Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5669_Библиотеки_им_академика_М_И_Перельмана
.pdf
352 T. Tagami et al.
is another application of machine learning which is a neural network with several
layers. Deep learning allows the prediction with incredible accuracy. In order to
predict the compositions of drug formulations for 3D printers (printer ink) and to
optimize experimental conditions, effective methods using machine learning have
been reported, although this field of investigation is still in its infancy. In our review,
we introduce the ways that machine learning can contribute to the development of
3D printed pharmaceuticals.
11.2 Utilization of Machine Learning to Predict and Improve
the Accuracy of 3D Printing
3D printing technology is an additive manufacturing method in that an object is
formed by lamination, one layer at a time, from the bottom up. When designing
an object, 3D computer-aided design software is used. The designed 3D object is
saved with the file name extension STL, and then various conditions related to the
3D printer are set using slicer software. The slicer software can determine settings
such as layer, shell, infill, and speed used in the process of 3D printing. The settings
can be adjusted to a fine level of detail, for example, to change and set specific
layers, infills, and speed of the 3D printing process, as well as set the fill pattern,
which can be used to produce a honeycomb structure and the amount of extrusion.
Material extrusion-type 3D printers need to set the temperature of the extruder and
the temperature of the stage, while SLA- and DLP-type 3D printers need to set
the light irradiation time and the lifting rate at which the platform is raised. The
printer condition is converted to a gcode file as the file extension, and 3D printing is
performed. From a recent review, neural network-based machine learning has been
well studied in the field of engineering and has been applied in FDM, binder jetting,
SLS, and other 3D printing methods using a number of parameters (Qi et al.
The settings of the neural network algorithm vary in terms of the number of hidden
layers, number of neurons in each layer, activation function, and loss function, and
obtaining the optimal settings requires a trial-and-error process.
The accuracy of the build depends largely on the type of 3D printer, the setting
conditions, and the materials, and even for the same type of 3D printer, the
performance varies among manufacturers. For this reason, it is necessary to search
for optimal conditions to see if 3D printing is possible using the intended material.
The optimization of 3D printing conditions typically requires user experience, so
it is often costly in terms of time and materials, involving trial and error. Besides
APIs, various pharmaceutical excipients are also included in the drug formulation. Each API has its own physical properties, and the melting point, thermal
stability, solubility, and stability in various solvents differ greatly. Furthermore,
pharmaceutical excipients may have different compositions and contents compared
to the pharmaceutical excipients used in conventional manufacturing methods. It
is expected that pharmaceutical excipients specialized for 3D printers (such as
photocrosslinking substances with vat photopolymerization-type 3D printers) will
be used. For this reason, it is necessary to determine whether the physical properties
2019).
11 Machine Learning in Additive Manufacturing of Pharmaceuticals 353
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
of the manufactured printer ink (formulation) are suitable for 3D printing. A
system that can predict the accurate conditions of 3D printing by machine learning
and artificial intelligence would greatly enhance the 3D printing technology for
pharmaceuticals in the pharmaceutical industry.
The group, FabRx, which is a start-up company focusing on 3D printed medicine;
University College London in the United Kingdom; and the University of A
Coruña in Spain published a research article on M3DISEEN, a web application
for predicting the characteristics of polymer filament, printability, temperature of
3D printing and hot-melt extrusion, and drug dissolution (Ong et al.
2022). In that
study, the authors collected the data of an FDM-type 3D printer and that of polymer
ink prepared by hot-melt extrusion. The in-house and literature-based data were then
utilized for machine learning, and they included parameters related to the extruder
and printer (brand, company, location, speed, temperature), platform temperature,
nozzle diameter, object, and shape. The target variables for prediction were the
extrusion temperature, filament mechanical characteristics, printing temperature,
and printability. The results obtained by random forest, artificial neural networks,
and support vector machines were shown as R
2
values. The accuracies of the
prediction of filament mechanical characteristics and printing temperature were
84% using the analysis of the optimized machine learning models, and random
forest showed the best prediction of extrusion temperature and printing temperature.
The machine learning model also predicted the temperature of 3D printing and hotmelt extrusion (8.4
◦
C for 3D printing; 5.5 ◦C for hot-melt extrusion). These results
showed an improvement in accuracy compared to those reported in their previous
study on M3DISEEN (Elbadawi et al.
2020a).
