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image of Personalised Transdermal Therapy for Chronic Pain with Digital Twin Technology

Abstract

Digital twin technology has emerged as a breakthrough development in healthcare, providing personalised transdermal drug delivery systems for chronic pain treatment. Digital twins provide accurate, customised therapy to enhance therapeutic outcomes and reduce risks by combining patient-specific computational models. This article aims to explore the applicability of digital twin technology in improving the transdermal delivery of drugs for successful chronic pain management. It is enabling personalised treatment through patient-specific simulations. By integrating physiological data with computational models, digital twins optimise drug absorption, patch application, and dosage adjustments in real-time, enhancing therapeutic outcomes while minimising side effects. Recent advancements highlight improvements in fentanyl patch optimisation, site-specific drug delivery, and thermally controlled systems. However, challenges such as ethical concerns, data security, and standardisation need to be addressed. Future research should focus on integrating AI and IoT to refine digital twin applications in precision medicine. It can be concluded from the findings of various studies that digital twin technology offers a promising future for precise and individualised transdermal drug delivery in chronic pain, paving the way for safer and more effective therapeutic interventions.

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2025-09-10
2025-09-14

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References

  1. IASP Announces Revised Definition of Pain. 2021 Available from: https://www.iasp-pain.org/citations/iasp-news/iasp-announces-revised-definition-of-pain/
  2. Leppert W. Malec-Milewska M. Zajaczkowska R. Wordliczek J. Transdermal and topical drug administration in the treatment of pain. Molecules 2018 23 3 681 10.3390/molecules23030681 29562618
    [Google Scholar]
  3. Schweiger V. Cacciapuoti M. Nizzero M. Simari S. Lombardi G. Gottin L. Stefani L. Martini A. Varrassi G. Finco G. Polati E. Gambaro G. Exploring chronic pain in hemodialysis patients: An observational study based on the new IASP classification for ICD-11. Pain Ther. 2025 14 1 375 385 10.1007/s40122‑024‑00698‑z 39755882
    [Google Scholar]
  4. Glare P. Aubrey K.R. Myles P.S. Transition from acute to chronic pain after surgery. Lancet 2019 393 10180 1537 1546 10.1016/S0140‑6736(19)30352‑6
    [Google Scholar]
  5. Hylands-White N. Duarte R.V. Raphael J.H. An overview of treatment approaches for chronic pain management. Rheumatol. Int. 2017 37 1 29 42 10.1007/s00296‑016‑3481‑8 27107994
    [Google Scholar]
  6. Turk D.C. Melzack R. The Measurement of Pain and the Assessment of People Experiencing Pain. Handbook of pain assessment 3rd ed Guilford Press 2011 3 16
    [Google Scholar]
  7. McGreevy K. Bottros M.M. Raja S.N. Preventing chronic pain following acute pain: Risk factors, preventive strategies, and their efficacy. Eur. J. Pain Suppl. 2011 5 S2 365 376 10.1016/j.eujps.2011.08.013 22102847
    [Google Scholar]
  8. Scholz J. Finnerup N.B. Attal N. Aziz Q. Baron R. Bennett M.I. Benoliel R. Cohen M. Cruccu G. Davis K.D. Evers S. First M. Giamberardino M.A. Hansson P. Kaasa S. Korwisi B. Kosek E. Lavand’homme P. Nicholas M. Nurmikko T. Perrot S. Raja S.N. Rice A.S.C. Rowbotham M.C. Schug S. Simpson D.M. Smith B.H. Svensson P. Vlaeyen J.W.S. Wang S.J. Barke A. Rief W. Treede R.D. The IASP classification of chronic pain for ICD-11: chronic neuropathic pain. Pain 2019 160 1 53 59 10.1097/j.pain.0000000000001365 30586071
    [Google Scholar]
  9. Treede R.D. Rief W. Barke A. Aziz Q. Bennett M.I. Benoliel R. Cohen M. Evers S. Finnerup N.B. First M.B. Giamberardino M.A. Kaasa S. Korwisi B. Kosek E. Lavand’homme P. Nicholas M. Perrot S. Scholz J. Schug S. Smith B.H. Svensson P. Vlaeyen J.W.S. Wang S-J. Chronic pain as a symptom or a disease: The IASP Classification of Chronic Pain for the International Classification of Diseases (ICD-11). Pain 2019 160 1 19 27 10.1097/j.pain.0000000000001384
    [Google Scholar]
  10. Malviya R. Rajput S. Metaverse-Based Digital Twins: Specialized Healthcare Applications. John Wiley & Sons 2025 10.1002/9781394315680
    [Google Scholar]
  11. Clauw D.J. Häuser W. Cohen S.P. Fitzcharles M.A. Considering the potential for an increase in chronic pain after the COVID-19 pandemic. Pain 2020 161 8 1694 1697 10.1097/j.pain.0000000000001950 32701829
    [Google Scholar]
  12. Domenichiello A.F. Ramsden C.E. The silent epidemic of chronic pain in older adults. Prog. Neuropsychopharmacol. Biol. Psychiatry 2019 93 284 290 10.1016/j.pnpbp.2019.04.006 31004724
    [Google Scholar]
  13. Nicholas M The IASP classification of chronic pain for ICD-11: Chronic primary pain. Pain 2018 160 30586068
    [Google Scholar]
  14. Cohen S.P. Vase L. Hooten W.M. Chronic pain: An update on burden, best practices, and new advances. Lancet 2021 397 10289 2082 2097 10.1016/S0140‑6736(21)00393‑7
    [Google Scholar]
  15. Fitzcharles M.A. Cohen S.P. Clauw D.J. Littlejohn G. Usui C. Häuser W. Nociplastic pain: Towards an understanding of prevalent pain conditions. Lancet 2021 397 10289 2098 2110 10.1016/S0140‑6736(21)00392‑5 34062144
    [Google Scholar]
  16. Devers A. Galer B.S. Topical lidocaine patch relieves a variety of neuropathic pain conditions: An open-label study. Clin. J. Pain 2000 16 3 205 208 10.1097/00002508‑200009000‑00005 11014393
    [Google Scholar]
  17. Clauw D.J. Essex M.N. Pitman V. Jones K.D. Reframing chronic pain as a disease, not a symptom: rationale and implications for pain management. Postgrad. Med. 2019 131 3 185 198 10.1080/00325481.2019.1574403 30700198
    [Google Scholar]
  18. Shi Y. Wu W. Multimodal non-invasive non-pharmacological therapies for chronic pain: mechanisms and progress. BMC Med. 2023 21 1 372 10.1186/s12916‑023‑03076‑2
