Skip to content
2000
image of The Use of Artificial Intelligence in the Formulation of Effervescent Tablets: A Review

Abstract

Artificial Intelligence (AI) is emerging as a valuable tool in pharmaceutical formulations, including the development of effervescent tablets (ETs). This review highlights how AI techniques are being explored to support ET formulation designs, optimize component ratios, predict disintegration and dissolution behavior, and control reactions through artificial neural networks, support vector machines, and machine learning. These techniques have been applied in recent studies to enhance stability, improve disintegration times, and flavor masking. Computational fluid dynamics simulations of effervescence and dissolution are underexplored for ETs. Data-driven approaches, like response surface modeling, require ingredient concentrations, tablet properties, consumer preferences, and predictive analytics for optimization. However, limited comprehensive datasets, complex reactions, environmental sensitivities, and ethical/regulatory considerations pose challenges. Overcoming these obstacles, as identified in the current literature, could enable AI to innovate ET development and personalization.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/mrmc/10.2174/0113895575402013250821121137
2025-09-01
2025-12-29

  • From This Site

    /content/journals/mrmc/10.2174/0113895575402013250821121137
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Vanhere K.G. Derle D.V. Khatale S.B. Nangude S.L. A comprehensive review on effervescent tablets. J. Drug Deliv. Ther. 2023 13 7 141 150 10.22270/jddt.v13i7.6120
    [Google Scholar]
  2. Nguyen V.T. Effect of binder and sweeteners on the production of effervescent artichoke ( Cynara scolymus L.) tea tablets. J. Food Process. Preserv. 2013 37 6 1078 1083 10.1111/j.1745‑4549.2012.00808.x
    [Google Scholar]
  3. Jaya D tanu. Formulation and Physical Stability Test of Effervescent Tablets Containinglactobacillus Bulgariscus Probiotic Bacteria with Wet Method. Indones Nat Res Pharm J. 2019 4 101 112
    [Google Scholar]
  4. Hastuti A. Mardiah Dewi L.D. Amalia L. Nur’utami D.A. Fitrilia T. Production of effervescent tablets of rosela flower (Hibiscus sabdariffa L.) and its effect on the in vivo immune system. ARGIPA 2023 8 1 81 93 10.22236/argipa.v8i1.11895
    [Google Scholar]
  5. Amela J. Salazar R. Cemeli J. Effervescent tablets of ascorbic acid. I. physical study of the possible components to be used. Drug Dev. Ind. Pharm. 1996 22 5 407 416 10.3109/03639049609069349
    [Google Scholar]
  6. Jacob S. Shirwaikar A. Nair A. Preparation and evaluation of fast-disintegrating effervescent tablets of glibenclamide. Drug Dev. Ind. Pharm. 2009 35 3 321 328 10.1080/03639040802337021 18821151
    [Google Scholar]
  7. Bani-Jaber A.K. Alkawareek M.Y. Al-Gousous J.J. Abu Helwa A.Y. Floating and sustained-release characteristics of effervescent tablets prepared with a mixed matrix of Eudragit L-100-55 and Eudragit E PO. Chem. Pharm. Bull. 2011 59 2 155 160 10.1248/cpb.59.155 21297292
    [Google Scholar]
  8. Zhang Y. Huang Z. Preparation of a kind of honey effervescent tablets. Proc 2013 4th Int Asia Conf Ind Eng Manag Innov IEMI2013. Qi E. Shen J. Dou R. Berlin, Heidelberg Springer Berlin Heidelberg 2014 3 9 10.1007/978‑3‑642‑40060‑5_1
    [Google Scholar]
  9. Sethuraman N. Artificial intelligence: A new paradigm for pharmaceutical applications in formulations development. Indian J. Pharm. Edu. Res. 2020 54 4 843 846 10.5530/ijper.54.4.176
    [Google Scholar]