Real-time monitoring and an auto-correction system to prevent over-extrusion
or under-extrusion of PLA filament in FDM 3D printers have also been proposed
(Jin et al.
2019). A camera was attached to the nozzle of a 3D printer to obtain a
real-time video, and images of the 3D printing process were continuously acquired
at 20 images/6s. If over-extrusion was detected, commands to change the printing
parameters (e.g., printing speed, flow rate, and nozzle height) were executed. The
convolutional neural network used for detection and classification was ResNet50, which was pre-trained and then adjusted for this application. The established
model can achieve an accuracy of 98% for predicting the printed quality of the PLA
filament being printed. Based on the literature introduced above, we showed the
concept of pretest and actual 3D printing incorporating machine learning expected
in future manufacturing (Fig.
11.2).
Nondestructive analysis such as the determination of drug content, water content,
and homogeneity of drug formulation in tablets is used as process analytical
technology (PAT) because it is important for controlling pharmaceutical quality.
3D printed polypills containing amlodipine and lisinopril were prepared using an
SLS-type 3D printer, and the drug contents were analyzed by a portable nearinfrared (NIR) spectrometer (Trenfield et al.
2020). This group previously studied
the feasibility of nondestructive analysis of 3D printed products containing a single
model drug, paracetamol, using NIR spectroscopy and Raman confocal microscopy
(Trenfield et al.
2018). The detection of two different drugs was performed in the
354 T. Tagami et al.
Fig. 11.2 Machine learning-mediated pretest of extrudability and actual 3D printing of medicine
using material extrusion-type 3D printer
second derivative spectrum in the range of specific NIR wavelengths, and the linear
relationship between the actual drug concentration determined by high-performance
liquid chromatography and NIR spectrometer data was acquired by partial linear
square calibration. The drying process after 3D printing is another important step
that can affect the water content and thus the quality of 3D printed tablets. While the
assessment of water content is commonly conducted in the pharmaceutical industry
using NIR spectrometers, 3D printed tablets prepared by micro-extrusion-based 3D
printer were also analyzed, and the method has been reported (Anderspuk et al.
2021).
Differences between batches of 3D printed tablets were investigated using NIR
and Fourier transform infrared (FTIR) (Macedo et al.
2022). The homogeneity of the
spectra of 3D printed tablets was analyzed by principal component analysis (PCA).
The authors prepared polyvinyl alcohol (PVA)-based tablets containing paracetamol
and hydroxypropyl cellulose (HPC)-based tablets containing hydrochlorothiazide
(90/10 or 70/30, w/w; polymer/drug ratio) using FDM 3D printers. The authors
mentioned that the Hotelling’s T
2
, which is the distance and scale from the center
point and the samples in a PCA plot, was within a critical limit. These results
suggested that the tablets had similar properties that were not dependent on the
batch production.
In another study, NIR spectroscopy chemical imaging was used to obtain distribution information and spatial information of the API-loaded materials prepared by
hot-melt extrusion and 3D printing (Khorasani et al. 2016). Extruded PCL-based
film containing indomethacin, extruded poly(oxy-ethylene)-based strand containing
nitrofurantoin, and 3D printed tablets composed of nitrofurantoin, hydroxyapatite,
and PLA using an FDM 3D printer were prepared and were assessed by NIR
11 Machine Learning in Additive Manufacturing of Pharmaceuticals 355
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
chemical imaging with multiple curve resolution-alternating least squares. The
results showed that the extruded films had a heterogeneous chemical distribution
after visual analysis. The detection of the dehydration process of nitrofurantoin was
also mentioned in the study.
3D printed candy-like drug formulations imitating the shape of Starmix confectionery were prepared using FDM 3D printer (Scoutaris et al.
2018). 3D printed
objects of various shapes were shown in the study. The 3D printed formulation
was composed of hydroxypropylmethylcellulose acetate succinate (HPMCAS),
polyethylene glycol (PEG) 6000, and indomethacin. The bulk compounds and drugloaded polymer filament were analyzed by Raman spectrometry. PCA results of
Raman mapping suggested the homogeneity of analyzed samples. In the latter part
of the study, the taste-masking effect of the 3D printed formulation was assessed by
human volunteers. The results indicated that the 3D printed formulation masked the
bitterness of the API remarkably.
