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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 409
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Fig. 12 Experimental results of the start-up operation: (a) torque profiles at different liquid–solid
ratios and (b) profiles of fractions of fine and oversized granules [12]
y = a − b exp−
t −d
,t ≥ d, (7)
c
where a, b, c, and d are the fitting parameters. The effects of process parameters
on the values of the fitting parameters were assessed by ANOVA. The particle size
distributions were monitored inline and were measured by an offline measurement.
Several indicators were used to find the appropriate indicator for the judgment of
start-up, e.g., median size, fractions of fine or oversized granules, and the principal
components of size distributions.
Results
The torque profiles of the two runs are summarized in Fig. 12a, where raw and fitted
data are shown as light and dark color, respectively. In all runs, the effects of the
start-up operation were observed through profiles dependent on runs. The liquid–
solid ratio was the most influential parameter on the torque profiles; significant
differences were measured in the value of a. The increase in torque was larger
for the high liquid–solid ratio, but the torque was slightly increased for the low
liquid–solid ratio. These differences were thought to be due to the differences in the
agglomeration rate and sticking.
A profile of fractions of fine and oversized granules is shown in Fig. 12b, where
a run of the low liquid–solid ratio was measured using an inline measurement.
The dark lines are the moving averages of the raw data, which are shown as
the light lines. Unlike the results of the torque profiles, the start-up effects were
not obvious in the results of particle size distributions. The principal component
analysis also showed that the effects of the start-up were small compared to the
deviations of particle size through the operation. While torques and particle sizes
have correlations in general, it was not observed in the investigated start-up.
410 K. Matsunami et al.
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4.4 Summary of Three Experiments
The large-scale experimental comparison specified two critical aspects of continuous technology, namely, scale-up issues and start-up operation. DoE-based
experiments determined key parameters for both product quality and productivity,
i.e., the liquid–solid ratio and circularity. Moreover, the definition of the start-up
operation was found to be different depending on torque and granule size. In the
context of the design framework in Figs. 2 and 3, these findings, despite being
limited to specific materials and processes, could still help specify appropriate input
parameters in activity A2. For example, when applying a high manufacturing rate
in continuous technology, the value of the liquid–solid ratio needs to be checked to
ascertain whether it is too high (see Fig. 11). In specifying the liquid–solid ratio, the
time required for the start-up operation could also be scrutinized, considering the
influence of the parameter on process behavior (see Fig. 12). If further experimental
support is needed to validate the simulation results, activity A3 could be activated.
5 Conclusion
This chapter presented a design framework and the associated new tools for solid
drug product manufacturing processes. IDEF0 activity modeling was applied to
define a process design pathway. To deal with the difficulty of the powder processes,
the developed activity model highlighted the interactions between simulation and
experimental investigation. Two newly developed mechanisms were introduced
for conducting simulation-oriented analysis. One was the superstructure-based
economic assessment tool for the comprehensive generation, analysis, and assessment of process alternatives. Stochastic simulation was integrated to propagate
the uncertainty of the input parameters into the NPV assessment result. The
algorithm was implemented as an original software called “SoliDecision.” The
other mechanism was the practical knowledge of continuous technology obtained
through experiments. Although the experiment was limited to specific raw materials
and process conditions, investigation of the scale-up and start-up operations and
key process parameters provided useful insights for process simulation. These new
mechanisms in the framework can effectively assist simulation-based, and more
rational and efficient, design of solid drug product manufacturing processes.
The proposed framework can serve as a “hub” for various other mechanisms
to be developed in the future. For example, population balance models can be
integrated with our economic assessment tool to incorporate material and process
characteristics in the calculation. Further experiments with different raw materials,
processes, and equipment setups could provide more general insights, which could
be also useful for the simulation-oriented design activities. To this end, more
collaborative efforts between simulation and experiment are desired.
Design Framework and Tools for Solid Drug Product Manufacturing Processes 411
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Acknowledgments The authors acknowledge Dr. Hiroshi Nakagawa, Dr. Shuichi Tanabe, and Mr.