  10. Sharma A.K. A study on the applicability of AI in pharmaceutical industry. 2022 1st International Conference on Computational Science and Technology (ICCST) Chennai, India, 09-10 November 2022, pp. 501-505. 10.1109/ICCST55948.2022.10040339
    [Google Scholar]
  11. Bhatt P. Singh S. Kumar V. Nagarajan K. Mishra S.K. Kumar Dixit P. Kumar V. Kumar S. Artificial intelligence in pharmaceutical industry: Revolutionizing drug development and delivery. Curr. Art. Intell. 2024 2 051223224198 10.2174/0129503752250813231124092946
    [Google Scholar]
  12. Jarallah S.J. Almughem F.A. Alhumaid N.K. Fayez N.A.L. Alradwan I. Alsulami K.A. Tawfik E.A. Alshehri A.A. Artificial intelligence revolution in drug discovery: A paradigm shift in pharmaceutical innovation. Int. J. Pharm. 2025 680 125789 10.1016/j.ijpharm.2025.125789 40451590
    [Google Scholar]
  13. Dangeti A. Bynagari D.G. Vydani K. Revolutionizing drug formulation: Harnessing artificial intelligence and machine learning for enhanced stability, formulation optimization, and accelerated development. Inter. J. Pharm. Sci. Med. 2023 8 8 18 29 10.47760/ijpsm.2023.v08i08.003
    [Google Scholar]
  14. Narayan A Chanana A Shanker O Kulkarni YR Gupta P Patel A Role of artificial intelligence in pharmaceutical drug development. Curr. Indian Sci. 2024 2 1 9 10.2174/012210299X313252240521111358
    [Google Scholar]
  15. Garg S. Arora K. Singh S. Nagarajan K. Artificial Intelligence and Machine Learning in Drug Discovery and Development. Jaber W. Pennsylvania, USA IGI Global 2023 42 61 10.4018/979‑8‑3693‑0368‑9.ch003
    [Google Scholar]
  16. Nihalani G. Applications of artificial intelligence in pharmaceutical research: An extensive review. Bioequiv. Bioavailab. Int. J. 2024 8 1 1 7 10.23880/beba‑16000231
    [Google Scholar]
  17. Noorain S.V. Srivastava V. Parveen B. Parveen R. Artificial intelligence in drug formulation and development: Applications and future prospects. Curr. Drug Metab. 2023 24 9 622 634 10.2174/0113892002265786230921062205 37779408
    [Google Scholar]
  18. Sahu A. Rathee S. Saraf S. Jain S.K. A review on the recent advancements and artificial intelligence in tablet technology. Curr. Drug Targets 2024 25 6 416 430 10.2174/0113894501281290231221053939 38213164
    [Google Scholar]
  19. Stanciu S. Tache I. Gurzun M. Sorici A. Croitoru A. Cuzino D. Tudor D. Sorin S. Artificial Intelligence in cardiovascular medical imaging. Roma. J. Milit. Med. 2020 123 4 310 316 10.55453/rjmm.2020.123.4.12
    [Google Scholar]
  20. Krarti M. An overview of artificial intelligence-based methods for building energy systems. J. Sol. Energy Eng. 2003 125 3 331 342 10.1115/1.1592186
    [Google Scholar]
  21. Yahaya B.H. Ahmed A.A. Anikajogun B.O. Economic sustainability of building and construction projects based on artificial intelligence techniques. Asian Rev. Civil Eng. 2023 12 1 34 40 10.51983/tarce‑2023.12.1.3677
    [Google Scholar]
  22. Manmeet Kaur A comprehensive overview of artificial intelligence-based classification techniques. Inter. J. Sci. Res. Arch. 2024 11 2 125 129 10.30574/ijsra.2024.11.2.0387
    [Google Scholar]
  23. Cui F. Zhang Z. Cao C. Zou Q. Chen D. Su X. Protein–DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data. Proteomics 2022 22 8 2100197 10.1002/pmic.202100197 35112474
    [Google Scholar]
  24. Dheer S. Tripathi S. Kim E. Neuro-psychological approaches for artificial intelligence. Fields Z. Pennsylvania, USA IGI Global 2023 29 43
    [Google Scholar]
  25. Banh L. Strobel G. Generative artificial intelligence. Electron. Mark. 2023 33 1 63 10.1007/s12525‑023‑00680‑1
    [Google Scholar]
  26. Akman A Schuller BW Audio explainable artificial intelligence: A review. Intell. Comput. 2024 2 1 10.34133/icomputing.0074
