Integrated PK-PD and agent-based modeling in oncology
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  • 作者:Zhihui Wang (1)
    Joseph D. Butner (2)
    Vittorio Cristini (1) (2) (3)
    Thomas S. Deisboeck (4)

    1. Department of Pathology
    ; University of New Mexico ; Albuquerque ; NM ; 87131 ; USA
    2. Department of Chemical Engineering and Center for Biomedical Engineering
    ; University of New Mexico ; Albuquerque ; NM ; 87131 ; USA
    3. Department of Mathematics
    ; Faculty of Science ; King Abdulaziz University ; Jeddah ; 21589 ; Saudi Arabia
    4. Department of Radiology
    ; Massachusetts General Hospital and Harvard Medical School ; Charlestown ; MA ; 02129 ; USA
  • 关键词:Chemotherapy ; Computer simulation ; Mathematical modeling ; Multiscale ; Tumor growth and invasion ; Translational research
  • 刊名:Journal of Pharmacokinetics and Pharmacodynamics
  • 出版年:2015
  • 出版时间:April 2015
  • 年:2015
  • 卷:42
  • 期:2
  • 页码:179-189
  • 全文大小:2,985 KB
  • 参考文献:1. Lowengrub, JS, Frieboes, HB, Jin, F, Chuang, YL, Li, X, Macklin, P, Wise, SM, Cristini, V (2010) Nonlinear modelling of cancer: bridging the gap between cells and tumours. Nonlinearity 23: pp. R1-R91 CrossRef
    2. Ventura, B, Lemerle, C, Michalodimitrakis, K, Serrano, L (2006) From in vivo to in silico biology and back. Nature 443: pp. 527-533 CrossRef
    3. Marx, V (2013) Biology: the big challenges of big data. Nature 498: pp. 255-260 CrossRef
    4. Wang, Z, Deisboeck, TS (2014) Mathematical modeling in cancer drug discovery. Drug Discov Today 19: pp. 145-150 CrossRef
    5. Ballesta, A, Clairambault, J, Dulong, S, Levi, F (2011) Theoretical optimization of Irinotecan-based anticancer strategies in the case of drug-induced efflux. Appl Math Lett 24: pp. 1251-1256 CrossRef
    6. Wong, H, Choo, EF, Alicke, B, Ding, X, La, H, McNamara, E, Theil, FP, Tibbitts, J, Friedman, LS, Hop, CE, Gould, SE (2012) Antitumor activity of targeted and cytotoxic agents in murine subcutaneous tumor models correlates with clinical response. Clin Cancer Res 18: pp. 3846-3855 CrossRef
    7. Wang, S, Guo, P, Wang, X, Zhou, Q, Gallo, JM (2008) Preclinical pharmacokinetic/pharmacodynamic models of gefitinib and the design of equivalent dosing regimens in EGFR wild-type and mutant tumor models. Mol Cancer Ther 7: pp. 407-417 CrossRef
    8. Walker, DC, Southgate, J (2009) The virtual cell鈥攁 candidate co-ordinator for 鈥榤iddle-out鈥?modelling of biological systems. Brief Bioinform 10: pp. 450-461 CrossRef
    9. Byrne, H, Drasdo, D (2009) Individual-based and continuum models of growing cell populations: a comparison. J Math Biol 58: pp. 657-687 CrossRef
    10. Wang, Z, Birch, CM, Sagotsky, J, Deisboeck, TS (2009) Cross-scale, cross-pathway evaluation using an agent-based non-small cell lung cancer model. Bioinformatics (Oxford, England) 25: pp. 2389-2396 CrossRef
    11. Gatenby, RA, Smallbone, K, Maini, PK, Rose, F, Averill, J, Nagle, RB, Worrall, L, Gillies, RJ (2007) Cellular adaptations to hypoxia and acidosis during somatic evolution of breast cancer. Br J Cancer 97: pp. 646-653 CrossRef
    12. Enderling, H, Anderson, AR, Chaplain, MA, Beheshti, A, Hlatky, L, Hahnfeldt, P (2009) Paradoxical dependencies of tumor dormancy and progression on basic cell kinetics. Cancer Res 69: pp. 8814-8821 CrossRef
    13. Wang, Z, Zhang, L, Sagotsky, J, Deisboeck, TS (2007) Simulating non-small cell lung cancer with a multiscale agent-based model. Theor Biol Med Model 4: pp. 50 CrossRef
    14. Rejniak, KA, Anderson, AR (2011) Hybrid models of tumor growth. Wiley interdisciplinary reviews. Syst Biol Med 3: pp. 115-125