Keita Yaginuma from Daiichi Sankyo Co., Ltd.; Mr. Koji Hasegawa and Mr. Takuya Nagato from
Powrex Corporation; Prof. Thomas De Beer, Prof. Ingmar Nopens, Dr. Alexander Ryckaert, and
Mr. Michiel Peeters at Ghent University; and Mr. Fabian Sternal at Friedrich-Alexander University
Erlangen-Nürnberg for useful discussions. H.S. is thankful for the financial support by a Grant-inAid for Young Scientists(A) [Grant number 17H04964]and a Grant-in-Aid forScientific Research
(B) [Grant number 21H01704] from the Japan Society for the Promotion of Science (JSPS) in
conducting part of this research. K. M. used the financial support of a Grant-in-Aid for JSPS
Research Fellows [Grant number 18 J22793] for conducting part of this research.
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st
Autumn Meeting, T216.
Challenges and Solutions in Drug
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Product Process Development
from a Material Science Perspective
Fanny Stauffer, Pierre-François Chavez, Julie Fahier, Corentin Larcy,
Mehrdad Pasha, and Gabrielle Pilcer
1 Introduction
Pharmaceutical process development is a delicate balance between scientific process
understanding, resource, and time constraints. While drug product (DP) development teams aim at designing optimal processes, the development time is usually
limited to accelerate the time to market [1]. In addition, the DP development is
usually performed in parallel to the chemical process development, limiting the
quantity of active pharmaceutical ingredient (API) available for the formulation and
DP process development.
The current regulatory environment clearly highlights the prerequisite of a thorough process understanding. The critical material attributes and process parameters
must be identified. In addition, the demonstration of an “enhanced knowledge of
product performance over a wide range of material attributes, processing options
and process parameters” is encouraged for the filing [2]. On the one hand, the
identification of critical process parameters and their impact on product and process
performance is well understood and is commonly done during process development.
On the other hand, the critical material attributes are more difficult to assess and are
less often considered. The considered physiochemical and biological properties of
the API are often limited to the parameters that are impacting the biopharmaceutical
activity of the product but rarely include considerations regarding the manufacturability ofthe compound and the interactions between process parametersand material
attributes.
The identification of the material attributes impacting the product and process
performance is nonetheless a critical aspect of the drug product development, and
F. Stauffer () · P.-F. Chavez · J. Fahier · C. Larcy · M. Pasha · G. Pilcer
UCB Pharma, Braine l’Alleud, Belgium
e-mail: fanny.stauffer@ucb.com
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
A. Fytopoulos et al. (eds.), Optimization of Pharmaceutical Processes, Springer
Optimization and Its Applications 189, https://doi.org/10.1007/978-3-030-90924-6_16
413
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appropriate studies are required to address this challenge. Due to the discrete
nature of particulate systems, it is important to understand single-particle properties, inter-particle and particle-boundary interactions, and bulk powder responses.
Characterization of single-particle properties is favorable due to limited availability
of APIs at early stages of drug product and process development. Although several
attempts have been made in the literature to link single-particle properties to the
bulk powder behavior either experimentally or via modelling approaches, carrying
such characterization is often challenging, time-consuming, and expensive [3–5].
Moreover, the properties at this length scale have variable distributions, which
questions the representability and relevance of single-particle measurements. For
instance, atomic force microscopy measurements of particles with complex surface
morphology and shape yield extremely variable values of adhesion force between
two particles [6].
Considering the representativeness and interpretation challenges associated with
single-particle measurements, characterizations are often performed at the bulk
level. It is hence crucial to account for the relevance and representability of the
characterization methods and properties at the bulk level. In bulk powder characterization, it is also most crucial that the samples selected for the measurements
should be physically and chemically representative of the bulk [7]. Moreover,
bulk powder properties are sensitive to the stress level of particles, dynamics
and strain rates of the process, and environmental conditions such as relative
humidity and temperature. Examples are as follows: (a) flow properties of cohesive
powders deteriorate under stress due to the increased number of contacts and
contact area [8], (b) compressibility response is very sensitive to the compaction
rate [9], and (c) the presence of moisture and prolonged storage time lead to
caking phenomena and deterioration of powder flow properties [10]. As a result,
multiplying characterization tests often results in contradictory findings that are
then difficult to interpret if the property measured by the selected characterization
technique cannot immediately be linked to the process of interest. Unlike liquid
and gas systems, fundamental scale-up rules are not established for particle systems
and processes [11]. In the absence of representative characterization technique, the
best solution can then be the miniaturization and pilot testing in addition to sample
characterization.
