Home Macroscopic transport models for drugs and vehicles in cancer tissues
Article
Licensed
Unlicensed Requires Authentication

Macroscopic transport models for drugs and vehicles in cancer tissues

  • Álvaro González-Garcinuño , Antonio Tabernero and Eva Martín del Valle ORCID logo EMAIL logo
Published/Copyright: February 19, 2025
Become an author with De Gruyter Brill

Abstract

Modeling drug release in solid tumors is a convergence point between chemical engineering and medicine. Consequently, many studies have been conducted to unravel the mechanisms behind drug distribution after administration. In addition, several approaches have been explored, ranging from pharmacokinetic and pharmacodynamic models to microscopic transport models through macroscopic transport models. This chapter focuses on the latter, macroscopic transport models, and discusses how these models can predict the processes involved in drug delivery, in free form or vehicle transported. We start by presenting some of the differentiating physiological parameters in cancer tissues and then the main equations used for modeling, including fluid flow, mass transport, and cell uptake. Also, the use of some dimensionless parameters explaining the processes that control transportation will be examined. Lastly, the final section will explore the process employed for building geometries to simulate solid tumors, as well as current research being conducted on patient-specific simulations made using medical images.


Corresponding authors: Álvaro González Garcinuño and Eva Martín del Valle, Department of Chemical Engineering, University of Salamanca, Salamanca, Spain; and IBSAL, Institute for Biomedical Research of Salamanca, Salamanca, Spain, E-mail: (A. González-Garcinuño), (E. Martín del Valle)

Award Identifier / Grant number: PID2022-1405990B-I00

Acknowledgment

Authors want to acknowledge the funding support from spanish ministry of Science, PID2022-1405990B-I00. Authors also want to acknowledge professors David Bogle and Tomas Sosnowski for the reviewing process.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: A.G.G.: writing, conceptualization, image preparation, research. A.T.: review and editing, image preparation, conceptualization. E.M.V: funding, review and editing, management.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The author states no conflict of interest.

  6. Research funding: Spanish Ministry of Science: PID2022-1405990B-I00.

  7. Data availability: Not applicable.

References

1. Thun, MJ, DeLancey, JO, Center, MM, Jemal, A, Ward, EM. The global burden of cancer: priorities for prevention. Carcinogenesis 2010;31:100–10. https://doi.org/10.1093/carcin/bgp263.Search in Google Scholar PubMed PubMed Central

2. McAleer, S. A history of cancer and its treatment. A history of cancer and its treatment. Ulster Med J 2022;91:124–9.Search in Google Scholar

3. Motofei, IG. Biology of cancer, from cellular and molecular mehcanisms to developmental processes and adaptation. Semin Cancer Biol 2022;86:600–15. https://doi.org/10.1016/j.semcancer.2021.10.003.Search in Google Scholar PubMed

4. He, W, Li, Q, Lu, Y, Ju, D, Gu, Y, Zhao, K, et al.. Cancer treatment evolution from traditional methods to stem cells and gene therapy. Curr Gene Ther 2022;22:368–85. https://doi.org/10.2174/1566523221666211119110755.Search in Google Scholar PubMed

5. Nizzero, S, Ziemys, A, Ferrari, M. Transport barriers and Oncophysics in cancer treatment. Trends Cancer 2018;4:277–80. https://doi.org/10.1016/j.trecan.2018.02.008.Search in Google Scholar PubMed PubMed Central

6. Kenjeres, S. On recent progress in modelling and simulations of multi-scale transfer of mass, momentum and particles in biomedical applications. Flow, Turbul Combust 2016;96:837–60. https://doi.org/10.1007/s10494-015-9669-2.Search in Google Scholar

7. Zhan, W, Alamer, M, Xu, XY. Computational modelling of drug delivery to solid tumour: understanding the interplay between chemotherapeutics and biological system for optimized delivery systems. Adv Drug Del Rev 2018;132:81–103. https://doi.org/10.1016/j.addr.2018.07.013.Search in Google Scholar PubMed

