Generic placeholder image

Drug Delivery Letters

Editor-in-Chief

ISSN (Print): 2210-3031
ISSN (Online): 2210-304X

Review Article

The Future of Artificial Intelligence in Pharmaceutical Product Formulation

Author(s): Lalit Singh*, Ritesh K. Tiwari, Shashi Verma and Vijay Sharma

Volume 9, Issue 4, 2019

Page: [277 - 285] Pages: 9

DOI: 10.2174/2210303109666190621144400

Price: $65

Abstract

Background: Conventional approach of formulating a new dosage form is a comprehensive task and uses various sources like man, money, time and experimental efforts. The use of AI can help to obtain optimized pharmaceutical formulation with desired (best) attributes. AI minimizes the use of resources and increases the understanding of impact, of independent variable over desired dependent responses/variables.

Objective: Thus, the aim of present work is to explore the use of Artificial intelligence in designing pharmaceutical products as well as the manufacturing process to get the pharmaceutical product of desired attributes with ease. The review is presenting various aspects of Artificial intelligence like Quality by Design (QbD) & Design of Experiment (DoE) to confirm the quality profile of drug product, reduce interactions among the input variables for the optimization, modelization and various simulation tools used in pharmaceutical manufacturing (scale up and production).

Conclusion: Hence, the use of QbD approach in Artificial intelligence is not only useful in understanding the products or process but also helps in building an excellent and economical pharmaceutical product.

Keywords: Artificial Intelligence, product quality profile, quality by design, design of experiment, critical quality attributes, current uses of artificial intelligence.

