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Nanotechnology in Cancer Drug Therapy: A Biocomputational Approach

  • Hermann B. Frieboes
  • John P. Sinek
  • Orhan Nalcioglu
  • John P. Fruehauf
  • Vittorio Cristini

Abstract

Although the clinical arsenal in treating cancer has been greatly extended in recent years with the application of new drugs and therapeutic modalities, the three basic approaches continue to be (in order of success) surgical resection, radiation, and chemotherapy. The latter treatment modality is primarily directed at metastatic cancer, which generally has a poor prognosis. A significant proportion of research investment is focused on improving the efficacy of chemotherapy, which is often the only hope in treating a cancer patient. Yet the challenges with chemotherapy are many. They include drug resistance by tumor cells, toxic effects on healthy tissue, inadequate targeting, and impaired transport to the tumor. Determination of proper drug dosage and scheduling, and optimal drug concentration can also be difficult. Finally, drug release kinetics at the tumor site is an important aspect of chemotherapy.

Keywords

Drug Release Tumor Vasculature Lump Parameter Model Drug Release Kinetic Higuchi Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    Alberts et al. Molecular Biology of the Cell. Taylor & Francis Group, New York, 2002.Google Scholar
  2. [2]
    R.P. Araujo and D.L.S. McElwain. A history of the study of solid tumour growth: the contribution of mathematical modeling. Bull. Math. Biol., June 2004. In press.Google Scholar
  3. [4]
    D. Barbolosi and A. Iliadis. Optimizing drug regimens in cancer chemotherapy: a simulation study using a PK-PD model. Comp. Biol. Med., 31:157–172, 2001.CrossRefGoogle Scholar
  4. [5]
    L.A. Bauer. Applied Clinical Pharmacokinetics. Mc-Graw Hill, New York. pp. 26–45, 2001.Google Scholar
  5. [6]
    L.T. Baxter and R.K. Jain. Pharmacokinetic analysis of the microscopic distribution of enzyme-conjugated antibodies and prodrugs: Comparison with experimental data. Brit. J. Canc., 73(4):447–456, 1996.Google Scholar
  6. [7]
    K.L. Black and N.S. Ningaraj. Modulation of brain tumor capillaries for enhanced drug delivery selectively to brain tumor. Cancer Control., 11(3):165–173, 2004.Google Scholar
  7. [8]
    G. Bonadonna, M. Zambetti, and P. Valagussa. Sequential or alternating doxorubicin and CMF regimens in breast cancer with more than three positive nodes: ten year results. J. Am. Med. Assoc., 273:542–547, 1995.CrossRefGoogle Scholar
  8. [9]
    I. Brigger, C. Dubernet, and P. Couvreur. Nanoparticles in cancer therapy and diagnosis. Adv. Drug Del. Reviews., 54(5):631–651, 2002.CrossRefGoogle Scholar
  9. [10]
    CCO Formulary, Liposomal Doxorubicin, October 2003.Google Scholar
  10. [11]
    M. Chaplain and A. Anderson. Mathematical modelling of tumour-induced angiogenesis: network growth and structure. Cancer Treat Res., 117:51–75, 2004.Google Scholar
  11. [12]
    M.L. Citron, D.A. Berry, C. Cirrincione, C. Hudis, E.P. Winer, W.J. Gradishar, N.E. Davidson, S. Martino, R. Livingston, J.N. Ingle, E.A. Perez, J. Carpenter, D. Hurd, J.F. Holland, B.L. Smith, C.I. Sartor, E.H. Leung, J. Abrams, R.L. Schilsky, H.B. Muss, and L. Norton. Randomized trial of dose-dense versus conventionally scheduled and sequential versus concurrent combination chemotherapy as postoperative adjuvant treatment of node-positive primary breast cancer: first report of intergroup trial C9741/cancer and leukemia group B trial 9741. J. Clin. Oncol., 21(8):1431–1439, 2003.CrossRefGoogle Scholar
  12. [13]
    P. Costa and J.M.S. Lobo. Modeling and comparison of dissolution profiles. Eur. J. Pharm. Sci., 13:123–133, 2001.CrossRefGoogle Scholar
  13. [14]