    [Google Scholar]
  19. Study of RVT-101 in patients with dementia with Lewy Bodies [ DLB]. 2016 Available from: https://www.cochranelibrary.com/central/doi/10.1002/central/CN-01457017/related-content
  20. Li J.X. Combining opioids and non-opioids for pain management: Current status. Neuropharmacology 2019 158 107619 10.1016/j.neuropharm.2019.04.025
    [Google Scholar]
  21. Martínez V. Abalo R. Peripherally acting opioid analgesics and peripherally-induced analgesia. Behav. Pharmacol. 2020 31 2&3 136 158 10.1097/FBP.0000000000000558 32168025
    [Google Scholar]
  22. Rikard S.M. Strahan A.E. Schmit K.M. Guy G.P. Jr Chronic pain among adults — United States, 2019–2021. MMWR Morb. Mortal. Wkly. Rep. 2023 72 15 379 385 10.15585/mmwr.mm7215a1 37053114
    [Google Scholar]
  23. Dahlhamer J. Lucas J. Zelaya C. Nahin R. Mackey S. DeBar L. Kerns R. Von Korff M. Porter L. Helmick C. Prevalence of chronic pain and high-impact chronic pain among adults — United States, 2016. MMWR Morb. Mortal. Wkly. Rep. 2018 67 36 1001 1006 10.15585/mmwr.mm6736a2 30212442
    [Google Scholar]
  24. Fayaz A. Croft P. Langford R.M. Donaldson L.J. Jones G.T. Prevalence of chronic pain in the UK: a systematic review and meta-analysis of population studies. BMJ Open 2016 6 6 e010364 10.1136/bmjopen‑2015‑010364 27324708
    [Google Scholar]
  25. Elliott A.M. Smith B.H. Hannaford P.C. Smith W.C. Chambers W.A. The course of chronic pain in the community: results of a 4-year follow-up study. Pain 2002 99 1 299 307 10.1016/S0304‑3959(02)00138‑0 12237208
    [Google Scholar]
  26. Steglitz J. Buscemi J. Ferguson M.J. The future of pain research, education, and treatment: a summary of the IOM report “Relieving pain in America: a blueprint for transforming prevention, care, education, and research”. Transl. Behav. Med. 2012 2 1 6 8 10.1007/s13142‑012‑0110‑2 24073092
    [Google Scholar]
  27. Deloitte Access Economics The cost of pain in Australia. https://www.deloitte.com/au/en/services/economics/analysis/cost-pain-australia.html
  28. Pickering G. Martin E. Tiberghien F. Delorme C. Mick G. Localized neuropathic pain: An expert consensus on local treatments. Drug Des. Devel. Ther. 2017 11 2709 2718 10.2147/DDDT.S142630 29066862
    [Google Scholar]
  29. Derry S. Wiffen P.J. Kalso E.A. Bell R.F. Aldington D. Phillips T. Gaskell H. Moore R.A. Topical analgesics for acute and chronic pain in adults - An overview of Cochrane Reviews. Cochrane Libr. 2017 2020 2 10.1002/14651858.CD008609.pub2
    [Google Scholar]
  30. Malviya R. Tyagi A. Fuloria S. Subramaniyan V. Sathasivam K. Sundram S. Karupiah S. Chakravarthi S. Meenakshi D.U. Gupta N. Sekar M. Sudhakar K. Fuloria N.K. Fabrication and characterization of chitosan—tamarind seed polysaccharide composite film for transdermal delivery of protein/peptide. Polymers 2021 13 9 1531 10.3390/polym13091531 34068768
    [Google Scholar]
  31. Han Y. Yan W. Zheng Y. Khan M.Z. Yuan K. Lu L. The rising crisis of illicit fentanyl use, overdose, and potential therapeutic strategies. Transl. Psychiatry 2019 9 1 282 10.1038/s41398‑019‑0625‑0
    [Google Scholar]
  32. Wang D.D. Ma T.T. Zhu H.D. Peng C.B. Transdermal fentanyl for cancer pain. J. Cancer Res. Ther. 2018 14 Suppl. 1 S14 S21 [8]. 10.4103/0973‑1482.171368
    [Google Scholar]
  33. Larsen R.H. Nielsen F. Sørensen J.A. Nielsen J.B. Dermal penetration of fentanyl: inter- and intraindividual variations. Pharmacol. Toxicol. 2003 93 5 244 248 10.1046/j.1600‑0773.2003.pto930508.x 14629737
    [Google Scholar]
  34. Defraeye T. Bahrami F. Ding L. Malini R.I. Terrier A. Rossi R.M. Predicting transdermal fentanyl delivery using mechanistic simulations for tailored therapy. Front. Pharmacol. 2020 11 585393 10.3389/fphar.2020.585393 33117179
    [Google Scholar]
  35. Zhang Q. Murawsky M. LaCount T.D. Hao J. Ghosh P. Raney S.G. Kasting G.B. Li S.K. Evaluation of heat effects on fentanyl transdermal delivery systems using in vitro permeation and in vitro release methods. J. Pharm. Sci. 2020 109 10 3095 3104 10.1016/j.xphs.2020.07.013 32702372
    [Google Scholar]
  36. Dąbrowska A.K. Spano F. Derler S. Adlhart C. Spencer N.D. Rossi R.M. The relationship between skin function, barrier properties, and body-dependent factors. Skin Res. Technol. 2018 24 2 165 174 10.1111/srt.12424
    [Google Scholar]
  37. Alkilani A. McCrudden M.T. Donnelly R. Transdermal drug delivery: Innovative pharmaceutical developments based on disruption of the barrier properties of the stratum corneum. Vol. 7. Pharmaceutics 2015 7 4 438 470 10.3390/pharmaceutics7040438 26506371
    [Google Scholar]
  38. Rajput S. Sharma P.K. Malviya R. Biomarkers and treatment strategies for breast cancer recurrence. Curr. Drug Targets 2023 24 15 1209 1220 10.2174/0113894501258059231103072025 38164731
    [Google Scholar]
  39. Phatale V. Vaiphei K.K. Jha S. Patil D. Agrawal M. Alexander A. Overcoming skin barriers through advanced transdermal drug delivery approaches. J. Control. Release 2022 351 361 380 10.1016/j.jconrel.2022.09.025 36169040
    [Google Scholar]
  40. Hadgraft J. Modulation of the barrier function of the skin. Skin Pharmacol. Physiol. 2001 14 1 Suppl. 1 72 81 10.1159/000056393 11509910
    [Google Scholar]
  41. Prodduturi S. Sadrieh N. Wokovich A.M. Doub W.H. Westenberger B.J. Buhse L. Transdermal delivery of fentanyl from matrix and reservoir systems: Effect of heat and compromised skin. J. Pharm. Sci. 2010 99 5 2357 2366 10.1002/jps.22004 19967778
    [Google Scholar]
  42. Albayati N. Talluri S.R. Dholaria N. Michniak-Kohn B. AI-driven innovation in skin kinetics for transdermal drug delivery: overcoming barriers and enhancing precision. Pharmaceutics 2025 17 2 188 10.3390/pharmaceutics17020188 40006555
    [Google Scholar]
  43. Roy S.D. Flynn G.L. Transdermal delivery of narcotic analgesics: Comparative permeabilities of narcotic analgesics through human cadaver skin. Pharm Res. 1989 6 10 825 832 10.1023/a:1015944018555.
