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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5395_Библиотеки_им_академика_М_И_Перельмана
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acceleration and reduction of material consumption, a methodology using only 10 g
of material has been developed to assess the suitability of a given formulation for
roller compaction.
Roller compaction is a dry granulation process in which a blend containing active
ingredients and excipients are agglomerated together, thanks to the mechanical
stress applied by two rollers of a compactor and directly milled into granules
afterward. The resulting granules can then be blended with an external phase prior
to compression into tablets or prior to capsule filling. The aim of roller compaction
is to increase powder flowability, prevent active ingredient segregation, and improve
product stability while decreasing bulk volume.
The development of roller compaction processes is currently performed on pilot
scale equipment. Despite best efforts in developing small-scale roller compactors,
this approach still requires few kilograms of blend and therefore a corresponding
amount of active ingredients that are not always available at the early development
stage of a new drug product compound. The amount of materials required is even
more significant when different blend compositions are investigated. Moreover,
material conveying and powder adhesion to rollers’ surfaceare additional challenges
that need to be addressed before evaluating the compaction itself.
According to literature, in order to ensure complete granulation and therefore
ensure suitable cohesiveness of the granules, roller compaction development should
target [19] (a) a solid fraction of around 0.7 and (b) a tensile strength – defined by
a three-point bending flexural test – of at least 1 MPa [20, 21]. Process parameters
allowing to obtain these attributes can be generated by roller compaction studies.
However, compaction simulators can also be used. In a first step, a compaction
simulator equipped with force and displacement sensors on each punch is used,
with a set of round flat punches, together with a semiautomatic tablet testing system
to characterize the manufactured tablets. Therefore, two tablet attributes can easily
and rapidly be evaluated:
1. The compressibility: the ability of a powder to decrease in volume under
pressure – expressed as the solid fraction of the tablets as a function of the
tableting compression.
2. The tabletability: the ability to form tablets of certain properties under pressure –
expressed as the tensile strength of the tablets in function of the tableting
compression.
A recent update of the Styl’One Evolution allows to mimic the roller compaction
process parameters with uniaxial compaction of the powder to ribbon-like tablets –
called ribblets – with a set of rectangular flat punches. This module, called RoCo
Pack, uses a model based on the thin layer model described by Peter et al., as seen in
Fig. 2 [23]. The model explains that in a roller compactor, the powder between the
rolls is divided into thin layers that consist of a mass which remains constant during
the process. The layers have a width determined by the roll width and a constant
height. Only the length differs, depending on the position between the rolls, which
decreases from the nip angle to the gap size. Considering this evolution of the layer
length, the density of each layer evolves as well.
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Fig. 2 Thin-layer model [23]
The evolution of the in-die density according to the applied compression along
with the characteristics of the formulation allows to convert a hydraulic pressure
(expressed in MPa) into a roll force per unit (expressed in kN/cm or MPa) and
vice versa. That learning phase of the module allows then to manufacture ribblets
corresponding to specific roller compaction process parameters.
The compaction assessment therefore uses the round flat punches to establish
the compressibility and tabletability profiles, identifying the hydraulic compression
force to apply on the blend to obtain (a) the suitable solid fraction and (b) the
aimed diametrical-compression tensile strength. As said before, it is usually advised
to target ribbons with a solid fraction of 0.7 and a three-point bending tensile
strength of at least 1 MPa. Hilden et al. [24] showed a correspondence 1:2 between
the diametrical compression tensile strength and the three-point bending tensile
strength. Therefore the target of at least 0.5 MPa of diametrical tensile strength
should be aimed. Finally, the RoCo Pack module is used with the rectangular flat
punches to determine the hydraulic compression force corresponding to the roller
compaction parameters (such as compaction force or gap size) on the equipment of
interest.
In this study, the downscaling was performed from a roller compactor to the
RoCo Pack for two in-house products and afterward with the 11.28-mm round
flat punch in order to verify the miniaturization approach. This would indicate the
targets in solid fraction and diametrical-compression tensile strength to aim for a
new product using the tablet press simulator. If the approach if verified, the amount
of material to be used for roller compaction feasibility and formulation development
studies could be reduced from a few kilograms per blend to about 10 g.
