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Review of machine learning algorithms´ application in pharmaceutical technology

Pregled primene algoritama mašinskog učenja u farmaceutskoj tehnologiji

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2021
Review_of_machine_pub_2021.pdf (782.4Kb)
Authors
Đuriš, Jelena
Kurćubić, Ivana
Ibrić, Svetlana
Article (Published version)
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Abstract
Machine learning algorithms, and artificial intelligence in general, have a wide range of applications in the field of pharmaceutical technology. Starting from the formulation development, through a great potential for integration within the Quality by design framework, these data science tools provide a better understanding of the pharmaceutical formulations and respective processing. Machine learning algorithms can be especially helpful with the analysis of the large volume of data generated by the Process analytical technologies. This paper provides a brief explanation of the artificial neural networks, as one of the most frequently used machine learning algorithms. The process of the network training and testing is described and accompanied with illustrative examples of machine learning tools applied in the context of pharmaceutical formulation development and related technologies, as well as an overview of the future trends. Recently published studies on more sophisticat...ed methods, such as deep neural networks and light gradient boosting machine algorithm, have been described. The interested reader is also referred to several official documents (guidelines) that pave the way for a more structured representation of the machine learning models in their prospective submissions to the regulatory bodies.

Algoritmi mašinskog učenja, kao i veštačka inteligencija u širem smislu, su veoma značajni i primenjuju se u razne svrhe u okviru farmaceutske tehnologije. Počevši od razvoja formulacija, preko izuzetnog potencijala za integraciju u koncept dizajna kvaliteta (engl. Quality by design), algoritmi mašinskog učenja omogućavaju bolje razumevanje uticaja kako formulacionih faktora tako i odgovarajućih procesnih parametara. Algoritmi mašinskog učenja mogu biti od naročitog značaja i za analizu velikog obima podataka koji se generišu korišćenjem procesnih analitičkih tehnologija. U ovom radu su ukratko predstavljene veštačke neuronske mreže, kao jedan od najčešće korišćenih algoritama mašinskog učenja. Prikazani su procesi treninga i testiranja mreža, kao i ilustrativni primeri algoritama primenjenih za različite potrebe razvoja i/ili optimizacije farmaceutskih formulacija i postupaka njihove izrade. Takođe, dat je i pregled budućih trendova u ovoj oblasti, kao i novijih studija o sofisticiran...im metodama, poput dubokih neuronskih mreža, i light gradient boosting algoritma. Zainteresovani čitaoci se takođe upućuju na nekoliko zvaničnih dokumenata (vodiča), po uzoru na koje mogu da se očekuju i preporuke za strukturiranu prezentaciju modela mašinskog učenja koji će se podnositi regulatornim telima u okviru dokumentacije koja se priprema za potrebe registracije novih lekova.

Keywords:
Machine learning / artificial neural networks / quality by design / pharmaceutical development / process analytical technologies / mašinsko učenje / veštačke neuronske mreže / razvoj lekova / dizajn kvaliteta / procesne analitičke tehnologije
Source:
Arhiv za farmaciju, 2021, 71, 4, 302-317
Publisher:
  • Beograd : Savez farmaceutskih udruženja Srbije
Funding / projects:
  • Ministry of Education, Science and Technological Development, Republic of Serbia, Grant no. 200161 (University of Belgrade, Faculty of Pharmacy) (RS-200161)

