Skip to content
2000
Volume 2, Issue 1
  • ISSN: 2210-299X
  • E-ISSN: 2210-3007
file format pdf download PDF
side by side viewer icon HTML

Abstract

In the field of pharmaceutical research, expert tool has become a game-changing tool that is altering many aspects of drug discovery and development. Understanding the physicochemical characteristics of drug candidates is essential for developing formulation methods, and this is where preformulation studies come into play. The incorporation of expert tools in preformulation research is thoroughly examined in this review, which also highlights the problems, applications, and future prospects of these techniques. Further, the present review focuses on role of SeDeM expert system to generate information about drug and excipients in association with Artificial Intelligence (AI). Researchers can identify safer and more effective therapies by utilizing AI-driven techniques to accelerate drug development processes, optimize formulations, and reduce risks related to drug delivery.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Loading

Article metrics loading...

/content/journals/cis/10.2174/012210299X338978241015155050
2024-12-10
2025-09-25

  • From This Site

    /content/journals/cis/10.2174/012210299X338978241015155050
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/cis/2/1/CIS-2-E2210299X338978.html?itemId=/content/journals/cis/10.2174/012210299X338978241015155050&mimeType=html&fmt=ahah

References

  1. AhirwarK. ShuklaR. Preformulation studies: A versatile tool in formulation design.Drug Formulation Design.IntechOpenLondon, UK202310.5772/intechopen.110346
     [Google Scholar]
  2. PatelN.A. ArefinP. A review on significance of identifying an appropriate solid form during drug discovery and product development.Material Science Research India202118215417010.13005/msri/180204
     [Google Scholar]
  3. KumarM. SainiM. PariharL. Preformulation studies of pantoprazole: Fundamental part of formulation design.Saud J Med Pharm Sci.20228837038010.36348/sjmps.2022.v08i08.001
     [Google Scholar]
  4. RouazK. Chiclana-RodríguezB. Nardi-RicartA. Suñé-PouM. Mercadé-FrutosD. Suñé-NegreJ.M. Pérez-LozanoP. García-MontoyaE. Excipients in the paediatric population: A review.Pharmaceutics202113338710.3390/pharmaceutics1303038733805830
     [Google Scholar]
  5. NgilirabangaJ.B. SamsodienH. Pharmaceutical co‐crystal: An alternative strategy for enhanced physicochemical properties and drug synergy.Nano Select20212351252610.1002/nano.202000201
     [Google Scholar]
  6. SolimanM.E. AdewumiA.T. AkawaO.B. SubairT.I. OkunlolaF.O. AkinsukuO.E. KhanS. Simulation models for prediction of bioavailability of medicinal drugs—the interface between experiment and computation.AAPS PharmSciTech20222338610.1208/s12249‑022‑02229‑535292867
     [Google Scholar]
  7. IoeleG. ChieffalloM. OcchiuzziM.A. De LucaM. GarofaloA. RagnoG. GrandeF. Anticancer drugs: Recent strategies to improve stability profile, pharmacokinetic and pharmacodynamic properties.Molecules20222717543610.3390/molecules2717543636080203
     [Google Scholar]
  8. ZarmpiP. FlanaganT. MeehanE. MannJ. FotakiN. Impact of magnesium stearate presence and variability on drug apparent solubility based on drug physicochemical properties.AAPS J.20202247510.1208/s12248‑020‑00449‑w32440810
     [Google Scholar]
  9. TambeS. JainD. MeruvaS.K. RongalaG. JuluriA. NihalaniG. MamidiH.K. NukalaP.K. BollaP.K. Recent advances in amorphous solid dispersions: preformulation, formulation strategies, technological advancements and characterization.Pharmaceutics20221410220310.3390/pharmaceutics1410220336297638
     [Google Scholar]