    [Google Scholar]
  27. Barbati G. Pasqualetti P. Matranga D. Scotti L. Franchi M. Simeon V. Signoriello S. Gandin I. Pacella D. Porreca A. Azzolina D. Berchialla P. Villani S. Study design and research protocol for diagnostic or prognostic studies in the age of artificial intelligence: A biostatistician’s perspective. Epidemiol. Biostat. Public Health 2024 18 2 10.54103/2282‑0930/22227
    [Google Scholar]
  28. 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 10.3390/pharmaceutics14112257 36365076
    [Google Scholar]
  29. Kim J.Y. Choi D.H. Deep learning-based image classification and quantification models for tablet sticking. Int. J. Pharm. 2025 678 125690 10.1016/j.ijpharm.2025.125690 40339626
    [Google Scholar]
  30. Vora L.K. Gholap A.D. Jetha K. Thakur R.R.S. Solanki H.K. Chavda V.P. Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics 2023 15 7 1916 10.3390/pharmaceutics15071916 37514102
    [Google Scholar]
  31. Chinnaiyan K. Mugundhan S.L. Narayanasamy D. Mohan M. Revolutionizing healthcare and drug discovery: The impact of artificial intelligence on pharmaceutical development. Curr. Drug Ther. 2024 19 10.2174/0115748855313948240711043701
    [Google Scholar]
  32. Suriyaamporn P. Pamornpathomkul B. Patrojanasophon P. Ngawhirunpat T. Rojanarata T. Opanasopit P. The artificial intelligence-powered new era in pharmaceutical research and development: A review. AAPS PharmSciTech 2024 25 6 188 10.1208/s12249‑024‑02901‑y 39147952
    [Google Scholar]
  33. Singh L. Tiwari R.K. Verma S. Sharma V. The future of artificial intelligence in pharmaceutical product formulation. Drug Deliv. Lett. 2019 9 4 277 285 10.2174/2210303109666190621144400
    [Google Scholar]
  34. Pandey D.R. Dash S.S. Mishra A. Advances of AI‑driven drug design and discovery in pharmaceuticals – Review. J. Angiother. 2024 8 1 1 10 10.25163/angiotherapy.819488
    [Google Scholar]
  35. Gillani H.A. Vitamin C and cranberry extract effervescent tablet formulation, characterization, and evaluation Int. J. Pharm. Integr. Health Sci. 2023 4 1 10.56536/ijpihs.v4i1.53
    [Google Scholar]
  36. Kasmawarni K. Utomo P.P. Aloe vera effervescent tablet formulation: Comparation study of acid-alkali-aloe powder and maltodextrin concentration. Biopropal Ind. 2010 1 1 9 10.20527/jps.v1i1.30534
    [Google Scholar]
  37. Soliman K.A. Ibrahim H.K. Ghorab M.M. Formulation of risperidone as self-nanoemulsifying drug delivery system in form of effervescent tablets. J. Dispers. Sci. Technol. 2012 33 8 1127 1133 10.1080/01932691.2011.599235
    [Google Scholar]
  38. Ali A. Ahmad U. Akhtar J. 3D Printing in Pharmaceutical Sector: An Overview. Ahmad U. Akhtar J. Pharmaceutical Formulation Design - Recent Practices United Kingdom IntechOpen 2020 10.5772/intechopen.90738
    [Google Scholar]
  39. Shinde P.S. Pawar A.Y. Talele S.G. The role of artificial intelligence in the pharmaceutical sector: A comprehensive analysis of its application from the discovery phase to industrial implementation. Inter. J. Drug Del. Tech. 2023 13 4 1283 1286 10.25258/ijddt.13.4.70
    [Google Scholar]
  40. Lynatra C. Wardiyah W. Elisya Y. Formulation of effervescent tablet of temulawak extract (curcuma xanthorrhiza roxb.) with variation of stevia as sweetener. J. Health Tech. Arts 2018 9 2 72 82 10.36525/sanitas.2018.9
    [Google Scholar]
  41. Violalita F. B R. The effect acid addition on characteristic effervescent tablet of tamarillo. Int. J. Adv. Sci. Eng. Inf. Technol. 2015 5 3 230 10.18517/ijaseit.5.3.528
    [Google Scholar]
