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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5637_Библиотеки_им_академика_М_И_Перельмана
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Design Framework and Tools for Solid Drug Product Manufacturing Processes 399
https://t.me/medicina_free
“batch technology” with stepwise implementation of the process units. New units
or combinations thereof could be added as needed to update the superstructure.
Formulation Strategy
In addition to process alternatives, the formulation concept for doses is another
important decision point. Two options are available: “proportional dosage” where all
doses have the same composition ratios but different product weights and “common
dosage” where all doses have the same weights but different composition ratios.
In clinical development, at least two doses are generally produced in either of the
formulation options or in combinations thereof. The choice greatly impacts the
amounts of processed materials for products and placebos and consequently impacts
the economic assessment.
An alternative that is an output of activity A2 was defined as a combination of
the process alternative and formulation strategy; that is, a maximum of 18,904 alternatives can be generated within the superstructure as the product of 9,452processes
and two formulation strategies. Each alternative is assessed individually. The number of practical alternatives can be obtained by applying case-specific constraints
such as resource availability for development and production, the characteristics
of the powder materials, and past production experience or market characteristics
affecting company preferences. For example, six alternatives, consisting of three
process alternatives and two formulation strategies, can be considered if a drug
product of interest is suitable for wet granulation.
3.2 Stochastic Economic Assessment
Overview of Economic Assessment
A stochasticeconomic assessment toolwas developed, where the probability density
functions (PDFs)of the netpresent value (NPV) can be calculated. In the present and
the next sections, figures and equations are presented by assuming the beginning of
phase II (clinical development) as the decision stage. The cash flow in the drug life
cycle can be illustrated as in Fig. 5. The design problem was defined as finding the
best combination of process alternatives (dosage forms, processing technologies,
and raw materials) and formulation strategy (proportional or common dosage) to
maximize the economic objective function, denoted by NPV (l) [USD], for a given
product formulation. Equation 1 defines the NPV for alternative l as
τ
C
NPV (l) = –
3
τ =0
(
dev
1 +r
(τ)
'
τ
prod
'
C
τ =0
invest
1 +r
(
'
–
τ
'
)
l
(τ)
)
'
'
τ
C
prod
τ =τ
sales
lau
'
+
'
τ
'
l
(τ)
1 +r
(
–C
'
'
(τ)
op
'
τ
)
,
'
l
(1)
400 K. Matsunami et al.
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Fig. 5 Overview of cash flow in the drug life cycle with an indication of the decision stage [9]
where C
[USD yr
(τ )[USDyr−1], C
dev
−1
] represent the development cost, investment cost, sales, and operating
(τ )[USDyr−1], C
invest
(τ )[USDyr−1], and Cop(τ )
sales
cost, respectively, when the time between the decision stage and target phases is τ
years. The dimensionless parameter r represents the interest rate. The parameters
τ
[yr], τ
3
inv
[yr], τ
[yr], and τ
lau
[yr] represent the periods from the decision
prod
stage to the clinical trials in phase III, the investment in production facilities
(e.g., the continuous manufacturing machine), the product launch, and the end of
the commercial production, respectively. For more details regarding the economic
assessment, refer to [9].
The design problem can be expressed, as shown in Eq. (2).
max E
NPV (l)
(
θ
)
s.t.
E
NPV (l))> 0
(
θ
Mass balance constraints
(
Processing time constraints
(
Pharma-specific constraints),
(
)
)
(2)
where the objective function is the expected value (E)ofNPV, the design variable
is alternative l, and the parameter θ represents the vector of uncertainty parameters.
The first constraint serves as the rejection criterion for an alternative. Mass balance
constraints consider the mass balance of raw materials and products/losses. The
examples of process time and pharma-specific constraints are the validated runtime
for continuous technology and safety stock, respectively. The following three
pharma-specific constraints were considered as worth incorporating in the model
[18]. At product launch, the production of a specific number of lots, typically three
in the pharmaceutical industry, is required for processvalidation. The number of lots
becomes flexible later in commercial production. Another important constraint is
avoiding drug shortages and therefore determining the required production volume
to provide sufficient inventory levels. The third constraint is expiration dates, which
also determines the shipping deadline.
Design Framework and Tools for Solid Drug Product Manufacturing Processes 401
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Equation 3 describes the difference in NPV between two alternatives l1and l
denoted as ΔNP V
The area of the positive region where ΔN P V
of alternative l
[USD].
l1,l
2
ΔNP V
being preferable to l2. This can be thus used as an additional
1
= NPV(l
l1,l
2
–NPV(l
)
1
)
2
> 0 gives the probability
l1,l
2
(3)
mechanism for comparison of alternatives.
