Annals of Biomedical Engineering

, Volume 44, Issue 9, pp 2626–2641

Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success

  • Thomas E. Yankeelov
  • Gary An
  • Oliver Saut
  • E. Georg Luebeck
  • Aleksander S. Popel
  • Benjamin Ribba
  • Paolo Vicini
  • Xiaobo Zhou
  • Jared A. Weis
  • Kaiming Ye
  • Guy M. Genin
Multi-Scale Modeling in the Clinic

Abstract

Hierarchical processes spanning several orders of magnitude of both space and time underlie nearly all cancers. Multi-scale statistical, mathematical, and computational modeling methods are central to designing, implementing and assessing treatment strategies that account for these hierarchies. The basic science underlying these modeling efforts is maturing into a new discipline that is close to influencing and facilitating clinical successes. The purpose of this review is to capture the state-of-the-art as well as the key barriers to success for multi-scale modeling in clinical oncology. We begin with a summary of the long-envisioned promise of multi-scale modeling in clinical oncology, including the synthesis of disparate data types into models that reveal underlying mechanisms and allow for experimental testing of hypotheses. We then evaluate the mathematical techniques employed most widely and present several examples illustrating their application as well as the current gap between pre-clinical and clinical applications. We conclude with a discussion of what we view to be the key challenges and opportunities for multi-scale modeling in clinical oncology.

Keywords

Cancer Mathematical modeling Predictive oncology Numerical modeling Computational modeling Agent-based modeling Cancer screening Epidemiology 