Graphical Abstract

[1]
Reddy, S.; Fox, J.; Purohit, M.P. Artificial intelligence-enabled healthcare delivery. J. R. Soc. Med., 2019, 112(1), 22-28.
[http://dx.doi.org/10.1177/0141076818815510] [PMID: 30507284]
[2]
Aksu, B.; Yurdasiper, A.; Ege, M.A.; Okur, N.U.; Karasulu, H.Y. Development and comparative evaluation of extended release indomethacin capsules. Afr. J. Pharm. Pharmacol., 2013, 7(30), 2201-2209.
[3]
Yu, L.X. Pharmaceutical quality by design: product and process development, understanding, and control. Pharm. Res., 2008, 25(4), 781-791.
[http://dx.doi.org/10.1007/s11095-007-9511-1] [PMID: 18185986]
[4]
Tomba, E.; Facco, P.; Bezzo, F.; Barolo, M. Latent variable modeling to assist the implementation of Quality-by-Design paradigms in pharmaceutical development and manufacturing: a review. Int. J. Pharm., 2013, 457(1), 283-297.
[http://dx.doi.org/10.1016/j.ijpharm.2013.08.074] [PMID: 24016743]
[5]
Noha, A.Y. A Review on optimal experimental design; London School of Economics, 2019, pp. 1-7.
[6]
Bhat, S. Quality by design approach to cGMP. Pharmatechnology review, 2011.
[7]
Hanjalic, K.; Popovac, M.; Hadziabdic, M. A robust near-wall elliptic-relaxation eddy-viscosity turbulence model for CFD. Int. J. Heat Fluid Flow, 2004, 25(6), 1047-1051.
[http://dx.doi.org/10.1016/j.ijheatfluidflow.2004.07.005]
[8]
Remy, B.; Khinast, J.G.; Glasser, B.J. Discrete element simulation of free flowing grains in a four-bladed mixer. AlChE J., 2009, 55(8), 2035-2048.
[http://dx.doi.org/10.1002/aic.11876]
[9]
Anderson, T.B.; Jackson, R. Fluid mechanical description of fluidized beds. Equations of motion. Ind. Eng. Chem. Fundam., 1967, 6(4), 527-539.
[http://dx.doi.org/10.1021/i160024a007]
[10]
Hulburt, H.M.; Katz, S. Some problems in particle technology: A statistical mechanical formulation. Chem. Eng. Sci., 1964, 19(8), 555-574.
[http://dx.doi.org/10.1016/0009-2509(64)85047-8]
[11]
Drug Discovery AI can do in a day without currently takes months. Available from: https://singularityhub.com/2017/05/07/drug-discovery-ai-can-do-in-a-day-what-currently-takes-months/ [Accessed June 20, 2018].
[12]
What to expect from artificial intelligence in pharma and how to get there. Available from: https://www.pharmaceuticalonline.com/doc/what-to-expect-from-artificial-intelligence-in-pharma-and-how-to-get-there-0001 [Accessed June 17, 2018].
[13]
Mesut, B.; Aksu, B.; Ozsoy, Y. Design of Sustained Release Tablet Formulations of AlfuzosinHCl by means of Neuro-Fuzzy Logic. Lat. Am. J. Pharm., 2013, 32(9), 1288-1297.
[14]
Gaspar, R.; Aksu, B.; Cuine, A.; Danhof, M.; Takac, M.J.; Linden, H.H.; Link, A.; Muchitsch, E.M.; Wilson, C.G.; Ohrngren, P.; Dencker, L. Towards a European strategy for medicines research (2014-2020): The EUFEPS position paper on Horizon 2020. Eur. J. Pharm. Sci., 2012, 47(5), 979-987.
[http://dx.doi.org/10.1016/j.ejps.2012.09.020] [PMID: 23046836]
[15]
Drug Discovery - AI Can Do in a Day What Currently Takes Months - Singularity Hub.
[16]
March-Vila, E.; Pinzi, L.; Sturm, N.; Tinivella, A.; Engkvist, O.; Chen, H.; Rastelli, G. On the Integration of In Silico Drug Design Methods for Drug Repurposing. Front. Pharmacol., 2017, 8, 298.
[http://dx.doi.org/10.3389/fphar.2017.00298] [PMID: 28588497]
[17]
Relevance of artificial intelligence in creative pharma marketing. Available from: http://www.tapanray.in/relevance-of-artificial-intelligence-in-creative-pharma-marketing/ [Accessed May 17, 2018].
[18]
Singh, L.; Sharma, V. Quality by Design (QbD) Approach in Pharmaceuticals: Status, Challenges and Next Steps. Drug Deliv. Lett., 2015, 5, 2-8.
[http://dx.doi.org/10.2174/2210303104666141112220253]
[19]
Aksu, B.; Yurdasiper, A.; Ege, M.A.; Okur, N.U.; Karasulu, H.Y. Development and Comparative Evaluation of Extended Release Indomethacin Capsules. Afr. J. Pharm. Pharmacol., 2013, 7(30), 2201-2209.
[20]
Yakovenko, O.; Jones, S.J.M. Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations. J. Comput. Aided Mol. Des., 2018, 32(1), 299-311.
[http://dx.doi.org/10.1007/s10822-017-0085-7] [PMID: 29134430]
[21]
Veerabhadra, S.N.; Khan, M.S.; Shukla, B.H.; Chaturvedi, P.R. Artificial intelligence in clinical research. Int J Clin Trials., 2016, 3(4), 187-193.