    V. Cristini, J. Lowengrub, and Q. Nie. Nonlinear simulation of tumor growth. J. Math. Biol., 46:191–224, 2003.zbMATHCrossRefMathSciNetGoogle Scholar
  14. [15]
    V. Cristini, H.B. Frieboes, R. Getemby, S. Corenta, M. Ferrari, and S. Sinek. Morphological instability and cancer inussion. Clin. Cancer Res., (in press) 2005.Google Scholar
  15. [16]
    Z. Cui, C.H. Hsu, and R.J. Mumper. Physical characterization and macrophage cell uptake of mannan-coated nanoparticles. Drug Dev. Ind. Pharm., 29(6):689–700, 2003.CrossRefGoogle Scholar
  16. [17]
    M.S. Dordal, A.C. Ho, M. Jackson-Stone, Y.F. Fu, C.L. Goolsby, and J.N. Winter. Flowcytometric assessment of the cellular pharmacokinetics of fluorescent drugs. Cytometry, 20:307–314, 1995.CrossRefGoogle Scholar
  17. [18]
    P. Elamanchili, M. Diwan, M. Cao, and J. Samuel. Characterization of poly(D,L-lactic-co-glycolic acid) based nanoparticulate system for enhanced delivery of antigens to dendritic cells. Vaccine, 22(19):2406–12, 2004.CrossRefGoogle Scholar
  18. [19]
    R.E. Eliaz, S. Nir, C. Marty, Jr. F.C. Szoka. Determination and modeling of kinetics of cancer cell killing by doxorubicin and doxorubicin encapsulated in targeted liposomes. Canc. Res., 64:711–718, 2004.CrossRefGoogle Scholar
  19. [21]
    N. Faisant, J. Siepmann, and J.P. Benoit. PLGA-based microparticles: elucidation of mechanisms and a new, simple mathematical model quantifying drug release. Eur. J. Pharm. Sci., 15(4):355–366, 2002.CrossRefGoogle Scholar
  20. [22]
    S.S. Feng and S. Chien. Chemotherapeutic engineering: application and further development of chemical engineering principles for chemotherapy of cancer and other diseases. Chem. Eng. Sci., 58:4087–4114, 2003.CrossRefGoogle Scholar
  21. [23]
    X. Gao, Y. Cui, R.M. Levenson, L.W. Chung, and S. Nie. In vivo cancer targeting and imaging with semiconductor quantum dots. Nat. Biotechnol., 22(8):969–76, 2004.CrossRefGoogle Scholar
  22. [24]
    B. Gompertz. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Philos. Trans. R Soc. Lond., 115:513–583, 1825.Google Scholar
  23. [25]
    R. Gref, P. Couvreur, G. Barratt, and E. Mysiakine. Surface-engineered nanoparticles for multiple ligand coupling. Biomaterials, 24(24):4529–4537, 2003.CrossRefGoogle Scholar
  24. [26]
    C.J. Gulledge and M.W. Dewhirst. Tumor oxygenation: a matter of supply and demand. Anticancer Res., 16:741–750, 1996.Google Scholar
  25. [27]
    H. Hashizume, P. Baluk, S. Morikawa, J.W. McLean, G. Thurston, S. Roberge, R.K. Jain, and D.M. McDonald. Openings between defective endothelial cells explain tumor vessel leakiness. Am. J. Pathol., 156(4):1363–1380, 2000.Google Scholar
  26. [28]
    T. Higuchi. Rate of release of medicaments from ointment bases containing drugs in suspension. J. Pharm. Sci., 50:874–875, 1961.CrossRefGoogle Scholar
  27. [29]
    S.K. Hobbs, W.L. Monsky, F. Yuan, W.G. Roberts, L. Griffith, V.P. Torchilin, and R.K. Jain. Regulation of transport pathways in tumor vessels: Role of tumor type and microenvironment. Proceedings of the National Academy of Sciences of the United States of America. vol. 95, pp. 4607–4612, 1998.CrossRefGoogle Scholar
  28. [30]
    M. Holz and A. Fahr. Compartment modeling. Adv. Drug Del. Rev., 48(2–3):249–264, 2001.CrossRefGoogle Scholar
  29. [31]
    M. Hombreiro-Perez, J. Siepmann, C. Zinutti, A. Lamprecht, N. Ubrich, M. Hoffman, R. Bodmeier, and P. Maincent. Non-degradable microparticles containing a hydrophilic and/or a lipophilic drug: preparation, characterization and drug release modeling. J. Control. Rel., 88(3):413–428, 2003.CrossRefGoogle Scholar
  30. [32]
    A. Ito, Y. Kuga, H. Honda, H. Kikkawa, A. Horiuchi, Y. Watanabe, and T. Kobayashi. Magnetite nanoparticleloaded anti-HER2 immunoliposomes for combination of antibody therapy with hyperthermia. Cancer Lett., 212(2):167–175, 2004.CrossRefGoogle Scholar