  42. Satish N. Kamble R. Kajal V. In vitro mucolytic activity determination of n-acetyl cysteine effervescent tablet using suspended level viscometer. Int. J. Appl. Pharm. 2023 141 144
    [Google Scholar]
  43. Mathaba M. Banza J. A comprehensive review on artificial intelligence in water treatment for optimization. Clean water now and the future. J. Environ. Sci. Health Part A Tox. Hazard. Subst. Environ. Eng. 2023 58 14 1047 1060 10.1080/10934529.2024.2309102 38293764
    [Google Scholar]
  44. Ain M.R.F. Yusof Y.A. Chin N.L. Amin N.A.M. Ghazali H.M. Optimisation of an effervescent pineapple tablet. J. Biol. Sci. 2016 16 7 247 255 10.3923/jbs.2016.247.255
    [Google Scholar]
  45. Dhawas P. Dhore A. Bhagat D. Pawar R.D. Kukade A. Kalbande K. Big Data Preprocessing, Techniques, Integration, Transformation, Normalisation, Cleaning, Discretization, and Binning. Darwish D. Pennsylvania, USA IGI Global 2024 159 182 10.4018/979‑8‑3693‑0413‑6.ch006
    [Google Scholar]
  46. Savant P.B. Qureshi M.A. N K. Kareppa M. B Thalkari A. Karwa P.N. Preparation and evaluation of diclofenac sodium effervescent tablet. Res. J. Pharm. Dos. Forms Technol. 2021 305 311 10.52711/0975‑4377.2021.00050
    [Google Scholar]
  47. Mahapatra A.P.K. Saraswat R. Botre M. Paul B. Prasad N. Application of response surface methodology (RSM) in statistical optimization and pharmaceutical characterization of a patient compliance effervescent tablet formulation of an antiepileptic drug levetiracetam. Future J. Pharm. Sci. 2020 6 1 82 10.1186/s43094‑020‑00096‑0
    [Google Scholar]
  48. Iwansyah A.C. Fauzi H. Cahyadi W. Hariadi H. Indriati A. Wardhani R. Abd Hamid H. Development, physiochemical and sensory evaluation of a new effervescent tablet formulation based on Moringa oleifera leaves extract. Int. J. Food Eng. 2023 19 3-4 133 141 10.1515/ijfe‑2022‑0170
    [Google Scholar]
  49. 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 1 122 131 10.1016/j.ejpb.2010.09.017 20934511
    [Google Scholar]
  50. Nagy B. Petra D. Galata D.L. Démuth B. Borbás E. Marosi G. Nagy Z.K. Farkas A. Application of artificial neural networks for Process Analytical Technology-based dissolution testing. Int. J. Pharm. 2019 567 118464 10.1016/j.ijpharm.2019.118464 31252145
    [Google Scholar]
  51. 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 3 321 331 10.1016/j.ejps.2011.08.012 21878388
    [Google Scholar]
  52. Zhang J. Huang Z. Preparation of a Kind of Bee pollen Effervescent Tablets. Proceedings of the 2nd International Conference on Science and Social Research (ICSSR 2013) Dordrecht, Netherlands, 2013, pp. 312-315. 10.2991/icssr‑13.2013.69
    [Google Scholar]
  53. Beaumont F. Popa C. Liger Belair G. Polidori G. Numerical modeling of bubble-induced flow patterns in champagne glasses. Int. J. Numer. Methods Heat Fluid Flow 2014 24 3 563 578 10.1108/HFF‑03‑2012‑0063
    [Google Scholar]
  54. Lettieri P. Saccone G. Cammarata L. Predicting the transition from bubbling to slugging fluidization using computational fluid dynamics. Chem. Eng. Res. Des. 2004 82 8 939 944 10.1205/0263876041580776
    [Google Scholar]
  55. Pei P. Zhang K. Lu E. Wen D. CFD simulation of bubbling and collapsing characteristics in a gas-solid fluidized bed. Petrol. Sci. 2009 6 1 69 75 10.1007/s12182‑009‑0013‑0
    [Google Scholar]
  56. Xu Y. Liu M. Tang C. Three-dimensional CFD–VOF–DPM simulations of effects of low-holdup particles on single-nozzle bubbling behavior in gas–liquid–solid systems. Chem. Eng. J. 2013 222 292 306 10.1016/j.cej.2013.02.065
    [Google Scholar]
  57. Aslani A. Fattahi F. Formulation, characterization and physicochemical evaluation of potassium citrate effervescent tablets. Adv. Pharm. Bull. 2013 3 1 217 225 10.5681/apb.2013.036