Monte Carlo Simulation (MCS)
Uncertainty is the highest at earlier stages, such as during clinical development,
with variables becoming better defined at later stages. Process variables such as
continuous manufacturing rates are classified as internal variables, while others
such as demand volume and success/failure of clinical development are defined as
external variables. Defining PDF of input parameters is used to quantify uncertainty
in clinical development and commercial production through MCS.
A triangular distribution was used to describe the PDF for parameters with potential ranges/multiple values. Triangular distributions specify minimum, maximum,
and peak parameter values. Such distributions are more convenient to use at early
design stages, e.g., phase II, since the required information of upper and lower
ends can be available then. Furthermore, the distribution can be asymmetric, adding
flexibility to the analysis. Equation 4 describes the PDF f(x) of continuous variables
such as the manufacturing rate.
f(x) =
⎧
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎩
2(x–x
)
x
(
max
x
(
max
–x
2(x
min
min
max
x
)(
–x
x
)(
std–xmin
max
min
–x
0|x<x
|
x
≤ x ≤ x
min
)
)
|
x
≤ x ≤ x
std
–x
)
std
,x > x
min
max
std
, (4)
max
2
, x
where x
min
max
, and x
peak) values of an input parameter, respectively.
After calculating the PDFs of ΔNP V
can be performed to identify critical parameters for decision-making. The Spearman
rank correlation coefficient (RCC), ρ
in Eq. 5:
where N [–] and d [–] represent the iteration number and the difference between the
ranks of the parameters x and ΔNP V
represent the minimum, maximum, and standard (i.e.,
std
, a global sensitivity analysis (GSA)
l1,l
2
[–] [19], was used as an indicator, as shown
x
2
ρ
x
= 1–
l1,l
NN2–1
2
6%d
, respectively.
, (5)
402 K. Matsunami et al.
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Fig. 6 Screenshot of the superstructure section in the “SoliDecision” software (a prototype
version)
3.3 Implementation of “SoliDecision”
The developed mechanism was implemented as an original software called “SoliDecision.” The software consists of three sections: superstructure, data input, and
result. The following describes the workflow when using the software.
Superstructure Section
A screenshot of the superstructure section in SoliDecision (a prototype version)
is presented in Fig. 6. As a first step of the economic assessment, alternatives
for the target product are generated (a maximum of 9452 process alternatives and
two formulation strategies). By activating or deactivating the boxes, the number of
alternatives is calculated and displayed.
Input Data Section
The input parameters are categorized according to the relevant stages (phase II,
phase III, and commercial production). The default values of all parameters that
are set in the software can be altered according to the product characteristics and
the company standards. The uncertainty of the input parameters can be specified by
setting the minimum, maximum, and standard values of each parameter. Triangular
or uniform distributions are available, besides simply using the deterministic value.
Design Framework and Tools for Solid Drug Product Manufacturing Processes 403
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Fig. 7 An example of the produced violin plots for PDFs of ΔNPV by SoliDecision (a prototype
version)
Fig. 8 The results of SoliDecision (a prototype) for the sensitivity analysis in the demonstration
Results Section
The results section provides the ranking of alternatives in terms of E
(NPV(l)),
θ
PDFs of NPV and ΔNPV, and the sensitivity analysis results. The alternative of
interest is highlighted in the superstructure with the results of E
(ΔNPV). PDFs of
θ
NPV and ΔNPV are presented by box plots and violin plots (Fig. 7); the Spearman
RCCs of critical parameters are shown by a bar chart (Fig. 8).
The practical use of the software was demonstrated for a case study involving
six alternatives: a combination of three process alternatives (continuous high shear,
batch high shear, and batch fluidized bed wet granulation methods) and two
formulation strategies (proportional and common). As a result, alternative number
1, consisting of the continuous high shear wet granulation method and proportional
dosage, showed the highest E
7. Alternative 1 (continuous) was the best in terms of E
(NPV(l)). The violin plots of ΔNPV are shown in Fig.
θ
(NPV(l)), but the possibility
θ
that alternative 2 (batch) became better than alternative 1 was 32%, which could be
too high to exclude alternative 2.
A sensitivity analysis was performed for ΔNPV
to find the relevant parameters
1, 2
to maintain the superiority of alternative 1. Theresults are shown in Fig. 8, wherethe
manufacturing rate in continuous technology was shown to be the most influential
parameter among the process-related parameters (i.e., the higher the rate is, the more
preferable continuous technology becomes). Because the manufacturing rate affects
both lossamounts and productiontime, the impactwas higher than other parameters.
By redefining a PDF of the manufacturing rate, a what-if analysis can be performed
404 K. Matsunami et al.
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to understand how much the change in manufacturing rate would affect the results
(see Ref. [9] for an example). Overall, alternative 1 can be chosen if the feasibility
of a high manufacturing rate is verified.