Drug homogeneity in 3D printed tablets was analyzed by NIR chemical imaging
or hyperspectral imaging, and the chemical composition (pixel) was assessed by
PCA after preprocessing (Samaro et al.
2021). In that study, a comparison was made
between an FDM 3D printer using polymer filaments and a direct powder extrusiontype 3D printer using a screw to extrude powder or pellets. FDM 3D printing is
highly dependent on the mechanical properties of the drug-loaded polymer filament,
and the amount of drug loading greatly affected the brittleness of the filament.
Several tests of mechanical properties such as tensile strength, three-point bending,
and buckling were conducted. In contrast, as the direct powder extrusion-type 3D
printer did not require the production of polymer filament, 3D printing was feasible
for many drug formulations. The overall porosity and appearance of the 3D printed
tablets were also analyzed by micro-computed tomography.
In a study by Rycerz et al., Lego™ blocklike chewable soft dosage forms were
fabricated, and drug-loaded printer ink was included in the drug formulation (Rycerz
et al.
2019). The soft dosage formulation was composed of gelatin, which produces
a texture similar to gummy candies and is a child-friendly formulation. The melted
formulation was heated at 75
◦
C and was cast into a 3D printed Lego blocklike mold,
and then the drug-loaded formulation was dispensed by 3D printer. In the study, the
dose amount in the 3D printed object was predicted by multiple regression analysis.
The dispensed dose was positively related with the extrusion multiplier and the inner
diameter of the needle and negatively related with the speed of needle movement.
Interestingly, the drug release showed a slowed rate. The authors mentioned that
the printer used locust bean gum, which may form a gel when embedded into
the formulation, and it is estimated that gelatin-based drug formulations probably
dissolve at 37
chewable formulations have also been published (Tagami et al.
◦
C. Related articles on 3D printed gummy drug formulations and
2021a; Zhu et al.
2022; Herrada-Manchón et al. 2020).
356 T. Tagami et al.
11.3 Utilization of Machine Learning to Predict the Physical
Properties of 3D Printed Formulations
As mentioned above, since the physical properties of APIs and pharmaceutical
excipients are diverse, the physical properties of drug formulations are also diverse,
so the construction of a good prediction model is important in terms of quality
control. When formulations are mixed and prepared, the resulting formulation
may exhibit completely different physical properties. Further, the obtained physical
properties may differ greatly depending on the method of mixing and preparation.
In addition to the uniformity of mixing, changes in the crystallinity of APIs and
excipients can occur. These changes directly affect the solubility and elution of the
drug and its absorption from the gastrointestinal tract. Solid dispersions, which are
dispersions of drug molecules or nanoparticles of drugs, are one option to improve
the drug dissolution of poorly water-soluble drugs and can be prepared by FDM 3D
printers. In this case, it is necessary to evaluate and predict the stability of the crystal
form of the drug substance in the solid dispersion, and it is also expected that the
crystal will change into a stable form in long-term storage.
Castro et al. assessed 968 drug formulations from 114 articles after data mining
literature that mentions drugs printed using FDM 3D printers (Castro et al.
They tried to predict the key aspects of 3D printed medicine, such as mechanical
properties of filament, printability, printing and extrusion temperature, and time of
drug release. Several machine learning algorithms were used, including random
forest, support vector machines, logistic regression, K-nearest neighbors, and
artificial neural networks. The same research group applied machine learning to the
prediction of drug dissolution from 3D printed tablets prepared by FDM 3D printing
(Elbadawi et al.
2020b). In that study, they tried to predict the drug dissolution
from the rheological properties of the melted polymer filament at 130
◦
170
C. The polymer filament contained polycaprolactone and PEGs with different
molecular weights, and ciprofloxacin as the API. Several machine learning models
were produced using partial linear square analysis, multi-linear regression, decision
trees, and support vector machines. The authors mentioned that the f
2
is commonly used to evaluate the similarity of drug dissolution, was an acceptable
value in the study.
Our group conducted a study on predicting drug release from 3D printed tablets
produced by DLP 3D printing (Tagami et al.
2021b). The 3D printed tablets, which
were composed of polyethylene glycol diacrylate (PEGDA), did not disintegrate
but released drug. This type of tablet is known as a ghost tablet, and three drugs
(paracetamol, theophylline, and carbamazepine), which have different aqueous
solubilities, were used in the study. The drug release profile fit the Higuchi model,
which corresponds to the pattern of sustained drug release from matrix tablets.