    15. Anderson, AR, Chaplain, MA (1998) Continuous and discrete mathematical models of tumor-induced angiogenesis. Bull Math Biol 60: pp. 857-899 CrossRef
    16. Schnell, S, Grima, R, Maini, PK (2007) Multiscale modeling in biology鈥擭ew insights into cancer illustrate how mathematical tools are enhancing the understanding of life from the smallest scale to the grandest. Am Sci 95: pp. 134-142 CrossRef
    17. Deisboeck, TS, Wang, Z, Macklin, P, Cristini, V (2011) Multiscale cancer modeling. Annu Rev Biomed Eng 13: pp. 127-155 CrossRef
    18. Cristini, V, Lowengrub, J (2010) Multiscale modeling of cancer: an integrated experimental and mathematical modeling approach. Cambridge University Press, Cambridge CrossRef
    19. Anderson, AR, Chaplain, MA, Rejniak, KA (2007) Single-cell-based models in biology and medicine. Mathematics and biosciences in interaction. Springer, Basel CrossRef
    20. Koch, G, Walz, A, Lahu, G, Schropp, J (2009) Modeling of tumor growth and anticancer effects of combination therapy. J Pharmacokinet Pharmacodyn 36: pp. 179-197 CrossRef
    21. Rocchetti, M, Del Bene, F, Germani, M, Fiorentini, F, Poggesi, I, Pesenti, E, Magni, P, Nicolao, G (2009) Testing additivity of anticancer agents in pre-clinical studies: a PK/PD modelling approach. Eur J Cancer (Oxford, England : 1990) 45: pp. 3336-3346 CrossRef
    22. Bissell, MJ, Radisky, D (2001) Putting tumours in context. Nat Rev Cancer 1: pp. 46-54 CrossRef
    23. Jain, RK (1990) Physiological barriers to delivery of monoclonal antibodies and other macromolecules in tumors. Cancer Res 50: pp. 814s-819s
    24. Enderling, H, Hlatky, L, Hahnfeldt, P (2009) Migration rules: tumours are conglomerates of self-metastases. Br J Cancer 100: pp. 1917-1925 CrossRef
    25. Frieboes, HB, Jin, F, Chuang, YL, Wise, SM, Lowengrub, JS, Cristini, V (2010) Three-dimensional multispecies nonlinear tumor growth-II: tumor invasion and angiogenesis. J Theor Biol 264: pp. 1254-1278 CrossRef
    26. Gatenby, RA, Silva, AS, Gillies, RJ, Frieden, BR (2009) Adaptive therapy. Cancer Res 69: pp. 4894-4903 CrossRef
    27. Gerlee, P, Anderson, AR (2009) Evolution of cell motility in an individual-based model of tumour growth. J Theor Biol 259: pp. 67-83 CrossRef
    28. Macklin, P, Edgerton, ME, Thompson, AM, Cristini, V (2012) Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): from microscopic measurements to macroscopic predictions of clinical progression. J Theor Biol 301: pp. 122-140 CrossRef
    29. Powathil, GG, Gordon, KE, Hill, LA, Chaplain, MA (2012) Modelling the effects of cell-cycle heterogeneity on the response of a solid tumour to chemotherapy: biological insights from a hybrid multiscale cellular automaton model. J Theor Biol 308: pp. 1-19 CrossRef
    30. Silva, AS, Gatenby, RA (2010) A theoretical quantitative model for evolution of cancer chemotherapy resistance. Biol Direct 5: pp. 25 CrossRef
    31. Smallbone, K, Gatenby, RA, Gillies, RJ, Maini, PK, Gavaghan, DJ (2007) Metabolic changes during carcinogenesis: potential impact on invasiveness. J Theor Biol 244: pp. 703-713 CrossRef
    32. Wang, Z, Birch, CM, Deisboeck, TS (2008) Cross-scale sensitivity analysis of a non-small cell lung cancer model: linking molecular signaling properties to cellular behavior. Biol Syst 92: pp. 249-258
    33. Wang, Z, Bordas, V, Deisboeck, TS (2011) Identification of critical molecular components in a multiscale cancer model based on the integration of Monte Carlo, resampling, and ANOVA. Front Physiol 2: pp. 35 CrossRef
    34. Wang, Z, Bordas, V, Sagotsky, J, Deisboeck, TS (2012) Identifying therapeutic targets in a combined EGFR-TGFbetaR signalling cascade using a multiscale agent-based cancer model. Math Med Biol 29: pp. 95-108 CrossRef