    [Google Scholar]
  44. Freise K.J. Newbound G.C. Tudan C. Clark T.P. Pharmacokinetics and the effect of application site on a novel, long-acting transdermal fentanyl solution in healthy laboratory Beagles. J. Vet. Pharmacol. Ther. 2012 35 s2 Suppl. 2 27 33 10.1111/j.1365‑2885.2012.01411.x 22731773
    [Google Scholar]
  45. Iordanskii A.L. Feldstein M.M. Markin V.S. Hadgraft J. Plate N.A. Modeling of the drug delivery from a hydrophilic transdermal therapeutic system across polymer membrane. Eur. J. Pharm. Biopharm. 2000 49 3 287 293 10.1016/S0939‑6411(00)00063‑1 10799821
    [Google Scholar]
  46. Anissimov Y.G. Jepps O.G. Dancik Y. Roberts M.S. Mathematical and pharmacokinetic modelling of epidermal and dermal transport processes. Adv. Drug Deliv. Rev. 2013 65 2 169 190 10.1016/j.addr.2012.04.009 22575500
    [Google Scholar]
  47. Walicka A Iwanowska-Chomiak B Drug diffusion transport through human skin. Int. J. Appl. Mech. 2018 23 4 977 988 10.2478/ijame‑2018‑0055
    [Google Scholar]
  48. Naegel A. Heisig M. Wittum G. Detailed modeling of skin penetration—An overview. Adv. Drug Deliv. Rev. 2013 65 2 191 207 10.1016/j.addr.2012.10.009 23142646
    [Google Scholar]
  49. Faulkner C. de Leeuw N.H. Predicting the membrane permeability of fentanyl and its analogues by molecular dynamics simulations. J. Phys. Chem. B 2021 125 30 8443 8449 10.1021/acs.jpcb.1c05438 34286980
    [Google Scholar]
  50. Rajput S. Malviya R. Uniyal P. Advancements in the diagnosis, prognosis, and treatment of retinoblastoma. Can. J. Ophthalmol. 2024 59 5 281 299 10.1016/j.jcjo.2024.01.018 38369298
    [Google Scholar]
  51. Rim J.E. Pinsky P.M. van Osdol W.W. Multiscale modeling framework of transdermal drug delivery. Ann. Biomed. Eng. 2009 37 6 1217 1229 10.1007/s10439‑009‑9678‑1 19319682
    [Google Scholar]
  52. Shenoy P. Rao M. Chokkadi S. Bhatnagar S. Salins N. Developing mathematical models to compare and analyse the pharmacokinetics of morphine and fentanyl. Indian J. Anaesth. 2024 68 1 111 117 10.4103/ija.ija_1036_23 38406346
    [Google Scholar]
  53. Pan S. Duffull S.B. Automated proper lumping for simplification of linear physiologically based pharmacokinetic systems. J. Pharmacokinet. Pharmacodyn. 2019 46 4 361 370 10.1007/s10928‑019‑09644‑5 31227954
    [Google Scholar]
  54. Madden J.C. Pawar G. Cronin M.T.D. Webb S. Tan Y.M. Paini A. in silico resources to assist in the development and evaluation of physiologically-based kinetic models. Comput. Toxicol. 2019 11 33 49 10.1016/j.comtox.2019.03.001
    [Google Scholar]
  55. Björkman S. Reduction and lumping of physiologically based pharmacokinetic models: prediction of the disposition of fentanyl and pethidine in humans by successively simplified models. J. Pharmacokinet. Pharmacodyn. 2003 30 4 285 307 10.1023/A:1026194618660 14650375
    [Google Scholar]
  56. Bahrami F. Rossi R.M. De Nys K. Defraeye T. An individualized digital twin of a patient for transdermal fentanyl therapy for chronic pain management. Drug Deliv. Transl. Res. 2023 13 9 2272 2285 10.1007/s13346‑023‑01305‑y 36897525
    [Google Scholar]
  57. La Count T.D. Zhang Q. Murawsky M. Hao J. Ghosh P. Dave K. Raney S.G. Talattof A. Kasting G.B. Li S.K. Evaluation of heat effects on transdermal nicotine delivery in vitro and in silico using heat-enhanced transport model analysis. AAPS J. 2020 22 4 82 10.1208/s12248‑020‑00457‑w 32488395
    [Google Scholar]
  58. Filipovic N. Saveljic I. Rac V. Graells B.O. Bijelic G. Computational and experimental model of transdermal iontophorethic drug delivery system. Int. J. Pharm. 2017 533 2 383 388 10.1016/j.ijpharm.2017.05.066 28576549
    [Google Scholar]
  59. Yan Q. Shen S. Wang Y. Weng J. Wan A. Yang G. The Finite Element Analysis Research on Microneedle Design Strategy and Transdermal Drug Delivery System. Vol. 14. Pharmaceutics 2022
    [Google Scholar]
  60. Bahrami F. Rossi R.M. Defraeye T. Predicting transdermal fentanyl delivery using physics-based simulations for tailored therapy based on the age. Drug Deliv. 2022 29 1 950 969 10.1080/10717544.2022.2050846 35319323
    [Google Scholar]
  61. The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles. https://ntrs.nasa.gov/api/citations/20120008178/downloads/20120008178.pdf
  62. Grieves M.W. Product lifecycle management: The new paradigm for enterprises. Int. J. Prod. Dev. 2005 2 1/2 71 [1–2]. 10.1504/IJPD.2005.006669
    [Google Scholar]
  63. Venkatesh K.P. Raza M.M. Kvedar J.C. Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation. NPJ Digit. Med. 2022 5 1 150 10.1038/s41746‑022‑00694‑7