Because this tablet did not disintegrate, drug molecules which were on the surface
of the tablet were released earlier, while drug molecules in the deeper regions of the
tablet needed a longer time to be released, resulting in the Higuchi model-type drug
release pattern. The drug release profile was predicted by (1) obtaining the drug
2021).
◦
C and
factor, which
11 Machine Learning in Additive Manufacturing of Pharmaceuticals 357
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
release amount at the intended time point, which was analyzed by multiple linear
regression, and (2) deciding on the drug release model (Higuchi model or first-order
model), which was analyzed by support vector machine binary classification. We
found that the 3D printed tablets containing paracetamol or carbamazepine exhibited
a good f
lower f
factor. In contrast, 3D printed tablets containing theophylline exhibited a
2
value. We consider the reason for this to be the smaller number of datasets.
2
Martinez et al. prepared PEGDA-based tablets containing paracetamol by SLA
3D printing (Martinez et al.
2018). Several object shapes (e.g., cylinder, sphere,
pyramid, torus, and cube) were prepared to investigate the influence of the geometry
of the tablet on drug release. They showed that the objects with a similar surface
area/volume ratio (SA/V) exhibited a similar drug release rate. Additionally, objects
with increased SA/V exhibited increased drug dissolution. The relationship between
drug release and variables was obtained from multiple regression analysis. The
drug release amount from PEGDA-based tablets was positively related with the
SA/V ratio and negatively related with the weight of the tablet. Since the value
of SA/V was statistically significant in the multiple regression analysis, SA/V was
determined to have an important role in the control of drug release.
PCA was conducted to understand the relationship between the different compositions of 3D printed tablets and the bitterness of the sample dissolved from the
tablets (Wang et al.
2021). In the study, differing amounts of flavors (sucralose and
spearmint) that can mask the bitterness of API were included in levetiracetamloaded orally disintegrating tablets produced using a binder jetting 3D printer,
and the tablets were compared and investigated. After the evaluation of in vitro
dispersion of the tablets and in vitro drug release from the tablets, the bitterness
of dissolved samples containing differing amounts of flavors was assessed using
an electronic tongue. The electronic tongue apparatus could detect seven different
response signals, and the properties of samples were classified with PCA axes 1
and 2, which covered 98.371% of the information. The visual analog scale, which
is a common human gustatory sensation test to evaluate the level of palatability,
was also conducted. Sample 4, which contained the highest amounts of sucralose in
the experimental condition, exhibited a remarkable bitter masking effect. This result
was also reflected in the results of PCA.
Drug loading into polymer filaments for FDM 3D printers was investigated
(Cerda et al.
2020). In this study, a soaking method, which used an organic
solvent to dissolve nifedipine and to load the API into the polymer filament, was
adopted. There are two methods to prepare drug-loaded polymer filaments: hotmelt extrusion and soaking, which is also known as passive diffusion. The soaking
method may be easier for adoption in future clinical settings compared to the hotmelt extrusion method due to the facile preparation. Drug-loaded polymer filaments
were prepared by soaking, and the selection of organic solvent was important for
the drug-loading efficiency (Tagami et al.
2019). The miscibility of the organic
solvent (ethanol or ethyl acetate) and filament (PVA, PLA, or polyurethane) was
investigated not only by measurements and diffusion kinetics of the API into
filament but also by calculating the Hansen solubility parameter (HSP), which is
the theory that materials having a similar cohesive energy are miscible. This theory
358 T. Tagami et al.
has been used for the assessment of drug cocrystals in the field of pharmaceutical
science (Walsh et al.
2018). The authors mentioned that HSP distance between drug,
organic solvent and filament is used as a prescreening tool. In addition, the drug
loading into the filament affected the mechanical properties, resulting in a lower
bending modulus. This tendency was also identified by PCA. Analysis of support
vector regression exhibited a good relationship between the predicted data and the
experimental data of filaments (R
2
= 0.91, for both organic solvents; R2 > 0.98, for
each organic solvent). The authors mentioned that support vector regression could
adopt a kernel function, which is suitable for nonlinear data.
Channeled spherical minitablets containing nifedipine were fabricated by FDM
3D printing (Ayyoubi et al.
2021). Channeled tablets can enhance drug dissolution
and are extensively studied in preparations by FDM 3D printing (Sadia et al.