  31. [33]
    T.L. Jackson and H.M. Byrne. A mathematical model to study the effects of drug resistance and vasculature on the response of solid tumors to chemotherapy. Math. Biosci., 164:17–38, 2000.zbMATHCrossRefMathSciNetGoogle Scholar
  32. [34]
    R.K. Jain and L.E. Gerlowski. Extravascular transport in normal and tumor tissues. Crit. Rev. Oncol. Hematol., 5(2):115–170, 1986.Google Scholar
  33. [35]
    R.K. Jain. Determinants of tumor blood flow: a review. Canc. Res., 48:2641–2658, 1988.Google Scholar
  34. [36]
    R.K. Jain and L.T. Baxter. Mechanisms of heterogeneous distribution of monoclonal antibodies and other macromolecules in tumors: Significance of elevated interstitial pressure. Cancer Res., 48:7022–7032, 1988.Google Scholar
  35. [37]
    R.K. Jain. Physiological barriers to delivery of monoclonal antibodies and other macromolecules in tumors. Cancer Res. (Suppl.), 50:814s–819s, 1990.Google Scholar
  36. [38]
    R.K. Jain. Delivery of molecular medicine to solid tumors: Lessons from in vivo imaging of gene expression and function. J. Control. Rel., 74:7–25, 2001.CrossRefGoogle Scholar
  37. [39]
    R.K. Jain. Normalizing tumor vasculature with anti-angiogenic therapy: A new paradigm for combination therapy. Nature Med., 7(9):987–989, 2001.CrossRefGoogle Scholar
  38. [40]
    H. Kitano. Computational systems biology. Nature, 420:206–210, 2002.CrossRefGoogle Scholar
  39. [41]
    N. Kohler, G.E. Fryxell, and M. Zhang. A bifunctional poly(ethylene glycol) silane immobilized on metallic oxide-based nanoparticles for conjugation with cell targeting agents. J. Am. Chem. Soc., 126(23):7206–7211, 2004.CrossRefGoogle Scholar
  40. [42]
    K.Kosmidis, P. Argyrakis, and P. Macheras.Areappraisal of drug release laws using Monte Carlo simulations: the prevalence of the Weibull function. Pharm. Res., 20(7):988–995, 2003.CrossRefGoogle Scholar
  41. [43]
    J. Kreuter. Nanoparticles. In J. Kreuter, (ed.), Colloidal Drug Delivery Systems, Marcel Dekker, Inc. New York, Basel, Hong Kong, 1994.Google Scholar
  42. [44]
    A.K. Laird. Dynamics of tumor growth. Br. J. Canc., 18:490–502, 1964.Google Scholar
  43. [45]
    R. Langer and N.A. Peppas. Chemical and physical structure of polymers as carriers for controlled release of bioactive agents: A review. Rev. Macromol. Chem. Phys., C23:61–126, 1983.Google Scholar
  44. [46]
    R. Langer. Drug delivery and targeting. Nature, 392:6679, 5–10, 1998.Google Scholar
  45. [47]
    R. Langer. Biomaterials in drug delivery and tissue engineering: One laboratory’s experience. Acc. Chem. Res., 33:94–101, 2000.CrossRefGoogle Scholar
  46. [48]
    J. Lankelma. Tissue transport of anti-cancer drugs. Curr. Pharm. Des., 8:1987–1993, 2002.CrossRefGoogle Scholar
  47. [49]
    K.W. Leong, B.C. Brott and R. Langer. Bioerodible polyanhydrides as drug-carrier matrices. I: Characterization, degradation and release characteristics. J. Biomed. Mat. Res., 19:941–955, 1985.CrossRefGoogle Scholar
  48. [50]
    A. Mantovani. Biology of disease. Tumor-associated macrophages in neoplastic progression: a paradigm for the in vitro function of chemokines. Lab. Invest., 71:5–16, 1994.Google Scholar
  49. [51]
    U. Massing and S. Fuxius. Liposomal formulations of anticancer drugs: selectivity and effectiveness. Drug Resis. Updat., 3:171–177, 2000.CrossRefGoogle Scholar
  50. [52]
    S.R. McDougall, A.R.A. Anderson, and M.A.J. Chaplain et al. Mathematical modelling of flow through vascular networks: Implications for tumour-induced angiogenesis and chemotherapy strategies. Bull. Math. Biol., 64(4):673–702, 2002.CrossRefGoogle Scholar
  51. [53]