[http://dx.doi.org/10.18203/2349-3259.ijct20163955]
[22]
Benefits of Artificial Intelligence in Drug Repurposing will be highlighted at 7th Annual Drug Repositioning, Repurposing and Rescue Conference. Available from: https://www.prweb.com/releases/2018/06/prweb15562678.htm [Accessed June 25, 2018].
[23]
Reshaping business with artificial intelligence. Available from: https://sloanreview.mit.edu/projects/artificial-intelligence-in-business-gets-real/ [Accessed July 21, 2018].
[24]
Abedini, F.; Ebrahimi, M.; Roozbehani, A.H.; Domb, A.J.; Hosseinkhani, H. Overview on natural hydrophilic polysaccharide polymers in drug delivery. Polym. Adv. Technol., 2018, 29, 2564-2573.
[http://dx.doi.org/10.1002/pat.4375]
[25]
Ghadiri, M.; Vasheghani-Farahani, E.; Atyabi, F.; Kobarfard, F.; Mohamadyar-Toupkanlou, F.; Hosseinkhani, H. Transferrin-conjugated magnetic dextran-spermine nanoparticles for targeted drug transport across blood-brain barrier. J. Biomed. Mater. Res. A, 2017, 105(10), 2851-2864.
[http://dx.doi.org/10.1002/jbm.a.36145] [PMID: 28639394]
[26]
Alibolandi, M.; Abnous, K.; Sadeghi, F.; Hosseinkhani, H.; Ramezani, M.; Hadizadeh, F. Folate receptor-targeted multimodal polymersomes for delivery of quantum dots and doxorubicin to breast adenocarcinoma: In vitro and in vivo evaluation. Int. J. Pharm., 2016, 500(1-2), 162-178.
[http://dx.doi.org/10.1016/j.ijpharm.2016.01.040] [PMID: 26802496]
[27]
Mottaghitalab, F.; Farokhi, M.; Shokrgozar, M.A.; Atyabi, F.; Hosseinkhani, H. Silk fibroin nanoparticle as a novel drug delivery system. J. Control. Release, 2015, 206, 161-176.
[http://dx.doi.org/10.1016/j.jconrel.2015.03.020] [PMID: 25797561]
[28]
He, W.; Hosseinkhani, H.; Mohammadinejad, R.; Roveimiab, Z.; Hueng, D.Y.; Ou, K.L.; Domb, A.J. Polymeric nanoparticles for therapy and imaging. Polym. Adv. Technol., 2014, 25(11), 1216-1225.
[http://dx.doi.org/10.1002/pat.3381]
[29]
Hosseinkhani, H. III 3D in vitro technology for drug discovery. Curr. Drug Saf., 2012, 7(1), 37-43.
[http://dx.doi.org/10.2174/157488612800492753] [PMID: 22663957]
[30]
Buket, A.; Anant, P. Marcel de, M.; Ozgen, O.; Tamer, G.; Peter, Y. Quality by Design Approach: Application of Artificial Intelligence Techniques of Tablets Manufactured by Direct Compression. AAPS PharmSciTech, 2012, 13(4)
[31]
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), 236-245.
[http://dx.doi.org/10.3109/10837450.2012.705294] [PMID: 22881350]
[32]
Patel, T.B.; Patel, L.D. Patel, Tr.; Suhagia, BN. Artificial neural network as tool for quality by design in formulation development of solid dispersion of fenofibrate. Bulletin of Pharmaceutical Research, 2015, 5(1), 20-27.
[33]
Wu, T.; Pan, W.; Chen, J.; Zhang, R. Formulation optimization technique based on artificial neural network in salbutamol sulfate osmotic pump tablets. Drug Dev. Ind. Pharm., 2000, 26(2), 211-215.
[http://dx.doi.org/10.1081/DDC-100100347] [PMID: 10697759]
[34]
Leane, M.M.; Cumming, I.; Corrigan, O.I. The use of artificial neural networks for the selection of the most appropriate formulation and processing variables in order to predict the in vitro dissolution of sustained release minitablets. AAPS PharmSciTech, 2003, 4(2)E26
[http://dx.doi.org/10.1208/pt040226] [PMID: 12916908]
[35]
Ozcelik, E.; Mesut, B.; Aksu, B.; Ozsoy, Y. Quetiapine Fumarate Extended-Release Tablet Formulation Design Using Artificial Neural Networks. Turk J Pharm Sci., 2017, 14(3), 213-221.
[http://dx.doi.org/10.4274/tjps.97720]
[36]
Aksu, B.; Matas, M.D.; Cevher, M.; Ozsoy, Y.; Guneri, T.; York, P. Quality by design approach for tablet formulations containing spray coated ramipril by using artificial intelligence techniques. Int. J. Drug Deliv., 2012, 4, 59-69.
[37]
Aksu, B.; Yegen, G.; Purisa, S.; Cevher, E.; Ozsoy, Y. Optimisation of ondansetron orally disintegrating tablets using artificial neural networks. Trop. J. Pharm. Res., 2014, 13(9), 1374-1383.
[http://dx.doi.org/10.4314/tjpr.v13i9.1]
[38]
Abdelbary, AA.; Al-Mahallawi, AA.; Abdelrahim, ME.; Ali, M. Preparation, optimization, and in vitro simulated inhalation delivery of carvedilol nanoparticles loaded on a coarse carrier intended for pulmonary administration; , 2015, 10, p. (1)6339-6353.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy