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Current Drug Metabolism

Editor-in-Chief

ISSN (Print): 1389-2002
ISSN (Online): 1875-5453

Review Article

Artificial Intelligence in Drug Formulation and Development: Applications and Future Prospects

Author(s): Noorain, Varsha Srivastava, Bushra Parveen and Rabea Parveen*

Volume 24, Issue 9, 2023

Published on: 28 September, 2023

Page: [622 - 634] Pages: 13

DOI: 10.2174/0113892002265786230921062205

Price: $65

Abstract

Artificial Intelligence (AI) has emerged as a powerful tool in various domains, and the field of drug formulation and development is no exception. This review article aims to provide an overview of the applications of AI in drug formulation and development and explore its future prospects. The article begins by introducing the fundamental concepts of AI, including machine learning, deep learning, and artificial neural networks and their relevance in the pharmaceutical industry. Furthermore, the article discusses the network and tools of AI and its applications in the pharmaceutical development process, including various areas, such as drug discovery, manufacturing, quality control, clinical trial management, and drug delivery. The utilization of AI in various conventional as well as modified dosage forms has been compiled. It also highlights the challenges and limitations associated with the implementation of AI in this field, including data availability, model interpretability, and regulatory considerations. Finally, the article presents the future prospects of AI in drug formulation and development, emphasizing the potential for personalized medicine, precision drug targeting, and rapid formulation optimization. It also discusses the ethical implications of AI in this context, including issues of privacy, bias, and accountability.

[1]
Zaslavsky, J.; Bannigan, P.; Allen, C. Re-envisioning the design of nanomedicines: Harnessing automation and artificial intelligence. Expert Opin. Drug Deliv., 2023, 20(2), 241-257.
[http://dx.doi.org/10.1080/17425247.2023.2167978] [PMID: 36644850]
[2]
Mishra, V. Artificial intelligence: The beginning of a new era in pharmacy profession. Asian J. Pharm., 2018, 12(02)
[3]
Kerasidou, C.X.; Kerasidou, A.; Buscher, M.; Wilkinson, S. Before and beyond trust: Reliance in medical AI. J. Med. Ethics, 2022, 48(11), 852-856.
[http://dx.doi.org/10.1136/medethics-2020-107095] [PMID: 34426519]
[4]
Sethuraman, N. Artificial intelligence: A new paradigm for pharmaceutical applications in formulations development. Ind. J. Pharmac. Educ. Res., 2020, 54(4), 843-846.
[http://dx.doi.org/10.5530/ijper.54.4.176]
[5]
Nihar, S.; Nishith, P.; Patel, K.R. A sequential review on intelligent drug delivery system. J. Pharm. Sci. Biosci. Res., 2013, 3(5), 158-162.
[6]
Paul, D.; Sanap, G.; Shenoy, S.; Kalyane, D.; Kalia, K.; Tekade, R.K. Artificial intelligence in drug discovery and development. Drug Discov. Today, 2021, 26(1), 80-93.
[http://dx.doi.org/10.1016/j.drudis.2020.10.010] [PMID: 33099022]
[7]
Alshawwa, S.Z.; Kassem, A.A.; Farid, R.M.; Mostafa, S.K.; Labib, G.S. Nanocarrier drug delivery systems: Characterization, limitations, future perspectives and implementation of artificial intelligence. Pharmaceutics, 2022, 14(4), 883.
[http://dx.doi.org/10.3390/pharmaceutics14040883] [PMID: 35456717]
[8]
Das, K.P. J, C. Nanoparticles and convergence of artificial intelligence for targeted drug delivery for cancer therapy: Current progress and challenges. Front. Med. Technol., 2023, 4, 1067144.
[http://dx.doi.org/10.3389/fmedt.2022.1067144] [PMID: 36688144]
[9]
Hassanzadeh, P.; Atyabi, F.; Dinarvand, R. The significance of artificial intelligence in drug delivery system design. Adv. Drug Deliv. Rev., 2019, 151-152, 169-190.