    K. Maruyama. In vivo targeting by liposomes. Biol. Pharm. Bull., 23(7):791–799, 2000.Google Scholar
  52. [54]
    L.D. Mayer and J.A. Shabbits. The role of liposomal drug delivery in molecular and pharmacological strategies to overcome multidrug resistance. Can. Metas. Rev., 20:87–93, 2001.CrossRefGoogle Scholar
  53. [55]
    B. Narasimhan. Mathematical models describing polymer dissolution: Consequences for drug delivery. Adv. Drug Del. Rev., 48(2–3):195–210, 2001.CrossRefGoogle Scholar
  54. [56]
    National Cancer Institute (NCI) website at http://press2.nci.nih.gov/sciencebehind/nanotech.Google Scholar
  55. [58]
    National Institute of Nanotechnology (NINT) of Canada at www.industrymailout.com/industry/do/news/view?id=9003&print=1.Google Scholar
  56. [59]
    P. Netti and R.K. Jain. Interstitial transport in solid tumors. In L. Preziosi (ed.), Cancer Modeling and Simulation, Chapman & Hall/CRC, Boca Raton, London, New York, Washington, pp. 62–65, 2003.Google Scholar
  57. [61]
    National Institutes of Health (NIH) website at http://press2.nci.nih.gov/sciencebehind/nanotech.Google Scholar
  58. [62]
    L. Norton. Implications of kinetic heterogeneity in clinical oncology. Semin. Oncol., 12:231–249, 1985.Google Scholar
  59. [63]
    L. Norton and R. Simon. The Norton-Simon hypothesis revisited. Cancer. Treat. Res., 70:163–169, 1986.Google Scholar
  60. [64]
    L. Norton. A Gompertzian model of human breast cancer growth. Cancer. Res., 48:7067–7071, 1988.Google Scholar
  61. [65]
    L. Norton. Theoretical concepts and the emerging role of taxanes in adjuvant therapy. Oncologist, 3(suppl):30–35, 2001.CrossRefGoogle Scholar
  62. [66]
    L. Norton. Karnofsky Memorial Lecture. ASCO, 2004.Google Scholar
  63. [67]
    T.P. Padera, B.R. Stoll, J.B. Tooredman, D. Capen, E. di Tomaso, and R.K. Jain. Cancer cells compress intratumour vessels. Nature, 427:695, 2004.CrossRefGoogle Scholar
  64. [68]
    R.S. Parker and F.J. Doyle III. Control-relevant modeling in drug delivery. Adv. Drug Del. Rev., 48(2–3):211–228, 2001.CrossRefGoogle Scholar
  65. [69]
    N.A. Peppas. Analysis of Fickian and non-Fickian drug release from polymers. Pharm. Acta. Helv., 60:110–111, 1985.Google Scholar
  66. [70]
    P.J. Polverini. How the extracellular matrix and macrophages contribute to angiogenesis-dependent diseases. Eur. J. Cancer, 32A(14):2430–2438, 1996.CrossRefGoogle Scholar
  67. [71]
    F. Quian, G.M. Saidel, D.M. Sutton, A. Exner, and J. Gao. Combined modeling and experimental approach for the development of dual-release polymer millirods. J. Control. Rel., 83:427–435, 2002.CrossRefGoogle Scholar
  68. [72]
    P. Sapra and T.M. Allen. Ligand-targeted liposomal anticancer drugs. Prog. Lipid Res., 42:439–462, 2003.CrossRefGoogle Scholar
  69. [73]
    M. Sarntinoranont, F. Rooney, and M. Ferrari. Interstitial stress and fluid pressure within a growing tumor. Ann. Biomed. Eng., 31:327–335, 2003.CrossRefGoogle Scholar
  70. [74]
    J. Siepmann and A. Goepferich. Mathematical modeling of bioerodible, polymeric drug delivery systems. Adv. Drug Del. Rev., 48(2–3):229–247, 2001.CrossRefGoogle Scholar
  71. [75]
    J. Siepmann, N. Faisant, and J.P. Benoit.Anewmathematical model quantifying drug release from bioerodible microparticles using Monte Carlo simulations. Pharm. Res. 19(12):1885–1893, 2002.CrossRefGoogle Scholar
  72. [76]
    J. Siepmann and N.A. Peppas. Modeling of drug release from delivery systems based on hydroxypropyl methylcellulose (HPMC). Adv. Drug Del. Rev., 48(2–3):139–157, 2003.Google Scholar
  73. [77]
    J. Siepmann, N. Faisant, J. Akiki, J. Richard, and J.P. Benoit. Effect of the size of biodegradable micro particles on drug release: experiment and theory. J. Control. Rel., 96(1):123–134, 2004.CrossRefGoogle Scholar