In this chapter, we identified specific challenges associated with material
attributes and solutions that can be applied to overcome them using industrial
case studies. In the first case study, quality-by-design (QbD) principles will be used
to identify critical material attributes (CMAs) with a limited number of API batches
for a product under development. In the second case study, miniaturization will be
applied to develop a new process with reduced material consumption. In the third
case study, the knowledge acquired on a marketed product will be transposed for the
development of a new process. In the fourth case study, targeted characterizations
will be applied to new API sources to mitigate troubleshooting for commercial
products.
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2 Case 1: Identifying CMAs with a Limited Number of API
Batches
This first case study describes the development of a new drug product. During
development phases, the amount of API batches is limited, and the batch-tobatch variability is unknown. It is therefore difficult to identify relevant CMAs
at this stage. Applying an enhanced QbD approach could nonetheless guide early
identification of the CMAs.
In the last decade, the pharmaceutical industry was encouraged by authorities to
enhance knowledge and understanding of products and manufacturing processes.
In order to help industries in this task, the International Conference on Harmonization (ICH) has published several guidelines such as the ICH Q8 guideline on
pharmaceutical development. This guideline mainly covers the concept of quality by
design (QbD) described as a science- and risk-based approach for which the quality
should not be tested into products but should be built in by design. Furthermore,
this QbD approach contributes to a continuous improvement of the product quality
by a systematic assessment and understanding and refining of the formulation and
processes throughout the product life cycle [2, 12].
ICH Q8 focuses on the pharmaceutical development that aims at designing
products and manufacturing processes to provide the intended performance of the
products and to meet the needs of patients. Therefore, pharmaceutical industries
should demonstrate that the proposed manufacturing processes and formulations
are suitable for the intended purpose. It can be eased by the achievement of
development studies that allow to acquire an enhanced understanding of product
performance over a wide range of material attributes and process parameters. This
understanding can be earned by preliminary knowledge, design of experiments, and
implementation of process analytical technology and can lead to the definition of a
design space.
According to ICH Q8(R2), all pharmaceutical development should include, at a
minimum, the following deliverables:
• Define the Quality Target Product Profile (QTPP)
• Identify potential Critical Quality Attributes (CQAs)
• Determine the CQAs
• Select an appropriate manufacturing process
• Define a control strategy.
In this case study, the QTPP was first defined, and then, from this QTPP,
CQAs that are “physical, chemical, biological or microbiological properties or
characteristics that should be within an appropriate limit, range, or distribution to
ensure the desired product quality” (ICH Q8) were identified. The DP process was
based on continuous twin-screw wet granulation. Wet granulation technology was
selected because of the API properties (i.e., poorly flowable, highly cohesive) and
relatively high drug load. The continuous manufacturing technology was selected
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as it allowed reducing API consumption during development and enabled faster
development time as no scale-up is necessary [13].
It was moreover decided to apply an enhanced QbD approach that also includes
the following deliverables:
• Identify the material attributes and process parameters that can have an effect on
product CQAs
• Determine the functional relationship that links material attributes and process
parameters to product CQAs
• Use the enhanced product and process understanding in combination with
Quality Risk Management (QRM) to establish an appropriate control strategy.
In consequence, knowledge collected during the pharmaceutical development
studies supplies an essential scientific understanding for the implementation of
design space, specifications, manufacturing controls, and quality risk management.
Quality risk management described in ICH Q9 document is useful to prioritize
the list of potential CQAs and to identify the relevant ones [14]. Risk assessment,
used in quality risk management, is also helpful to identify material attributes and
process parameters that have an impact on product CQAs based on preliminary
knowledge and experimental data (screening experiments). In the present case
study, a Potential Risk Identification using Severity Matrix (PRISM) analysis was
performed to guide the development. A priori risks related to raw material attributes
and process parameters were listed and prioritized to design development studies.