References

  1. 1.
    Adam, J., and N. Bellomo. A survey of models for tumor-immune system dynamics. Berlin: Springer, 2012.Google Scholar
  2. 2.
    Aerts, H. J., E. R. Velazquez, R. T. Leijenaar, C. Parmar, P. Grossmann, S. Carvalho, J. Bussink, R. Monshouwer, B. Haibe-Kains, D. Rietveld, F. Hoebers, M. M. Rietbergen, C. R. Leemans, A. Dekker, J. Quackenbush, R. J. Gillies, and P. Lambin. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5:4006, 2014.PubMedPubMedCentralGoogle Scholar
  3. 3.
    Ambrosi, D., and L. Preziosi. On the closure of mass balance modes for tumors growth. Math. Models Methods Appl. Sci. 12(05):737–753, 2002.CrossRefGoogle Scholar
  4. 4.
    An, G. Closing the scientific loop: bridging correlation and causality in the petaflop age. Sci. Transl. Med. 2(41):41, 2010.CrossRefGoogle Scholar
  5. 5.
    An, G., and S. Kulkarni. An agent-based modeling framework linking inflammation and cancer using evolutionary principles: description of a generative hierarchy for the hallmarks of cancer and developing a bridge between mechanism and epidemiological data. Math. Biosci. 260:16–24, 2015.PubMedCrossRefGoogle Scholar
  6. 6.
    An, G., Q. Mi, J. Dutta-Moscato, and Y. Vodovotz. Agent-based models in translational systems biology. Wiley Interdiscip. Rev. Syst. Biol. Med. 1(2):159–171, 2009.PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Andasari, V., R. T. Roper, M. H. Swat, and M. A. Chaplain. Integrating intracellular dynamics using CompuCell 3D and Bionetsolver: applications to multiscale modelling of cancer cell growth and invasion. PLoS One 7(3):e33726, 2012.PubMedPubMedCentralCrossRefGoogle Scholar
  8. 8.
    Anderson, A. R. A hybrid mathematical model of solid tumour invasion: the importance of cell adhesion. Math. Med. Biol. 22(2):163–186, 2015.CrossRefGoogle Scholar
  9. 9.
    Bae, Y. H. Drug targeting and tumor heterogeneity. J. Control. Release 133(1):2, 2009.PubMedCrossRefGoogle Scholar
  10. 10.
    Barrett, J. S., M. J. Fossler, K. D. Cadieu, and M. R. Gastonguay. Pharmacometrics: a multidisciplinary field to facilitate critical thinking in drug development and translational research settings. J. Clin. Pharmacol. 48:632–649, 2008.PubMedCrossRefGoogle Scholar
  11. 11.
    Begam, B. F., and J. S. Kumar. A study on chemoinformatics and its applications on modern drug discovery. Procedia Eng. 38:1264–1275, 2012.CrossRefGoogle Scholar
  12. 12.
    Bellouquid, A., and M. Delitala. Mathematical methods and tools of kinetic theory towards modelling complex biological systems. Math. Models Methods Appl. Sci. 15(11):1639–1666, 2005.CrossRefGoogle Scholar
  13. 13.
    Benzekry, S., A. Gandolfi, and P. Hahnfeldt. Global dormancy of metastases due to systemic inhibition of angiogenesis. PLoS One 9(1):e84249-11, 2014.CrossRefGoogle Scholar
  14. 14.
    Bozic, I., and M. A. Nowak. Timing and heterogeneity of mutations associated with drug resistance in metastatic cancers. Proc. Natl. Acad. Sci. USA. 111(45):15964–15968, 2014.PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    Brikci, F. B., J. Clairambault, B. Ribba, and B. Perthame. An age-and-cyclin-structured cell population model for healthy and tumoral tissues. J. Math. Biol. 57(1):91–110, 2008.CrossRefGoogle Scholar
  16. 16.
    Byrne, H. M. Dissecting cancer through mathematics: from the cell to the animal model. Nat. Rev. Cancer 10(3):221–230, 2010.PubMedCrossRefGoogle Scholar
  17. 17.
    Cappuccio, A., P. Tieri, and F. Castiglione. Multi-scale modelling in immunology: a review. Brief Bioinform. 17(3):408–418, 2015.PubMedCrossRefGoogle Scholar
  18. 18.
    Cavallo, F., C. De Giovanni, P. Nanni, G. Forni, and P. L. Lollini. The immune hallmarks of cancer. Cancer Immunol. Immunother. 60:319–326, 2011.PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Chakrabarti, A., S. Verbridge, A. D. Stroock, C. Fischbach, and J. D. Varner. Multi-scale models of breast cancer progression. Ann. Biomed. Eng. 40(11):2488–2500, 2012.PubMedCrossRefGoogle Scholar
  20. 20.
    Chang, R. L., L. Xie, P. E. Bourne, and B. O. Paisson. Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Comput. Biol. 6(9):e10000938, 2010.CrossRefGoogle Scholar
  21. 21.
    Claret, L., and R. Bruno. Assessment of tumor growth inhibition metrics to predict overall survival. Clin Pharmacol Ther. 96(2):135–137, 2014.PubMedCrossRefGoogle Scholar
  22. 22.
    Clark, A. M., A. J. Williams, and S. Ekins. Machines first, humans second: on the importance of algorithmic interpretation of open chemistry data. J. Cheminform. 7(9):1–20, 2015.Google Scholar
  23. 23.
    Clegg, L. W., and F. Gabhann. Site-specific phosphorylation of VEGFR2 is mediated by receptor trafficking: insights from a computational model. PLoS Comput. Biol. 11(6):1004158, 2015.CrossRefGoogle Scholar
