(466a) Application of Quality by Design Principles for An Existing Product for the Control of the Dimer Impurity in the Manufacturing Process

Application
of Quality by Design principles for an existing product for the Control of the
Dimer Impurity in the Manufacturing Process

 
Gautam Samanta, Praveen
K Sharma, S Srividya, Kamlesh Pai Fondekar, K R Janardan Sarma, Saurabh Gupta, GRB
Ravish, and Y Raghuram
 
Dr. Reddy's Laboratories
Integrated Product Development
Organization
Bachupally, Hyderabad
India

The
philosophy of quality by design is all about the patients' need. The designing,
developing, and manufacturing of the product must meet those needs. Most of the
innovator and few generic companies have adopted the concept of QbD in their
product development. Dr. Reddy's Laboratories has started implementing the QbD principles
for all new APIs development. The QbD implementation for new products is well
documented but not for existing products. The key drivers for the implementation
of QbD for the existing products are to reduce variability in the product
quality, improve yield, reduce cycle time, solve manufacturing issues, reduce
cost of quality, and introduction of real time release testing for
manufacturing process.

The
objective of this work is to provide a perspective of QbD application for
existing API manufacturing where a stepwise reverse QbD approach has been
applied. The unique features of this work comprise methodology and
implementation strategy in defining design space and thereafter executing the
control strategy for consistent production of the API.

Irbesartan
is an angiotensin II receptor antagonist used mainly for the treatment of
hypertension. Dr. Reddy's Laboratories developed the generic version and filed
DMF in March 2009. In stage 1, IRB-1 is formed by reaction between 2 n
butyl-4-spiro cyclopentane-2-imidazoline-5-one and 4-bromo
methyl-2'-cyanobiphenyl at -5 to -10 °C in the alkaline medium (Figure
1). The reaction time is 2-3 h. In stage 2, IRB-1 is converted to tetrazole by
the reaction with NaN₃ at elevated temperature and the final product
Irbesartan (IRB-2) is obtained after crystallization.

Figure 1: Stage 1 for the synthesis of
Irbesartan and the structure of the Dimer impurity (CQA)

 In
stage 1, there is a significant variation (upto 5%) in the formation of a dimer
impurity, which is difficult to remove in the subsequent stages. If the dimer
content is more than 1%, significant yield loss occurs for its removal. So the
cost of quality is high. The dimer impurity is the critical quality attributes
(CQA) in this stage. The QbD principles have been applied to address the issue.

The
following stepwise QbD approaches has been adopted:

1.     
Historical data and analysis of
data to identify the problem

There is a significant variation in
the formation of the dimer in the process. So some variables are playing critical
role. To understand the variability in the dimer quantity experiments were
conducted at pre-determined time under the same experimental conditions.Figure 2
shows the experimental results, which indicate that the formation of the dimer
depends on the reaction time. Even within the reaction time 2-3 h, the dimer
quantity is varying significantly and the variation is more after 3 h which
indicates that reaction time is very critical. The best fit with the data is
the third-order polynomial equation (R²= 0.994).

Figure 2: Best
fitting of the experimental data

2.     
Quality risk assessment for
material attributes and process parameters

To identify the critical variables
the cause-effect diagram (Figure 3) has been used. All material attributes and
process parameters have been listed in this diagram. Based on the chemistry
knowledge as well as practical experience it has been concluded that material
attributes are not responsible for the dimer. So only the process parameters
have been considered for risk analysis.

 

Figure 3:
Cause-effect or fishbone diagram for MA and PP to understand the effect on CQAs

In
the case of PPs, the first phase is to identify those potential CPPs that may
affect the CQAs of the API (i.e., the severity). The second phase of the
assessment is to estimate the probability of a parameter affecting the API (i.e.,
the risk). The parameters with a high probability of influencing the API
with enough severity to produce unacceptable API are critical parameters. The
overall risk is the combination of severity and probability and risk assessment
is the impact of the process on the API. Table 1 shows the risk assessment of
the process parameters.

