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_5580_Библиотеки_им_академика_М_И_Перельмана
.pdf
17 Design of Biomaterials Using Informatics 319
https://t.me/med1917
Fig. 17.5 Chemical structures of monomers used for the fabrication of polymer brush films. a N-
(2-hydroxypropyl) methacrylamide (HPMA), b Carboxybetaine methacrylate (CBMA), c Ethylene
glycol (EG), d 2-hydroxyethyl methacrylate (HEMA), and e 2-(dimethyl amino)ethyl methacrylate
(DMAEMA). (Redrawn based on the figure in [
11])
component adsorption experiments. We measured the quantity of adsorption using
surface plasmon resonance (SPR) spectroscopy.
We employed machine learning to construct and compare several regression
models, including artificial neural networks (ANN), support vector machines (SVM),
and random forests (RF). After considering the accuracy of the predictions, we
selected RF as the algorithm to use.
Figure 17.6 displays the results of training the RF using the aforementioned dataset
to predict the amount of serum component adsorption. In this study, we were able
to predict the quantity of serum component adsorption with an R-value accuracy of
0.87. Consequently, it became apparent that it’s feasible to predict the quantity of
biomolecule adsorption on polymer films, which exhibit varying properties based
on manufacturing conditions unlike SAMs. This is achieved by introducing the film
structure parameters obtained experimentally from the molecular structure forming
the film as explanatory variables.
17.3 Concluding Remarks
In this chapter, we explored numerous applications of informatics in biomaterial
design and the prediction of material functions. We discussed the inherent difficulties in predicting the function and performance of biomaterials using theoretical
approaches and simulations. Given these complexities, the datasets used tend to be
320 T. Hayashi
https://t.me/med1917
Fig. 17.6 Predicted amounts
of serum protein on polymer
brush films with the trained
RF algorithm plotted as a
function of the
corresponding experimental
values. The solid line is a
plot of y = x for an eye guide
for accurate prediction.
(Redrawn based on the figure
in [
11])
small, making the choice of algorithm and the optimization of hyperparameters critical aspects. We demonstrated that it is possible to analyze the correlation between
materials’ chemical structures (SAMs and polymer films) and protein adsorption
onto them.
The process of importance analysis aids in ranking structural parameters in terms
of their significance for material function or performance. This offers valuable information for material design. Moreover, the model we’ve trained can serve as an initial
screening tool for candidate materials, provided the dataset adequately encompasses
the applicable domain.
However, it’s important to note that understanding the mechanisms underlying
biomaterials’ functions based solely on the correlation between chemical structures
and functions can often prove challenging. For instance, while we can predict the
chemical structures of protein-resistant materials, the specific interfacial interaction
responsible for protein resistance remains unclear. As such, both material design and
the elucidation of underlying mechanisms must be pursued concurrently.
Acknowledgements We acknowledge Ms. Kazue Taki for her help in arranging this chapter. This
work was supported by JSPS KAKENHI Grant Number (JP23H04059 and JP 22H04530). This
work was performed under the Research Program for CORE lab of “Five-star Alliance” in “NJRC
Mater. & Dev.” This work was also supported by the Japan–Taiwan Exchange Association.
17 Design of Biomaterials Using Informatics 321
https://t.me/med1917
References
1. White A (2012) The Materials Genome Initiative: One Year On. MRS Bull 37:715–716. https://
doi.org/10.1557/mrs.2012.194
2. Ling C (2022) A Review of the Recent Progress in Battery Informatics. NPJ Comput Mater
8:1–22.
