Pregled primene algoritama mašinskog učenja u farmaceutskoj tehnologiji
Sažetak
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.
Reference
Damiati SA. Digital Pharmaceutical Sciences. AAPS PharmSciTech. 2020;21(6):206.
Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. In: Bohr A, Memarzadeh K, editors. Artificial Intelligence in Healthcare: Elsevier; 2020; p. 25-60.
Djuris J, Vidovic B, Ibric S. Release modeling of nanoencapsulated food ingredients by artificial intelligence algorithms. In: Jafari SM, editor. Release and Bioavailability of Nanoencapsulated Food Ingredients: Elsevier; 2020; p. 311-47.
Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discov Today. 2018;23(6):1241-50.
Belič A, Škrjanc I, Božič DZ, Karba R, Vrečer F. Minimisation of the capping tendency by tableting process optimisation with the application of artificial neural networks and fuzzy models. Eur J Pharm Biopharm. 2009;73(1):172-8.
Walia N, Singh H, Sharma A. ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey. Int J Comp App. 2015;123(13):32-8.
Barmpalexis P, Karagianni A, Karasavvaides G, Kachrimanis K. Comparison of multi-linear regression, particle swarm optimization artificial neural networks and genetic programming in the development of mini-tablets. Int J Pharm. 2018;551(1–2):166-76.
ICH Q8(R2). Pharmaceutical Development. [Internet]. Available from: https://database.ich.org/sites/default/files/Q8_R2_Guideline.pdf. [cited 2021 May 15].
Amasya G, Aksu B, Badilli U, Onay-Besikci A, Tarimci N. QbD guided early pharmaceutical development study: Production of lipid nanoparticles by high pressure homogenization for skin cancer treatment. Int J Pharm. 2019;563:110-21.
Barmpalexis P, Kachrimanis K, Georgarakis E. Solid dispersions in the development of a nimodipine floating tablet formulation and optimization by artificial neural networks and genetic programming. Eur J Pharm Biopharm. 2011;77(1):122-31.
Koletti AE, Tsarouchi E, Kapourani A, Kontogiannopoulos KN, Assimopoulou AN, Barmpalexis P. Gelatin nanoparticles for NSAID systemic administration: Quality by design and artificial neural networks implementation. Int J Pharm. 2020;578:119118.
Chansanroj K, Petrović J, Ibrić S, Betz G. Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks. Eur J Pharm Sci. 2011;44(3):321-31.
Miletić T, Ibrić S, Đurić Z. Combined Application of Experimental Design and Artificial Neural Networks in Modeling and Characterization of Spray Drying Drug: Cyclodextrin Complexes. Drying Tech. 2014;32(2):167-79.
Aksu B, Paradkar A, de Matas M, Özer Ö, Güneri T, York P. A quality by design approach using artificial intelligence techniques to control the critical quality attributes of ramipril tablets manufactured by wet granulation. Pharm Dev Technol. 2013;18(1):236-45.
Nguyen CN, Tran BN, Do TT, Nguyen H, Nguyen TN. D-Optimal Optimization and Data-Analysis Comparison Between a DoE Software and Artificial Neural Networks of a Chitosan Coating Process onto PLGA Nanoparticles for Lung and Cervical Cancer Treatment. J Pharm Innov. 2019;14(3):206-20.
Onuki Y, Kawai S, Arai H, Maeda J, Takagaki K, Takayama K. Contribution of the Physicochemical Properties of Active Pharmaceutical Ingredients to Tablet Properties Identified by Ensemble Artificial Neural Networks and Kohonen’s Self-Organizing Maps. J Pharm Sci. 2012;101(7):2372-81.
Kinnunen H, Hebbink G, Peters H, Shur J, Price R. Defining the Critical Material Attributes of Lactose Monohydrate in Carrier Based Dry Powder Inhaler Formulations Using Artificial Neural Networks. AAPS PharmSciTech. 2014;15(4):1009-20.
Simões MF, Silva G, Pinto AC, Fonseca M, Silva NE, Pinto RMA, et al. Artificial neural networks applied to quality-by-design: From formulation development to clinical outcome. Eur J Pharm Biopharm. 2020;152:282-95.
Lou H, Lian B, Hageman MJ. Applications of Machine Learning in Solid Oral Dosage Form Development. J Pharm Sci. 2021; doi: 10.1016/j.xphs.2021.04.013.
Rowe RC, Roberts RJ. Artificial intelligence in pharmaceutical product formulation: neural computing and emerging technologies. Pharm Sci Technol Today. 1998;1(5):200-5.