Two in-house validated products have been used to develop this methodology:
Product A, for which an external phase is added prior to tableting, and Product B,
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for which the granules are directly tableted. Three batches from those two products
have been used, and homogenized blends have been sampled in the commercial
facilities. Blends were characterized by true density using helium pycnometry.
For the RoCo Pack learning phase, blends weremanually filled intothe die cavity.
This learning phase allowed to obtain the correspondence between hydraulic force
and roll compaction process parameters. Ribblets were characterized in terms of
mass and thickness. Once the learning phase was performed and the commercial
process parameters (i.e., gap size and compaction force) were entered into the
recipe, hydraulic pressures of 140 MPa and 42 MPa appeared to correspond for
all batches of Product A and Product B, respectively.
For each blend, the compaction behavior was then characterized using the 1compression mode provided as a default by manufacturer. At each compaction,
300 mg of powder was accurately weighed and was manually poured into the
die cavity. Then, the powder was compacted with the pressure applied from the
punch-die set. To correspond to the stress applied during roller compaction, a range
of hydraulic pressure from 20 to 200 MPa has been investigated. At least three
tablets per compression force have been manufactured. Tablets were characterized
in termsof mass, hardness,and thickness. Theenvelop density(Eq. 1) and the tensile
strength (Eq. 2) have been calculated for each tablet. Using the blend true density,
the out-of-die solid fraction (Eq. 3) has been determined.
Envelop density
g
mL
=
π × Diameter
4 ×Tablet masse(mg
2
× Thickness(mm
(mm)
)
(1)
)
Solid fraction =
Tensile strength(MPa)=
Therefore, two tablet attributes can be evaluated: (a) the compressibility of the
material, by plotting the solid fraction as a function of the compression applied, and
(b) the tabletability of the material, by plotting the tensile strength in function of the
compression applied.
The compressibility profiles of each batch of Product A and Product B are displayed in Fig. 3 by plotting the obtained round flat tablet solid fraction according to
the compression applied. The compressibility profiles were fitted with a logarithmic
trendline leading to a fitted R
solid fraction obtained at the hydraulic pressures estimated for both products. Solid
fractions of 0.85 and 0.81 were obtained for Product A and Product B, respectively.
Envelop density
Preblend true density
2 ×Tablet Hardness(N
mL
g
g
mL
)
π × Tablet diameter(mm)× Tablet thickness(mm
2
> 0.98. This fitting was used to interpolate the
(2)
)
(3)
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0.91
0.89
0.87
0.85
0.83
0.81
SOLID FRACTION
0.79
0.77
0.75
20 40 60 80 100 120 140 160
COMPRESSION (MPA)
Fig. 3 Compressibility profiles of Product A and Product B
Product A Batch #1
Product A Batch #2
Product A Batch #3
Product B Batch #1
Product B Batch #2
Product B Batch #3
Compared to the reference value of0.7 found in the literature, it was seen that higher
solid fractions were needed to obtain appropriate cohesiveness for the products
under investigation.
The tabletability profiles of each batch of Product A and Product B are displayed
in Fig. 4 by plotting the obtained round flat tablet tensile strength according to
the compression applied. The compressibility profiles were fitted with a linear
trendline leading to a fitted R
2
> 0.98. This fitting was used to interpolate the tensile
strength obtained at the hydraulic pressures estimated for both products. Diametrical
compression tensile strengths of 0.52 MPa and 0.41 MPa were obtained for Product
A and Product B, respectively. These values are aligned with the one mentioned in
the introduction, taking into consideration that 0.5 MPa of diametrical compression
tensile strength corresponds to 1 MPa of three-point bending tensile strength.
Through the characterization of the two in-house products via the Styl’One
Evolution mounted with round flat tablets manufactured mimicking the commercial
process, a solid fraction up to 0.85 and a diametrical-compression tensile strength
around 0.5 MPa should be targeted to achieve cohesive ribblets. By miniaturizing
the roller compaction process, it was possible to perform the early assessment of this
process with only 10 g of blend per tested batch. This methodology could also be
used to run feasibility studies for new chemical entities or formulation development
rapidly and with minimum amount of materials. Also, the low amount of material
required allows to assess different qualitative and quantitative compositions of
blends before applying the optimal parameter conditions to a pilot-scale roller
compactor. It is therefore well suited to evaluate the impact of various material
attributes. It has nonetheless to be noticed that this miniaturization approach
cannot anticipate feeding performance and potential powder adhesion to the roller’s
surface.