    35. Wang, Z, Deisboeck, TS, Cristini, V (2014) Development of a sampling-based global sensitivity analysis workflow for multiscale computational cancer models. IET Syst Biol 8: pp. 191-197 CrossRef
    36. Alarcon, T, Byrne, HM, Maini, PK (2003) A cellular automaton model for tumour growth in inhomogeneous environment. J Theor Biol 225: pp. 257-274 CrossRef
    37. Wang, Z, Deisboeck, TS (2008) Computational modeling of brain tumors: discrete, continuum or hybrid?. Sci Model Simul 15: pp. 381-393 CrossRef
    38. Gerlee, P, Anderson, AR (2007) An evolutionary hybrid cellular automaton model of solid tumour growth. J Theor Biol 246: pp. 583-603 CrossRef
    39. Marcu, L, Bezak, E, Olver, I, Doorn, T (2005) Tumour resistance to cisplatin: a modelling approach. Phys Med Biol 50: pp. 93-102 CrossRef
    40. Marcu, L, Doorn, T, Zavgorodni, S, Olver, I (2002) Growth of a virtual tumour using probabilistic methods of cell generation. Aust Phys Eng Sci Med 25: pp. 155-161 CrossRef
    41. Marias, K, Dionysiou, D, Sakkalis, V, Graf, N, Bohle, RM, Coveney, PV, Wan, S, Folarin, A, Buchler, P, Reyes, M, Clapworthy, G, Liu, E, Sabczynski, J, Bily, T, Roniotis, A, Tsiknakis, M, Kolokotroni, E, Giatili, S, Veith, C, Messe, E, Stenzhorn, H, Kim, YJ, Zasada, S, Haidar, AN, May, C, Bauer, S, Wang, T, Zhao, Y, Karasek, M, Grewer, R, Franz, A, Stamatakos, G (2011) Clinically driven design of multi-scale cancer models: the ContraCancrum project paradigm. Interface Focus 1: pp. 450-461 CrossRef
    42. Kurbatova, P, Bernard, S, Bessonov, N, Crauste, F, Demin, I, Dumontet, C, Fischer, S, Volpert, V (2011) Hybrid model of erythropoiesis and leukemia treatment with cytosine arabinoside. SIAM J Appl Math 71: pp. 2246-2268 CrossRef
    43. Sieniek, M, Gurgul, P, Ko艂odziejczyk, P, Paszy艅ski, M (2010) Agent-based parallel system for numerical computations. Proc Comput Sci 1: pp. 1971-1981 CrossRef
    44. Neilson, MP, Mackenzie, JA, Webb, SD, Insall, RH (2011) Modeling cell movement and chemotaxis using pseudopod-based feedback. SIAM J Sci Comput 33: pp. 1035-1057 CrossRef
    45. Zahedmanesh, H, Lally, C (2012) A multiscale mechanobiological modelling framework using agent-based models and finite element analysis: application to vascular tissue engineering. Biomech Model Mechanobiol 11: pp. 363-377 CrossRef
    46. Plank, MJ, Simpson, MJ (2012) Models of collective cell behaviour with crowding effects: comparing lattice-based and lattice-free approaches. J R Soc, Interface 9: pp. 2983-2996 CrossRef
    47. Johnston, ST, Simpson, MJ, Plank, MJ (2013) Lattice-free descriptions of collective motion with crowding and adhesion. Phys Rev E 88: pp. 062720 CrossRef
    48. Macklin, P, Edgerton, ME, Lowengrub, JS, Cristini, V Discrete cell modeling. In: Cristini, V, Lowengrub, JS eds. (2010) Multiscale modeling of cancer: an integrated experimental and mathematical modeling approach. Cambridge University Press, Cambridge, pp. 88-122 CrossRef
    49. Abbott, RG, Forrest, S, Pienta, KJ (2006) Simulating the hallmarks of cancer. Artif Life 12: pp. 617-634 CrossRef
    50. Zhang, LS, Strouthos, CG, Wang, Z, Deisboeck, TS (2009) Simulating brain tumor heterogeneity with a multiscale agent-based model: linking molecular signatures, phenotypes and expansion rate. Math Comput Model 49: pp. 307-319 CrossRef
    51. Railsback, SF, Lytinen, SL, Jackson, SK (2006) Agent-based simulation platforms: review and development recommendations. SIMULATION 82: pp. 609-623 CrossRef
    52. Meibohm, B, Derendorf, H (1997) Basic concepts of pharmacokinetic/pharmacodynamic (PK/PD) modelling. Int J Clin Pharmacol Ther 35: pp. 401-413