  74. [78]
    S. Simoes, J.N. Moreira, C. Fonseca, N. Duezguenes, and M. Pedroso de Lima. On the formulation of pH-sensitive liposomes with long circulation times. Adv. Drug Del. Rev., 56:947–965, 2004.CrossRefGoogle Scholar
  75. [79]
    J. Sinek, H.B. Frieboes, X. Zheng, and V. Cristini. Chemotherapy simulations demonstrate fundamental transport limitations involving nanoparticles. Biomed. Microdev., 6(4):297–309, 2004.CrossRefGoogle Scholar
  76. [80]
    T. Sonoda, H. Kobayashi, T. Kaku, T. Hirakawa, and H. Nakano. Expression of angiogenesis factors in monolayer culture, multicellular spheroid and in vivo transplanted tumor by human ovarian cancer cell lines. Cancer Lett., 196:229–237, 2003.CrossRefGoogle Scholar
  77. [81]
    M.Y. Su, A.A. Najafi, and O. Nalcioglu. Regional comparison of tumor vascularity and permeability parameters measured by albumin-Gd-DTPA and Gd-DTPA. Magn. Reson. Med., 34(3):402–411, 1995.CrossRefGoogle Scholar
  78. [82]
    M.Y. Su, A. Muhler, X. Lao, and O. Nalcioglu. Tumor characterization with dynamic contrast-enhanced MRI using MR contrast agents of various molecular weights. Magn. Reson. Med., 39(2):259–69, 1998.CrossRefGoogle Scholar
  79. [83]
    B.G. Sumpter, D.W. Noid, and M.D. Barnes. Recent developments in the formation, characterization, and simulation of micron and nano-scale droplets of amorphous polymer blends and semi-crystalline polymers. Polymer, 44(16):4389–4403, 2003.CrossRefGoogle Scholar
  80. [84]
    Trends in NanoTechnology (TNT) 2001 at nanoword.net/library/weekly/ aa091201a.htm.Google Scholar
  81. [85]
    P. Veng-Pedersen. Noncompartmentally-based pharmacokinetic modeling. Adv. Drug Del. Rev., 48(2–3):265–300, 2001.CrossRefGoogle Scholar
  82. [86]
    C.H. Wang, J. Li, and C.S. Teo et al. The delivery of BCNU to brain tumors. J. Control. Rel., 61(1–2):21–41, 1999.CrossRefGoogle Scholar
  83. [87]
    W. Weibull. A statistical distribution of wide applicability. J. Appl. Mechan., 18:293–297, 1951.zbMATHGoogle Scholar
  84. [88]
    C.P. Winsor. The Gompertz curve as a growth curve. Proc. Natl. Acad. Sci., 18:1–8, 1932.CrossRefGoogle Scholar
  85. [89]
    F. Yuan, M. Dellian, D. Fukumura, M. Leunig, D.A. Berk, V.P. Torchilin, and R.K. Jain.Vascular permeability in a human tumor xenograft: molecular size dependence and cutoff size. Cancer Res., 55(17):3752–3756, 1995.Google Scholar
  86. [90]
    H.S. Zaki, M.D. Jenkinson, D.G. Du Plessis, T. Smith, and N.G. Rainov. Vanishing contrast enhancement in malignant glioma after corticosteroid treatment. Acta Neurochir., 146:841–845, 2004.CrossRefGoogle Scholar
  87. [91]
    M. Zhang, Z. Yan, L.L. Chow, and C.H. Wan. Simulation of drug release from biodegradable polymeric microspheres with bulk and surface erosions. J. Pharm. Sci., 92(10):2040–2056, 2003.CrossRefGoogle Scholar
  88. [92]
    X. Zheng, S.M. Wise, and V. Cristini. Nonlinear simulation of tumor necrosis, neo-vascularization and tissue invasion via an adaptive finite-element/level-set method. Bull. Math. Bio., 67:211–259, 2005.CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Hermann B. Frieboes
    • 1
  • John P. Sinek
    • 2
  • Orhan Nalcioglu
    • 3
  • John P. Fruehauf
    • 4
  • Vittorio Cristini
    • 1
    • 2
    • 5
  1. 1.Department of Biomedical EngineeringUniversity of CaliforniaIrvine
  2. 2.Department of MathematicsUniversity of CaliforniaIrvine
  3. 3.Department of Radiological Sciences and Tu & Yuen Center for Functional Onco-ImagingUniversity of CaliforniaIrvine
  4. 4.Department of Medicine—Hematology/OncologyUniversity of CaliforniaIrvine
  5. 5.Department of Biomedical Engineering, REC 204University of CaliforniaIrvine

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