[http://dx.doi.org/10.1016/j.addr.2019.05.001] [PMID: 31071378]
[10]
Das, S.; Dey, R.; Nayak, A.K. Artificial intelligence in pharmacy. Ind. J. Pharmac. Educ. Res., 2021, 55(2), 304-318.
[http://dx.doi.org/10.5530/ijper.55.2.68]
[11]
Wirtz, B.W.; Weyerer, J.C.; Geyer, C. Artificial intelligence and the public sector—applications and challenges. Int. J. Public Adm., 2019, 42(7), 596-615.
[http://dx.doi.org/10.1080/01900692.2018.1498103]
[12]
Lamberti, M.J.; Wilkinson, M.; Donzanti, B.A.; Wohlhieter, G.E.; Parikh, S.; Wilkins, R.G.; Getz, K. A study on the application and use of artificial intelligence to support drug development. Clin. Ther., 2019, 41(8), 1414-1426.
[http://dx.doi.org/10.1016/j.clinthera.2019.05.018] [PMID: 31248680]
[13]
Jiang, F.; Jiang, Y.; Zhi, H.; Dong, Y.; Li, H.; Ma, S.; Wang, Y.; Dong, Q.; Shen, H.; Wang, Y. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc. Neurol., 2017, 2(4), 230-243.
[http://dx.doi.org/10.1136/svn-2017-000101] [PMID: 29507784]
[14]
Sakiyama, Y. The use of machine learning and nonlinear statistical tools for ADME prediction. Expert Opin. Drug Metab. Toxicol., 2009, 5(2), 149-169.
[http://dx.doi.org/10.1517/17425250902753261] [PMID: 19239395]
[15]
Colombo, S. Applications of artificial intelligence in drug delivery and pharmaceutical development. In: Artificial Intelligence in Healthcare; Academic Press, 2020; pp. 85-116.
[http://dx.doi.org/10.1016/B978-0-12-818438-7.00004-6]
[16]
Ye, Z.; Yang, W.; Yang, Y.; Ouyang, D. Interpretable machine learning methods for in vitro pharmaceutical formulation development. Food Front., 2021, 2(2), 195-207.
[http://dx.doi.org/10.1002/fft2.78]
[17]
Jiang, J.; Ma, X.; Ouyang, D.; Williams, R.O. III Emerging artificial intelligence (AI) technologies used in the development of solid dosage forms. Pharmaceutics, 2022, 14(11), 2257.
[http://dx.doi.org/10.3390/pharmaceutics14112257] [PMID: 36365076]
[18]
Beneke, F.; Mackenrodt, M.O. Artificial intelligence and collusion. IIC, 2019, 50, 109-134.
[http://dx.doi.org/10.1007/s40319-018-00773-x]
[19]
Kalyane, D.; Sanap, G.; Paul, D.; Shenoy, S.; Anup, N.; Polaka, S.; Tambe, V.; Tekade, RK. Artificial intelligence in the pharmaceutical sector: Current scene and future prospect. In: The future of pharmaceutical product development and research; Academic Press, 2020; pp. 73-107.
[http://dx.doi.org/10.1016/B978-0-12-814455-8.00003-7]
[20]
Korkmaz, S.; Zararsiz, G.; Goksuluk, D. Drug/nondrug classification using support vector machines with various feature selection strategies. Comput. Methods Programs Biomed., 2014, 117(2), 51-60.
[http://dx.doi.org/10.1016/j.cmpb.2014.08.009] [PMID: 25224081]
[21]
Aliper, A.; Plis, S.; Artemov, A.; Ulloa, A.; Mamoshina, P.; Zhavoronkov, A. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol. Pharm., 2016, 13(7), 2524-2530.
[http://dx.doi.org/10.1021/acs.molpharmaceut.6b00248] [PMID: 27200455]
[22]
Gams, M.; Horvat, M.; Ožek, M.; Luštrek, M.; Gradišek, A. Integrating artificial and human intelligence into tablet production process. AAPS PharmSciTech, 2014, 15(6), 1447-1453.