  10. KramzerL.F. HamorskyK.T. GraebingP.W. WangL. FuquaJ.L. MatobaN. LasnikA.B. MonclaB.J. ZhangJ. PalmerK.E. RohanL.C. Preformulation characterization of griffithsin, a biopharmaceutical candidate for HIV prevention.AAPS PharmSciTech20212238310.1208/s12249‑021‑01931‑0
     [Google Scholar]
  11. DeruyverL. RigautC. LambertP. HautB. GooleJ. The importance of pre-formulation studies and of 3D-printed nasal casts in the success of a pharmaceutical product intended for nose-to-brain delivery.Adv. Drug Deliv. Rev.202117511382610.1016/j.addr.2021.11382634119575
     [Google Scholar]
  12. DamiatiS.A. Digital pharmaceutical sciences.AAPS PharmSciTech202021620610.1208/s12249‑020‑01747‑432715351
     [Google Scholar]
  13. SethuramanN. Artificial intelligence: A new paradigm for pharmaceutical applications in formulations development.Indian J Pharm Educ Res.202054484384610.5530/ijper.54.4.176
     [Google Scholar]
  14. WangS. DiJ. WangD. DaiX. HuaY. GaoX. State-of-the-art review of artificial neural networks to predict, characterize and optimize pharmaceutical formulation.Pharmaceutics.2022141183
     [Google Scholar]
  15. WadamsR.C. AkseliI. AlbrechtJ. FerreiraA.P. GambleJ.F. LeaneM. ThomasS. SchumanY. TaylorL. TobynM. Particle property characterization and data curation for effective powder property modeling in the pharmaceutical industry.AAPS PharmSciTech202223828610.1208/s12249‑022‑02434‑236261755
     [Google Scholar]
  16. UlusoyU. A review of particle shape effects on material properties for various engineering applications: From macro to nanoscale.Minerals (Basel)20231319110.3390/min13010091
     [Google Scholar]
  17. UhlemannJ. DiedamH. HoheiselW. SchikarskiT. PeukertW. Modeling and simulation of process technology for nanoparticulate drug formulations—A particle technology perspective.Pharmaceutics20201312210.3390/pharmaceutics1301002233374375
     [Google Scholar]
  18. AlshawwaS.Z. KassemA.A. FaridR.M. MostafaS.K. LabibG.S. Nanocarrier drug delivery systems: Characterization, limitations, future perspectives and implementation of artificial intelligence.Pharmaceutics202214488310.3390/pharmaceutics1404088335456717
     [Google Scholar]
  19. Rauf-ur-Rehman ShoaibM.H. AhmedF.R. YousufR.I. SiddiquiF. SaleemM.T. QaziF. KhanM.Z. IrshadA. BashirL. NazS. FarooqM. MahmoodZ.A. SeDeM expert system with I-optimal mixture design for oral multiparticulate drug delivery: An encapsulated floating minitablets of loxoprofen Na and its in silico physiologically based pharmacokinetic modeling.Front. Pharmacol.202314106601810.3389/fphar.2023.106601836937845
     [Google Scholar]
  20. AliK.A. MohinS.K. MondalP. GoswamiS. GhoshS. ChoudhuriS. Influence of artificial intelligence in modern pharmaceutical formulation and drug development.Future J. Pharm. Sci.20241015310.1186/s43094‑024‑00625‑1
     [Google Scholar]
  21. DawoudM.H.S. MannaaI.S. Abdel-DaimA. SweedN.M. Integrating artificial intelligence with quality by design in the formulation of lecithin/chitosan nanoparticles of a poorly water-soluble drug.AAPS PharmSciTech202324616910.1208/s12249‑023‑02609‑537552427
     [Google Scholar]
  22. SinghI. ThakurA.K. BalaR. MadanR. SeDeM expert system, an innovative tool for developing directly compressible tablets: A review.Curr. Drug Res. Rev.2021131162410.2174/258997751266620092811371632988362
     [Google Scholar]
  23. LuraA. Systematic investigations for the development and manufacturing of pharmaceutical mini-tabletsDoctoral dissertation, Dissertation, Düsseldorf, Heinrich Heine University,2022
     [Google Scholar]
  24. TranováT. LoskotJ. NavrátilO. BrniakW. MužíkováJ. Effect of co-processed excipient type on properties of orodispersible tablets containing captopril, tramadol, and domperidone.Int. J. Pharm.202363612283810.1016/j.ijpharm.2023.12283836921743
     [Google Scholar]