In the context of the process design framework shown in Figs. 2 and 3, “SoliDecision” as new mechanism 1 can help specify key parameters (e.g., manufacturing
rate) that need to be clarified in the earlier stage, as well as the economically optimal
alternative (e.g., alternative 1 in the case above). Based on these simulation results
as an output of activity A2, the promising alternatives can be determined in activity
A4, or a further experiment in activity A3 can be triggered (through activities A4
and A1).
4 New Mechanism 2: Practical Knowledge of Continuous
Technology
Generally, in process simulation, a priori knowledge is required to set up appropriate
conditions and to interpret the results appropriately. Due to its novelty, such
knowledge is yet to be established for continuous technology. New mechanism 2
aims to cover this gap. Three types of experiments were performed for continuous
technology: large-scale performance comparison with batch technology [10], key
parameter determination regarding product quality and productivity [11], and the
analysis of the start-up operation [12, 13].
4.1 Large-Scale Comparison
Material and Methods
The wet granulation process was investigated with small- (5–10 kg) and largescale (100 kg) experiments. In these experiments, batch fluidized bed granulation,
batch high shear granulation, and continuous high shear granulation were tested
and compared. In all experiments, 29.4 wt%-ethenzamide was used as the API
and mixed with mannitol, microcrystalline cellulose, hydroxypropyl cellulose, and
magnesium stearate.
Granules and tablets were sampled and tested to assess the yield and relevant
quality attributes. Tablet quality targets were set as (a) tablet hardness ≥40 N,
(b) 95% ≤API content ≤105% of the target composition (i.e., 29.4 wt%), and (c)
dissolution rate ≥80% of the API dissolved within 30 min. The impact of scale-up
was analyzed by comparing the results of small- and large-scale experiments. For
the comparison of different dissolution behaviors, a similarity factor f
as shown in Eq. 6.
[–] was used
2
Design Framework and Tools for Solid Drug Product Manufacturing Processes 405
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where n [–], R
⎧
-
⎨
f2= 50 · log
[%], and Tt[%] represent the number of time points, the dissolution
t
1 +
⎩
n
1
R
(
n
t
t=1
− T
.
−0.5
2
)
t
× 100
⎫
⎬
⎭
(6)
of the reference product, and the dissolution of the test product at time t, respectively
[20]. Two dissolution profiles are considered similar if the value of f
is larger than
2
50.
For process performance, product losses and their sources, for example, powders
sticking to the granulator surface, have been analyzed to determine the process
yield in addition to quantifying the amounts of the final product. For the large-scale
experiments, the yield was calculated as the ratio of the mass of the final product
and inputraw materials. In the case ofcontinuous technologies, thefinal product was
defined as that obtained after steady-state operation has been reached with a stable
output. Tablets produced during start-up operations and during the initial condition
setting of the compression were written off as losses.
Results
All tablets in the large-scale experiment achieved the targets mentioned above
of tablet hardness, API content, and dissolution. Comparison of the three tested
technologies showed that the tablet hardness in batch high shear granulation was the
lowest, while API content was lowest with the batch fluidized bed granulation. The
latter result is assumed to be due to higher losses of API as higher concentration
powders escape the bag filter in fluidized bed granulation. Figure 9a shows the
different dissolution profiles in the large-scale experiments, where three profiles
were judged as equivalent because all values of f
were larger than 60. These
2
results confirmed that all tested manufacturing technologies, including continuous
technologies, could achieve the tested tablet quality targets at an industrial scale.
Fig. 9 Comparison of dissolution profiles (a) between technologies and (b) between small- and
large-scale experiments in continuous technology [10]
406 K. Matsunami et al.
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Further findings were obtained regarding scale-up. The dissolution profiles of
each technology were compared between the small- and large-scale experiments.
As presented in Fig. 9b, the dissolution profiles changed significantly in continuous
technology from small- to large-scale experiments, where f
Both profiles fulfilled the product quality targets in the experiments, but the scaledependent change in dissolution profiles highlights scale-up is a key factor that
could affect the final product quality. In continuous operations, scale-up through
runtime extension was found to be not straightforward when using the same
core granulation and drying units in both the small- and large-scale experiments.
When the large-scale experiment was conducted under the same conditions as the
small-scale experiment, the run failed due to equipment clogging and subsequent
malfunction. Additional adjustments were required in the large-scale experiments
to complete the required 4-h operation needed for a 100-kg production scale.
First, the ratio of binder–water (liquid–solid ratio) was reduced from 22 wt% to
18 wt%. Second, the blade rotation speed was reduced from 6000 to 5000 rpm.
Lastly, the screw type in the kneading part of the granulator was changed to reduce
the equipment’s pressure. These changes affected the dissolution profiles. This
experiment was an actual case where unexpected complications could arise with
scale-up.
From the results of process performance, continuous technology showed the
lowest yield. The yield was more than 93% in batch technologies with both fluidized
bed and high shear granulation, whereas the yield in continuous technology was
90.6%. Critical causes of loss in continuous technology were determined to be
residues remaining in the feeder and losses during the start-up process, which
accounted for 4.95% of input materials. The inline monitoring results revealed that
the median granule diameter was higher at the first 4.5 min and stabilized afterward.
To summarize the results, the experimental results provided a feasibility analysis
of new continuous technology. The product quality equivalence was confirmed,
but critical technical challenges were also found, i.e., scale-up issues and start-up
operation. The key factors for solving the scale-up issues in the experiments were
the liquid–solid ratio and the screw specification.
was lower than 50.
2
4.2 Key Parameter Determination
Material and Methods
The same equipment and raw materials were used as in the previous subsection
with ethenzamide as the API. Design of experiment (DoE) was applied. Five
input parameters were selected as factors and changed during the experiments: two
material parameters (API content and the molecular weight of the binder) and three
process parameters (manufacturing rate, blade rotation speed, and the liquid–solid
ratio). As intermediate parameters, granule properties were measured, e.g., granule
Design Framework and Tools for Solid Drug Product Manufacturing Processes 407
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size distribution and circularity distribution. As output parameters, tablet quality,
e.g., dissolution, and productivity, e.g., drying time, were measured.
Based on fractional factorial designs, 22 experimental runs were planned and
performed. The experimental results were analyzed using a five-way analysis of
variance (ANOVA) and the partial least squares (PLS) method. ANOVA was used
to analyze the effects of input parameters, whereas PLS was used to observe the
impacts of intermediate parameters.
Results
The effects of input parameters on granule and product quality were judged by p-
values of ANOVA. The null hypothesis was “there is no difference in the mean
of each property when varying the factors.” Two material parameters showed high
impacts on tablet properties, but the effects were not observed on measured granule
properties. The effects of process parameters on granule properties were confirmed
in the experiments, and the liquid–solid ratio further affected tablet properties such
as dissolution.
The PLS regression coefficients of raw material and granule properties were
calculated for each item of tablet quality. The coefficients for the % of API dissolved
at 3 min, which represented tablet dissolution, are summarized in Fig. 10. Besides
raw material properties (granule properties 1 and 2 in Fig. 10), 10-percentile
circularity was the most relevant parameter; the high impacts of circularity were
also observed for other tablet properties such as disintegration time. This PLS
analysis showed that circularity is a new key parameter that can be monitored to
predict tablet quality. A possible explanation for the correlation between circularity
and dissolution is that high circularity is the result of high levels of agglomeration
during granulation. This would result in a higher granule true density and a delay in
dissolution.
For the drying time as a measure of productivity, ANOVA determined that the
manufacturing rate and the liquid–solid ratio were the most relevant parameters. The
Fig. 10 PLS regression coefficients of granule properties for % of API dissolved at 3 min [11]
408 K. Matsunami et al.
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Fig. 11 Results generated from the effects on drying time: (a) regression model of drying time
and (b) the relationship between liquid–solid ratio and maximum acceptable manufacturing rate
[11]
regression model of drying time was then developed with a 95% prediction interval
(Fig. 11a). Because the acceptable drying time is governed by the manufacturing
rate, an inequality equation of the manufacturing rate and the liquid–solid ratio was
derived (see Ref. [11] for details) and applied (Fig. 11b). The results thus show
that low liquid–solid ratios are preferable in high-speed continuous manufacturing.
This could be further used in product development, e.g., for the selection and
development of excipients for continuous processing.
Through the experiments, the liquid–solid ratio and granule circularity were
determined as the key parameters of continuous wet granulation in terms of tablet
quality and productivity. More specifically, the following practical findings were
obtained: (1) lower liquid–solid ratios are preferred for high-speed manufacturing
and (2) the impact of high circularity on slowing down tablet disintegration and
dissolution.
4.3 Start-Up Operation
Material and Methods
Premixed powders containing 5.0 wt% theophylline were used for the experiments.
A full factorial experimental design was performed to assess the effects of screw
speed, manufacturing rate, and the liquid–solid ratio on the start-up operation in
continuous wet granulation. The torque profiles in the twin-screw granulator and
the particle size distribution of granules were measured during each 1-h production
run.
The torque y at time t was fitted by the first-order system, as shown in Eq. (7).
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