[http://dx.doi.org/10.1208/s12249-014-0174-z] [PMID: 24970587]
[23]
Fogel, D.B. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review. Contemp. Clin. Trials Commun., 2018, 11, 156-164.
[http://dx.doi.org/10.1016/j.conctc.2018.08.001] [PMID: 30112460]
[24]
Harrer, S.; Shah, P.; Antony, B.; Hu, J. Artificial intelligence for clinical trial design. Trends Pharmacol. Sci., 2019, 40(8), 577-591.
[http://dx.doi.org/10.1016/j.tips.2019.05.005] [PMID: 31326235]
[25]
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]
[26]
Rantanen, J.; Khinast, J. The future of pharmaceutical manufacturing sciences. J. Pharm. Sci., 2015, 104(11), 3612-3638.
[http://dx.doi.org/10.1002/jps.24594] [PMID: 26280993]
[27]
Singh, J.; Flaherty, K.; Sohi, R.S.; Deeter-Schmelz, D.; Habel, J.; Le Meunier-FitzHugh, K.; Malshe, A.; Mullins, R.; Onyemah, V. Sales profession and professionals in the age of digitization and artificial intelligence technologies: Concepts, priorities, and questions. J. Pers. Sell. Sales Manage., 2019, 39(1), 2-22.
[http://dx.doi.org/10.1080/08853134.2018.1557525]
[28]
Sellwood, M.A.; Ahmed, M.; Segler, M.H.S.; Brown, N. Artificial intelligence in drug discovery. Future Med. Chem., 2018, 10(17), 2025-2028.
[http://dx.doi.org/10.4155/fmc-2018-0212] [PMID: 30101607]
[29]
Mak, K.K.; Pichika, M.R. Artificial intelligence in drug development: Present status and future prospects. Drug Discov. Today, 2019, 24(3), 773-780.
[http://dx.doi.org/10.1016/j.drudis.2018.11.014] [PMID: 30472429]
[30]
Tsigelny, I.F. Artificial intelligence in drug combination therapy. Brief. Bioinform., 2019, 20(4), 1434-1448.
[http://dx.doi.org/10.1093/bib/bby004] [PMID: 29438494]
[31]
Yang, X.; Wang, Y.; Byrne, R.; Schneider, G.; Yang, S. Concepts of artificial intelligence for computer-assisted drug discovery. Chem. Rev., 2019, 119(18), 10520-10594.
[http://dx.doi.org/10.1021/acs.chemrev.8b00728] [PMID: 31294972]
[32]
Chan, H.C.S.; Shan, H.; Dahoun, T.; Vogel, H.; Yuan, S. Advancing drug discovery via artificial intelligence. Trends Pharmacol. Sci., 2019, 40(8), 592-604.
[http://dx.doi.org/10.1016/j.tips.2019.06.004] [PMID: 31320117]
[33]
Firth, N.C.; Atrash, B.; Brown, N.; Blagg, J. MOARF, an integrated workflow for multiobjective optimization: Implementation, synthesis, and biological evaluation. J. Chem. Inf. Model., 2015, 55(6), 1169-1180.
[http://dx.doi.org/10.1021/acs.jcim.5b00073] [PMID: 26054755]
[34]
Rashid, M.B.M.A.; Toh, T.B.; Hooi, L.; Silva, A.; Zhang, Y.; Tan, P.F.; Teh, A.L.; Karnani, N.; Jha, S.; Ho, C.M.; Chng, W.J.; Ho, D.; Chow, E.K.H. Optimizing drug combinations against multiple myeloma using a quadratic phenotypic optimization platform (QPOP). Sci. Transl. Med., 2018, 10(453), eaan0941.
[http://dx.doi.org/10.1126/scitranslmed.aan0941] [PMID: 30089632]
[35]
Hessler, G.; Baringhaus, K.H. Artificial intelligence in drug design. Molecules, 2018, 23(10), 2520.