8. Schuck, E, Bohnert, T, Chakravarty, A, Damian-Iordache, V, Gibson, C, Hsu, CP, et al.. Preclinical pharmacokinetic/pharmacodynamic modeling and simulation in the pharmaceutical industry: an IQ consortium survey examining the current landscape. AAPS J 2015;17:462–73. https://doi.org/10.1208/s12248-014-9716-2.Search in Google Scholar PubMed PubMed Central

9. Eigenmann, MJ, Frances, N, Lavé, T, Walz, AC. PKPD modeling of acquired resistance to anti-cancer drug treatment. J Pharmacokinet Pharmacodyn 2017;44:617–30. https://doi.org/10.1007/s10928-017-9553-x.Search in Google Scholar PubMed PubMed Central

10. Bertuzzi, A, Gandolfi, A. Cell kinetics in a tumour cord. J Theor Biol 2000;204:587–99. https://doi.org/10.1006/jtbi.2000.1079.Search in Google Scholar PubMed

11. Sefidgar, M, Soltani, M, Raahemifar, K, Sadeghi, M, Bazmara, H, Bazargan, M, et al.. Numerical modeling of drug delivery in a dynamic solid tumor microvasculature. Microvasc Res 2015;99:43–56. https://doi.org/10.1016/j.mvr.2015.02.007.Search in Google Scholar PubMed

12. Chauhan, VP, Stylianopoulos, T, Martin, JD, Popovic, Z, Chen, O, Kamoun, WS, et al.. Normalization of tumour blood vessels improves the delivery of nanomedicines in a size-dependent manner. Nat Nanotech 2012;7:383–8. https://doi.org/10.1038/nnano.2012.45.Search in Google Scholar PubMed PubMed Central

13. Rejniak, KA, Estrella, V, Chen, T, Cohen, AS, Lloyd, M, Morse, DL. The role of tumor tissue architecture in treatment penetration and efficacy: an integrative study. Front Oncol 2013;3:111–23. https://doi.org/10.3389/fonc.2013.00111.Search in Google Scholar PubMed PubMed Central

14. Penta, R, Ambrosi, D, Quarteroni, A. Multiscale homogenization for fluid and drug transport in vascularized malignant tissues. Math Model Methods Appl Sci 2015;25:79–108. https://doi.org/10.1142/s0218202515500037.Search in Google Scholar

15. Kashkooli, FM, Soltani, M, Momeni, MM. Computational modeling of drug delivery to solid tumors: a pilot study based on a real image. J Drug Deliv Sci Technol 2021;62:102347.10.1016/j.jddst.2021.102347Search in Google Scholar

16. Liu, ZG, Jiao, D. Necroptosis, tumor necrosis and tumorigenesis. Cell Stress 2020;4:1–8. https://doi.org/10.15698/cst2020.01.208.Search in Google Scholar PubMed PubMed Central

17. Kashkooli, FM, Soltani, M, Hamedi, MH. Drug delivery to solid tumors with heterogeneous microvascular networks: novel insights from image-based numerical modeling. Eur J Pharm Sci 2020;151. https://doi.org/10.1016/j.ejps.2020.105399.Search in Google Scholar PubMed

18. Kim, HG, Yu, AR, Lee, JJ, Lee, YJ, Lim, SM, Kim, JS. Measurement of tumor pressure and strategies of imaging tumor pressure for radioimmunotherapy. Nucl Med Mol Imag 2019;53:235–41. https://doi.org/10.1007/s13139-019-00598-7.Search in Google Scholar PubMed PubMed Central

19. Heldin, CH, Rubin, K, Pietras, K, Ostman, A. High interstitial fluid pressure - an obstacle in cancer therapy. Nat Rev Cancer 2004;4:806–13. https://doi.org/10.1038/nrc1456.Search in Google Scholar PubMed