    [Google Scholar]
  64. Cukic M. Annaheim S. Bahrami F. Defraeye T. De Nys K. Jörger M. Is personal physiology-based rapid prediction digital twin for minimal effective fentanyl dose better than standard practice: a pilot study protocol. BMJ Open 2024 14 9 e085296 10.1136/bmjopen‑2024‑085296 39317494
    [Google Scholar]
  65. Diniz P Grimm B Garcia F Fayad J Ley C Mouton C Digital twin systems for musculoskeletal applications: A current concepts review. Knee Surg Sports Traumatol Arthrosc 2025 33 5 1892 1910 10.1002/ksa.12627
    [Google Scholar]
  66. Mulder S.T. Omidvari A.H. Rueten-Budde A.J. Huang P.H. Kim K.H. Bais B. Rousian M. Hai R. Akgun C. van Lennep J.R. Willemsen S. Rijnbeek P.R. Tax D.M.J. Reinders M. Boersma E. Rizopoulos D. Visch V. Steegers-Theunissen R. Dynamic digital twin: diagnosis, treatment, prediction, and prevention of disease during the life course. J. Med. Internet Res. 2022 24 9 e35675 10.2196/35675 36103220
    [Google Scholar]
  67. Rasheed A. San O. Kvamsdal T. Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access 2020 8 21980 22012 10.1109/ACCESS.2020.2970143
    [Google Scholar]
  68. Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning. 2025 https://openreview.net/forum?id=kG4GSdzjDt
  69. Xie K. Zhang L. Yang Y. Li X. Khedri R. Chen Z. An X Language-Driven Framework for Systematic Development of Digital Twin Healthcare Systems. ACM Transactions on Multimedia Computing, Communications and Applications 2023 10.1145/3729230
    [Google Scholar]
  70. Viola F. Del Corso G. De Paulis R. Verzicco R. GPU accelerated digital twins of the human heart open new routes for cardiovascular research. Sci. Rep. 2023 13 1 8230 10.1038/s41598‑023‑34098‑8 37217483
    [Google Scholar]
  71. Rajput S. Malviya R. Sridhar S.B. Nanoparticle-based photodynamic therapy for targeted treatment of breast cancer. Nano-Structures & Nano-Objects 2024 40 101405 10.1016/j.nanoso.2024.101405
    [Google Scholar]
  72. Cau F.M. Explaining black box models through twin systems. International Conference on Intelligent User Interfaces, Proceedings IUI 2020, P.27 - 28 10.1145/3379336.3381511
    [Google Scholar]
  73. Brennan R.W. Lesage J. Evaluating the use of grey-box system identification for digital twins in manufacturing automation. 2024 https://www.tandfonline.com/doi/pdf/10.1080/0951192X.2024.2386980
    [Google Scholar]
  74. Qin Y. Liu H. Wang Y. Mao Y. Inverse physics–informed neural networks for digital twin–based bearing fault diagnosis under imbalanced samples. Int. J. Comput. Integr. 2024 38 7 10.1016/j.knosys.2024.111641
    [Google Scholar]
  75. Yang S. Kim H. Hong Y. Yee K. Maulik R. Kang N. Data-driven physics-informed neural networks: A digital twin perspective. Comput. Methods Appl. Mech. Eng. 2024 428 117075 10.1016/j.cma.2024.117075
    [Google Scholar]
  76. He M. Zhu L. Yang N. Li H. Yang Q. Recent advances of oral film as platform for drug delivery. Int. J. Pharm. 2021 604 120759 10.1016/j.ijpharm.2021.120759
    [Google Scholar]
  77. Alkilani A.Z. Nasereddin J. Hamed R. Nimrawi S. Hussein G. Abo-Zour H. Donnelly R.F. Beneath the Skin: A review of current trends and future prospects of transdermal drug delivery systems. Pharmaceutics 2022 14 6 1152 10.3390/pharmaceutics14061152 35745725
    [Google Scholar]
  78. Jeong W.Y. Kwon M. Choi H.E. Kim K.S. Recent advances in transdermal drug delivery systems: a review. Biomater. Res. 2021 25 1 24 10.1186/s40824‑021‑00226‑6 34321111
    [Google Scholar]
  79. Kim E.J. Choi D.H. Quality by design approach to the development of transdermal patch systems and regulatory perspective. J. Pharm. Investig. 2021 51 6 669 690 10.1007/s40005‑021‑00536‑w
    [Google Scholar]
  80. Khan S.U. Ullah M. Saeed S. Saleh E.A.M. Kassem A.F. Arbi F.M. Wahab A. Rehman M. ur Rehman K. Khan D. Zaman U. Khan K.A. Khan M.A. Lu K. Nanotherapeutic approaches for transdermal drug delivery systems and their biomedical applications. Eur. Polym. J. 2024 207 112819 10.1016/j.eurpolymj.2024.112819
    [Google Scholar]
  81. Joshi N. Azizi Machekposhti S. Narayan R.J. Evolution of transdermal drug delivery devices and novel microneedle technologies: A historical perspective and review. JID Innov 2023 3 6 10.1016/j.xjidi.2023.100225
    [Google Scholar]
  82. Akhtar N. Singh V. Yusuf M. Khan R.A. Non-invasive drug delivery technology: Development and current status of transdermal drug delivery devices, techniques and biomedical applications. Biomed Tech 2020 65 3 343 272 10.1515/bmt‑2019‑0019.