[http://dx.doi.org/10.3390/molecules23102520] [PMID: 30279331]
[36]
Öztürk, H.; Özgür, A.; Ozkirimli, E. DeepDTA: Deep drug–target binding affinity prediction. Bioinformatics, 2018, 34(17), i821-i829.
[http://dx.doi.org/10.1093/bioinformatics/bty593] [PMID: 30423097]
[37]
Das, MK.; Chakraborty, T. ANN in pharmaceutical product and process development. In: Artificial neural network for drug design, delivery and disposition; Academic Press, 2016; pp. 277-293.
[http://dx.doi.org/10.1016/B978-0-12-801559-9.00014-4]
[38]
Pereira, J.C.; Caffarena, E.R.; dos Santos, C.N. Boosting docking-based virtual screening with deep learning. J. Chem. Inf. Model., 2016, 56(12), 2495-2506.
[http://dx.doi.org/10.1021/acs.jcim.6b00355] [PMID: 28024405]
[39]
Kumar, R.; Sharma, A.; Siddiqui, M.H.; Tiwari, R.K. Prediction of human intestinal absorption of compounds using artificial intelligence techniques. Curr. Drug Discov. Technol., 2017, 14(4), 244-254.
[http://dx.doi.org/10.2174/1570163814666170404160911] [PMID: 28382857]
[40]
Feng, Q.; Dueva, E.; Cherkasov, A.; Ester, M. Padme: A deep learning-based framework for drug-target interaction prediction. arXiv:1807.09741,, 2018.
[41]
Karimi, M.; Wu, D.; Wang, Z.; Shen, Y. DeepAffinity: Interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks. Bioinformatics, 2019, 35(18), 3329-3338.
[http://dx.doi.org/10.1093/bioinformatics/btz111] [PMID: 30768156]
[42]
Mayr, A.; Klambauer, G.; Unterthiner, T.; Hochreiter, S. DeepTox: Toxicity prediction using deep learning. Front. Environ. Sci., 2016, 3, 80.
[http://dx.doi.org/10.3389/fenvs.2015.00080]
[43]
Pu, L.; Naderi, M.; Liu, T.; Wu, H.C.; Mukhopadhyay, S.; Brylinski, M. eToxPred: A machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol. Toxicol., 2019, 20(1), 2.
[http://dx.doi.org/10.1186/s40360-018-0282-6] [PMID: 30611293]
[44]
Lysenko, A.; Sharma, A.; Boroevich, K.A.; Tsunoda, T. An integrative machine learning approach for prediction of toxicity-related drug safety. Life Sci. Alliance, 2018, 1(6), e201800098.
[http://dx.doi.org/10.26508/lsa.201800098] [PMID: 30515477]
[45]
Gayvert, K.M.; Madhukar, N.S.; Elemento, O. A data-driven approach to predicting successes and failures of clinical trials. Cell Chem. Biol., 2016, 23(10), 1294-1301.
[http://dx.doi.org/10.1016/j.chembiol.2016.07.023] [PMID: 27642066]
[46]
Mason, D.J.; Eastman, R.T.; Lewis, R.P.I.; Stott, I.P.; Guha, R.; Bender, A. Using machine learning to predict synergistic antimalarial compound combinations with novel structures. Front. Pharmacol., 2018, 9, 1096.
[http://dx.doi.org/10.3389/fphar.2018.01096] [PMID: 30333748]
[47]
Farizhandi, A.A.K.; Alishiri, M.; Lau, R. Machine learning approach for carrier surface design in carrier-based dry powder inhalation. Comput. Chem. Eng., 2021, 151, 107367.
[http://dx.doi.org/10.1016/j.compchemeng.2021.107367]
[48]
Chauhan, S.; O’Callaghan, S.; Wall, A.; Pawlak, T.; Doyle, B.; Adelfio, A.; Trajkovic, S.; Gaffney, M.; Khaldi, N. Using peptidomics and machine learning to assess effects of drying processes on the peptide profile within a functional ingredient. Processes, 2021, 9(3), 425.