3. Takahashi K, Takahashi L (2022) Data in Materials and Catalysts Informatics. In: Machine
4. Takahashi K, Ohyama J, Nishimura S, Fujima J, Takahashi L, Uno T, Taniike T (2023) Catalysts
5. Mosalam H, Hussien S, Abdellatif SO (2023) OFET Informatics: Observing the Impact of
6. Workman M, Chen DZ, Musa SM (2020) Machine Learning for Predicting Perovskite Solar Cell
7. Steiner S, Wolf J, Glatzel S, et al (2019) Organic Synthesis in a Modular Robotic System Driven
8. Zhou Z, Li X, Zare RN (2017) Optimizing Chemical Reactions with Deep Reinforcement
9. Gupta A, Müller AT, Huisman BJH, Fuchs JA, Schneider P, Schneider G (2018) Generative
10. Lourdes Sánchez M, Will A, Rodríguez A, Gónzalez-Salcedo LO (2020) 1 A Review of Bone
11. Palai D, Tahara H, Chikami S, Latag GV, Maeda S, Komura C, Kurioka H, Hayashi T
12. Kwaria RJ, Mondarte EAQ, Tahara H, Chang R, Hayashi T (2020) Data-Driven Prediction of
13. Xue Y, Che J, Ji X, Li Y, Xie J, Chen X (2022) Recent Advances in Biomaterial-Boosted
14. Eldeeb AE, Salah S, Elkasabgy NA (2022) Biomaterials for Tissue Engineering Applications
15. Tanaka M, Hayashi T, Morita S (2013) Theroles of Water Molecules at t he Biointerface of
16. Tanaka M, Morita S, Hayashi T (2021) Role of Interfacial Water in Determining the Interactions
17. Israelachvili JN (2011) Intermolecular and Surface Forces. Academic Press. https://doi.org/10.
18. Hayashi T (2021) Water at Interfaces: Its Behavior and Roles in Interfacial Phenomena. Chem
19. Khalili AA, Ahmad MR (2015) A Review of Cell Adhesion Studies for Biomedical and
https://doi.org/10.1038/s41524-022-00713-x
Learning in Materials Informatics: Methods and Applications. American Chemical Society, pp
239–246.
Informatics: Paradigm Shift Towards Data-Driven Catalyst Design. Chem Commun 59:2222–
2238.
Organic Transistor’s Design Parameters on the Device Output Performance Using a Machine
Learning Algorithm. Int J Numer Model.
Opto-Electronic Properties. In: 2020 International Conference on Data Analytics for Business
and Industry: Way Towards a Sustainable Economy (ICDABI). IEEE, pp 1–5.
10.1109/ICDABI51230.2020.9325629
by a Chemical Programming Language. Science.
Learning. ACS Cent Sci 3:1337–1344.
Recurrent Networks for De Novo Drug Design. Mol Inform.
700111
Tissue Engineering for the Application of Artificial Intelligence in Cellular Adhesion Prediction. In: Srivastava R, Mallick PK, Rautaray SS, Pandey M (eds) Computational Intelligence
for Machine Learning and Healthcare Informatics. De Gruyter, Boston, Berlin.
10.1515/9783110648195-001
(2022) Prediction of Serum Adsorption Onto Polymer Brush Films by Machine Learning.
ACS Biomater Sci Eng. 8:3765-3772.
Protein Adsorption on Self-Assembled Monolayers Toward Material Screening and Design.
ACS Biomater Sci Eng 6:4949–4956.
Adoptive Cell Therapy. Chem Soc Rev 51:1766–1794.
and Current Updates in the Field: A Comprehensive Review. AAPS PharmSciTech 23:267.
https://doi.org/10.1208/s12249-022-02419-1
Medical Polymers. Polym J 45:701–710.
of Proteins and Cells with Hydrated Materials. Colloids Surf B Biointerfaces 198:111449.
https://doi.org/10.1016/j.colsurfb.2020.111449
1016/C2009-0-21560-1
Lett 50:1173–1180.
Biological Applications. Int J Mol Sci 16:18149–18184.