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2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
TENSILE STRENGTH (MPA)
0.4
0.2
0.0
0 20406080100120140160180
COMPRESSION (MPA)
Product A Batch #1
Product A Batch #2
Product A Batch #3
Product B Batch #1
Product B Batch #2
Product B Batch #3
Fig. 4 Tabletability of Product A and Product B
4 Case 3: Coping with Batch-to-Batch Variability in Drug
Product Process Improvement
This third case study describes the development of a new process for a commercial
product. Process improvement and product extension are common during product
life cycle. Existing processes might benefit from process improvement to reduce
the cost of good and environmental footprint during product life cycle. Product
extension can be considered to develop modified release forms to reduce pill burden.
In both cases, the knowledge acquired during initial process development can be
utilized to develop a robust improved process or a new formulation.
In the present case, the aim of the process change was to improve the drug
product manufacturing process in order to reduce the cost of good. The original
process was based on batch high shear wet granulation. Continuous twin-screw
wet granulation was considered as an alternative to the batch process. Continuous
manufacturing is indeed renowned for being more efficient, more flexible, and
greener [13, 25–28]. The main challenges associated with the change of process
from batch to continuous were the poor flowability of the API, its high drug load in
the formulation, and its batch-to-batch variability which impact on the process was
not fully understood. The aim was therefore to design a continuous process, keeping
the same formulation and mitigating the impact of the API batch-to-batch variability
on processability while ensuring consistent product quality attributes between the
batch and the continuous process.
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4.1 Identification of the API Variability
The first step toward designing a new and more robust drug product process was to
better understand the API batch-to-batch variability. Raw materials indeed present
slight variations of physical properties not only from manufacturer to manufacturer
but also from batch to batch. In order to capture this variability and to then account
for it during process development, a thorough material characterization should be
performed on each batch of raw materials. The API was manufactured at different
manufacturing sites and using different synthetic routes, crystallization, drying and
delumping processes, and manufacturing equipment. The API manufacturing could
be grouped into four processes called Processes 1, 2, 3, and 4 in this section. From
the experience acquired on the batch drug product process, the API variability
existed notonly between but also within drug substance processes. The link between
API characteristics and drug product manufacturability was, however, missing,
leading to drug product process adjustments.
In recent years, scientific researches investigating the link between material
attributes and process performance increased dramatically. Standardized approaches
involving material science, particle engineering, and process development are
indeed being developed to rationalize product development [1, 29] or guide process
selection [20].
While historical studies usually based their conclusions on a few selected
materials to investigate the impact of specific material attributes [30, 31], the
current standard relies on the use of broad material databases and extensive material
characterization [32–34]. These databases are then exploited using multivariate
statistical analysis to select relevant materials and to link material attributes and
process performance. This was especially done for twin-screw feeding [35–38],
dry granulation [39], or compression processes [40] for instance. The end use is
to predict the behavior of a new compound based on its characteristics or to guide
the process selection.
This type of studies has demonstrated their relevance when comparing materials
with large differences in terms of material attributes (e.g., cohesive APIs versus
free-flowing excipients). It is, however, not suited to address the challenges related
to batch-to-batch variability and to identify critical material attributes for a given
compound. The methodology is nonetheless transferable, the material database
being dedicated to a specific compound and no longer to multiple ones. It was
successfully applied to excipients [41–43] but also API [44].
In this project, API batches were systematically characterized in terms of PSD,
density, and agglomeration profile. Based on this initial database, eight API batches
were selected for process screening, and four more were selected for process
optimization. Three batches were selected from drug substance process 1 (P1/1-3),
three from process 2 (P2/1-3), two from process 3 (P3/1-2), and three from process 4
(P4/1-3). All batches were within specifications in terms of purity, residual solvents,
crystallinity, and polymorphism.
