reporting yield

yield reporting after OVI corrections

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Table 5: Summary of FMEA output (CPP4) from Table 3. s. no.

unit operations or process parameters (PPs)

8 9

charge of Toluene 10 volumes into the reactor at 30±5°C charge of compound 3 into the reactor at 30±5°C stir the reaction mass for 10-15 minutes at 30±5°C heat the reaction mass to 55±5°C eq. of 40% dimethyl amine solution (CPP4-1) add methylamine solution at 55±5°C (CPP4-2) maintain the reaction mass at 55±5°C for 5 Hrs (CPP4-3) separate the organic layer in 15 minutes concentrate the organic layer to remove toluene

10

calculate the yield of 5 in crude mass

1 2 3 4 5 6 7

RPN number ≤ 45

whether critical? no

control strategy 9-12 vol

≤ 45 9 ≤ 45 225 120-243 200-280

no no no yes yes yes

30±5°C 15 minutes 55±5°C to be tested to be tested to be tested

45 75 75

no no no

30 minutes OVI correction to be given

To summarize, there were three CPP4 which were to be studied for their impact on the CMA5 of compound 4 and rest seven PPs were held constant as summarized in Table 5. Apart from this, two CMA4 (Table 3) were also well defined prior to any further optimization. 4. Optimizing the effect of CMA4 and CPP4 on the CMA5 4.1 Optimizing the CMAs It is important to control the CMA4 (i.e. the quality of compound 3 and methylamine) in order to have a control over CMA5 (desired specifications of compound 4). The CMA4 were already well defined as shown in Table 3. It was then time for optimizing the CPP4 affecting the conversion of compound 3 to compound 4. 4.2 Optimizing the effect CPP4 on CMA5 A 23 full factorial experimental design was planned to study the effect of three CPP4 (outcome of FMEA analysis, Table 4 and 5) on CMA5, keeping all the other PPs constant at the desired level



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(Table 5). The investigational range for all the three CPP4s considered for DoE is given in Table 6 and the results of full factorial design are given in Table 7. Table 6: Range for three CPPs considered for DoE Symbol A B C

variables

unit low (-) high (+)

CPP4-1 reaction temperature ˚C CPP4-2 methylamine eq. CPP4-3 reaction time Hrs.

50 7 4

70 13 6

Table 7: Results of 23 full factorial design

reaction temperature 50 70 50 70 50 70 50 70

Factors methyl amine eq. 7 7 13 13 7 7 13 13

responses (CMA5 from table 5) reaction unreacted hydrolyzed dimer yield (%) time (Hr) (3) impurity (6) impurity (7) 4 0.08 2.26 1.49 85.63 4 0.5 2.69 2.55 75.00 4 0.01 0.62 0.25 87.20 4 0.1 1.19 0.56 85.00 6 0.07 1.32 0.94 83.35 6 0.52 1.83 1.55 80.37 6 0.01 0.54 0.13 86.63 6 0.08 0.60 0.21 79.98

4.2.1: Analysis of DoE results for various CMA5 4.2.1.1:

Effect of three CPP4 on Unreacted 3:

The Half-normal Plot and the Pareto Chart show that the unreacted starting materials 3 in the reaction mass were not only influenced by the reaction temperature and methylamine quantity, but also by their interaction effect. This is evident by Figure 2 and ANOVA Table 8. Lower reaction temperature and excess Methylamine leads to less unreacted 3 and more of product 4. Higher level of unreacted 3 may be due to the loss of Methylamine at higher temperature. The same is depicted in the contour graph given in Figure 3.


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Figure 2: Half-Normal Plot and Pareto Chart for unreacted 3 Table 8: ANOVA table for unreacted 3 source model A-reaction temperature B- methylamine eq. AB residual cor total

sum of squares 0.31 0.13 0.12 0.06 0.00 0.31

df 3 1 1 1 4 7

mean square 0.10 0.13 0.12 0.06 0.00

F value 928.11 1178.78 1045.44 560.11

Figure 3: Effect of CPPs on Unreacted 3 after 5 Hrs


p-value prob > F < 0.0001 < 0.0001 < 0.0001 < 0.0001

significant


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4.2.1.2:

Page 16 of 27

Effect of CPP4 on the Hydrolyzed impurity 6:

In this case, the Pareto and Half-Normal plot (Figure 4) indicate that the hydrolyzed impurity 6 was affected inversely by the methylamine equivalents and the reaction time, whereas the reaction temperature did not have any impact on this impurity. The same conclusion can be drawn from ANOVA analysis (Table 9) and the Contour Graph (Figure 5). In other words, a higher equivalent of methyl amine and higher reaction time favors less of impurity 6.