  25. KokottM. LuraA. BreitkreutzJ. WiedeyR. Evaluation of two novel co-processed excipients for direct compression of orodispersible tablets and mini-tablets.Eur. J. Pharm. Biopharm.202116812213010.1016/j.ejpb.2021.08.01634474110
     [Google Scholar]
  26. VasiljevićI. TurkovićE. AleksićI. ParojčićJ. Mathematical approaches for powders and multiparticulate units processability characterization in pharmaceutical development.Arch. Pharm. Pract.202272Notebook 663766010.5937/arhfarm72‑40961
     [Google Scholar]
  27. DestroF. García MuñozS. BezzoF. BaroloM. Powder composition monitoring in continuous pharmaceutical solid-dosage form manufacturing using state estimation – Proof of concept.Int. J. Pharm.202160512080810.1016/j.ijpharm.2021.12080834144142
     [Google Scholar]
  28. ErvastiT. NiinikoskiH. Mäki-LohiluomaE. LeppinenH. KetolainenJ. KorhonenO. LakioS. The comparison of two challenging low dose APIs in a continuous direct compression process.Pharmaceutics202012327910.3390/pharmaceutics1203027932244950
     [Google Scholar]
  29. Figuera-FigueraA. Suñé-PouM. Pérez-LozanoP. García-MontoyaE. Amela-NavarroJ. Suñé-NegreJ.M. SeDeM as a tool to validate drug substance manufacturing processes and assess scalability and suitability for direct compression: Supplier screening.Pharmaceutics2023158203410.3390/pharmaceutics1508203437631248
     [Google Scholar]
  30. BehjaniM.A. MotlaghY.G. BaylyA.E. HassanpourA. Assessment of blending performance of pharmaceutical powder mixtures in a continuous mixer using discrete element method (DEM).Powder Technol.2020366738110.1016/j.powtec.2019.10.102
     [Google Scholar]
  31. Aguilar-DíazJ.E. García-MontoyaE. Pérez-LozanoP. Suñé-NegreJ.M. MiñarroM. TicóJ.R. SeDeM expert system a new innovator tool to develop pharmaceutical forms.Drug Dev. Ind. Pharm.201440222223610.3109/03639045.2012.75600723343308
     [Google Scholar]
  32. TadweeI. ShahiS. Formulation development of losartan potassium immediate release tablets and process optimization using SeDeM expert system.J. Appl. Pharm. Sci.201882033043
     [Google Scholar]
  33. SunH. SeDeM expert system: A review and new perspectives.J. Pharm. Biopharm. Res.201911364710.25082/JPBR.2019.01.005
     [Google Scholar]
  34. ButtiniF. RozouS. RossiA. ZoumpliouV. RekkasD.M. The application of quality by design framework in the pharmaceutical development of dry powder inhalers.Eur. J. Pharm. Sci.2018113647610.1016/j.ejps.2017.10.04229104067
     [Google Scholar]
  35. VoraL.K. GholapA.D. JethaK. ThakurR.R.S. SolankiH.K. ChavdaV.P. Artificial intelligence in pharmaceutical technology and drug delivery design.Pharmaceutics2023157191610.3390/pharmaceutics1507191637514102
     [Google Scholar]
  36. SoniS. SahuP. DikkatwarM.S. Quality by design: Modern approach in pharmaceutics for quality pharmaceuticals.J. Pharm. Negat. Results202320522062
     [Google Scholar]
  37. GrossI.P. LimaA.L. Sá-BarretoL. GratieriT. GelfusoG.M. Cunha-FilhoM. In 3D & 4D printing methods for pharmaceutical manufacturing and personalised drug delivery: Opportunities and challenges.Preformulation of 3D Printable Pharmaceutical Dosage FormsSpringer International PublishingCham2023149179
     [Google Scholar]
  38. KostewiczE.S. AbrahamssonB. BrewsterM. BrouwersJ. ButlerJ. CarlertS. DickinsonP.A. DressmanJ. HolmR. KleinS. MannJ. McAllisterM. MinekusM. MuensterU. MüllertzA. VerweiM. VertzoniM. WeitschiesW. AugustijnsP. In vitro models for the prediction of in vivo performance of oral dosage forms.Eur. J. Pharm. Sci.20145734236610.1016/j.ejps.2013.08.02423988843
     [Google Scholar]
  39. SubishR. Formulation development and evaluation of etoricoxib oral disintegration tablet by using ion exchange resin complexation techniqueDoctoral dissertation, Arulmigu Kalasalingam College of Pharmacy, Krishnankoil,2017