8149
https://doi.org/10.1021/bk-2022-1416.ch010
https://doi.org/10.1039/D2CC05938J
https://doi.org/10.1002/jnm.3132
https://doi.org/
https://doi.org/10.1126/science.aav2211
https://doi.org/10.1021/acscentsci.7b00492
https://doi.org/10.1002/minf.201
https://doi.org/
https://doi.org/10.1021/acsbiomaterials.2c00441
https://doi.org/10.1021/acsbiomaterials.0c01008
https://doi.org/10.1039/D1CS00786F
https://doi.org/10.1038/pj.2012.229
https://doi.org/10.1246/cl.210049
https://doi.org/10.3390/ijms16081
322 T. Hayashi
https://t.me/med1917
20. Hayashi T, Makiuchi N, Hara M (2009) Self-Assembled Monolayers with Chemical Gradients:
Fabrication and Protein Adsorption Experiments. Jpn J Appl Phys 48:095503.
10.1143/JJAP.48.095503
21. Sekine T, Tanaka Y, Sato C, Tanaka M, Hayashi T (2015) Evaluation of Factors To Determine Platelet Compatibility by Using Self-Assembled Monolayers with a Chemical Gradient.
Langmuir 31:7100–7105.
22. Rodenstein M, Zürcher S, Tosatti SGP, Spencer ND (2010) Fabricating Chemical Gradients
on Oxide Surfaces by Means of Fluorinated, Catechol-Based, Self-Assembled Monolayers.
Langmuir 26:16211–16220.
23. Koo H-J, Waynant KV, Zhang C, Haasch RT, Braun PV (2014) General Method for Forming
Micrometer-Scale Lateral Chemical Gradients in Polymer Brushes. Chem Mater 26:2678–
2683.
24. Harris BP, Metters AT (2006) Generation and Characterization of Photopolymerized Polymer
25. Zhu L, Ye X, Tang G, Zhao N, Gong Y, Zhao Y, Zhao J, Zhang X (2006) Corrosion Test,
26. Vasilevich A, de Boer J (2018) Robot-Scientists Will Lead Tomorrow’s Biomaterials Discovery.
27. Tamasi MJ, Gormley AJ (2022) Biologic Formulation in a Self-Driving Biomaterials Lab. Cell
28. Gauthier J, Vincent AT, Charette SJ, Derome N (2019) A Brief History of Bioinformatics. Brief
29. White AD, Nowinski AK, Huang W, Keefe AJ, Sun F, Jiang S (2012) Decoding Nonspecific
30. Chang R, Quimada Mondarte EA, Palai D, Sekine T, Kashiwazaki A, Murakami D, Tanaka M,
31. Chen S, Cao Z, Jiang S (2009) Ultra-Low Fouling Peptide Surfaces Derived from Natural Amino
32. Love JC, Estroff LA, Kriebel JK, Nuzzo RG, Whitesides GM (2005) Self-Assembled Mono-
33. Hayashi T, Morikawa Y, Nozoye H (2001) Adsorption State of Dimethyl Disulfide on Au(111):
34. Hayashi T, Kodama C, Nozoye H (2001) Structural Evolution of Dibutyldisulfide Adsorbed on
35. Hayashi T, Fricke A, Katsura K, Kodama C, Nozoye H (1999) Adsorption State of Diethyld-
36. Kodama C, Hayashi T, Nozoye H (2001) Decomposition of Alkanethiols Adsorbed on Au (1 1
37. Pyun J, Kowalewski T, Matyjaszewski K (2003) Synthesis of Polymer Brushes Using Atom
38. Badoux M, Billing M, Klok H-A (2019) Polymer Brush Interfaces for Protein Biosensing
https://doi.org/10.1021/cm5004388
Brush Gradients. Macromolecules 39:2764–2772.
Cell Behavior Test, and In Vivo Study of Gradient TiO
Electrochemical Oxidation. J Biomed Mater Res A 78:515–522.
a.30745
Curr Opin Biomed Eng 6:74–80.
Rep Phys Sci 3:101041.
Bioinform 20:1981–1996.
Interactions From Nature. Chem Sci 3:3488–3494.
