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International Journal of Bioprinting Advances for 3D-printed oral drug delivery systems

preparation, by the pressurized-assisted microsyringes 100. Cisco, 2023, What is cybersecurity?, viewed May 25, 2023,
technique. Int J Pharm, 621(2022): 121756.
https://www.cisco.com/c/en/us/products/security/what-is-
https://doi.org/10.1016/j.ijpharm.2022.121756 cybersecurity.html
95. Zhang J, Thakkar R, Kulkarni VR, et al., 2021, Investigation 101. Kok XW, Singh A, Raimi-Abraham BT, 2022, A design
of the fused deposition modeling additive manufacturing approach to optimise secure remote three-dimensional (3D)
I: Influence of process temperature on the quality and printing: A proof-of-concept study towards advancement in
crystallinity of the dosage forms. AAPS PharmSciTech, telemedicine. Healthcare (Basel, Switzerland), 10(6): 1114.
22(8): 258.
https://doi.org/10.3390/healthcare10061114
https://doi.org/10.1208/s12249-021-02094-8
102. Trenfield SJ, Xian Tan H, Awad A, et al., 2019, Track-and-
96. Wang Y, Genina N, Müllertz A, et al., 2023, Coating of trace: Novel anti-counterfeit measures for 3D printed
primary powder particles improves the quality of binder personalized drug products using smart material inks.
jetting 3D printed oral solid products. J Pharm Sci, 112(2). Int J Pharm, 567(2019): 118443.
https://doi.org/10.1016/j.xphs.2022.08.030 https://0-doi-org.biblioteca-ils.tec.mx/10.1016/j.
ijpharm.2019.06.034
97. O’Reilly CS, Elbadawi M, Desai N, et al., 2021, Machine learning
and machine vision accelerate 3D printed orodispersible film 103. Oh BC, Jin G, Park C, et al., 2020, Preparation and evaluation
development. Pharmaceutics, 13(12): 2187. of identifiable quick response (QR)-coded orodispersible
films using 3D printer with directly feeding nozzle.
http://dx.doi.org/10.3390/pharmaceutics13122187
Int J Pharm, 584 (2020): 119405.
98. Obeid S, Madžarević M, Krkobabić M, et al., 2021, Predicting
drug release from diazepam FDM printed tablets using deep https://0-doi-org.biblioteca-ils.tec.mx/10.1016/j.
learning approach: Influence of process parameters and ijpharm.2020.119405
tablet surface/volume ratio. Int J Pharm, 601(2021): 120507. 104. Windolf H, Chamberlain R, Delmotte A, et al., 2022,
Blind-Watermarking—Proof-of-concept of a novel
https://doi.org/10.1016/j.ijpharm.2021.120507
approach to ensure batch traceability for 3D printed tablets.
99. Mazur H, Erbrich L, Quodbach J, 2023, Investigations into Pharmaceutics, 14(2): 432.
the use of machine learning to predict drug dosage form
design to obtain desired release profiles for 3D printed oral http://dx.doi.org/10.3390/pharmaceutics14020432
medicines. Pharm Dev Technol, 28(2): 219–231. 105. Xu X, Seijo-Rabina A, Awad A, et al., 2021, Smartphone-enabled
3D printing of medicines. Int J Pharm, 609(2021): 121199.
https://doi.org/10.1080/10837450.2023.2173778
https://doi.org/10.1016/j.ijpharm.2021.121199

Volume 9 Issue 6 (2023) 523 https://doi.org/10.36922/ijb.1119