    [Google Scholar]
  83. Pires L.R. Vinayakumar K.B. Turos M. Miguel V. Gaspar J. A perspective on microneedle-based drug delivery and diagnostics in paediatrics. J. Pers. Med. 2019 9 4 49 10.3390/jpm9040049 31731656
    [Google Scholar]
  84. Ruby P.K. Pathak S.M. Aggarwal D. Critical attributes of transdermal drug delivery system (TDDS) – A generic product development review. Drug Dev. Ind. Pharm. 2014 40 11 1421 1428 10.3109/03639045.2013.879720 24467407
    [Google Scholar]
  85. Ruela A.L.M. Perissinato A.G. Lino M.E.S. Mudrik P.S. Pereira G.R. Evaluation of skin absorption of drugs from topical and transdermal formulations. Braz. J. Pharm. Sci. 2016 52 3 527 544 [3]. 10.1590/s1984‑82502016000300018
    [Google Scholar]
  86. Singh I. Morris A. Performance of transdermal therapeutic systems: Effects of biological factors. Int. J. Pharm. Investig. 2011 1 1 4 9 10.4103/2230‑973X.76721 23071913
    [Google Scholar]
  87. Jang HH Lee SN Epidermal Skin Barrier. Asian j. beauty cosmetol 2016 14 3 339 347 10.20402/ajbc.2016.0039
    [Google Scholar]
  88. Sahle F.F. Gebre-Mariam T. Dobner B. Wohlrab J. Neubert R.H.H. Skin diseases associated with the depletion of stratum corneum lipids and stratum corneum lipid substitution therapy. Skin Pharmacol. Physiol. 2015 28 1 42 55 10.1159/000360009 25196193
    [Google Scholar]
  89. Rajabalaya R. Musa M.N. Kifli N. David S.R. Oral and transdermal drug delivery systems: Role of lipid-based lyotropic liquid crystals. Drug Des. Devel. Ther. 2017 11 393 406 10.2147/DDDT.S103505 28243062
    [Google Scholar]
  90. Barry B.W. Novel mechanisms and devices to enable successful transdermal drug delivery. Eur. J. Pharm. Sci. 2001 14 2 101 114 10.1016/S0928‑0987(01)00167‑1 11500256
    [Google Scholar]
  91. Malek-Khatabi A. Sadat Razavi M. Abdollahi A. Rahimzadeghan M. Moammeri F. Sheikhi M. Tavakoli M. Rad-Malekshahi M. Faraji Rad Z. Recent progress in PLGA-based microneedle-mediated transdermal drug and vaccine delivery. Biomater. Sci. 2023 11 16 5390 5409 10.1039/D3BM00795B
    [Google Scholar]
  92. Jain S.K. Verma A. Jain A. Hurkat P. Transfollicular drug delivery: Current perspectives. Research and Reports in Transdermal Drug Delivery 2016 5 1
    [Google Scholar]
  93. Petersen B. Rovati S. Diclofenac epolamine (Flector) patch: Evidence for topical activity. Clin. Drug Investig. 2009 29 1 1 9 10.2165/0044011‑200929010‑00001 19067470
    [Google Scholar]
  94. Banning M. Topical diclofenac: Clinical effectiveness and current uses in osteoarthritis of the knee and soft tissue injuries. Expert Opin. Pharmacother. 2008 9 16 2921 2929 10.1517/14656566.9.16.2921 18937623
    [Google Scholar]
  95. Duragesic Label. 2025 Available from: https://www.accessdata.fda.gov/drugsatfda_docs/label/2005/19813s039lbl.pdf
  96. Muijsers R.B.R. Wagstaff A.J. Transdermal Fentanyl. Drugs 2001 61 15 2289 2307 10.2165/00003495‑200161150‑00014 11772140
    [Google Scholar]
  97. Hans G. Robert D. Transdermal buprenorphine – A critical appraisal of its role in pain management. J. Pain Res. 2009 117 10.2147/JPR.S6503
    [Google Scholar]
  98. Poliwoda S. Noor N. Jenkins J.S. Stark C.W. Steib M. Hasoon J. Varrassi G. Urits I. Viswanath O. Kaye A.M. Kaye A.D. Buprenorphine and its formulations: A comprehensive review. Health Psychol. Res. 2022 10 3 37517 10.52965/001c.37517 35999975
    [Google Scholar]
  99. Poplawski S. Johnson M. Philips P. Eberhart L.H.J. Koch T. Itri L.M. Use of fentanyl iontophoretic transdermal system (ITS) (IONSYS®) in the management of patients with acute postoperative pain: A case series. Pain Ther. 2016 5 2 237 248 10.1007/s40122‑016‑0061‑2 27817153
    [Google Scholar]
  100. Goddard J.M. Reaney R.L. Lidocaine 5%–medicated plaster (Versatis) for localised neuropathic pain: results of a multicentre evaluation of use in children and adolescents. Br. J. Pain 2018 12 3 189 193 10.1177/2049463718756431 30057764
    [Google Scholar]
  101. Bajaj S. Whiteman A. Brandner B. Transdermal drug delivery in pain management. Contin. Educ. Anaesth. Crit. Care Pain 2011 11
    [Google Scholar]
  102. Pavelka K. Loet X.L. Bjorneboe O. Herrero-Beaumont G. Richarz U. Benefits of transdermal fentanyl in patients with rheumatoid arthritis or with osteoarthritis of the knee or hip: an open-label study to assess pain control. Curr. Med. Res. Opin. 2004 20 12 1967 1977 10.1185/030079904X14120 15701214
    [Google Scholar]
  103. Likar R. Transdermal buprenorphine in the management of persistent pain - safety aspects. Ther. Clin. Risk Manag. 2006 2 1 115 125 18360586
    [Google Scholar]
  104. Gallagher A.M. Leighton-Scott J. van Staa T.P. Utilization characteristics and treatment persistence in patients prescribed low-dose buprenorphine patches in primary care in the United Kingdom: A retrospective cohort study. Clin. Ther. 2009 31 8 1707 1715 10.1016/j.clinthera.2009.08.022 19808129
    [Google Scholar]
  105. Rajput S. Malviya R. Prajapati B.G. Sridhar S.B. Shareef J. Nanoparticle troopers: Infiltrating cancer cells for targeted therapies. Nano-Structures & Nano-Objects 2025 41 101453 10.1016/j.nanoso.2025.101453