    53. Wada, R, Erickson, HK, Lewis Phillips, GD, Provenzano, CA, Leipold, DD, Mai, E, Johnson, H, Tibbitts, J (2014) Mechanistic pharmacokinetic/pharmacodynamic modeling of in vivo tumor uptake, catabolism, and tumor response of trastuzumab maytansinoid conjugates. Cancer Chemother Pharmacol 74: pp. 969-980 CrossRef
    54. Shah, DK, Haddish-Berhane, N, Betts, A (2012) Bench to bedside translation of antibody drug conjugates using a multiscale mechanistic PK/PD model: a case study with brentuximab-vedotin. J Pharmacokinet Pharmacodyn 39: pp. 643-659 CrossRef
    55. Tate, SC, Cai, S, Ajamie, RT, Burke, T, Beckmann, RP, Chan, EM, Dios, A, Wishart, GN, Gelbert, LM, Cronier, DM (2014) Semi-mechanistic pharmacokinetic/pharmacodynamic modeling of the antitumor activity of LY2835219, a new cyclin-dependent kinase 4/6 inhibitor, in mice bearing human tumor xenografts. Clin Cancer Res 20: pp. 3763-3774 CrossRef
    56. Zhou, Q, Gallo, JM (2011) The pharmacokinetic/pharmacodynamic pipeline: translating anticancer drug pharmacology to the clinic. AAPS J 13: pp. 111-120 CrossRef
    57. Agoram, BM, Martin, SW, Graaf, PH (2007) The role of mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modelling in translational research of biologics. Drug Discov Today 12: pp. 1018-1024 CrossRef
    58. Chabner BA, Longo DL (2010) Cancer chemotherapy and biotherapy: principles and practice. Cancer chemotherapy and biotherapy, 5th edn. Lippincott Williams & Wilkins, Philadelphia
    59. Grudzinski, JJ, Tome, W, Weichert, JP, Jeraj, R (2010) The biological effectiveness of targeted radionuclide therapy based on a whole-body pharmacokinetic model. Phys Med Biol 55: pp. 5723-5734 CrossRef
    60. Norton, L, Simon, R, Brereton, HD, Bogden, AE (1976) Predicting the course of Gompertzian growth. Nature 264: pp. 542-545 CrossRef
    61. Norton, L (1988) A Gompertzian model of human breast cancer growth. Cancer Res 48: pp. 7067-7071
    62. Citron, ML, Berry, DA, Cirrincione, C, Hudis, C, Winer, EP, Gradishar, WJ, Davidson, NE, Martino, S, Livingston, R, Ingle, JN, Perez, EA, Carpenter, J, Hurd, D, Holland, JF, Smith, BL, Sartor, CI, Leung, EH, Abrams, J, Schilsky, RL, Muss, HB, Norton, L (2003) 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: pp. 1431-1439 CrossRef
    63. Noble, SL, Sherer, E, Hannemann, RE, Ramkrishna, D, Vik, T, Rundell, AE (2010) Using adaptive model predictive control to customize maintenance therapy chemotherapeutic dosing for childhood acute lymphoblastic leukemia. J Theor Biol 264: pp. 990-1002 CrossRef
    64. Mackey, MC (1978) Unified hypothesis for the origin of aplastic anemia and periodic hematopoiesis. Blood 51: pp. 941-956
    65. Parra-Guillen, ZP, Berraondo, P, Ribba, B, Troconiz, IF (2013) Modeling tumor response after combined administration of different immune-stimulatory agents. J Pharmacol Exp Ther 346: pp. 432-442 CrossRef
    66. Zitvogel, L, Apetoh, L, Ghiringhelli, F, Kroemer, G (2008) Immunological aspects of cancer chemotherapy. Nat Rev Immunol 8: pp. 59-73 CrossRef
    67. Yates, JW (2009) An implementation of the Expectation-Maximisation (EM) algorithm for population pharmacokinetic-pharmacodynamic modelling in ACSLXTREME. Comput Methods Programs Biomed 96: pp. 49-62 CrossRef
    68. Li, M, Li, H, Cheng, X, Wang, X, Li, L, Zhou, T, Lu, W (2013) Preclinical pharmacokinetic/pharmacodynamic models to predict schedule-dependent interaction between erlotinib and gemcitabine. Pharm Res 30: pp. 1400-1408 CrossRef
    69. Wang, Z, Butner, JD, Kerketta, R, Cristini, V, Deisboeck, TS (2015) Simulating cancer growth with multiscale agent-based modeling. Semin Cancer Biol.