20. Milosevic, M, Fyles, A, Hedley, D, Pintilie, M, Levin, W, Manchul, L, et al.. Interstitial fluid pressure predicts survival in patients with cervix cancer independent of clinical prognostic factors and tumor oxygen measurements. Cancer Res 2001;61:6400–5.Search in Google Scholar

21. Liu, LJ, Brown, SL, Ewing, JR, Ala, BD, Scheneider, KM, Schlesinger, M. Estimation of tumor insterstitial fluid pressure (TIFP) noninvasively. PLoS One 2016;11:e0140892. https://doi.org/10.1371/journal.pone.0140892.Search in Google Scholar PubMed PubMed Central

22. Netti, PA, Baxter, LT, Boucher, Y, Jain, RK, Skalak, R. Time-dependent behavior of interstitial fluid pressure in solid tumors: implications for drug delivery. Cancer Res 1995;20:20.Search in Google Scholar

23. Milosevic, MF, Fyles, AW, Hill, RP. The relationship between elevated interstitial fluid pressure and blood flow in tumors: a bioengineering analysis. Int J Rad Oncol Biol Phys 1999;43:1111–23. https://doi.org/10.1016/s0360-3016(98)00512-4.Search in Google Scholar PubMed

24. Lee, GH, Huang, SA, Aw, WY, Rathod, ML, Cho, C, Ligler, FS, et al.. Multilayer microfluidic platform for the study of luminal, transmural, and interstitial flow. Biofabrication 2022;14:025007. https://doi.org/10.1088/1758-5090/ac48e5.Search in Google Scholar PubMed PubMed Central

25. Siemann, DW. The unique characteristics of tumor vasculature and preclinical evidence for its selective disruption by tumor-vascular disrupting agents. Cancer Treat Rev 2011;37:63–74. https://doi.org/10.1016/j.ctrv.2010.05.001.Search in Google Scholar PubMed PubMed Central

26. Konerding, MA, Fait, E, Gaumann, A. A 3D microvascular architecture of pre-cancerous lesions and invasive carcinomas of the colon. Br J Cancer 2001;84:1354–62. https://doi.org/10.1054/bjoc.2001.1809.Search in Google Scholar PubMed PubMed Central

27. Brigger, I, Dubernet, C, Couvreur, P. Nanoparticles in cancer therapy and diagnosis. Adv Drug Deliv Rev 2002;54:631–51. https://doi.org/10.1016/s0169-409x(02)00044-3.Search in Google Scholar PubMed

28. Baik, AH. Hypoxia signaling and oxygen metabolism in cardio-oncology. J Mol Cell Cardiol 2022;165:64–75. https://doi.org/10.1016/j.yjmcc.2021.12.013.Search in Google Scholar PubMed

29. Al Tameemi, W, Dale, TP, Kh Al-Jumaily, RM, Forsyth, NR. Hypoxia-modified cancer cell metabolism. Front Cell Dev Biol 2019;7:4. https://doi.org/10.3389/fcell.2019.00004.Search in Google Scholar PubMed PubMed Central

30. Tu, J, Tu, K, Xu, H, Wang, L, Yuan, X, Qin, X, et al.. Improving tumor hypoxia and radiotherapy resistance via in situ nitric oxide release strategy. Eur J Pharm Biopharm 2020;150:96–107. https://doi.org/10.1016/j.ejpb.2020.03.003.Search in Google Scholar PubMed

31. Chen, YH, Peng, CC, Cheng, YJ, Wu, JG, Tung, YC. Generation of nitric oxide gradients in microfluidic devices for cell culture using spatially controlled chemical reactions. Biomicrofluidics 2013;7:064104. https://doi.org/10.1063/1.4829775.Search in Google Scholar PubMed PubMed Central

32. Padera, T, Stoll, B, Tooredman, J. Cancer cells compress intratumour vessels. Nature 2004;427:695. https://doi.org/10.1038/427695a.Search in Google Scholar PubMed

