Advancing Liposomal Drug Delivery through Physiologically-Based Pharmacokinetic Modeling

References

1. van der Koog L. Gandek T.B. Nagelkerke A. Liposomes and extracellular vesicles as drug delivery systems: A comparison of composition, pharmacokinetics, and functionalization. Adv. Healthc. Mater. 2022 11 5 2100639 10.1002/adhm.202100639 34165909
   [Google Scholar]
2. He H. Yuan D. Wu Y. Cao Y. Pharmacokinetics and pharmacodynamics modeling and simulation systems to support the development and regulation of liposomal drugs. Pharmaceutics 2019 11 3 110 10.3390/pharmaceutics11030110 30866479
   [Google Scholar]
3. Ozbek O. Genc D.E.O. Ulgen K. Advances in physiologically based pharmacokinetic (PBPK) modeling of nanomaterials. ACS Pharmacol. Transl. Sci. 2024 7 8 2251 2279 10.1021/acsptsci.4c00250 39144562
   [Google Scholar]
4. Minnema J. Borgos S.E.F. Liptrott N. Vandebriel R. Delmaar C. Physiologically based pharmacokinetic modeling of intravenously administered nanoformulated substances. Drug Deliv. Transl. Res. 2022 12 9 2132 2144 10.1007/s13346‑022‑01159‑w 35551616
   [Google Scholar]
5. Lin W. Chen Y. Unadkat J.D. Zhang X. Wu D. Heimbach T. Applications, challenges, and outlook for pbpk modeling and simulation: A regulatory, industrial and academic perspective. Pharm. Res. 2022 39 8 1701 1731 10.1007/s11095‑022‑03274‑2 35552967
   [Google Scholar]
6. Li M. Zou P. Tyner K. Lee S. Physiologically Based Pharmacokinetic (PBPK) modeling of pharmaceutical nanoparticles. AAPS J. 2017 19 1 26 42 10.1208/s12248‑016‑0010‑3 27834047
   [Google Scholar]
7. Hartmanshenn C. Scherholz M. Androulakis I.P. Physiologically-based pharmacokinetic models: Approaches for enabling personalized medicine. J. Pharmacokinet. Pharmacodyn. 2016 43 5 481 504 10.1007/s10928‑016‑9492‑y 27647273
   [Google Scholar]
8. Vizirianakis I.S. Mystridis G.A. Avgoustakis K. Fatouros D.G. Spanakis M. Enabling personalized cancer medicine decisions: The challenging pharmacological approach of PBPK models for nanomedicine and pharmacogenomics. (Review)Oncol. Rep. 2016 35 4 1891 1904 10.3892/or.2016.4575 26781205
   [Google Scholar]
9. Jablonka L. Ashtikar M. Gao G.F. Thurn M. Modh H. Wang J.W. Preuß A. Scheglmann D. Albrecht V. Röder B. Wacker M.G. Predicting human pharmacokinetics of liposomal temoporfin using a hybrid in silico model. Eur. J. Pharm. Biopharm. 2020 149 121 134 10.1016/j.ejpb.2020.02.001 32035970
   [Google Scholar]
10. Talkington A.M. McSweeney M.D. Wessler T. Rath M.K. Li Z. Zhang T. Yuan H. Frank J.E. Forest M.G. Cao Y. Lai S.K. A PBPK model recapitulates early kinetics of anti-PEG antibody-mediated clearance of PEG-liposomes. J. Control. Release 2022 343 518 527 10.1016/j.jconrel.2022.01.022 35066099
    [Google Scholar]
11. Montanha M.C. Howarth A. Mohamed D.A. Loier E. Main L. Rösslein M. Delmaar C. Prina-Mello A. Siccardi M. A physiologically based pharmacokinetic model to predict pegylated liposomal doxorubicin disposition in rats and human. Drug Deliv. Transl. Res. 2022 12 9 2178 2186 10.1007/s13346‑022‑01175‑w 35551629
    [Google Scholar]
12. Badhan R. Khadke S. Perrie Y. Application of pharmacokinetics modelling to predict human exposure of a cationic liposomal subunit antigen vaccine system. Pharmaceutics 2017 9 4 57 10.3390/pharmaceutics9040057 29215597
    [Google Scholar]
13. Saldanha L. Langel Ü. Vale N. A physiologically based pharmacokinetic (PBPK) study to assess the adjuvanticity of three peptides in an oral vaccine. Pharmaceutics 2024 16 6 780 10.3390/pharmaceutics16060780 38931901
    [Google Scholar]
14. Lin Z. Chou W.C. Cheng Y.H. He C. Monteiro-Riviere N.A. Riviere J.E. Predicting nanoparticle delivery to tumors using machine learning and artificial intelligence approaches. Int. J. Nanomedicine 2022 17 1365 1379 10.2147/IJN.S344208 35360005
    [Google Scholar]
15. Chou W.C. Chen Q. Yuan L. Cheng Y.H. He C. Monteiro-Riviere N.A. Riviere J.E. Lin Z. An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice. J. Control. Release 2023 361 53 63 10.1016/j.jconrel.2023.07.040 37499908
    [Google Scholar]