     [Google Scholar]
  40. LaiS.M.H. An expert system for development of powder filled hard gelatin capsule formulations.Health and environmental sciences; Gelatin capsules1996
     [Google Scholar]
  41. HickeyA. J. Inhaled pharmaceutical product development perspectives: Challenges and opportunitiesElsevierElsevierAmsterdam, Netherlands20171
     [Google Scholar]
  42. HangN.T. LongN.T. DuyN.D. ChienN.N. Van PhuongN. Towards safer and efficient formulations: Machine learning approaches to predict drug-excipient compatibility.Int. J. Pharm.202465312388410.1016/j.ijpharm.2024.12388438341049
     [Google Scholar]
  43. WangN. SunH. DongJ. OuyangD. PharmD.E. PharmDE: A new expert system for drug-excipient compatibility evaluation.Int. J. Pharm.202160712096210.1016/j.ijpharm.2021.12096234339812
     [Google Scholar]
  44. NambiarA.G. SinghM. MaliA.R. SerranoD.R. KumarR. HealyA.M. AgrawalA.K. KumarD. Continuous manufacturing and molecular modeling of pharmaceutical amorphous solid dispersions.AAPS PharmSciTech202223724910.1208/s12249‑022‑02408‑436056225
     [Google Scholar]
  45. AlhijjajM. BeltonP. FábiánL. WellnerN. ReadingM. QiS. Novel thermal imaging method for rapid screening of drug–polymer miscibility for solid dispersion based formulation development.Mol. Pharm.201815125625563610.1021/acs.molpharmaceut.8b0079830372612
     [Google Scholar]
  46. SimõesM.F. PintoR.M.A. SimõesS. Hot-melt extrusion in the pharmaceutical industry: toward filing a new drug application.Drug Discov. Today20192491749176810.1016/j.drudis.2019.05.01331132415
     [Google Scholar]
  47. de CassanD. HoheiselA.L. GlasmacherB. MenzelH. Impact of sterilization by electron beam, gamma radiation and X-rays on electrospun poly-(ε-caprolactone) fiber mats.J. Mater. Sci. Mater. Med.20193044210.1007/s10856‑019‑6245‑730919082
     [Google Scholar]
  48. ChuH. LiuZ. JiT. YangC. XuN. Recent advances in the preparation of superhydrophobic coatings based on low-surface-energy modifiers: Diversified properties and potential applications.Appl. Therm. Eng.202425112359110.1016/j.applthermaleng.2024.123591
     [Google Scholar]
  49. BeheraA. BeheraA. Advanced semiconductor/conductor materialsAdvanced MaterialsSpringerNew York City.2022557596
     [Google Scholar]
  50. ŠestákJ. HubíkP. MarešJ.J. Thermal physics and thermal analysis: From macro to micro, highlighting thermodynamics, kinetics and nanomaterials.Thermal Physics and Thermal AnalysisSpringerSpringer2017Vol. 1110.1007/978‑3‑319‑45899‑1
     [Google Scholar]
  51. CharooN.A. MohamedE.M. EltiganiM. KhanM.A. NutanM.T. RahmanZ. Drug and formulation development processes.From Current to Future Trends in Pharmaceutical Technology.Academic Press202425729210.1016/B978‑0‑323‑91111‑5.00007‑X
     [Google Scholar]
  52. ShuklaR. KuznetsovA. AliA. Drug Formulation Design.BoD–Books on DemandIntechOpenLondon, United Kingdom202310.5772/intechopen.105307
     [Google Scholar]
  53. YuL.X. AmidonG. KhanM.A. HoagS.W. PolliJ. RajuG.K. WoodcockJ. Understanding pharmaceutical quality by design.AAPS J.201416477178310.1208/s12248‑014‑9598‑324854893
     [Google Scholar]
  54. AliJ. PramodK. TahirM.A. CharooN.A. AnsariS.H. Pharmaceutical product development: A quality by design approach.Int. J. Pharm. Investig.20166312913810.4103/2230‑973X.18735027606256
     [Google Scholar]
  55. GrangeiaH.B. SilvaC. SimõesS.P. ReisM.S. Quality by design in pharmaceutical manufacturing: A systematic review of current status, challenges and future perspectives.Eur. J. Pharm. Biopharm.2020147193710.1016/j.ejpb.2019.12.00731862299
     [Google Scholar]
  56. ChapmanK.L. HolzgrefeH. BlackL.E. BrownM. ChellmanG. CopemanC. CouchJ. CretonS. GehenS. HobermanA. KinterL.B. MaddenS. MattisC. StempleH.A. WilsonS. Pharmaceutical toxicology: Designing studies to reduce animal use, while maximizing human translation.Regul. Toxicol. Pharmacol.20136618810310.1016/j.yrtph.2013.03.00123524271