Hayashi T (2021) Protein- and Cell-Resistance of Zwitterionic Peptide-Based Self-Assembled
Monolayers: Anti-Biofouling Tests and Surface Force Analysis. Front Chem 9:748017.
doi.org/10.3389/fchem.2021.748017
Acids. Biomaterials 30:5892–5896.
layers of Thiolates on Metals as a Form of Nanotechnology. Chem Rev 105:1103–1169.
doi.org/10.1021/cr0300789
Evidence for Adsorption as Thiolate at the Bridge Site. J Chem Phys 114:7615–7621.
doi.org/10.1063/1.1360245
Au(111). Appl Surf Sci 169–170:100–103,
isulfide on Au(111) Studied with a Combined System of HREELS and STM. Surf Sci
427–428:393–397.
1) at Low Temperature. Appl Surf Sci 169–170:264–267.
2(00)00664-4
Transfer Radical Polymerization. Macromol Rapid Commun 24:1043–1059.
10.1002/marc.200300078
Prepared by Surface-Initiated Controlled Radical Polymerization. Polym Chem 10:2925–2951.
https://doi.org/10.1039/C9PY00163H
https://doi.org/10.1021/acs.langmuir.5b01216
https://doi.org/10.1021/la100805z
https://doi.org/10.1021/ma0512051
Layers Produced by Compound
2
https://doi.org/10.1016/j.cobme.2018.03.005
https://doi.org/10.1016/j.xcrp.2022.101041
https://doi.org/10.1093/bib/bby063
https://doi.org/10.1039/C2SC21135A
https://doi.org/10.1016/j.biomaterials.2009.07.001
https://doi.org/10.1016/S0169-4332(00)00647-435
https://doi.org/10.1016/S0039-6028(99)00308-8
https://doi.org/10.1016/S0169-433
https://doi.org/10.1002/jbm.
https://doi.org/
https://
https://
https://
https://doi.org/
Chapter 18
https://t.me/med1917
Monitoring and Controlling
in Continuous Manufacturing Process
Kimito Funatsu
18.1 Introduction—Movement to Introduce Continuous
Processes in the Pharmaceutical Industry
Since the regulation was introduced by the U.S. Food and Drug Administration (FDA)
in 2004 [
has been recommended. The realization of continuous manufacturing is expected to
increase efficiency and reduce costs. This is also supported by advances in process
sensors and control technology.
ents (APIs) and excipients are mixed. At this point, the need to guarantee mixing
uniformity means that near-infrared spectroscopy (NIRS)-based online monitoring
methods are used. In addition, when the synthesized API is crystallized, it can be
controlled to obtain a powder with the desired particle size distribution based on the
concentration of the components estimated online [
years, the pharmaceutical industry has been trying to shift manufacturing facilities
to a continuous process, and provisional calculations have been made for shifting the
API manufacturing process to a continuous process [
production can be carried out on the same equipment, making scale-up easy. On
the other hand, it is essential to introduce process monitoring methods to guar-
1], the use of process monitoring sensors and continuous manufacturing
In the mixing process for tablet production, the active pharmaceutical ingredi-
2
]. On the other hand, in recent
].
3
Continuous processes are attracting attention because development and actual
toring methods using Partial Least Squares (PLS) require a large amount of data
acquisition through analysis using expensive APIs and require a lot of cost and
effort to build models [
tion model using NIRS, it is necessary to acquire concentration and NIRS pairs
K. Funatsu (B)
Data Science Center, Nara Institute of Science and Technology, Nara, Japan
e-mail: funatsu@dsc.naist.jp
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
H. Satoh et al. (eds.), Drug Development Supported by Informatics,
https://doi.org/10.1007/978-981-97-4828-0_18
]. For example, when building a concentration predic-
4
323
324 K. Funatsu
https://t.me/med1917
using high-performance liquid-phase chromatography (HPLC) and NIRS instruments; measuring concentrations by HPLC is time-consuming and labor-intensive,
thus hindering the advantage of continuous processes—faster production. To solve
this problem, it is necessary to develop calibration-free approaches that do not use
training data or calibration-minimum methods that enable process monitoring from
a small amount of training data. This will be discussed again later in this chapter.