33. Kataru, RP, Ly, CL, Shin, J, Park, HJ, Baik, JE, Rehal, S, et al.. Tumor lymphatic function regulates tumor inflammatory and immunosuppressive microenvironments. Cancer Immunol Res 2019;7:1345–58. https://doi.org/10.1158/2326-6066.cir-18-0337.Search in Google Scholar PubMed PubMed Central

34. Alitalo, A, Detmar, M. Interaction of tumor cells and lymphatic vessels in cancer progression. Oncogene 2012;31:4499–508. https://doi.org/10.1038/onc.2011.602.Search in Google Scholar PubMed

35. Cote, B, Rao, D, Alany, RG, Kwon, GS, Alani, AWG. Lymphatic changes in cancer and drug delivery to the lymphatics in solid tumors. Adv Drug Deliv Rev 2019;144:16–34. https://doi.org/10.1016/j.addr.2019.08.009.Search in Google Scholar PubMed

36. Bagby, TR, Cai, S, Duan, S, Thahi, S, Aires, DJ, Forrest, L. Impact of molecular weight on lymphatic drainage of a biopolymer-based imaging agent. Pharmaceutics 2012;4:276–95. https://doi.org/10.3390/pharmaceutics4020276.Search in Google Scholar PubMed PubMed Central

37. Oussoren, C, Zuidema, J, Crommelin, DJA, Storm, G. Lymphatic uptake and biodistribution of liposomes after subcutaneous injection. Biochim Biophys Acta Biomembr 1997;1328:261–72. https://doi.org/10.1016/s0005-2736(97)00122-3.Search in Google Scholar PubMed

38. Xie, Y, Bagby, TR, Cohen, MS, Forrest, ML. Drug delivery to the lymphatic system: importance in future cancer diagnosis and therapies. Expet Opin Drug Deliv 2009;6:785–92. https://doi.org/10.1517/17425240903085128.Search in Google Scholar PubMed PubMed Central

39. Soltani, M, Chen, P. Numerical modeling of fluid flow in solid tumors. PLoS One 2011;6:e20344. https://doi.org/10.1371/journal.pone.0020344.Search in Google Scholar PubMed PubMed Central

40. Baxter, LT, Jain, RK. Transport of fluid and macromolecules in tumors I. Role of interstitial pressure and convection. Microvasc Res 1989;37:77–104. https://doi.org/10.1016/0026-2862(89)90074-5.Search in Google Scholar PubMed

41. Forster, JC, Harriss-Phillips, WM, Douglass, MJJ, Bezak, E. A review of the development of tumor vasculature and its effects on the tumor microenvironment. Hypoxia (Auckl) 2017;5:21–32. https://doi.org/10.2147/hp.s133231.Search in Google Scholar PubMed PubMed Central

42. Jafarnejad, M, Ismail, AZ, Duarte, D, Vyas, C, Ghahramani, A, Zawieja, DC, et al.. Quantification of the whole lymph node vasculature based on tomography of the vessel corrosion casts. Sci Rep 2019;9:13380. https://doi.org/10.1038/s41598-019-49055-7.Search in Google Scholar PubMed PubMed Central

43. Shore, AG. Capillaroscopy and the measurement of capillary pressure. Br J Clin Pharmacol 2000;50:501–13. https://doi.org/10.1046/j.1365-2125.2000.00278.x.Search in Google Scholar PubMed PubMed Central

44. Voutouri, C, Stylianopoulos, T. Evolution of osmotic pressure in solid tumors. J Biomech 2014;47:3441–7. https://doi.org/10.1016/j.jbiomech.2014.09.019.Search in Google Scholar PubMed PubMed Central

45. Rasouli, SS, Jolma, IW, Friis, HA. Impact of spatially varying hydraulic conductivities on tumor interstitial fluid pressure distribution. Inform Med Unlock 2019;16. https://doi.org/10.1016/j.imu.2019.100175.Search in Google Scholar