     [Google Scholar]
  57. TapkirN. SoniF. SahuA.K. JadavT. TekadeR.K. SenguptaP. A comprehensive review on assessment and key control strategies for impurities in drug development with a special emphasis on post-marketing surveillance.J. Pharm. Innov.2021•••120
     [Google Scholar]
  58. SardellaM. BelcherG. LunguC. IgnoniT. CamisaM. StenverD.I. PorcelliP. D’AntuonoM. CastiglioneN.G. AdamsA. FurlanG. GrisoniI. HallS. BogaL. ManciniV. CiucaM. ChonziD. EdwardsB. MangoniA.A. TuccoriM. ProkofyevaE. De GregorioF. Bertazzoli Grabinski BroglioM. van LeeuwenB. KrugerP. RauschC. Le LouetH. Monitoring the manufacturing and quality of medicines: A fundamental task of pharmacovigilance.Ther. Adv. Drug Saf.20211210.1177/2042098621103843634394910
     [Google Scholar]
  59. HaleemR.M. SalemM.Y. FatahallahF.A. AbdelfattahL.E. Quality in the pharmaceutical industry – A literature review.Saudi Pharm. J.201523546346910.1016/j.jsps.2013.11.00426594110
     [Google Scholar]
  60. SangshettiJ.N. DeshpandeM. ZaheerZ. ShindeD.B. AroteR. Quality by design approach: Regulatory need.Arab. J. Chem.201710S3412S342510.1016/j.arabjc.2014.01.025
     [Google Scholar]
  61. Jones-SalkeyO. ChuZ. IngramA. Windows-YuleC.R.K. Reviewing the impact of powder cohesion on continuous direct compression (CDC) performance.Pharmaceutics2023156158710.3390/pharmaceutics1506158737376036
     [Google Scholar]
  62. HörmannT.R. JägerN. FunkeA. MürbR.K. KhinastJ.G. PaudelA. Formulation performance and processability window for manufacturing a dual-polymer amorphous solid dispersion via hot-melt extrusion and strand pelletization.Int. J. Pharm.20185531-240842110.1016/j.ijpharm.2018.10.03530326284
     [Google Scholar]
  63. MatićJ. PaudelA. BauerH. GarciaR.A.L. BiedrzyckaK. KhinastJ.G. Developing HME-based drug products using emerging science: a fast-track roadmap from concept to clinical batch.AAPS PharmSciTech202021517610.1208/s12249‑020‑01713‑032572701
     [Google Scholar]
  64. SacherS. KhinastJ. G. An overview of pharmaceutical manufacturing for solid dosage formsProcess Simulation and Data Modeling in Solid Oral Drug Development and ManufactureSpringer NatureNew York City2016311383
     [Google Scholar]
  65. NandiU. TrivediV. RossS.A. DouroumisD. Advances in twin-screw granulation processing.Pharmaceutics202113562410.3390/pharmaceutics1305062433925577
     [Google Scholar]
  66. MehdipourI. KhayatK.H. Understanding the role of particle packing characteristics in rheo-physical properties of cementitious suspensions: A literature review.Constr. Build. Mater.201816134035310.1016/j.conbuildmat.2017.11.147
     [Google Scholar]
  67. YazarG. DemirkesenI. Linear and non-linear rheological properties of gluten-free dough systems probed by fundamental methods.Food Eng. Rev.2023151568510.1007/s12393‑022‑09321‑3
     [Google Scholar]
  68. MaP.C. SiddiquiN.A. MaromG. KimJ.K. Dispersion and functionalization of carbon nanotubes for polymer-based nanocomposites: A review.Compos., Part A Appl. Sci. Manuf.201041101345136710.1016/j.compositesa.2010.07.003
     [Google Scholar]
  69. GaldónE. CasasM. GayangoM. CaraballoI. First study of the evolution of the SeDeM expert system parameters based on percolation theory: Monitoring of their critical behavior.Eur. J. Pharm. Biopharm.201610915816410.1016/j.ejpb.2016.10.00427769825
     [Google Scholar]
  70. Aguilar-de-LeyvaÁ. CampiñezM.D. JostF. GaviraM. CaraballoI. Study of the critical points in combined matrix tablets containing both inert and swelling excipients.J. Drug Deliv. Sci. Technol.20195288589410.1016/j.jddst.2019.06.005
     [Google Scholar]