Figure 4: Half-Normal and Pareto Chart for impurity 6 Table 9: ANOVA table for hydrolyzed impurity 6 source model B- methylamine eq. C- reaction time (Hrs) residual cor total

sum of squares 4.10 3.33 0.76 0.54 4.63

df 2 1 1 5 7

mean square 2.05 3.33 0.76 0.11

F value 19.04 31.00 7.08


p-value prob > F 0.0046 0.0026 0.0449

significant

Page 17 of 27

6.00

0.50

5.50

C: Reaction time (Hrs)

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


1.00 1.50

5.00

2.00 4.50

4.00 7.00

8.00

9.00

10.00

11.00

12.00

13.00

B: Methyl Amine eq.

Figure 5: Effect of CPPs on hydrolyzed impurity 6 at 60 °C 4.2.1.2:

Effect of CPP4 on the Dimer impurity 7:

It is evident from the Pareto Chart and Half-Normal Plot (Figure 6) that the impurity 7 was affected inversely by the equivalents of Methylamine. The other two CPPs had no effect on it. This fact was augmented by the ANOVA analysis (Table 10) and also by the Contour Plot (Figure 7)

Figure 6: Half-Normal Plot and Pareto Chart for impurity 7



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Page 18 of 27

Table 10: ANOVA table for impurity 7 source model B- methylamine eq. residual cor total

sum of squares 3.62 3.62 1.45 5.07

df 1 1 6 7

mean square 3.62 3.62 0.24

F value 14.92 14.92

p-value prob > F 0.0083 0.0083

significant

Figure 7: Effect of CPP4 on Dimer impurity 7 at 60 °C 4.2.1.2:

Effect of CPP4 on the Yield of compound 4:

The Half-Normal Plot, Pareto Chart (Figure 8), ANOVA analysis (Table 11), and the Contour Plot (Figure 9) show that the yield had inverse relation with the reaction temperature, while the other two CPP4s had no effect. It might be possible that at higher temperature, methylamine would be escaping from the reaction mass thereby decreasing the yield and increasing the intermediate hydrolyzed impurity 6.


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Figure 8: Half-Normal Plot and Pareto Chart for % yield Table 11: ANOVA table for % yield source model A-reaction temperature residual cor total

sum of squares 68.86 68.86 21.65 90.51

df 1 1 6 7

mean square 68.86 68.86 3.61

F value 19.08 19.08

Figure 9: Effect of CPP4 on % yield for reaction time of 5 Hrs


p-value prob > F 0.0047 0.0047

significant


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To summarize, the contribution of all the three CPP4s and their interaction on the four CMA5s of compound 4 are captured in Figure 10.

Figure 10: Contribution of CPP4 on CMA5 of compound 4 4.3: Defining the Design Space for compound 4. Finally, a design space was generated by defining constraints for all the three CPP4s and CMA4s involved in the process as shown in Table 12. It is worth mentioning that the rest of the process parameters that were not critical were held within their range as defined in FMEA (see Table 3 and 4). Table 12: Criterion for defining design space

name of process/material attributes.

unit

goal

A: reaction temperature B: methyl amine eq.

°C eq.

in range in range

CPP4


lower limit 50 10

upper limit 70 12

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C: reaction time unreacted 3 hydrolyzed imp (6) dimer impurity (7) yield

CMA5 Or response

Hrs % HPLC % HPLC % HPLC %

target minimize minimize minimize maximize

5.5 0.01 1 0.53 3 0.13 3 NLT 82

Based on the constraints defined for CMA5 as shown in Table 12, an overlay plot of all the CPP4swas generated, thereby defining a boundary within which CPP5 could be varied with no effect on CMA5. This amicable region or the proven acceptable range (yellow region, Figure 11) within which the process meets all specifications or CMA5 is captured in Table 12. However, the rectangle ABCD inside the yellow region, being our normal operating range, becomes the desired design space.

Figure 11: Design Space (red rectangle) defined for the reaction time of 5.5 Hrs. 5. Defining Control Strategies3 for all CMAs and CPPs: The control strategies for all CMA5s were discussed in Table 3 and the control strategies for all other non-critical process parameters were determined after FMEA analysis (Table 4 and 5).