46. Graczyk, KM, Matyka, M. Predicting porosity, permeability, and tortuosity of porous media from images by deep learning. Sci Rep 2020;10:21488. https://doi.org/10.1038/s41598-020-78415-x.Search in Google Scholar PubMed PubMed Central

47. Koponen, A, Kataja, M, Timonen, J. Permeability and effective porosity of porous media. Phys Rev E 1997;56. https://doi.org/10.1103/physreve.56.3319.Search in Google Scholar

48. Majumder, S, Islam, MR, Righetti, R. Non-invasive imaging of interstitial fluid transport parameters in solid tumors in vivo. Sci Rep 2023;13:7132. https://doi.org/10.1038/s41598-023-33651-9.Search in Google Scholar PubMed PubMed Central

49. Ramazanilar, M, Mojra, A. Characterization of breast tissue permeability for detection of vascular breast tumors: an in vitro study. Mat Sci Eng C 2020;107. https://doi.org/10.1016/j.msec.2019.110222.Search in Google Scholar PubMed

50. González-Garcinuño, A, Tabernero, A, Nieto, C, Martín del Valle, E, Kenjeres, S. Mutiphysics simulation of liposome release from hydrogels for cavity filling following patient-specific breast tumor surgery. Eur J Pharm Sci 2025;204. https://doi.org/10.1016/j.ejps.2024.106966.Search in Google Scholar PubMed

51. Salavati, H, Pullens, P, Debbaut, C, Ceelen, W. Hydraulic conductivity of human cancer tissue: a hybrid study. Bioeng Trans Med 2023;9:e10617. https://doi.org/10.1002/btm2.10617.Search in Google Scholar PubMed PubMed Central

52. Yang, Y, Zhan, W. Role of tissue hydraulic permeability in convection-enhanced delivery of nanoparticle-encapsulated chemotherapy drugs to brain tumour. Pharm Res (N Y) 2022;39:877–92. https://doi.org/10.1007/s11095-022-03261-7.Search in Google Scholar PubMed PubMed Central

53. Stapleton, S, Milosevic, M, Allen, C, Zheng, J, Dunne, M, Yeung, I, et al.. A mathematical model for enhanced permeability retention effect for liposome transport in solid tumors. PLoS One 2013;8:e81157. https://doi.org/10.1371/journal.pone.0081157.Search in Google Scholar PubMed PubMed Central

54. Duzgunes, N, Nir, S. Mechanisms and kinetics of liposome-cell interactions. Adv Drug Del Rev 1999;40:3–18. https://doi.org/10.1016/s0169-409x(99)00037-x.Search in Google Scholar PubMed

55. Vainsht, I, Roskos, LK, Cheng, J, Sleeman, MA, Wang, B, Liang, M. Quantitative measurement of the target-mediated internalization kinetics of biopharmaceuticals. Pharm Res (N Y) 2015;32:286–99. https://doi.org/10.1007/s11095-014-1462-8.Search in Google Scholar PubMed PubMed Central

56. Zhan, W, Wang, CH. Convection enhanced delivery of chemotherapeutic drugs into brain tumour. J Contr Release 2018;271:74–87. https://doi.org/10.1016/j.jconrel.2017.12.020.Search in Google Scholar PubMed

57. Soltani, M, Chen, P. Effect of tumor shape and size on drug delivery to solid tumors. J Biol Eng 2012;6:4. https://doi.org/10.1186/1754-1611-6-4.Search in Google Scholar PubMed PubMed Central

58. de Monte, F, Pontrelli, G, Becker, S. Chapter 3: drug release in biological tissues. Transport in Biological Media 2013:59–118.10.1016/B978-0-12-415824-5.00003-5Search in Google Scholar