    [Google Scholar]
  106. Drugs. 2025 Available from: https://www.fda.gov/drugs
  107. Power I. Fentanyl HCl iontophoretic transdermal system (ITS): Clinical application of iontophoretic technology in the management of acute postoperative pain. Br. J. Anaesth. 2007 98 1 4 11 10.1093/bja/ael314 17158126
    [Google Scholar]
  108. Barricelli B.R. Casiraghi E. Fogli D. A survey on digital twin: Definitions, characteristics, applications, and design implications. Vol. 7. IEEE Access 2019 7 167653 167671 10.1109/ACCESS.2019.2953499
    [Google Scholar]
  109. Popa E.O. van Hilten M. Oosterkamp E. Bogaardt M.J. The use of digital twins in healthcare: socio-ethical benefits and socio-ethical risks. Life Sci. Soc. Policy 2021 17 1 6 10.1186/s40504‑021‑00113‑x 34218818
    [Google Scholar]
  110. Javaid M. Haleem A. Pratap Singh R. Khan S. Suman R. Blockchain technology applications for Industry 4.0: A literature-based review. Blockchain: Research and Applications 2021 2 4 10.1016/j.bcra.2021.100027
    [Google Scholar]
  111. Ahmed H. Devoto L. The potential of a digital twin in surgery. Surg. Innov. 2021 28 4 509 510 10.1177/1553350620975896
    [Google Scholar]
  112. Rivera L.F. Villegas N.M. Jiménez M. Tamura G. Angara P. Müller H.A. Towards continuous monitoring in personalized healthcare through digital twins. Conference of the Centre for Medicine, Computer Science, Engineering 2020
    [Google Scholar]
  113. Garg H. Sharma B. Shekhar S. Agarwal R. Spoofing detection system for e-health digital twin using EfficientNet Convolution Neural Network. Multimed Tools Appl 2022 81 26873 26888
    [Google Scholar]
  114. Jimenez J.I. Jahankhani H. Kendzierskyj S. Health Care in the Cyberspace: Medical Cyber-Physical System and Digital Twin Challenges.Digital Twin Technologies and Smart Cities 2020 79 92
    [Google Scholar]
  115. Al-Ali A.R. Gupta R. Zaman Batool T. Landolsi T. Aloul F. Al Nabulsi A. Digital twin conceptual model within the context of internet of things. Future Internet 2020 12 10 163 [10]. 10.3390/fi12100163
    [Google Scholar]
  116. Newrzella S.R. Franklin D.W. Haider S. Three-dimension digital twin reference architecture model for functionality, dependability, and life cycle development across industries. IEEE Access 2022 10 95390 95410 10.1109/ACCESS.2022.3202941
    [Google Scholar]
  117. Angulo C. Gonzalez-Abril L. Raya C. Ortega J.A. A Proposal to Evolving Towards Digital Twins in Healthcare. Bioinformatics and Biomedical Engineering 2020, p.418-426 10.1007/978‑3‑030‑45385‑5_37
    [Google Scholar]
  118. Shamanna P. Saboo B. Damodharan S. Mohammed J. Mohamed M. Poon T. Kleinman N. Thajudeen M. Reducing HbA1c in type 2 diabetes using digital twin technology-enabled precision nutrition: A retrospective analysis. Diabetes Ther. 2020 11 11 2703 2714 10.1007/s13300‑020‑00931‑w 32975712
    [Google Scholar]
  119. Cellina M. Cè M. Alì M. Irmici G. Ibba S. Caloro E. Fazzini D. Oliva G. Papa S. Digital Twins: The New frontier for personalized medicine? Appl. Sci. 2023 13 13 7940 [Switzerland]. 10.3390/app13137940
    [Google Scholar]
  120. Sun T. He X. Song X. Shu L. Li Z. The Digital Twin in Medicine: A Key to the Future of Healthcare? Front. Med. 2022 9 907066 10.3389/fmed.2022.907066 35911407
    [Google Scholar]
  121. Sun T. He X. Li Z. Digital twin in healthcare: Recent updates and challenges. Digit. Health 2023 9 20552076221149651 10.1177/20552076221149651
    [Google Scholar]
  122. Riascos R. Ostrosi E. Sagot J.C. Stjepandić J. Conceptual Approach for a Digital Twin of Medical Devices. 2022 320 329 10.3233/ATDE220661
    [Google Scholar]
  123. Xu Y. Liu X. Cao X. Huang C. Liu E. Qian S. Artificial intelligence: A powerful paradigm for scientific research. Vol. 2. Innovation. 2021 2 4 10.1016/j.xinn.2021.100179
    [Google Scholar]
  124. Lupton D. Language matters: The ‘digital twin’ metaphor in health and medicine. J. Med. Ethics 2021 47 6 409 10.1136/medethics‑2021‑107517
    [Google Scholar]
  125. Braun M. Represent me: Please! Towards an ethics of digital twins in medicine. J. Med. Ethics 2021 47 6 394 400 10.1136/medethics‑2020‑106134 33722986
    [Google Scholar]
  126. Marinette B. Hui X. The Principal as a Curriculum-instructional Leader: A Strategy for Curriculum Implementation in Cameroon Secondary Schools. Int. J. Educ. Res. 2020 8
    [Google Scholar]
  127. Cheng W. Lian W. Tian J. Building the hospital intelligent twins for all-scenario intelligence health care. Digit. Health 2022 8 10.1177/20552076221107894 35720617
    [Google Scholar]
  128. Fukawa N. Rindfleisch A. Enhancing innovation via the digital twin. J. Prod. Innov. Manage. 2023 40
    [Google Scholar]
  129. Gkouskou K. Vlastos I. Karkalousos P. Chaniotis D. Sanoudou D. Eliopoulos A.G. The “Virtual Digital Twins” Concept in Precision nutrition. Adv. Nutr. 2020 11 6 1405 1413 10.1093/advances/nmaa089 32770212
    [Google Scholar]
  130. Subramanian K. Digital Twin for Drug Discovery and Development—The Virtual Liver. J. Indian Inst. Sci. 2020 100 4 653 662 10.1007/s41745‑020‑00185‑2
