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Lyons B, Balkaran JPR, Dunn-Lawless D, Lucian V, Keller SB, O’Reilly CS, Hu L, Rubasingham J, Nair M, Carlisle R, Stride E, Gray M, Coussios C. Sonosensitive Cavitation Nuclei-A Customisable Platform Technology for Enhanced Therapeutic Delivery. Molecules 2023; 28:7733. [PMID: 38067464 PMCID: PMC10708135 DOI: 10.3390/molecules28237733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/14/2023] [Accepted: 11/16/2023] [Indexed: 12/18/2023] Open
Abstract
Ultrasound-mediated cavitation shows great promise for improving targeted drug delivery across a range of clinical applications. Cavitation nuclei-sound-sensitive constructs that enhance cavitation activity at lower pressures-have become a powerful adjuvant to ultrasound-based treatments, and more recently emerged as a drug delivery vehicle in their own right. The unique combination of physical, biological, and chemical effects that occur around these structures, as well as their varied compositions and morphologies, make cavitation nuclei an attractive platform for creating delivery systems tuned to particular therapeutics. In this review, we describe the structure and function of cavitation nuclei, approaches to their functionalization and customization, various clinical applications, progress toward real-world translation, and future directions for the field.
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Affiliation(s)
- Brian Lyons
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK; (J.P.R.B.); (D.D.-L.); (V.L.); (S.B.K.); (L.H.); (J.R.); (M.N.); (R.C.); (E.S.); (M.G.)
| | - Joel P. R. Balkaran
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK; (J.P.R.B.); (D.D.-L.); (V.L.); (S.B.K.); (L.H.); (J.R.); (M.N.); (R.C.); (E.S.); (M.G.)
| | - Darcy Dunn-Lawless
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK; (J.P.R.B.); (D.D.-L.); (V.L.); (S.B.K.); (L.H.); (J.R.); (M.N.); (R.C.); (E.S.); (M.G.)
| | - Veronica Lucian
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK; (J.P.R.B.); (D.D.-L.); (V.L.); (S.B.K.); (L.H.); (J.R.); (M.N.); (R.C.); (E.S.); (M.G.)
| | - Sara B. Keller
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK; (J.P.R.B.); (D.D.-L.); (V.L.); (S.B.K.); (L.H.); (J.R.); (M.N.); (R.C.); (E.S.); (M.G.)
| | - Colm S. O’Reilly
- Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford OX1 3PJ, UK;
| | - Luna Hu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK; (J.P.R.B.); (D.D.-L.); (V.L.); (S.B.K.); (L.H.); (J.R.); (M.N.); (R.C.); (E.S.); (M.G.)
| | - Jeffrey Rubasingham
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK; (J.P.R.B.); (D.D.-L.); (V.L.); (S.B.K.); (L.H.); (J.R.); (M.N.); (R.C.); (E.S.); (M.G.)
| | - Malavika Nair
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK; (J.P.R.B.); (D.D.-L.); (V.L.); (S.B.K.); (L.H.); (J.R.); (M.N.); (R.C.); (E.S.); (M.G.)
| | - Robert Carlisle
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK; (J.P.R.B.); (D.D.-L.); (V.L.); (S.B.K.); (L.H.); (J.R.); (M.N.); (R.C.); (E.S.); (M.G.)
| | - Eleanor Stride
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK; (J.P.R.B.); (D.D.-L.); (V.L.); (S.B.K.); (L.H.); (J.R.); (M.N.); (R.C.); (E.S.); (M.G.)
| | - Michael Gray
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK; (J.P.R.B.); (D.D.-L.); (V.L.); (S.B.K.); (L.H.); (J.R.); (M.N.); (R.C.); (E.S.); (M.G.)
| | - Constantin Coussios
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK; (J.P.R.B.); (D.D.-L.); (V.L.); (S.B.K.); (L.H.); (J.R.); (M.N.); (R.C.); (E.S.); (M.G.)
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O’Reilly CS, Elbadawi M, Desai N, Gaisford S, Basit AW, Orlu M. Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development. Pharmaceutics 2021; 13:2187. [PMID: 34959468 PMCID: PMC8706962 DOI: 10.3390/pharmaceutics13122187] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 12/11/2021] [Accepted: 12/13/2021] [Indexed: 01/17/2023] Open
Abstract
Orodispersible films (ODFs) are an attractive delivery system for a myriad of clinical applications and possess both large economical and clinical rewards. However, the manufacturing of ODFs does not adhere to contemporary paradigms of personalised, on-demand medicine, nor sustainable manufacturing. To address these shortcomings, both three-dimensional (3D) printing and machine learning (ML) were employed to provide on-demand manufacturing and quality control checks of ODFs. Direct ink writing (DIW) was able to fabricate complex ODF shapes, with thicknesses of less than 100 µm. ML algorithms were explored to classify the ODFs according to their active ingredient, by using their near-infrared (NIR) spectrums. A supervised model of linear discriminant analysis was found to provide 100% accuracy in classifying ODFs. A subsequent partial least square algorithm was applied to verify the dose, where a coefficient of determination of 0.96, 0.99 and 0.98 was obtained for ODFs of paracetamol, caffeine, and theophylline, respectively. Therefore, it was concluded that the combination of 3D printing, NIR and ML can result in a rapid production and verification of ODFs. Additionally, a machine vision tool was used to automate the in vitro testing. These collective digital technologies demonstrate the potential to automate the ODF workflow.
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Affiliation(s)
| | | | | | | | - Abdul W. Basit
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29–39 Brunswick Square, London WC1N 1AX, UK (M.E.); (N.D.); (S.G.)
| | - Mine Orlu
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29–39 Brunswick Square, London WC1N 1AX, UK (M.E.); (N.D.); (S.G.)
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