    70. Kazmi, N, Hossain, MA, Phillips, RM (2012) A hybrid cellular automaton model of solid tumor growth and bioreductive drug transport. IEEE/ACM Trans Comput Biol Bioinform 9: pp. 1595-1606 CrossRef
    71. McKeown SR, Cowen RL, Williams KJ (2007) Bioreductive drugs: from concept to clinic. Clin Oncol (Royal College of Radiologists (Great Britain)) 19(6):427鈥?42. doi:10.1016/j.clon.2007.03.006
    72. Das, H, Wang, Z, Niazi, MK, Aggarwal, R, Lu, J, Kanji, S, Das, M, Joseph, M, Gurcan, M, Cristini, V (2013) Impact of diffusion barriers to small cytotoxic molecules on the efficacy of immunotherapy in breast cancer. PLoS One 8: pp. e61398 CrossRef
    73. Edgerton, ME, Chuang, YL, Macklin, P, Yang, W, Bearer, EL, Cristini, V (2011) A novel, patient-specific mathematical pathology approach for assessment of surgical volume: application to ductal carcinoma in situ of the breast. Anal Cell Pathol (Amst) 34: pp. 247-263 CrossRef
    74. Koay, EJ, Truty, MJ, Cristini, V, Thomas, RM, Chen, R, Chatterjee, D, Kang, Y, Bhosale, PR, Tamm, EP, Crane, CH, Javle, M, Katz, MH, Gottumukkala, VN, Rozner, MA, Shen, H, Lee, JE, Wang, H, Chen, Y, Plunkett, W, Abbruzzese, JL, Wolff, RA, Varadhachary, GR, Ferrari, M, Fleming, JB (2014) Transport properties of pancreatic cancer describe gemcitabine delivery and response. J Clin Investig 124: pp. 1525-1536 CrossRef
    75. Pascal, J, Ashley, CE, Wang, Z, Brocato, TA, Butner, JD, Carnes, EC, Koay, EJ, Brinker, CJ, Cristini, V (2013) Mechanistic modeling identifies drug-uptake history as predictor of tumor drug resistance and nano-carrier-mediated response. ACS Nano 7: pp. 11174-11182 CrossRef
    76. Pascal, J, Bearer, EL, Wang, Z, Koay, EJ, Curley, SA, Cristini, V (2013) Mechanistic patient-specific predictive correlation of tumor drug response with microenvironment and perfusion measurements. Proc Natl Acad Sci.
    77. Frieboes, HB, Smith, BR, Chuang, YL, Ito, K, Roettgers, AM, Gambhir, SS, Cristini, V (2013) An integrated computational/experimental model of lymphoma growth. PLoS Comput Biol 9: pp. e1003008 CrossRef
    78. Prakasha Gowda, AS, Polizzi, JM, Eckert, KA, Spratt, TE (2010) Incorporation of gemcitabine and cytarabine into DNA by DNA polymerase beta and ligase III/XRCC1. Biochemistry 49: pp. 4833-4840 CrossRef
    79. Momparler, RL (1974) A model for the chemotherapy of acute leukemia with 1-beta-d-arabinofuranosylcytosine. Cancer Res 34: pp. 1775-1787
    80. Gevertz, JL (2011) Computational modeling of tumor response to vascular-targeting therapies鈥損art I: validation. Comput Math Methods Med 2011: pp. 830515 CrossRef
    81. Gevertz, JL, Torquato, S (2006) Modeling the effects of vasculature evolution on early brain tumor growth. J Theor Biol 243: pp. 517-531 CrossRef
    82. Bergers, G, Hanahan, D (2008) Modes of resistance to anti-angiogenic therapy. Nat Rev Cancer 8: pp. 592-603 CrossRef
    83. Gatenby, RA (2009) A change of strategy in the war on cancer. Nature 459: pp. 508-509 CrossRef
    84. Sorenson, CM, Barry, MA, Eastman, A (1990) Analysis of events associated with cell cycle arrest at G2 phase and cell death induced by cisplatin. J Natl Cancer Inst 82: pp. 749-755 CrossRef
    85. Wu, M, Frieboes, HB, McDougall, SR, Chaplain, MA, Cristini, V, Lowengrub, J (2013) The effect of interstitial pressure on tumor growth: coupling with the blood and lymphatic vascular systems. J Theor Biol 320: pp. 131-151 CrossRef
    86. Frieboes, HB, Edgerton, ME, Fruehauf, JP, Rose, FR, Worrall, LK, Gatenby, RA, Ferrari, M, Cristini, V (2009) Prediction of drug response in breast cancer using integrative experimental/computational modeling. Cancer Res 69: pp. 4484-4492 CrossRef