Finally, the control strategy for the three CPP4s was defined after DoE study and is captured in Table 13. These CPP4s and CMA4s would be controlled and monitored closely in the future, during commercialization, using various PAT tools and statistical process control tools. 4 Table 13: Control strategy for the three CPP4s with their revised RPN after FMEA-2

acceptable range

control strategy

severity

detection(not)

RPN

FMEA-2 occurrence

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1

equivalents of dimethyl amine solution (CPP41)

9.5 – 11 Eq

reanalysis of methylamine solution just before use. specification of assay to be fixed between 35-40%

3

7

3

63

2

reaction temperature 55±5°C (CPP42)

~ 52-60 °C


3

7

3

63

3

maintain the reaction mass at 55±5°C for 5 Hrs (CPP4-3)

5.5 - 6 Hrs


3

7

3

63

S. No.

factors

Finally, the specification (CMA5) for compound 4 was optimized based on the design space as captured in Table 14. It is worth mentioning that even though high level of impurities at stage-4 could be tolerated at the next stage, the QbD helped in optimizing the reaction conditions that resulted in much lower levels of these impurities (compare Table 3 and 14). Table 14: Final Specifications for compound 4


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specifications (CMA5) maximum tolerable limits 1.1

process control limits*

whether CMA5 or not? No

assay as per analysis

1.2

residual toluene

No

1.3 1.4

unreacted (3) hydrolyzed impurity (6)

NMT 1% NMT 3%

0.5% 1.5%

Yes Yes

1.5

dimer impurity (7)

NMT 3%

0.5%

Yes

remarks

it is taken to the next stage based on the assay of 4. even though these would not participate in the next stage, it was desired to keep these at minimum level.

*these limits were the outcome of DoE 6. FMEA 2: Assessing the risk mitigation The last step of the QbD process was to assess the effect of DoE on the RPN of all CPP4s and compare it with the RPN that was there before DoE, i.e. with FMEA-1. For the three CPP4s, these RPNs decreased significantly as shown in Table 13 (compare with RPN in Table 4). Conclusion: This article demonstrates the stepwise methodology of implementing QbD for determining the CMAs for any KSM. The emphasis was on optimizing the CMAs of KSM so that the quality of the final API stage becomes consistent in the future. In addition, this exercise would eliminate at least one source of variation from the process. It is also evident that if a manufacturer is obtaining a KSM from outside/third party, then it is beneficial for the manufacturer to include the supplier in the QbD journey. Further, the case study illustrates how FMEA could be used for the unbiased selection of the CPPs and CMAs, which was then used as an input for DoE studies. Finally, the operating ranges for all CPPs were finalized based on the design space obtained after DoE, thereby providing a robust process.



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AUTHOR INFORMATION * [email protected], [email protected]. * [email protected] Notes DRL Communication Number IPDO-IPM 00423. The present article represents the author’s personal views on the subject. ACKNOWLEDGMENT We thank the DRL management for supporting this initiative. ABBREVIATIONS ANOVA analysis of variance API

active pharmaceutical ingredient

CMA

critical material attribute

CPP

critical process parameter

CQA

critical quality attribute

DoE

design of experiments

EMEA

the European agency for the evaluation of medicinal products

eq

equivalents

FDA

food and drug administration

FMEA

failure mode and effect analysis

Hrs

Hours

ICH

international conference on harmonization of technical requirements for the registration of pharmaceuticals for


Page 24 of 27

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human use KSM

key starting material

MAs

material attributes

NLT

not less than

NMT

not more than

PP

process parameters

QbD

quality by design

QTPP

quality target product profile

RPN

risk priority number

wrt

with respect to

σ2

Variance



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REFERENCES

1. Qayum,

M. A.; Sunkari, P. K.; Srinivas, P.; Roy, A. K.; Org. Process Res. Dev., 2015, (under

publication). 2. Note on Detectability: Risk number given to detectability actually shows slack of detectability.

Higher number means that CQA is not detectible by the analytical method.

3. (a) Lobben, P. C.; Barlow, E.; James S. Bergum, J. S.; Braem, A.; Chang, S. Y.; Gibson, F.; Kopp, N.; Lai, C.; LaPorte, T. L.; Leahy, D. K.; Müslehiddinoğlu, J.; Quiroz, F.; Skliar, D.; Spangler, L.; Srivastava, S.; Wasser, D.; Wasylyk, J.; Wethman, R.; Xu, Z.; Org. Process Res. Dev., 2014, just accepted, DOI: 10.1021/op500126u. (b) Zhou, G.; Moment, A.; Cuff, J.; Schafer, W.; Orella, C.; Sirota, E.; Gong, X.; Welch, C.; Org. Process Res. Dev., 2014, just accepted, DOI: 10.1021/op5000978. 4. Mukundam, K.; Varma, R. N. D.; Deshpande, G. R.; Dahanukar, V. H.; Roy, A. K.; Org. Process Res. Dev. 2013, 17, 1002


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TOC 290x152mm (150 x 150 DPI)


Quality by Design in Action 2: Controlling Critical ... - ACS Publications

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