Shao Q, Rowe RC, York P. Investigation of an artificial intelligence technology—Model trees. Eur J Pharm Sci. 2007;31(2):137-44.
Colbourn EA, Rowe RC. Novel approaches to neural and evolutionary computing in pharmaceutical formulation: challenges and new possibilities. Future Med Chem. 2009;1(4):713-26.
Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000;22(5):717-27.
Djuris J, Ibric S, Djuric Z. Neural computing in pharmaceutical products and process development. In: Djuris J, editor. Computer-Aided Applications in Pharmaceutical Technology: Elsevier; 2013; p. 91-175.
Hassanzadeh P, Atyabi F, Dinarvand R. The significance of artificial intelligence in drug delivery system design. Adv Drug Deliv Rev. 2019;151–152:169-90.
Sun Y, Peng Y, Chen Y, Shukla AJ. Application of artificial neural networks in the design of controlled release drug delivery systems. Adv Drug Deliv Rev. 2003;55(9):1201-15.
Millen N, Kovačević A, Djuriš J, Ibrić S. Machine Learning Modeling of Wet Granulation Scale-up Using Particle Size Distribution Characterization Parameters. J Pharm Innov. 2020;15(4):535-46.
Landin M. Artificial Intelligence Tools for Scaling Up of High Shear Wet Granulation Process. J Pharm Sci. 2017;106(1):273-7.
Khalid GM, Usman AG. Application of data-intelligence algorithms for modeling the compaction performance of new pharmaceutical excipients. Future J Pharm Sci. 2021;7(1):31.
Gams M, Horvat M, Ožek M, Luštrek M, Gradišek A. Integrating Artificial and Human Intelligence into Tablet Production Process. AAPS PharmSciTech. 2014;15(6).
Colombo S. Applications of artificial intelligence in drug delivery and pharmaceutical development. In: Bohr A, Memarzadeh K, editors. Artificial Intelligence in Healthcare: Elsevier; 2020; p. 85-116.
Adir O, Poley M, Chen G, Froim S, Krinsky N, Shklover J, et al. Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine. Adv Mat. 2020;32(13):1901989.
Egorov E, Pieters C, Korach-Rechtman H, Shklover J, Schroeder A. Robotics, microfluidics, nanotechnology and AI in the synthesis and evaluation of liposomes and polymeric drug delivery systems. Drug Deliv Trans Res. 2021;11(2):345-52.
Lee C, Kwon O, Kim M, Kwon D. Early identification of emerging technologies: A machine learning approach using multiple patent indicators. Technol Forecast Soc Change. 2018;127:291-303.
Lin H-H, Ouyang D, Hu Y. Intelligent Classifier: a Tool to Impel Drug Technology Transfer from Academia to Industry. J Pharm Innov. 2019;14(1):28-34.
Yang Y, Ye Z, Su Y, Zhao Q, Li X, Ouyang D. Deep learning for in vitro prediction of pharmaceutical formulations. Acta Pharm Sin B. 2019;9(1):177-85.
Ekins S. The Next Era: Deep Learning in Pharmaceutical Research. Pharm Res. 2016;33(11):2594-603.
Zhao Q, Ye Z, Su Y, Ouyang D. Predicting complexation performance between cyclodextrins and guest molecules by integrated machine learning and molecular modeling techniques. Acta Pharm Sin B. 2019;9(6):1241-52.
Gao H, Ye Z, Dong J, Gao H, Yu H, Li H, et al. Predicting drug/phospholipid complexation by the lightGBM method. Chem Phys Lett. 2020;747:137354.
He Y, Ye Z, Liu X, Wei Z, Qiu F, Li H-F, et al. Can machine learning predict drug nanocrystals? J Control Release. 2020;322:274-85.
Öztürk AA, Gündüz AB, Ozisik O. Supervised Machine Learning Algorithms for Evaluation of Solid Lipid Nanoparticles and Particle Size. Comb Chem High Throughput Screen. 2018;21(9):693-9.
Abbas K, Afaq M, Ahmed Khan T, Song W-C. A Blockchain and Machine Learning-Based Drug Supply Chain Management and Recommendation System for Smart Pharmaceutical Industry. Electronics. 2020;9(5):852.
Henstock PV. Artificial Intelligence for Pharma: Time for Internal Investment. Trends Pharmacol Sci. 2019;40(8):543-6.
Artificial Intelligence and Machine Learning in Software as a Medical Device [Internet]. Available from: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device. [cited 2021 May 15]
Joint HMA/EMA workshop on artificial intelligence in medicines regulation. [Internet]. Available from: https://www.ema.europa.eu/en/events/joint-hmaema-workshop-artificial-intelligence-medicines-regulation [cited 2021 May 15].