2018) because typical 3D printed polymer-based matrix tablets can show slower
and sustained-release properties. Minitablets are studied as flexible dosage forms
and are expected to be better for pediatric patients and elderly patients, who may
have difficulty swallowing. The authors prepared PVA-based tablets from drugloaded polymer filament loaded with the soaking method, and Kollidon VA 64-based
and ethyl cellulose-based filament using the hot-melt extrusion method. The drug
dissolution profiles of the 3D printed tablets were compared. PCA was performed
for the drug dissolution, and it was found that the ethyl cellulose-based tablets had
a negative impact on drug dissolution, while the Kollidon VA 64-based tablets had
a positive impact.
In another study, the physical characteristics of 3D printed tablets were classified
by PCA (Hamed et al.
2021). Amorphous lopinavir-loaded Kollicoat IR-based
tablets were produced using an SLS-type 3D printer. The crystallization content
of the API in the 3D printed tablets was dependent on the printing parameters and
the composition. The authors mentioned that the laser scanning speed affects the
duration of exposure to the laser, which affects the crystallinity of the API. The
crystallinity of the API was analyzed by X-ray powder diffraction. The authors also
mentioned that the physical stability was deemed stable without significant change
after conducting a short-term accelerated test.
11.4 Utilization of Machine Learning to Predict Printability
and Detect Defects During 3D Printing
Preventing print failures during 3D printing and detecting signs of print failures as
early as possible are important for efficient planning (Fig.
causes of failures in 3D printing, including failures in the printing of each layer
and poor crimping between layers. In the case of the material extrusion type or
the inkjet type of 3D printer, these failures are typically because the printer inks
cannot be effectively ejected by the nozzle, or the intended 3D printing cannot be
achieved due to excess ejection. If this is caused by the material, it is possible that the
quality of the material varies and thus 3D printing cannot be carried out smoothly
because some of the components are not homogeneous, resulting in nozzle clog.
11.2). There are various
11 Machine Learning in Additive Manufacturing of Pharmaceuticals 359
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
In the case of the stereolithography method, some of the causes of 3D printing
failure may be that light irradiation does not harden the intended layer effectively,
or overcuring occurs and makes the 3D printed object thicker. Since the optimal
amount of printer ink depends not only on the formulation composition but also
on the physical properties of the API used, a trial-and-error process is required.
Regarding the problems involving the 3D printer itself, failure to print may be due
to a variety of factors. For example, the machine may malfunction due to long-term
operation or deformation or damage of parts due to age-related deterioration. For
these reasons, the use of machine learning and artificial intelligence is expected
to be an effective method to predict various troubles during manufacturing and to
effectively perform quality control.
Various types of equipment can aid in the detection of 3D printing defects.
For example, 3D printing has been monitored by pyrometers, thermocouples,
displacement sensors, and IR imaging, which is used as a temperature sensor.
Although these applications are mainly reported in the 3D printing of metal-based
materials, the methodology has also been applied to FDM 3D printing (Li et al.
2019). In one study, the PLA-based part of an engine intake flange was produced by
an FDM 3D printer, and the surface roughness of the 3D printed part was assessed
by the combination of thermocouples to measure the temperature of the build plate
and extruder and an accelerometer to measure the vibration of the build plate
and extruder. Online prediction performance of surface roughness was evaluated
using six different machine learning algorithms, namely, random forest, AdaBoost,
classification and regression tree, support vector regression, ridge regression, and
random vector functional link network. Based on the literature introduced above,
we showed the concept of 3D printing incorporating PAT (and machine learning)
expected in future manufacturing (Fig.
11.3).
Another method to detect defects involves the monitoring of 3D printed parts
by camera. If there is a potential defect found in the 3D printing process, it is
necessary to pause the 3D printing at the appropriate time. In one study, images
of the 3D printing process of acrylonitrile butadiene styrene (ABS) and PLA by
an FDM 3D printer were taken at preset checkpoints. Red, green, and blue color
values of all pixels in the images were calculated (Delli and Chang
2018). Support
Fig. 11.3 Process analytical
technology in the 3D printing
of medicine using material
extrusion-type 3D printer
360 T. Tagami et al.
vector machines, which is a binary classification method, and a supervised machine
learning method were used to classify parts as either good or defective.
Henry et al. investigated the extrusion defects in polymer-blended filament
for FDM 3D printing (Henry et al.