59. Yadav, KS, Dalal, DC. Penetration and distribution efficacy of chemotherapeutic drugs in biological tissues: a computational investigation. Mathem Comp Simul 2023;214:152–71. https://doi.org/10.1016/j.matcom.2023.06.025.Search in Google Scholar

60. Trucillo, P. Drug carriers: a review on the most used mathematical models for drug release. Processes 2022;10:1094. https://doi.org/10.3390/pr10061094.Search in Google Scholar

61. Paul, DR. Elaborations on the Higuchi model for drug delivery. Int. J. Pharm. 2011;418:13–17. https://doi.org/10.1016/j.ijpharm.2010.10.037.Search in Google Scholar PubMed

62. Peppas, NA. A model of dissolution-controlled solute release from porous drug delivery polymeric systems. J Biomed Mater Res 1983;17:1079–87. https://doi.org/10.1002/jbm.820170615.Search in Google Scholar PubMed

63. Peppas, NA, Sahlin, JJ. A simple equation for the description of solute release. III. Coupling of diffusion and relaxation. Int. J. Pharm. 1989;57:169–72. https://doi.org/10.1016/0378-5173(89)90306-2.Search in Google Scholar

64. Costa, P, Sousa Lobo, JM. Modeling and comparison of dissolution profiles. Eur J Pharm Sci 2001;13:123–33. https://doi.org/10.1016/s0928-0987(01)00095-1.Search in Google Scholar PubMed

65. Corrigan, OI, Li, X. Quantifying drug release from PLGA nanoparticulates. Eur. J. Pharm. Sci. 2009;37:477–85. https://doi.org/10.1016/j.ejps.2009.04.004.Search in Google Scholar PubMed

66. Hopfenberg, HB. Membranes. In: Polymers in Medicine and Surgery. Boston, MA, USA: Springer; 1975:99–107 pp.10.1007/978-1-4684-7744-3_7Search in Google Scholar

67. Eikenberry, S. A tumor cord model for Doxorubicin delivery and dose optimization in solid tumors. Theor Biol Med Model 2009;6:16. https://doi.org/10.1186/1742-4682-6-16.Search in Google Scholar PubMed PubMed Central

68. Di, J, Hou, P, Corpstein, CD, Wu, K, Xu, Y, Li, T. Multiphysics modelling and simulation of local transport and absorption kinetics of intramuscularly injected lipid nanoparticles. J Contr Release 2023;359:234–43. https://doi.org/10.1016/j.jconrel.2023.05.048.Search in Google Scholar PubMed

69. Zheng, F, Hou, P, Corpstein, CD, Xing, L, Li, T. Multiphysics modeling and simulation of subcutaneous injection and absorption of biotherapeutics: model development. Pharm Res (N Y) 2021;38:607–24. https://doi.org/10.1007/s11095-021-03032-w.Search in Google Scholar PubMed

70. Steuperaert, M, D’Urso Labate, GF, Debbaut, C, De Wever, O, Vanhove, C, Ceelen, W, et al.. Mathematical modelling of intraperitoneal drug delivery: simulation of drug distribution in a single tumor nodule. Drug Deliv 2017;24:491–501.10.1080/10717544.2016.1269848Search in Google Scholar PubMed PubMed Central

71. Bhandari, A, Gu, B, Kashkooli, FM, Zhan, W. Image-based predictive modelling frameworks for personalized drug delivery in cancer therapy. J Contr Release 2024;370:721–46. https://doi.org/10.1016/j.jconrel.2024.05.004.Search in Google Scholar PubMed

72. Adabbo, G, Andreozzi, A, Iasiello, M, Vanoli, GP. Numerical evaluation of heat-triggered drug release via thermos-sensitive liposomes: a comparison between image-based vascularized tumor and volume-averaged porous media models. Int J Heat Mass Transfer 2024;220. https://doi.org/10.1016/j.ijheatmasstransfer.2023.124942.Search in Google Scholar