EMA Regulatory Science to 2025 Strategic reflection. [Internet]. Available from: https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/ema-regulatory-science-2025-strategic-reflection_en.pdf [cited 2021 May 15].
Suggested criteria for using AI/ML algorithms in GxP. [Internet]. Available from: https://laegemiddelstyrelsen.dk/en/licensing/supervision-and-inspection/inspection-of-authorised-pharmaceutical-companies/using-aiml-algorithms-in-gxp/ [cited 2021 May 15].
Rasheed H, Höllein L, Holzgrabe U. Future Information Technology Tools for Fighting Substandard and Falsified Medicines in Low- and Middle-Income Countries. Front Pharmacol. 2018;9:995.
Klemenčič J, Mihelič J. Application of Algorithms and Machine Learning Methods in Pharmaceutical Manufacture. IPSI Trans Internet Res. 2019;15(1):16–22.
Wong W, Chee E, Li J, Wang X. Recurrent Neural Network-Based Model Predictive Control for Continuous Pharmaceutical Manufacturing. Mathematics. 2018;6(11):242.
Moss GP, Shah AJ, Adams RG, Davey N, Wilkinson SC, Pugh WJ, et al. The application of discriminant analysis and Machine Learning methods as tools to identify and classify compounds with potential as transdermal enhancers. Eur J Pharm Sci. 2012;45(1–2):116-27.
Boobier S, Hose DRJ, Blacker AJ, Nguyen BN. Machine learning with physicochemical relationships: solubility prediction in organic solvents and water. Nat Commun. 2020;11(1):5753.
Djuris J, Cirin-Varadjan S, Aleksic I, Djuris M, Cvijic S, Ibric S. Application of Machine-Learning Algorithms for Better Understanding of Tableting Properties of Lactose Co-Processed with Lipid Excipients. Pharmaceutics. 2021;13(5):663.
Khalid MH, Tuszynski PK, Szlek J, Jachowicz R, Mendyk A. From Black-Box to Transparent Computational Intelligence Models: A Pharmaceutical Case Study. In: 2015 13th International Conference on Frontiers of Information Technology (FIT). IEEE; 2015.
Han R, Xiong H, Ye Z, Yang Y, Huang T, Jing Q, et al. Predicting physical stability of solid dispersions by machine learning techniques. J Control Release. 2019;(311–2):16-25.
Lou H, Chung JI, Kiang Y-H, Xiao L-Y, Hageman MJ. The application of machine learning algorithms in understanding the effect of core/shell technique on improving powder compactability. Int J Pharm. 2019;555:368-79.
Han R, Yang Y, Li X, Ouyang D. Predicting oral disintegrating tablet formulations by neural network techniques. Asian J Pharm Sci. 2018;13(4):336-42.
Chen S, Liu T, Xu D, Huo Y, Yang Y. Image based Measurement of Population Growth Rate for L-Glutamic Acid Crystallization. In: 2019 Chinese Control Conference (CCC). IEEE; 2019.
Mehle A, Likar B, Tomaževič D. In-line recognition of agglomerated pharmaceutical pellets with density-based clustering and convolutional neural network. IPSJ Trans Comput Vis Appl. 2017;9(1):1-6.
Hirschberg C, Edinger M, Holmfred E, Rantanen J, Boetker J. Image-Based Artificial Intelligence Methods for Product Control of Tablet Coating Quality. Pharmaceutics. 2020;12(9):877.
Nagy B, Petra D, Galata DL, Démuth B, Borbás E, Marosi G, et al. Application of artificial neural networks for Process Analytical Technology-based dissolution testing. Int J Pharm. 2019;567:118464.
Korteby Y, Mahdi Y, Azizou A, Daoud K, Regdon G. Implementation of an artificial neural network as a PAT tool for the prediction of temperature distribution within a pharmaceutical fluidized bed granulator. Eur J Pharm Sci. 2016;88:219-32.
von Stosch M, Hamelink J-M, Oliveira R. Hybrid modeling as a QbD/PAT tool in process development: an industrial E. coli case study. Bioprocess Biosyst Eng. 2016;39(5):773-84.
Chiappini FA, Teglia CM, Forno ÁG, Goicoechea HC. Modelling of bioprocess non-linear fluorescence data for at-line prediction of etanercept based on artificial neural networks optimized by response surface methodology. Talanta. 2020;210:120664.
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