[http://dx.doi.org/10.3390/pr9030425]
[49]
Keskes, S.; Hanini, S.; Hentabli, M.; Laidi, M. Artificial intelligence and mathematical modelling of the drying kinetics of pharmaceutical powders. Kem. Ind., 2020, 69(3-4), 137-152.
[http://dx.doi.org/10.15255/KUI.2019.038]
[50]
Zhao, J.; Tian, G.; Qiu, Y.; Qu, H. Rapid quantification of active pharmaceutical ingredient for sugar-free Yangwei granules in commercial production using FT-NIR spectroscopy based on machine learning techniques. Spectrochim. Acta A Mol. Biomol. Spectrosc., 2021, 245, 118878.
[http://dx.doi.org/10.1016/j.saa.2020.118878] [PMID: 32919149]
[51]
Landin, M. Artificial intelligence tools for scaling up of high shear wet granulation process. J. Pharm. Sci., 2017, 106(1), 273-277.
[http://dx.doi.org/10.1016/j.xphs.2016.09.022] [PMID: 27816264]
[52]
Ma, X.; Kittikunakorn, N.; Sorman, B.; Xi, H.; Chen, A.; Marsh, M.; Mongeau, A.; Piché, N.; Williams, R.O., III; Skomski, D. Application of deep learning convolutional neural networks for internal tablet defect detection: High accuracy, throughput, and adaptability. J. Pharm. Sci., 2020, 109(4), 1547-1557.
[http://dx.doi.org/10.1016/j.xphs.2020.01.014] [PMID: 31982393]
[53]
Obeid, S. Madžarević, M.; Krkobabić, M.; Ibrić, S. Predicting drug release from diazepam FDM printed tablets using deep learning approach: Influence of process parameters and tablet surface/volume ratio. Int. J. Pharm., 2021, 601, 120507.
[http://dx.doi.org/10.1016/j.ijpharm.2021.120507] [PMID: 33766640]
[54]
Westphal, E.; Seitz, H. A machine learning method for defect detection and visualization in selective laser sintering based on convolutional neural networks. Addit. Manuf., 2021, 41, 101965.
[http://dx.doi.org/10.1016/j.addma.2021.101965]
[55]
Zhou, J.; He, J.; Li, G.; Liu, Y. Identifying capsule defect based on an improved convolutional neural network. Shock Vib., 2020, 2020, 1-9.
[http://dx.doi.org/10.1155/2020/8887723]
[56]
Kumar, K.; Panpalia, G.; Priyadarshini, S. Application of artificial neural networks in optimizing the fatty alcohol concentration in the formulation of an O/W emulsion. Acta Pharm., 2011, 61(2), 249-256.
[http://dx.doi.org/10.2478/v10007-011-0013-7] [PMID: 21684851]
[57]
Agatonovic-Kustrin, S.; Glass, B.D.; Wisch, M.H.; Alany, R.G. Prediction of a stable microemulsion formulation for the oral delivery of a combination of antitubercular drugs using ANN methodology. Pharm. Res., 2003, 20(11), 1760-1765.
[http://dx.doi.org/10.1023/B:PHAM.0000003372.56993.39] [PMID: 14661919]
[58]
Petrović, J.; Ibrić, S.; Betz, G.; Đurić, Z. Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees. Int. J. Pharm., 2012, 428(1-2), 57-67.
[http://dx.doi.org/10.1016/j.ijpharm.2012.02.031] [PMID: 22402474]
[59]
Galata, D.L.; Könyves, Z.; Nagy, B.; Novák, M.; Mészáros, L.A.; Szabó, E.; Farkas, A.; Marosi, G.; Nagy, Z.K. Real-time release testing of dissolution based on surrogate models developed by machine learning algorithms using NIR spectra, compression force and particle size distribution as input data. Int. J. Pharm., 2021, 597, 120338.