    [Google Scholar]
  58. Sendall F.E.J. Staniforth J.N. A study of powder adhesion to metal surfaces during compression of effervescent pharmaceutical tablets. J. Pharm. Pharmacol. 1986 38 7 489 493 10.1111/j.2042‑7158.1986.tb04620.x 2875146
    [Google Scholar]
  59. Xu J. Li Y. The formulation and preparation technology of aspirin and vitamin C effervescent tablets. Pharm. Care Res. 2012 12 2 129 131 10.5428/pcar20120218
    [Google Scholar]
  60. Momeni M. Afkanpour M. Rakhshani S. Mehrabian A. Tabesh H. A prediction model based on artificial intelligence techniques for disintegration time and hardness of fast disintegrating tablets in pre-formulation tests. BMC Med. Inform. Decis. Mak. 2024 24 1 88 10.1186/s12911‑024‑02485‑4 38539201
    [Google Scholar]
  61. Wang X. Liu Z. Lin X. Hong Y. Shen L. Zhao L. A novel paradigm on data and knowledge-driven drug formulation development: Opportunities and challenges of machine learning. J. Ind. Inf. Integr. 2025 44 100796 10.1016/j.jii.2025.100796
    [Google Scholar]
  62. Patel S. Patel M. Kulkarni M. Patel M.S. DE-INTERACT: A machine-learning-based predictive tool for the drug-excipient interaction study during product development—Validation through paracetamol and vanillin as a case study. Int. J. Pharm. 2023 637 122839 10.1016/j.ijpharm.2023.122839 36931538
    [Google Scholar]
  63. Hang N.T. Long N.T. Duy N.D. Chien N.N. Van Phuong N. Towards safer and efficient formulations: Machine learning approaches to predict drug-excipient compatibility. Int. J. Pharm. 2024 653 123884 10.1016/j.ijpharm.2024.123884 38341049
    [Google Scholar]
  64. Azad M.A. Olawuni D. Kimbell G. Badruddoza A.Z.M. Hossain M.S. Sultana T. Polymers for extrusion-based 3D printing of pharmaceuticals: A holistic materials–process perspective. Pharmaceutics 2020 12 2 124 10.3390/pharmaceutics12020124 32028732
    [Google Scholar]
  65. Thakur N. Sharma A. Ethical considerations in AI-driven financial decision making. J. Manag. Publ. Pol. 2024 15 3 41 57 10.47914/jmpp.2024.v15i3.003
    [Google Scholar]
  66. Amini A. Guijt R.M. Themelis T. De Vos J. Eeltink S. Recent developments in digital light processing 3D-printing techniques for microfluidic analytical devices. J. Chromatogr. A 2023 1692 463842 10.1016/j.chroma.2023.463842 36745962
    [Google Scholar]
  67. Sywelem M.M.G. Mahklouf A.M.E-S. Ethical considerations in the integration of artificial intelligence in education: an overview. Comp. Sci. Infor. Tech. 2024 1 15 10.5121/csit.2024.141201
    [Google Scholar]
  68. Wang J. Mao W. Wenjie W. The ethics of artificial intelligence: Sociopolitical and legal dimensions. Interd. Stud. Soc. Law Polit. 2023 2 2 27 32 10.61838/kman.isslp.2.2.6
    [Google Scholar]
  69. Banerji B. Feroz M.A. Elements of AI ethical regulatory framework and SDGs. Explor Ethical Dimens Environ Sustain Use AI. Kannan H. Rodriguez R.V. Paprika Z.Z. Ade-Ibijola A. Pennsylvania, USA IGI Global 2024 126 139
    [Google Scholar]
/content/journals/mrmc/10.2174/0113895575402013250821121137
Loading
The Use of Artificial Intelligence in the Formulation of Effervescent Tablets: A Review
Bentham Science Publishers ; https://doi.org/10.2174/0113895575402013250821121137
/content/journals/mrmc/10.2174/0113895575402013250821121137
/content/journals/mrmc/10.2174/0113895575402013250821121137
Loading

Data & Media loading...


  • Article Type:     Review Article
Keywords: Artificial Intelligence ; improve flavor masking ; effervescent tablets ; machine learning ; formulation ; artificial neural network ; computer-assisted drug design

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test