    87. Sinek, JP, Sanga, S, Zheng, X, Frieboes, HB, Ferrari, M, Cristini, V (2009) Predicting drug pharmacokinetics and effect in vascularized tumors using computer simulation. J Math Biol 58: pp. 485-510 CrossRef
    88. Wise, SM, Lowengrub, JS, Frieboes, HB, Cristini, V (2008) Three-dimensional multispecies nonlinear tumor growth鈥揑 Model and numerical method. J Theor Biol 253: pp. 524-543 CrossRef
    89. Wu, M, Frieboes, HB, Chaplain, MA, McDougall, SR, Cristini, V, Lowengrub, JS (2014) The effect of interstitial pressure on therapeutic agent transport: coupling with the tumor blood and lymphatic vascular systems. J Theor Biol 355: pp. 194-207 CrossRef
    90. Welter, M, Rieger, H (2013) Interstitial fluid flow and drug delivery in vascularized tumors: a computational model. PLoS One 8: pp. e70395 CrossRef
    91. Jain, RK (1987) Transport of molecules in the tumor interstitium: a review. Cancer Res 47: pp. 3039-3051
    92. Jain, RK, Tong, RT, Munn, LL (2007) Effect of vascular normalization by antiangiogenic therapy on interstitial hypertension, peritumor edema, and lymphatic metastasis: insights from a mathematical model. Cancer Res 67: pp. 2729-2735 CrossRef
    93. Winslow RL, Trayanova N, Geman D, Miller MI (2012) Computational medicine: translating models to clinical care. Sci Transl Med 4(158):158rv111. doi:10.1126/scitranslmed.3003528
    94. Tamascelli, D, Dambrosio, FS, Conte, R, Ceotto, M (2014) Graphics processing units accelerated semiclassical initial value representation molecular dynamics. J Chem Phys 140: pp. 174109 CrossRef
    95. Gu, X, Pan, H, Liang, Y, Castillo, R, Yang, D, Choi, D, Castillo, E, Majumdar, A, Guerrero, T, Jiang, SB (2010) Implementation and evaluation of various demons deformable image registration algorithms on a GPU. Phys Med Biol 55: pp. 207-219 CrossRef
    96. Chen, X, Summers, R, Yao, J (2011) FEM-based 3-D tumor growth prediction for kidney tumor. IEEE Trans Bio-Med Eng 58: pp. 463-467 CrossRef
    97. Wang, Z, Sagotsky, J, Taylor, T, Shironoshita, P, Deisboeck, TS (2013) Accelerating cancer systems biology research through Semantic Web technology. Wiley interdisciplinary reviews. Syst Biol Med 5: pp. 135-151
    98. Powathil, GG, Adamson, DJ, Chaplain, MA (2013) Towards predicting the response of a solid tumour to chemotherapy and radiotherapy treatments: clinical insights from a computational model. PLoS Comput Biol 9: pp. e1003120 CrossRef
  • 刊物类别:Biomedical and Life Sciences
  • 刊物主题:Biomedicine
    Pharmacology and Toxicology
    Pharmacy
    Veterinary medicine
    Biomedical Engineering
    Biochemistry
  • 出版者:Springer Netherlands
  • ISSN:1573-8744
文摘
Mathematical modeling has become a valuable tool that strives to complement conventional biomedical research modalities in order to predict experimental outcome, generate new medical hypotheses, and optimize clinical therapies. Two specific approaches, pharmacokinetic-pharmacodynamic (PK-PD) modeling, and agent-based modeling (ABM), have been widely applied in cancer research. While they have made important contributions on their own (e.g., PK-PD in examining chemotherapy drug efficacy and resistance, and ABM in describing and predicting tumor growth and metastasis), only a few groups have started to combine both approaches together in an effort to gain more insights into the details of drug dynamics and the resulting impact on tumor growth. In this review, we focus our discussion on some of the most recent modeling studies building on a combined PK-PD and ABM approach that have generated experimentally testable hypotheses. Some future directions are also discussed.

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