[http://dx.doi.org/10.1016/j.ijpharm.2021.120338] [PMID: 33545285]
[60]
Han, R.; Yang, Y.; Li, X.; Ouyang, D. Predicting oral disintegrating tablet formulations by neural network techniques. Asian. J. Pharmac. Sci., 2018, 13(4), 336-342.
[http://dx.doi.org/10.1016/j.ajps.2018.01.003] [PMID: 32104407]
[61]
Tan, C. Degim, İ.T. Development of sustained release formulation of an antithrombotic drug and application of fuzzy logic. Pharm. Dev. Technol., 2012, 17(2), 242-250.
[http://dx.doi.org/10.3109/10837450.2010.531739] [PMID: 21062232]
[62]
Nemati, P.; Imani, M.; Farahmandghavi, F.; Mirzadeh, H.; Marzban-Rad, E.; Nasrabadi, A.M. Dexamethasone-releasing cochlear implant coatings: Application of artificial neural networks for modelling of formulation parameters and drug release profile. J. Pharm. Pharmacol., 2013, 65(8), 1145-1157.
[http://dx.doi.org/10.1111/jphp.12086] [PMID: 23837582]
[63]
Belič A.; Grabnar, I.; Karba, R.; Mrhar, A. Pathways of paracetamol absorption from layered excipient suppositories: Artificial intelligence approach. Eur. J. Drug Metab. Pharmacokinet., 2003, 28(1), 31-40.
[http://dx.doi.org/10.1007/BF03190864] [PMID: 14503662]
[64]
Sankalia, M.G.; Mashru, R.C.; Sankalia, J.M.; Sutariya, V.B. Papain entrapment in alginate beads for stability improvement and site-specific delivery: Physicochemical characterization and factorial optimization using neural network modeling. AAPS PharmSciTech, 2005, 6(2), E209-E222.
[http://dx.doi.org/10.1208/pt060231] [PMID: 16353980]
[65]
Labouta, H.I.; El-Khordagui, L.K.; Molokhia, A.M.; Ghaly, G.M. Multivariate modeling of encapsulation and release of an ionizable drug from polymer microspheres. J. Pharm. Sci., 2009, 98(12), 4603-4615.
[http://dx.doi.org/10.1002/jps.21753] [PMID: 19645004]
[66]
Zhang, A.Y.; Fan, T.Y. Optimization of calcium alginate floating microspheres loading aspirin by artificial neural networks and response surface methodology. Beijing Da Xue Xue Bao, 2010, 42(2), 197-201.
[PMID: 20396364]
[67]
Medarević, D.P.; Kleinebudde, P.; Djuriš, J.; Djurić, Z.; Ibrić, S. Combined application of mixture experimental design and artificial neural networks in the solid dispersion development. Drug Dev. Ind. Pharm., 2016, 42(3), 389-402.
[http://dx.doi.org/10.3109/03639045.2015.1054831] [PMID: 26065534]
[68]
Barmpalexis, P.; Koutsidis, I.; Karavas, E.; Louka, D.; Papadimitriou, S.A.; Bikiaris, D.N. Development of PVP/PEG mixtures as appropriate carriers for the preparation of drug solid dispersions by melt mixing technique and optimization of dissolution using artificial neural networks. Eur. J. Pharm. Biopharm., 2013, 85(3), 1219-1231.
[http://dx.doi.org/10.1016/j.ejpb.2013.03.013] [PMID: 23541514]
[69]
Gao, H.; Wang, W.; Dong, J.; Ye, Z.; Ouyang, D. An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design. Eur. J. Pharm. Biopharm., 2021, 158, 336-346.
[http://dx.doi.org/10.1016/j.ejpb.2020.12.001] [PMID: 33301864]
[70]
Han, R.; Xiong, H.; Ye, Z.; Yang, Y.; Huang, T.; Jing, Q.; Lu, J.; Pan, H.; Ren, F.; Ouyang, D. Predicting physical stability of solid dispersions by machine learning techniques. J. Control. Release, 2019, 311-312, 16-25.