  71. MillánM. CaraballoI. Effect of drug particle size in ultrasound compacted tablets.Int. J. Pharm.20063101-216817410.1016/j.ijpharm.2005.12.00516431046
     [Google Scholar]
  72. LeuenbergerH. Pharmaceutical technology: Drug delivery, formulation, and process research.Chimia (Aarau)2006601-2404010.2533/000942906777675092
     [Google Scholar]
  73. BanniganP. AldeghiM. BaoZ. HäseF. Aspuru-GuzikA. AllenC. Machine learning directed drug formulation development.Adv. Drug Deliv. Rev.202117511380610.1016/j.addr.2021.05.01634019959
     [Google Scholar]
  74. HassanzadehP. AtyabiF. DinarvandR. The significance of artificial intelligence in drug delivery system design.Adv. Drug Deliv. Rev.2019151-15216919010.1016/j.addr.2019.05.00131071378
     [Google Scholar]
  75. TakayamaK. FujikawaM. NagaiT. Artificial neural network as a novel method to optimize pharmaceutical formulations.Pharm. Res.19991611610.1023/A:10119868238509950271
     [Google Scholar]
  76. TakayamaK. FujikawaM. ObataY. MorishitaM. Neural network based optimization of drug formulations.Adv. Drug Deliv. Rev.20035591217123110.1016/S0169‑409X(03)00120‑012954200
     [Google Scholar]
  77. YangY. YeZ. SuY. ZhaoQ. LiX. OuyangD. Deep learning for in vitro prediction of pharmaceutical formulations.Acta Pharm. Sin. B20199117718510.1016/j.apsb.2018.09.01030766789
     [Google Scholar]
  78. SunY. PengY. ChenY. ShuklaA.J. Application of artificial neural networks in the design of controlled release drug delivery systems.Adv. Drug Deliv. Rev.20035591201121510.1016/S0169‑409X(03)00119‑412954199
     [Google Scholar]
  79. HashadR.A. IshakR.A.H. FahmyS. MansourS. GeneidiA.S. Chitosan-tripolyphosphate nanoparticles: Optimization of formulation parameters for improving process yield at a novel pH using artificial neural networks.Int. J. Biol. Macromol.201686505810.1016/j.ijbiomac.2016.01.04226783636
     [Google Scholar]
  80. TummalaS.R. GorrepatiN. AI-driven predictive analytics for drug stability studies. J. Pharm.Insight. Res.202422188198
     [Google Scholar]
  81. GlassB. Agatonovic-KustrinS. WischM. Artificial neural networks to optimize formulation components of a fixed-dose combination of rifampicin, isoniazid and pyrazinamide in a microemulsion.Curr. Drug Discov. Technol.20052319520110.2174/157016305486686416472228
     [Google Scholar]
  82. SiposE. OlteanA.R. SzabóZ.I. RédaiE.M. NagyG.D. Application of SeDeM expert systems in preformulation studies of pediatric ibuprofen ODT tablets.Acta Pharm.201767223724610.1515/acph‑2017‑001728590910
     [Google Scholar]
  83. SauríJ. MillánD. Suñé-NegreJ.M. Pérez-LozanoP. SarrateR. FàbregasA. CarrilloC. MiñarroM. TicóJ.R. García-MontoyaE. The use of the SeDeM diagram expert system for the formulation of Captopril SR matrix tablets by direct compression.Int. J. Pharm.20144611-2384510.1016/j.ijpharm.2013.11.02924284019
     [Google Scholar]
  84. GibsonM. Pharmaceutical preformulation and formulation.Drugs Pharm. Sci.2001199199
     [Google Scholar]
  85. Castañeda HernándezO. Domínguez-RoblesJ. CaraballoI. BernadM.J. Melgoza ContrerasL.M. Comparison between polymeric excipients using SeDeM expert system in combination with mathematical modeling and quality control tools.J. Drug Deliv. Sci. Technol.20238610475010.1016/j.jddst.2023.104750
     [Google Scholar]
  86. ScholtzJ.C. SteenekampJ.H. HammanJ.H. TiedtL.R. The SeDeM expert diagram system: Its performance and predictability in direct compressible formulations containing novel excipients and different types of active ingredients.Powder Technol.201731222223610.1016/j.powtec.2017.02.019
     [Google Scholar]
  87. WanS. YangR. ZhangH. LiX. GuM. GuanT. RenJ. SunH. DaiC. Application of the SeDeM expert system in studies for direct compression suitability on mixture of rhodiola extract and an excipient.AAPS PharmSciTech201920310510.1208/s12249‑019‑1320‑4