    [Google Scholar]
  131. Drummond D. Coulet A. Technical, ethical, legal, and societal challenges with digital twin systems for the management of chronic diseases in children and young people. J. Med. Internet Res. 2022 24 10 e39698 10.2196/39698 36315239
    [Google Scholar]
  132. Bruynseels K. Santoni de Sio F. van den Hoven J. Digital Twins in health care: Ethical implications of an emerging engineering paradigm. Front. Genet. 2018 9 31 10.3389/fgene.2018.00031 29487613
    [Google Scholar]
  133. Emmert-Streib F. Yli-Harja O. Dehmer M. Explainable artificial intelligence and machine learning: A reality rooted perspective. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2020 10 6 e1368 [6]. 10.1002/widm.1368
    [Google Scholar]
  134. Kelly J.T. Campbell K.L. Gong E. Scuffham P. The internet of things: Impact and implications for health care delivery. J. Med. Internet Res. 2020 22 11 e20135 10.2196/20135 33170132
    [Google Scholar]
  135. Manickam P. Mariappan S.A. Murugesan S.M. Hansda S. Kaushik A. Shinde R. Thipperudraswamy S.P. Artificial intelligence (AI) and internet of medical things (IoMT) assisted biomedical systems for intelligent healthcare. Biosensors 2022 12 8 562 10.3390/bios12080562 35892459
    [Google Scholar]
  136. Alnaim A.K. Alwakeel A.M. Machine-learning-based iot–edge computing healthcare solutions. Electronics 2023 12 [Switzerland].
    [Google Scholar]
  137. Dang V.A. Vu Khanh Q. Nguyen V.H. Nguyen T. Nguyen D.C. Intelligent Healthcare: Integration of emerging technologies and internet of things for humanity. Sensors 2023 23 9 4200 10.3390/s23094200
    [Google Scholar]
  138. Ali O Abdelbaki W Shrestha A Elbasi E Alryalat MAA Dwivedi YK A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. Journal of Innovation & Knowledge 2023
    [Google Scholar]
  139. Haleem A. Javaid M. Pratap Singh R. Suman R. Medical 4.0 technologies for healthcare: Features, capabilities, and applications. Internet of Things and Cyber-Physical Systems 2022 2 12 30 10.1016/j.iotcps.2022.04.001
    [Google Scholar]
  140. Peng G.C.Y. Alber M. Buganza Tepole A. Cannon W.R. De S. Dura-Bernal S. Garikipati K. Karniadakis G. Lytton W.W. Perdikaris P. Petzold L. Kuhl E. Multiscale modeling meets machine learning: What can we learn? Arch. Comput. Methods Eng. 2021 28 3 1017 1037 10.1007/s11831‑020‑09405‑5 34093005
    [Google Scholar]
  141. Meier-Schellersheim M. Fraser I.D.C. Klauschen F. Multiscale modeling for biologists. Wiley Interdiscip. Rev. Syst. Biol. Med. 2009 1 1 4 14 10.1002/wsbm.33 20448808
    [Google Scholar]
  142. Botín-Sanabria D.M. Mihaita A.S. Peimbert-García R.E. Ramírez- Moreno M.A. Ramírez-Mendoza R.A. Lozoya-Santos J.J. Digital twin technology challenges and applications: A comprehensive review. Remote Sens. 2022 14 6 1335 10.3390/rs14061335
    [Google Scholar]
  143. Liu Y. Zhang L. Yang Y. Zhou L. Ren L. Wang F. Liu R. Pang Z. Deen M.J. A novel cloud-based framework for the elderly healthcare services using digital twin. IEEE Access 2019 7 49088 49101 10.1109/ACCESS.2019.2909828
    [Google Scholar]
  144. Möller J. Pörtner R. Digital twins for tissue culture techniques—concepts, expectations, and state of the art. Vol. 9. Processes 2021 9 3 447 10.3390/pr9030447
    [Google Scholar]
  145. Emmert-Streib F. Yli-Harja O. What Is a Digital Twin? Experimental Design for a Data-Centric Machine Learning Perspective in Health. Int. J. Mol. Sci. 2022 23 21 13149 10.3390/ijms232113149 36361936
    [Google Scholar]
  146. Armeni P. Polat I. De Rossi L.M. Diaferia L. Meregalli S. Gatti A. Digital Twins in Healthcare: Is it the beginning of a new era of evidence-based medicine? A critical Review. J. Pers. Med. 2022 12 8 1255 10.3390/jpm12081255 36013204
    [Google Scholar]
  147. Nagaraj D. Khandelwal P. Steyaert S. Gevaert O. Augmenting digital twins with federated learning in medicine. Lancet Digit. Health 2023 5 5 e251 e253 10.1016/S2589‑7500(23)00044‑4 37100540
    [Google Scholar]
  148. Hussain I. Hossain M.A. Park S.J. A Healthcare Digital Twin for Diagnosis of Stroke. Proceedings of 2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health, BECITHCON 2021 2021 10.1109/BECITHCON54710.2021.9893641
    [Google Scholar]
  149. Wickramasinghe N. Jayaraman P.P. Forkan A.R.M. Ulapane N. Kaul R. Vaughan S. Zelcer J. A vision for leveraging the concept of digital twins to support the provision of personalized cancer care. IEEE Internet Comput. 2022 26 5 17 24 10.1109/MIC.2021.3065381
    [Google Scholar]
  150. Sager S. Digital twins in oncology. J. Cancer Res. Clin. Oncol. 2023 149 9 5475 5477 10.1007/s00432‑023‑04633‑1
    [Google Scholar]
  151. Thiong’o G.M. Rutka J.T. Digital Twin Technology: The future of predicting neurological complications of pediatric cancers and their treatment. Front. Oncol. 2022 11 781499 10.3389/fonc.2021.781499 35127487
    [Google Scholar]
  152. Kaul R. Ossai C. Forkan A.R.M. Jayaraman P.P. Zelcer J. Vaughan S. The role of AI for developing digital twins in healthcare: The case of cancer care. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2023 13