DOI: 10.5937/arhfarm71-32499

ISSN: 0004-1963

[ Google Scholar ]
URI
https://farfar.pharmacy.bg.ac.rs/handle/123456789/3952
Collections
  • Radovi istraživača / Researchers’ publications
Institution/Community
Pharmacy
TY  - JOUR
AU  - Đuriš, Jelena
AU  - Kurćubić, Ivana
AU  - Ibrić, Svetlana
PY  - 2021
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3952
AB  - Machine learning algorithms, and artificial intelligence in general, have a wide range of
applications in the field of pharmaceutical technology. Starting from the formulation
development, through a great potential for integration within the Quality by design framework,
these data science tools provide a better understanding of the pharmaceutical formulations and
respective processing. Machine learning algorithms can be especially helpful with the analysis of
the large volume of data generated by the Process analytical technologies. This paper provides a
brief explanation of the artificial neural networks, as one of the most frequently used machine
learning algorithms. The process of the network training and testing is described and accompanied
with illustrative examples of machine learning tools applied in the context of pharmaceutical
formulation development and related technologies, as well as an overview of the future trends.
Recently published studies on more sophisticated methods, such as deep neural networks and light
gradient boosting machine algorithm, have been described. The interested reader is also referred
to several official documents (guidelines) that pave the way for a more structured representation
of the machine learning models in their prospective submissions to the regulatory bodies.
AB  - Algoritmi mašinskog učenja, kao i veštačka inteligencija u širem smislu, su veoma značajni i primenjuju se u razne svrhe u okviru farmaceutske tehnologije. Počevši od razvoja formulacija, preko izuzetnog potencijala za integraciju u koncept dizajna kvaliteta (engl. Quality by design), algoritmi mašinskog učenja omogućavaju bolje razumevanje uticaja kako formulacionih faktora tako i odgovarajućih procesnih parametara. Algoritmi mašinskog učenja mogu biti od naročitog značaja i za analizu velikog obima podataka koji se generišu korišćenjem procesnih analitičkih tehnologija. U ovom radu su ukratko predstavljene veštačke neuronske mreže, kao jedan od najčešće korišćenih algoritama mašinskog učenja. Prikazani su procesi treninga i testiranja mreža, kao i ilustrativni primeri algoritama primenjenih za različite potrebe razvoja i/ili optimizacije farmaceutskih formulacija i postupaka njihove izrade. Takođe, dat je i pregled budućih trendova u ovoj oblasti, kao i novijih studija o sofisticiranim metodama, poput dubokih neuronskih mreža, i light gradient boosting algoritma. Zainteresovani čitaoci se takođe upućuju na nekoliko zvaničnih dokumenata (vodiča), po uzoru na koje mogu da se očekuju i preporuke za strukturiranu prezentaciju modela mašinskog učenja koji će se podnositi regulatornim telima u okviru dokumentacije koja se priprema za potrebe registracije novih lekova.
PB  - Beograd : Savez farmaceutskih udruženja Srbije
T2  - Arhiv za farmaciju
T1  - Review of machine learning algorithms´ application in pharmaceutical technology
T1  - Pregled primene algoritama mašinskog učenja u farmaceutskoj tehnologiji
VL  - 71
IS  - 4
SP  - 302
EP  - 317
DO  - 10.5937/arhfarm71-32499
ER  - 
@article{
author = "Đuriš, Jelena and Kurćubić, Ivana and Ibrić, Svetlana",
year = "2021",
abstract = "Machine learning algorithms, and artificial intelligence in general, have a wide range of
applications in the field of pharmaceutical technology. Starting from the formulation
development, through a great potential for integration within the Quality by design framework,
these data science tools provide a better understanding of the pharmaceutical formulations and
respective processing. Machine learning algorithms can be especially helpful with the analysis of
the large volume of data generated by the Process analytical technologies. This paper provides a
brief explanation of the artificial neural networks, as one of the most frequently used machine
learning algorithms. The process of the network training and testing is described and accompanied
with illustrative examples of machine learning tools applied in the context of pharmaceutical
formulation development and related technologies, as well as an overview of the future trends.
Recently published studies on more sophisticated methods, such as deep neural networks and light
gradient boosting machine algorithm, have been described. The interested reader is also referred
to several official documents (guidelines) that pave the way for a more structured representation
of the machine learning models in their prospective submissions to the regulatory bodies., Algoritmi mašinskog učenja, kao i veštačka inteligencija u širem smislu, su veoma značajni i primenjuju se u razne svrhe u okviru farmaceutske tehnologije. Počevši od razvoja formulacija, preko izuzetnog potencijala za integraciju u koncept dizajna kvaliteta (engl. Quality by design), algoritmi mašinskog učenja omogućavaju bolje razumevanje uticaja kako formulacionih faktora tako i odgovarajućih procesnih parametara. Algoritmi mašinskog učenja mogu biti od naročitog značaja i za analizu velikog obima podataka koji se generišu korišćenjem procesnih analitičkih tehnologija. U ovom radu su ukratko predstavljene veštačke neuronske mreže, kao jedan od najčešće korišćenih algoritama mašinskog učenja. Prikazani su procesi treninga i testiranja mreža, kao i ilustrativni primeri algoritama primenjenih za različite potrebe razvoja i/ili optimizacije farmaceutskih formulacija i postupaka njihove izrade. Takođe, dat je i pregled budućih trendova u ovoj oblasti, kao i novijih studija o sofisticiranim metodama, poput dubokih neuronskih mreža, i light gradient boosting algoritma. Zainteresovani čitaoci se takođe upućuju na nekoliko zvaničnih dokumenata (vodiča), po uzoru na koje mogu da se očekuju i preporuke za strukturiranu prezentaciju modela mašinskog učenja koji će se podnositi regulatornim telima u okviru dokumentacije koja se priprema za potrebe registracije novih lekova.",
publisher = "Beograd : Savez farmaceutskih udruženja Srbije",
journal = "Arhiv za farmaciju",
title = "Review of machine learning algorithms´ application in pharmaceutical technology, Pregled primene algoritama mašinskog učenja u farmaceutskoj tehnologiji",
volume = "71",
number = "4",
pages = "302-317",
doi = "10.5937/arhfarm71-32499"
}
Đuriš, J., Kurćubić, I.,& Ibrić, S.. (2021). Review of machine learning algorithms´ application in pharmaceutical technology. in Arhiv za farmaciju
Beograd : Savez farmaceutskih udruženja Srbije., 71(4), 302-317.
https://doi.org/10.5937/arhfarm71-32499
Đuriš J, Kurćubić I, Ibrić S. Review of machine learning algorithms´ application in pharmaceutical technology. in Arhiv za farmaciju. 2021;71(4):302-317.
doi:10.5937/arhfarm71-32499 .
Đuriš, Jelena, Kurćubić, Ivana, Ibrić, Svetlana, "Review of machine learning algorithms´ application in pharmaceutical technology" in Arhiv za farmaciju, 71, no. 4 (2021):302-317,
https://doi.org/10.5937/arhfarm71-32499 . .

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