[http://dx.doi.org/10.1016/j.jconrel.2019.08.030] [PMID: 31465824]
[71]
Takayama, K.; Takahara, J.; Fujikawa, M.; Ichikawa, H.; Nagai, T. Formula optimization based on artificial neural networks in transdermal drug delivery. J. Control. Release, 1999, 62(1-2), 161-170.
[http://dx.doi.org/10.1016/S0168-3659(99)00033-4] [PMID: 10518647]
[72]
Leonardi, D.; Salomón, C.J.; Lamas, M.C.; Olivieri, A.C. Development of novel formulations for Chagas’ disease: Optimization of benznidazole chitosan microparticles based on artificial neural networks. Int. J. Pharm., 2009, 367(1-2), 140-147.
[http://dx.doi.org/10.1016/j.ijpharm.2008.09.036] [PMID: 18938233]
[73]
dos Santos, A.M.; Carvalho, F.C.; Teixeira, D.A.; Azevedo, D.L.; de Barros, W.M.; Gremião, M.P.D. Computational and experimental approaches for development of methotrexate nanosuspensions by bottom-up nanoprecipitation. Int. J. Pharm., 2017, 524(1-2), 330-338.
[http://dx.doi.org/10.1016/j.ijpharm.2017.03.068] [PMID: 28359822]
[74]
Mehta, C.H.; Narayan, R.; Nayak, U.Y. Computational modeling for formulation design. Drug Discov. Today, 2019, 24(3), 781-788.
[http://dx.doi.org/10.1016/j.drudis.2018.11.018] [PMID: 30502513]
[75]
Ho, D.; Wang, P.; Kee, T. Artificial intelligence in nanomedicine. Nanoscale Horiz., 2019, 4(2), 365-377.
[http://dx.doi.org/10.1039/C8NH00233A] [PMID: 32254089]
[76]
Asadi, H.; Rostamizadeh, K.; Salari, D.; Hamidi, M. Preparation of biodegradable nanoparticles of tri-block PLA–PEG–PLA copolymer and determination of factors controlling the particle size using artificial neural network. J. Microencapsul., 2011, 28(5), 406-416.
[http://dx.doi.org/10.3109/02652048.2011.576784] [PMID: 21736525]
[77]
Li, Y.; Abbaspour, M.R.; Grootendorst, P.V.; Rauth, A.M.; Wu, X.Y. Optimization of controlled release nanoparticle formulation of verapamil hydrochloride using artificial neural networks with genetic algorithm and response surface methodology. Eur. J. Pharm. Biopharm., 2015, 94, 170-179.
[http://dx.doi.org/10.1016/j.ejpb.2015.04.028] [PMID: 25986587]
[78]
Baharifar, H.; Amani, A. Size, loading efficiency, and cytotoxicity of albumin-loaded chitosan nanoparticles: An artificial neural networks study. J. Pharm. Sci., 2017, 106(1), 411-417.
[http://dx.doi.org/10.1016/j.xphs.2016.10.013] [PMID: 27866686]
[79]
Luo, M.; Feng, Y.; Wang, T.; Guan, J. Micro-/nanorobots at work in active drug delivery. Adv. Funct. Mater., 2018, 28(25), 1706100.
[http://dx.doi.org/10.1002/adfm.201706100]
[80]
Fu, J.; Yan, H. Controlled drug release by a nanorobot. Nat. Biotechnol., 2012, 30(5), 407-408.
[http://dx.doi.org/10.1038/nbt.2206] [PMID: 22565965]
[81]
Narayanan, R.R.; Durga, N.; Nagalakshmi, S. Impact of artificial intelligence (AI) on drug discovery and product development. Ind. J. Pharmac. Educ. Res., 2022, 56(3s), s387-s397.
[http://dx.doi.org/10.5530/ijper.56.3s.146]
[82]
Hortelao, A.C.; Simó, C.; Guix, M.; Guallar-Garrido, S.; Julián, E.; Vilela, D.; Rejc, L.; Ramos-Cabrer, P.; Cossío, U.; Gómez-Vallejo, V.; Patiño, T.; Llop, J.; Sánchez, S. Swarming behavior and in vivo monitoring of enzymatic nanomotors within the bladder. Sci. Robot., 2021, 6(52), eabd2823.