    [Google Scholar]
  153. James L. Digital twins will revolutionise healthcare. Engineering and Technology 2021 16 2 10.1049/et.2021.0210
    [Google Scholar]
  154. Kamel Boulos M.N. Zhang P. Digital twins: From personalised medicine to precision public health. Vol. 11. J. Pers. Med. 2021 11 8 745 10.3390/jpm11080745 34442389
    [Google Scholar]
  155. Elayan H. Aloqaily M. Guizani M. Digital twin for intelligent context-aware iot healthcare systems. IEEE Internet Things J. 2021 8 23 16749 16757 10.1109/JIOT.2021.3051158
    [Google Scholar]
  156. Lai X. Geier O.M. Fleischer T. Garred Ø. Borgen E. Funke S.W. Kumar S. Rognes M.E. Seierstad T. Børresen-Dale A.L. Kristensen V.N. Engebraaten O. Köhn-Luque A. Frigessi A. Toward personalized computer simulation of breast cancer treatment: A multiscale pharmacokinetic and pharmacodynamic model informed by multitype patient data. Cancer Res. 2019 79 16 4293 4304 10.1158/0008‑5472.CAN‑18‑1804 31118201
    [Google Scholar]
  157. Haleem A. Javaid M. Pratap Singh R. Suman R. Exploring the revolution in healthcare systems through the applications of digital twin technology. Biomedical Technology 2023 4 28 38 10.1016/j.bmt.2023.02.001
    [Google Scholar]
  158. Kalozoumis P.G. Marino M. Carniel E.L. Iakovidis D.K. Towards the Development of a Digital Twin for Endoscopic Medical Device Testing. Studies in Systems. Decision and Control 2022 10.1007/978‑3‑030‑96802‑1_7
    [Google Scholar]
  159. Peng Y Zhang M Yu F Xu J Gao S. Digital Twin Hospital Buildings: An Exemplary Case Study through Continuous Lifecycle Integration. Advances in Civil Engineering. 2020
    [Google Scholar]
  160. Bahrami F. Rossi R.M. De Nys K. Joerger M. Radenkovic M.C. Defraeye T. Implementing physics-based digital patient twins to tailor the switch of oral morphine to transdermal fentanyl patches based on patient physiology. Eur. J. Pharm. Sci. 2024 195 106727 10.1016/j.ejps.2024.106727 38360153
    [Google Scholar]
  161. Turab M. Jamil S. A comprehensive survey of digital twins in healthcare in the era of metaverse. BioMedInformatics 2023 3 3 563 584 10.3390/biomedinformatics3030039
    [Google Scholar]
  162. Giansanti D. Morelli S. Exploring the Potential of Digital Twins in Cancer Treatment: A Narrative Review of Reviews. J. Clin. Med. 2025 14 10 3574 10.3390/jcm14103574 40429568
    [Google Scholar]
  163. Schwartz S.M. Wildenhaus K. Bucher A. Byrd B. Digital Twins and the Emerging Science of Self: Implications for Digital Health Experience Design and “Small” Data. Front. Comput. Sci. 2020 2 31 10.3389/fcomp.2020.00031
    [Google Scholar]
  164. Malviya R Rajput S. Advances and Insights into AI-Created Disability Supports. 10.1007/978‑981‑96‑6069‑8
    [Google Scholar]
  165. SDTC—Swedish Digital Twin Consortium SDTC—Swedish Digital Twin Consortium. Available from: https://www.sdtc.se/#consortium
  166. Digital Twin Consortium Digital Twin Consortium. Available from: https://www.digitaltwinconsortium.org/
  167. Di Shen M. Chen S.B. Ding X.D. The effectiveness of digital twins in promoting precision health across the entire population: a systematic review. NPJ Digit. Med. 2024 7 1 1 10 38172429
    [Google Scholar]
  168. Papachristou K. Katsakiori P.F. Papadimitroulas P. Strigari L. Kagadis G.C. Digital Twins’ Advancements and applications in healthcare, towards precision medicine. J. Pers. Med. 2024 14 11 1101 10.3390/jpm14111101 39590593
    [Google Scholar]
  169. Katsoulakis E. Wang Q. Wu H. Shahriyari L. Fletcher R. Liu J. Achenie L. Liu H. Jackson P. Xiao Y. Syeda-Mahmood T. Tuli R. Deng J. Digital twins for health: A scoping review. NPJ Digit. Med. 2024 7 1 77 10.1038/s41746‑024‑01073‑0
    [Google Scholar]
  170. Quaranta L. Hossein Homaei M. Weinberger N. Hery D. Mahr D. Adler S.O. Beyond the gender data gap: Co-creating equitable digital patient twins. Front Digit Health 2025 7 10.3389/fdgth.2025.1584415
    [Google Scholar]
  171. Tretter M. Perspectives on digital twins and the (im)possibilities of control. J. Med. Ethics 2021 47 6 410 411 10.1136/medethics‑2021‑107460 34001527
    [Google Scholar]
  172. Mittelstadt B. Near-term ethical challenges of digital twins. J. Med. Ethics 2021 47 6 405 406 10.1136/medethics‑2021‑107449
    [Google Scholar]
  173. Ethics and governance of artificial intelligence for health. 2021 Available from: https://www.who.int/citations/i/item/9789240029200
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Personalised Transdermal Therapy for Chronic Pain with Digital Twin Technology
Bentham Science Publishers ; https://doi.org/10.2174/0113894501395522250903070018
/content/journals/cdt/10.2174/0113894501395522250903070018
/content/journals/cdt/10.2174/0113894501395522250903070018
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  • Article Type:     Review Article
Keywords: transdermal drug delivery ; fentanyl patches ; non-invasive drug delivery ; Chronic pain ; chronic pain management ; personalised medicine

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