18.2 Types of Analysis in Continuous Manufacturing
Processes
The process of continuous mixing of raw materials, granulation, tableting, and
coating in that order is schematically illustrated in Fig.
tion requires real-time monitoring of each process and feedback control based on the
monitoring content.
There are several challenges in continuous production. These are summarized
below.
1.
The measurement conditions of the data for building statistical models need to be
the same as during actual production (data during operation is generally difficult
to obtain).
Consideration of methods to realize process monitoring without disturbing actual
2.
production.
3. To deal with fouling (=clogging), which reduces the sensitivity of the spectrom-
eter.
18.1. Continuous produc-
Fig. 18.1 Continuous manufacturing process
18 Monitoring and C ontrolling in Continuous Manufacturing Process 325
https://t.me/med1917
4. Investigation of Process Analytical Technology (PAT) using methods other than
near-infrared spectroscopy.
5. Study on control using PAT.
6. Study on reducing the cost of data acquisition for building models for process
monitoring.
Of these, issues 5 and 6 need to be resolved urgently in the future to make the
continuous production process a reality. This will be discussed in the next section.
18.3 PAT Issues: Acquisition of Data for Model Building
In the past, to predict the concentrations of components such as the main drug, a
prediction model has been constructed using PLS and other methods between the
concentrations of each component and the NIRS. However, the high cost of the main
drug and the time and cost i nvolved in the destructive testing of samples have been
problematic. To solve this problem, Iterative Optimization Technology (IOT) [
has been proposed for NIRS-based component concentration prediction, utilizing
Lambert–Beer’s law, which expresses the linearity between NIRS absorbance and
component concentration. Multivariate Curve Resolution Alternating Least Squares
(MCR-ALS) [
spectral data. However, MCR-ALS is mainly used to elucidate molecular interactions
and requires a large number of training data when used for concentration prediction.
Another method, called Indirect Hard Modeling (IHM) [
quantification after fitting the peak function to the NIRS of a mixture. This method
can be used to detect impurities and is an excellent predictive model, but it requires
training data equal to or greater than the number of components [
makes it difficult to reduce the number of training data and predict concentrations
using methods other than IOT.
In this section, IOT and its extension methods are briefly introduced based on
these considerations. IOT performs concentration prediction by the method described
below, but there are several problems as shown in Table
IOT combined with latent variable models such as Principal Component Analysis
(PCA) to address the problem of multi-collinearity between wavelengths or between
pure spectra, which reduces the prediction accuracy of IOT [
prediction by IOT is possible even for systems with a large number of components. The use of PCA enables predictions to be made by IOT even for systems
with a large number of components. In the same paper, the author also proposed IOT
combined with wavelength-region selection methods for NIRS data with wavelength
regions that are not required for prediction, such as noisy regions. Combining the
Genetic Algorithm-based Wavelength Selection (GAWLS) method [
wavelength-region selection methods, with IOT has been shown to increase prediction accuracy. Furthermore, the combination of PCA and GAWLS enables highly
accurate concentration prediction for systems consisting of multiple components
6] has been proposed as a calibration-free method that can be applied to
7], has been proposed for
8
]. The above
18.1. Funatsu et al. proposed
9]. By using PCA,
10], one of the
5]
326 K. Funatsu
https://t.me/med1917
Table 18.1 Problems and response methods for each IOT application destination
Application destination IOT problems Response methods IOT extension
Systems mixing
multiple components
Powder mixing
processes
Solution-based process
(e.g., crystallization
processes)
Multiple linearity
between wave-length
and pure spectra
High influence of noise
multi-linearity between
pure spectra
Influenced by
intermolecular
interactions
Correlationless between
wavelengths
Non-correlation of pure
spectra
Wavelength range
selection
Application of
non-linear
Modelling the effects of
molecular interactions
Wavelength range
selection
methods
PCA-IOT
GAWLS-IOT
Non-linear IOT
IOT-VIS
WLSEA-IOT
with similar NIR spectral shapes by selecting only the wavelength region required
for the prediction. Applications of GAWLS-IOT and PCA-GAWLS-IOT include
powder mixing processes for tablet production. Online monitoring methods applied
to powder mixing processes only need to be able to achieve highly accurate predictions around the concentration when the powder is uniformly mixed and are not
required to achieve highly accurate predictions for a variety of composition ratios.