The API under development was a BCS class II compound. The modeling of the
dissolution highlighted the API particle size as a potential CMA for the dissolution
rate of the final DP. In the absence of ICH definition, a CMA can be described as a
measurable characteristic of a starting material or raw material, whose variability
has an impact on a CQA and therefore should be monitored or controlled to
ensure the process produces the desired quality. Yu et al. especially mentioned that
“drug substance, excipients, and in-process materials may have many CMAs” [15].
Other potential CMAs were indeed identified by the risk analysis such as the API
flowability, which could impact feeding stability and therefore final product content
uniformity.
Extensive material characterization was performed on each API batch in order
to characterize the variability of the API physical attributes. However, as the
drug substance (DS) manufacturing process was still under development, a limited
variability had been observed between the API batches. The observed variability
was moreover not necessarily representative of the future commercial process due to
the diversification of API sources. In order to evaluate the impact of API variability
on the CQAs and manufacturability, API batches having extreme properties based
on the predicted biological activity were manufactured. These extreme batches
were then used for process development. The impact of potential CMAs on the
CQAs were studied together with the potential CPPs using design of experiments
(DoEs). The API particle size being confirmed as a CMA, this material attribute,
and the identified CPPs were included in the full line optimization DoE. The design
space is defined in ICH Q8 as “the multidimensional combination and interaction
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Fig. 1 Map of the probability of success over the experimental space for tablet assay. Blue:
probability of success ≥0.95, red: probability of success <0.95
of input variables (e.g., material attributes) and process parameters that have been
demonstrated toprovide assurance of quality.” Design space iscommonly calculated
based on process optimization results [16, 17]. The full line design space was
defined based on DoE results as illustrated in Fig. 1. In this figure, each combination
of process parameters is colored based on their probability of success. The design
space corresponds to the blue areas. As seen in the picture, the product temperature
(T) is the main driver for the product assay, a low value resulting in a high risk of
failure.
The generation of a design space as a sound development practice does not
preclude or determine if a design space is included in the registration file. In
this case, proven acceptance ranges (PAR) were extracted from the design space
study for the registration file. As seen in the design space, the API particle size
(PSD median), though being varied only within an acceptable range to ensure
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adequate biological activity, impacted the tablet assay to an extent that could lead
to out-of-specification assay results. It is, however, not advantageous to reduce
API specification for commercial manufacturing. The PAR was therefore defined
to accommodate for all API batches by restricting the accepted process range. In
this case, the design space contained but was not limited to the PAR [18].
In addition to the enhanced scientific understanding and PAR definition, the
development of a design space was also used for control strategy with a real-time
monitoring of impacting CPPs allowing to decrease end-product testing. A control
strategy is defined in ICH Q10 guideline as “a planned set of controls, derived
from current product and process understanding, that ensures process performance
and product quality” ([12], p. 10). The controls included parameters and attributes
related to drug substance and drug product:
• Control of the CMAs including API particle size
• Control of the CPPs
• Product specifications
• Control of critical manufacturing steps
• In-process analysis and control of the CQAs instead of end-product testing
• A planned maintenance to check the multivariate prediction models as the design
space was considered for predicting some CQAs
As seen in this case study, applying an enhanced QbD approach during process
development allowed identifying API CMAs with limited amount of materials
and batches. The risk analysis combined with extensive material characterization
identified the potential CMAs and their correlation. The inclusion of API batches
manufactured to map the expected future variability in the DoEs then allowed to
confirm the CMAs and to design a robust process that can accommodate for this
variability. This is only the first step as the knowledge on the raw material CMAs
should be continuously built during life cycle management as will be demonstrated
in the fourth case study.
This first case study described the development of a new drug product. During
development phases, the amount of API batches is limited and the batch-to-batch
variability unknown. It is therefore difficult to identify relevant CMAs at this
stage. Applying an enhanced QbD approach could nonetheless guide in an early
identification of the CMAs.
3 Case 2: Accelerating Process Development Using
Miniaturized Equipment
The previous case study used continuous manufacturing to reduce development time
and materialconsumption. Process miniaturizationcan offer a valuable alternative to
full-scale development to characterize raw materials, develop formulation, and run
feasibility studies with reduced amount of materials. In the context of development
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