2021). The authors found “fir tree” cracking
defects in the longitudinal and transverse directions when Kollidon VA 64/poly
(ethylene oxide) (PEO)-blended polymer placebo filament was prepared by hotmelt extrusion. This phenomenon is known as melt fracture and can affect filament
feedability. They obtained Raman spectroscopy data of the PEO sample, the
Kollidon VA 64 sample, and the blended filament sample around fissures, as well
as the sample with smooth parts, and PCA was conducted. After the measurement
of the viscosity of the polymer at different temperatures, the authors discussed
methods to avoid melt fracture, such as reduction of die shear using a higher die
landing temperature, reduction of viscosity, use of a wider die and lower extrusion
rate, and possible use of materials with lower molecular weight because viscosity
is dependent on molecular weight. Then zolpidem-loaded caplets were prepared
aiming for withdrawal therapy, and the influence of nozzle size on drug dissolution
was investigated.
Feedability (extrudability) of pharmaceutical polymer filament and paracetamolloaded HPMCAS polymer filament for FDM 3D printer was predicted and assessed
(Nasereddin et al.
2018). The compression test to simulate the feeding process was
conducted using a texture analyzer. The relationship between the flexibility profiles
of the filament and feedability was explained by PCA. Because the feedable group
and unfeedable group were separated successfully by PCA, the authors mentioned
that the flexibility profile of a polymer filament can predict its feedability.
The printability of pharmaceutical polymer filaments using an FDM 3D printer
was assessed in terms of mechanical properties (e.g., maximum tensile strength,
Young’s modulus, and elongation at break), and PCA was conducted to understand
the properties of polymers (Tabriz et al.
2021). This study provided useful basic
information for the prediction of the mechanical properties of drug-loaded polymer
filaments and the similarity of polymer filaments.
Detection of abnormalities in the production of microneedles prepared by FDM
3D printer was conducted by machine learning (Sarabi et al.
2022). In the study,
ten types of needles with different sizes of needle base diameter, needle height, and
draft angle were 3D printed, and chemical etching was conducted. Images of the
needles were obtained by digital camera. After imaging processing, labeled datasets
were prepared by expert manual annotation. Then, classical machine learning
algorithms, including stochastic gradient descent, decision trees, naïve Bayes,
multilayer perceptron, support vector machines, and PCA, were used. Additionally,
deep learning models were used for comparison, including a convolutional neural
network with ResNet-34 as the baseline and MobileNetV2 as the light network, and
ConvNeXt_Base as what was considered to be a state-of-the-art network.
11 Machine Learning in Additive Manufacturing of Pharmaceuticals 361
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
11.5 Application of DoE for QbD in 3D Printed Medicine
Quality by design (QbD) is a scientific and systemic approach to control risk and
is employed in the manufacture of pharmaceuticals in factories. QbD can enhance
the reliability of the resulting product, so this approach is studied for the prediction
of the quality of various kinds of medicine, including 3D printed medicine. QbDbased studies to predict the physical properties of drug formulations and process
parameters have been conducted. QbD has several specific processes (Fig.
For example, the Ishikawa diagram (also known as a fishbone diagram or causeeffect diagram) is used to set the critical quality attributes (CQAs) and the quality
target product profile, which involves the enumeration of assessed risk. Then,
statistical methods using design of experiment (DoE) and response surface methods
are adopted. DoE is a method based on the analysis of variance, and orthogonal
arrays such as central composite design and D-optimal are used to reduce the
number of unnecessary experiments efficiently. The prediction of output (response)
can be analyzed by multiple regression analysis and response surface methodology.
The statistical methods can be used to evaluate the contribution of CQAs against
outputs. In particular, a management area called design space is set up to obtain a
formulation of the desired quality from important factors that affect the qualityrelated characteristics. Within the design space, quality can be ensured flexibly.
As this kind of analysis is conducted by using commercially available software,
similar analysis is possible using the Python programming language. However, there
have been cases in which sufficient formulation quality cannot be predicted even by
using the DoE method. One possible reason for this seems to be that the factors are
too complex and important factors cannot be extracted statistically. In such cases,
prediction by other methods such as machine learning or deep learning may be a
suitable option.
11.4).
Fig. 11.4 Typical process of quality by design approach in drug development
Соседние файлы в папке Библиотека им академика М.И. Перельмана