[http://dx.doi.org/10.1126/scirobotics.abd2823] [PMID: 34043548]
[83]
Tran, T.T.V.; Tayara, H.; Chong, K.T. Artificial intelligence in drug metabolism and excretion prediction: Recent advances, challenges, and future perspectives. Pharmaceutics, 2023, 15(4), 1260.
[http://dx.doi.org/10.3390/pharmaceutics15041260] [PMID: 37111744]
[84]
Kazmi, S.R.; Jun, R.; Yu, M.S.; Jung, C.; Na, D. In silico approaches and tools for the prediction of drug metabolism and fate: A review. Comput. Biol. Med., 2019, 106, 54-64.
[http://dx.doi.org/10.1016/j.compbiomed.2019.01.008] [PMID: 30682640]
[85]
Litsa, E.E.; Das, P.; Kavraki, L.E. Machine learning models in the prediction of drug metabolism: Challenges and future perspectives. Expert Opin. Drug Metab. Toxicol., 2021, 17(11), 1245-1247.
[http://dx.doi.org/10.1080/17425255.2021.1998454] [PMID: 34706606]
[86]
Banerjee, P.; Dunkel, M.; Kemmler, E.; Preissner, R. SuperCYPsPred: A web server for the prediction of cytochrome activity. Nucleic Acids Res., 2020, 48(W1), W580-W585.
[http://dx.doi.org/10.1093/nar/gkaa166] [PMID: 32182358]
[87]
Yang, K.K.; Wu, Z.; Arnold, F.H. Machine-learning-guided directed evolution for protein engineering. Nat. Methods, 2019, 16(8), 687-694.
[88]
Strokach, A.; Becerra, D.; Corbi-Verge, C.; Perez-Riba, A.; Kim, P.M. Fast and flexible protein design using deep graph neural networks. Cell Syst., 2020, 11(4), 402-411.
[PMID: 32971019]
[89]
Jang, W.D.; Kim, G.B.; Kim, Y.; Lee, S.Y. Applications of artificial intelligence to enzyme and pathway design for metabolic engineering. Curr. Opin. Biotechnol., 2022, 73, 101-107.
[http://dx.doi.org/10.1016/j.copbio.2021.07.024] [PMID: 34358728]
[90]
Mesko, B. The role of artificial intelligence in precision medicine. Expert Rev. Precis. Med. Drug Dev., 2017, 2(5), 239-241.
[http://dx.doi.org/10.1080/23808993.2017.1380516]
[91]
Eraslan, G.; Avsec, Ž.; Gagneur, J.; Theis, F.J. Deep learning: New computational modelling techniques for genomics. Nat. Rev. Genet., 2019, 20(7), 389-403.
[http://dx.doi.org/10.1038/s41576-019-0122-6] [PMID: 30971806]
[92]
Zou, J.; Huss, M.; Abid, A.; Mohammadi, P.; Torkamani, A.; Telenti, A. A primer on deep learning in genomics. Nat. Genet., 2019, 51(1), 12-18.
[http://dx.doi.org/10.1038/s41588-018-0295-5] [PMID: 30478442]
[93]
Johnson, K.B.; Wei, W.Q.; Weeraratne, D.; Frisse, M.E.; Misulis, K.; Rhee, K.; Zhao, J.; Snowdon, J.L. Precision medicine, AI, and the future of personalized health care. Clin. Transl. Sci., 2021, 14(1), 86-93.
[http://dx.doi.org/10.1111/cts.12884] [PMID: 32961010]
[94]
Filipp, F.V. Opportunities for artificial intelligence in advancing precision medicine. Curr. Genet. Med. Rep., 2019, 7(4), 208-213.
[http://dx.doi.org/10.1007/s40142-019-00177-4] [PMID: 31871830]

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