Table 1: Risk assessment for the process
parameters

Based
on the preliminary risk analysis of the process parameters, RPM, NaOH quantity,
and reaction time have been identified as critical process parameters. Though the
reaction temp is critical, it has not been considered for DoE study because the
reaction is not completing above -5 °C and the reaction mass is
freezing below -18 °C. Due to a narrow window of operation
it was decided to fix it at -7.5
± 2.5 °C.
To establish the relation between the critical process parameters and CQAs the
design of experiments (DoE) has been applied.

3.     
DoE, understanding of process, and
design space

Initially, two-level
factorial design was selected and twelve experiments (2³ + 4 center
points) were conducted in the lab considering three variables and their
respective ranges as mentioned in Table 2. The normal plot for the dimer
(Figure 4) reveals that all the three variables are making significant positive
impact on the dimer formation (NaOH 36%, RPM 21%, and Reaction time 13%). It is
interesting to note that the three-variable interaction (ABC) has negative
impact (15%) on the dimer formation. Due to significant curvature a central
composite design was selected and remaining experiments were conducted.

Table 2: Critical process parameters and
their ranges

Figure 4: Half-normal plot for two-level
factorial design

The
data from CCD are fitted to a cubic model (R² = 0.9999, Adj R²
= 0.9997, Pred R² = 0.9496, and PRESS = 2.76). Analysis of variance
testing (Table 3) confirms the significance of three-factor interaction (ABC),
as well as of other terms A²B, AB², A²C, A²,
B², C² and two-factor interactions AB, AC, and BC. The
three-dimensional surface plot, illustrated in Figure 5 is generated according
to the cubical model and demonstrates the dependence of dimer on the amount of
NaOH and RPM at optimal level of reaction time (2.5 h). To see the influence of
three-factor interaction (ABC), the model term (ABC) has been excluded and the
analysis of variance indicates that in the absence of that term almost all the
terms become insignificant (Table 4) and the lack of fit becomes significant.
This indicates that the three-factor interaction term is playing very critical
role in the formation of dimer.

Figure
6 is an overlay plot of yield, dimer, and other QAs, setting the foundation for
continued improvement of the process. The yellow region is design space where
the desired QA levels will be obtained. Laboratory validation experiments were
performed under the experimental conditions (NaOH 1.3 eq, Reaction time 200 ±
10 min, RPM 300 ± 20) chosen from the design space and the dimer (0.29 ± 0.06)
and other quality attributes were within the specifications.

Table 3: ANOVA table and associated
statistics for response surface cubic model for dimer

Figure 5: Influence of NaOH and RPM on
the formation of dimer impurity at reaction time 2.5 h

Table 4: ANOVA table and associated
statistics for response surface cubic model for dimer excluding ABC interaction
term

Figure 6: Overlay plot of Yield, starting
materials, purity, dimer impurity, and single maximum unknown impurity for
stage 1, which provides a process operating window

4.     
Control strategy

Does the
output from the laboratory-scale multivariate design get reflected during scale-up?
It is important to do the appropriate scale-up for the scale dependent
variables before manufacturing. Based on the process understanding and
criticality a control strategy has been developed to consistently ensure
product quality. To control the formation of the dimer in the plant the scale
dependent variable, RPM was scaled-up based on tip-speed and control on other
variables was achieved through automation. In-process analysis was not
available to monitor the formation of the dimer at that time. The consecutive
six batches data in the plant indicates that the dimer is under control within
the specification (<1%).

The QbD
principles provide a structured approach for gaining process knowledge and
developing robust manufacturing process. The application of the QbD principles
for the existing products is very limited. This has been applied successfully
to reduce the variability of the dimer impurity in the process and reduces the
cost of quality. On the basis of the process understanding, it has been
demonstrated that the control of the dimer impurity can be achieved by controlling
the critical process variables.