     [Google Scholar]
  88. ParkH. OtteA. ParkK. Evolution of drug delivery systems: From 1950 to 2020 and beyond.J. Control. Release2022342536510.1016/j.jconrel.2021.12.03034971694
     [Google Scholar]
  89. VargasonA.M. AnselmoA.C. MitragotriS. The evolution of commercial drug delivery technologies.Nat. Biomed. Eng.20215995196710.1038/s41551‑021‑00698‑w33795852
     [Google Scholar]
  90. PageS. KhanT. KühlP. SchwachG. StorchK. ChokshiH. Patient centricity driving formulation innovation: improvements in patient care facilitated by novel therapeutics and drug delivery technologies.Annu. Rev. Pharmacol. Toxicol.202262134136310.1146/annurev‑pharmtox‑052120‑09351734990203
     [Google Scholar]
  91. RagelleH. RahimianS. GuzziE.A. WestenskowP.D. TibbittM.W. SchwachG. LangerR. Additive manufacturing in drug delivery: Innovative drug product design and opportunities for industrial application.Adv. Drug Deliv. Rev.202117811399010.1016/j.addr.2021.11399034600963
     [Google Scholar]
  92. MilanesiM. RunfolaA. GuerciniS. Pharmaceutical industry riding the wave of sustainability: Review and opportunities for future research.J. Clean. Prod.202026112120410.1016/j.jclepro.2020.121204
     [Google Scholar]
  93. WangW. YeZ. GaoH. OuyangD. Computational pharmaceutics - A new paradigm of drug delivery.J. Control. Release202133811913610.1016/j.jconrel.2021.08.03034418520
     [Google Scholar]
  94. GholapA.D. UddinM.J. FaiyazuddinM. OmriA. GowriS. KhalidM. Advances in artificial intelligence for drug delivery and development: A comprehensive review.Comput. Biol. Med.202417810870210.1016/j.compbiomed.2024.10870238878397
     [Google Scholar]
  95. ChaudharyS. MuthudossP. MadheswaranT. PaudelA. GaikwadV. Artificial intelligence (AI) in drug product designing, development, and manufacturing.A Handbook of Artificial Intelligence In Drug Delivery.Academic Press202339544210.1016/B978‑0‑323‑89925‑3.00015‑0
     [Google Scholar]
  96. GuptaV. DhoteV. PaulB.N. TrivediP. Development of novel topical drug delivery system containing cisplatin and imiquimod for dual therapy in cutaneous epithelial malignancy.J. Liposome Res.201424215016210.3109/08982104.2013.86521624328725
     [Google Scholar]
  97. Jiménez-LunaJ. GrisoniF. WeskampN. SchneiderG. Artificial intelligence in drug discovery: Recent advances and future perspectives.Expert Opin. Drug Discov.202116994995910.1080/17460441.2021.190956733779453
     [Google Scholar]
  98. AhmadZ. RahimS. ZubairM. Abdul-GhafarJ. Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: Present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review.Diagn. Pathol.20211612410.1186/s13000‑021‑01085‑433731170
     [Google Scholar]
  99. OsabaE. Villar-RodriguezE. Del SerJ. NebroA.J. MolinaD. LaTorreA. SuganthanP.N. Coello CoelloC.A. HerreraF. A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems.Swarm Evol. Comput.20216410088810.1016/j.swevo.2021.100888
     [Google Scholar]
/content/journals/cis/10.2174/012210299X338978241015155050
Loading
Expert Systems in Preformulation and Formulation Development with Special Reference to SeDeM System: An Innovative, Problem Solving, Intelligent and Optimization Algorithm Tool
Curr. Indian Sci. 2, e2210299X338978 (2024); https://doi.org/10.2174/012210299X338978241015155050
/content/journals/cis/10.2174/012210299X338978241015155050
/content/journals/cis/10.2174/012210299X338978241015155050
Loading

Data & Media loading...


  • Article Type:     Review Article
Keyword(s): Drug development processes; Expert system; Mitigate risks associated with drug delivery; Optimize formulations; Preformulation; SeDeM expert

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