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For all batches, the physical characterization was extended to better identify
what characteristics are changing between batches and what variability is impacting
the drug product process. The selected API batches were therefore characterized
for PSD, density, compressibility, agglomeration profile, specific surface area,
surface energy, electrostatic charging, angle of avalanche, and rheology. In total, 17
properties were measured for each API batch. Principal component analysis (PCA)
was used to extract relevant information from this API database. The methodology
is further detailed in a dedicated article [22]. The PCA consisted of three principal
components (PCs) and explained 92.9% of the API variability. Figure 5 presents
the PC1/PC2 and PC2/PC3 loading scatter plots. The first PC was related to crystal
length, agglomerates, flowability, and electrostatic charging. The second PC was
related to the span of the PSD and the agglomerate strength. Finally, the third PC
was related to surface energy of the API.
The API batches could then be clustered using the corresponding score plots as
seen in Fig. 6. It was, for instance, observed that the selected API batches from
process 2 were characterized by larger particles and better flowability in opposition
to the selected API batches from process 4.
4.2 Process Development
Thanks to the PCA, the API variability was quantified. The next step consisted of
including this variability in the process development to ensure the development of
a drug process that accounts for the API variability. The drug product consisted of
an intra-granular phase and an extra-granular phase. The API represented 64% of
the intra-granular phase explaining its prominent impact on processability. For each
process step (i.e., feeding [45], granulation [46], drying, milling, final blending, and
tableting), both the potential material attributes and the process parameters were
included in the design of experiments. In order to quantify the impact of the API
variability on process performance, the three PC scores associated to each API
were used as quantitative factors to design and interpret process development trials.
This allowed including the evaluated variability of the API properties with a limited
number of uncorrelated factors.
During granulation screening for instance, the impact of the API variability was
studied together with the screw speed and the liquid-to-solid (L/S) ratio. Thanks to
this approach, the two first PCs were identified as potential CMA and the L/S ratioas
a potential CPP. This information was then used to optimize the full manufacturing
process (from pre-blend to tablet) applying the same approach [47]. As a result, a
multistep design space including both the API CMAs and the CPPs was defined.
The design space (in green in Fig. 7) demonstrated the ability to define process
settings leading to conforming product CQAs regardless of the API characteristics.
The design space was finally verified with external API batches.
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Fig. 5 PC1/PC2 and PC2/PC3 loading plots – colored according to properties characterized. In
blue: PSD parameter, in light blue: agglomerate parameters, in orange: flowability parameters, in
purple: surface parameters, in red: electrostatic charges, in green: density [22]
In this case study, the prior knowledge on API batch-to-batch variability was
used to design a new process for an existing product. The existing characterization
data were complemented for a selected number of API batches in order to identify
potential CMAs. API batches were then enrolled in the process development studies
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Fig. 6 PC1/PC2 and PC1/PC3 score plot – colored according to the drug substance process. In
green: process 1, in blue: process 2, in red: process 3, in yellow: process 4 [22]
to link API properties to process and product performance. This approach allowed
to design a robust DP process able to cope with the previously identified API batchto-batch variability.
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Fig. 7 Design space of the process line with a risk of failure of 1% guaranteeing granule LOD
below 3.0%, dryer air flow deviation below 5.0%, and tablet press punch displacement deviation
below 0.5% [47]
5 Case 4: Mitigating Troubleshooting Induced by New API
Sources on Commercial Drug Product Processes
The following case study focuses on examples of a continual improvement across
the product life cycle of a commercial drug product, namely, the new sourcing
of the drug substance (DS). Indeed, the development and improvement of API
manufacturing process usually continue over its life cycle.
The outsourcing of API manufacturing has several objectives such as the capacity
and market extension and the reduction of the cost of good. However, any change
should beevaluated for the impacton the qualityof the DSand potentially onthe DP.
This evaluation is based on scientific understanding of the manufacturing process,
and appropriate testing should be determined to analyze the impact of the proposed
change. Studies should therefore be carried out to demonstrate the equivalence of
the sources, usually by comparing the new API source to a reference material. Thus,
no influence on DP process and quality should be expected. While this comparison
should at least be based on specifications, including relevant material properties
that affect the DP process is key to mitigate future troubleshooting. This type of
evaluation is even more complex as processability differences often result from a
combination of multiple API properties (e.g., granulometry, surface properties, etc.)
as highlighted in the previous case study. In extreme cases, DP process adjustment
is performed to cope with the new properties of the new API source.
5.1 Impact of Storage Conditions and Shipment
The first example concerns a drug substance A in the form of powder. The drug
product process was a dry granulation followed by tableting leading to coated
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