73. Jarrett, AM, Hormuth, DA, Wu, C, Kazerouni, AS, Erkut, DA, Virostko, J, et al.. Evaluating patient-specific neoadjuvant regimens for breast cancer via mathematical model constrained by quantitative magnetic resonance imaging data. Neoplasia 2020;22:820–30. https://doi.org/10.1016/j.neo.2020.10.011.Search in Google Scholar PubMed PubMed Central

74. Linninger, AA, Somayaji, MR, Mekarski, M, Zhang, L. Prediction of convection-enhanced drug delivery to the human brain. J Theor Biol 2008;250:125–38. https://doi.org/10.1016/j.jtbi.2007.09.009.Search in Google Scholar PubMed

75. May, CP, Kolokotroni, E, Stamatakos, GS, Büchler, P. Coupling biomechanics to a cellular level model: an approach to patient-specific image driven multi-scale and Multiphysics tumor simulation. Prog Biophys Mol Biol 2011;107:193–9. https://doi.org/10.1016/j.pbiomolbio.2011.06.007.Search in Google Scholar PubMed

76. Bhandari, A, Bansal, A, Singh, A, Sinha, N. Perfusion kinetics in human brain tumor with DCE-MRI derived model and CFD analysis. J Biomech 2017;59:80–9. https://doi.org/10.1016/j.jbiomech.2017.05.017.Search in Google Scholar PubMed

77. Bhandari, A, Bansal, A, Singh, A, Gupta, RK, Sinha, N. Comparison of transport of chemotherapeutic drug in voxelized heterogeneous model of human brain tumor. Microvasc Res 2019;124:76–90. https://doi.org/10.1016/j.mvr.2019.03.003.Search in Google Scholar PubMed

78. Zhan, W, Gedroyc, W, Xu, XY. Effect of heterogeneous microvasculature distribution on drug delivery to solid tumour. J Phys D Appl Phys 2014;47. https://doi.org/10.1088/0022-3727/47/47/475401.Search in Google Scholar

79. Wu, C, Hormuth, DA, Lorenzo, G, Jarrett, AM, Pineda, F, Howard, FM, et al.. Towards patient-specific optimization of neoadjuvant treatment protocols for breast cancer based on image-guided fluid dynamics. IEEE Trans Biomed Eng 2022;69:3334–44. https://doi.org/10.1109/tbme.2022.3168402.Search in Google Scholar

80. Bhandari, A, Jaiswal, K, Singh, A, Zhan, W. Convection-enhanced delivery of antiangiogenic drugs and liposomal cytotoxic drugs to heterogeneous brain tumor for combination therapy. Cancers 2022;14:4177. https://doi.org/10.3390/cancers14174177.Search in Google Scholar PubMed PubMed Central

81. Vidotto, M, Pederzani, M, Castellano, A, Pieri, V, Falini, A, Dini, D, et al.. Integrating diffusion tensor imaging and neurite orientation dispersion and density imaging to improve the predictive capabilities of CED models. Ann Biomed Eng 2021;49:689–702. https://doi.org/10.1007/s10439-020-02598-7.Search in Google Scholar PubMed PubMed Central

82. Caddy, G, Stebbing, J, Wakefield, G, Adair, M, Xu, XY. Multiscale modelling of nanoparticle distribution in a realistic tumour geometry following local injection. Cancers 2022;14:5729. https://doi.org/10.3390/cancers14235729.Search in Google Scholar PubMed PubMed Central

83. Zhan, W, Rodriguez y Baena, F, Dini, D. Effect of tissue permeability and drug diffusion anisotropy on convection-enhanced delivery. Drug Deliv 2019;26:773–81. https://doi.org/10.1080/10717544.2019.1639844.Search in Google Scholar PubMed PubMed Central

Received: 2024-09-30
Accepted: 2024-12-05
Published Online: 2025-02-19

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 13.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/psr-2024-0059/html
Scroll to top button