The IOT combined with GAWLS achieves the former and can determine the end of
powder mixing from a small number of training data. It is useful as a concentration
prediction method to determine the end of powder mixing from a small number of
training data.
On the other hand, Nonlinear IOT [11] has been used to apply IOT to mixtures
in solution systems, and IOT with Virtual molecular Interaction Spectra (IOT-VIS)
], Wavelength Selection-based on Nonlinear IOT and IOT-VIS take into account
12
[
the influence of molecular interactions on infrared (IR) by utilizing non-linear transformations and non-linear terms, respectively. In contrast, Wavelength Selection
] enables concentration predic-
Based on Excess Absorption (WLSEA)-IOT [
12
tion by selecting regions where Lambert–Beer’s law holds from a small number of
training data. These methods are expected to be applied to monitoring crystallization
processes and chemical synthesis reactions.
18.4 IOT and Novel IOT Methods
Figure 18.2 schematically illustrates this IOT concept. The basis for the application
of IOT is Lambert–Beer’s law, which states that the NIR absorbance of a mixture is
expressed as a linear combination of the NIR absorbance of each pure component,
18 Monitoring and C ontrolling in Continuous Manufacturing Process 327
https://t.me/med1917
Fig. 18.2 Iterative Optimization Technology (IOT) [5]
holds. The advantage of the IOT is that the concentration of each component in the
mixture can be determined without the need to construct a calibration model.
Using the IOT, it is possible to determine the endpoint in the mixing process in
real-time, for example, as shown in Fig.
18.3.
Fig. 18.3 Predicting product quality in mixing processes [5]
328 K. Funatsu
https://t.me/med1917
However, only the wavelength range required to apply the IOT should be selected,
as the accuracy of quantification is reduced if there are wavelength ranges that are
unnecessary for the application of Lambert–Beer’s law. Combined with the wavelength region selection method using genetic algorithms, the prediction accuracy
is improved because regions that reduce the prediction accuracy, such as regions
with high noise and regions affected by intermolecular interactions, are excluded. In
addition, as the number of components in a mixture increases, the spectral shape of
the pure components may become similar. The problem of multi-collinearity arises,
which also reduces the accuracy of quantification. To solve this problem, as described
in the previous section, the elimination of collinearity using PCA has been carried
10
out, resulting in improved accuracy [
].
An example of this application is presented below. This is the application of I OT to
real-time monitoring of a continuous mixing process consisting of five components.
Two components in particular have a small content of 1% (SA) and 3% (SSG). If
IOT is simply applied, the theoretical and predicted values do not match at all, as
shown in Fig.
18.4. When wavelength-domain selection is taken into account, very
good predictions are made, except for the component with a content of 1% (SA),
as showninFig.
18.5. The reason for the poor predictions for the components with
small content is due to the collinearity between the component spectra, and when the
collinearity is removed by PCA, real-time monitoring can be carried out with very
good accuracy, as shown in Fig.
18.6.
The above was a case where Lambert–Beer’s law holds. However, even if the
Lambert–Beer rule does not hold over the entire wavelength range due to intermolecular interactions between components or other reasons, it can be found by
wavelength-region selection methods if it holds in some parts of the wavelength
range. This wavelength-region selection requires only a set of mixture of NIRS and
Fig. 18.4 Prediction results from IOT
Соседние файлы в папке Библиотека им академика М.И. Перельмана