  24. 24.
    Colin, T., F. Cornelis, J. Jouganous, J. Palussière, and O. Saut. Patient specific simulation of tumor growth, response to the treatment and relapse of a lung metastasis: a clinical case. J. Comput. Surg. 2:1, 2015.CrossRefGoogle Scholar
  25. 25.
    Colin, T., A. Iollo, D. Lombardi, and O. Saut. System identification in tumor growth modeling using semi-empirical eigenfunctions. Math. Models Methods Appl. Sci. 22(06):1250003-1, 2012.CrossRefGoogle Scholar
  26. 26.
    Cross, W. The code: an authorized history of the ASME boiler and pressure vessel code. New York: American Society of Mechanical Engineers, 1990.Google Scholar
  27. 27.
    Curtius, K., W. D. Hazelton, J. Jeon, and E. G. Luebeck. A multi-scale model evaluates screening for neoplasia in Barrett’s Esophagus. PLoS Comput. Biol. 11(5):e1004272, 2015.PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Diaz, Jr, L. A., R. T. Williams, J. Wu, I. Kinde, J. R. Hecht, J. Berlin, B. Allen, I. Bozic, J. G. Reiter, M. A. Nowak, K. W. Kinzler, K. S. Oliner, and B. Vogelstein. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature. 486(7404):537–540, 2012.PubMedPubMedCentralGoogle Scholar
  29. 29.
    EFPIA MID3 Workgroup, S. Marshall, R. Burghaus, V. Cosson, S. Cheung, M. Chenel, O. Dellapasqua, N. Frey, B. Hamrén, L. Harnisch, F. Ivanow, T. Kerbusch, J. Lippert, P. Milligan, S. Rohou, A. Staab, J. Steimer, C. Tornøe, and S. Visser. Good practices in model-informed drug discovery and development: practice, application, and documentation. CPT Pharmacomet. Syst. Pharmacol. 5:93–122, 2016.CrossRefGoogle Scholar
  30. 30.
    Elson, E. L., and G. M. Genin. Tissue constructs: platforms for basic research and drug discovery. Interface Focus. 6(1):20150095, 2016.PubMedCrossRefGoogle Scholar
  31. 31.
    Engelberg, J. A., G. E. Ropella, and C. A. Hunt. Essential operating principles for tumor spheroid growth. BMC Syst Biol. 2(1):110, 2009.CrossRefGoogle Scholar
  32. 32.
    Engler, A. J., P. O. Humbert, B. Wehrle-Haller, and V. M. Weaver. Multi-scale modeling of form and function. Science 324(5924):208–212, 2009.PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Ette, E. I., and P. J. Williams. Pharmacometrics: the science of quantitative pharmacology. Hoboken: Wiley, 2007.CrossRefGoogle Scholar
  34. 34.
    Finley, S. D., P. Angelikopoulos, P. Koumoutsakos, and A. S. Popel. Pharmacokinetics of anti-VEGF agent aflibercept in cancer predicted by data driven, molecular-detailed model. CPT: Pharmacomet. Syst. Pharmacol. 4(11):641–649, 2015.Google Scholar
  35. 35.
    Finley, S. D., L. H. Chu, and A. S. Popel. Computational systems biology approaches to anti-angiogenic cancer therapeutics. Drug Discov. Today 20:187–197, 2015.PubMedCrossRefGoogle Scholar
  36. 36.
    Finley, S. D., and A. S. Popel. Effect of tumor microenvironment on tumor VEGF during anti-VEGF treatment: systems biology predictions. J. Natl. Cancer Inst. 105(11):802–811, 2013.PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Fitzgerald, J. B., B. Schoeberl, U. B. Nielson, and P. K. Sorger. Systems biology and combination therapy in the quest for clinical efficacy. Nat. Chem. Biol. 2(9):458–466, 2006.PubMedCrossRefGoogle Scholar
  38. 38.
    Gallaher, J., and A. R. Anderson. Evolution of intratumoral phenotypic heterogeneity: the role of trait inheritance. Interface Focus. 3(4):20130016, 2013.PubMedPubMedCentralCrossRefGoogle Scholar
  39. 39.
    Garny, A., J. Cooper, and P. J. Hunter. Toward a VPH/Physiome ToolKit. Wiley Interdiscip. Rev. Syst. Biol. Med. 2(2):134–147, 2010.PubMedCrossRefGoogle Scholar
  40. 40.
    Gatenby, R. A., and E. T. Gawlinski. A reaction-diffusion model of cancer invasion. Cancer Research 56(24):5745–5753, 1996.PubMedGoogle Scholar
  41. 41.
    Gerlinger, M., A. J. Rowan, S. Horswell, J. Larkin, D. Endesfelder, E. Gronroos, P. Martinez, N. Matthews, A. Stewart, P. Tarpey, I. Varela, B. Phillimore, S. Begum, N. Q. McDonald, A. Butler, D. Jones, K. Raine, C. Latimer, C. R. Santos, M. Nohadani, A. C. Eklund, B. Spencer-Dene, G. Clark, L. Pickering, G. Stamp, M. Gore, Z. Szallasi, J. Downward, P. A. Futreal, and C. Swanton. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366:883–892, 2012.PubMedPubMedCentralCrossRefGoogle Scholar
  42. 42.
    Gillies, R. J., P. E. Kinahan, and H. Hricak. Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577, 2016.PubMedCrossRefGoogle Scholar
  43. 43.
    Green, Jr, S. E., and Y. Li. Rhetorical institutionalism: Language, agency, and structure in institutional theory since Alvesson. J. Manag. Stud. 48(7):1662–1697, 1993.CrossRefGoogle Scholar
  44. 44.
    Gross, S., R. Rahal, N. Stransky, C. Lengauer, and K. P. Hoeflich. Targeting cancer with kinase inhibitors. J. Clin. Invest. 125:1780–1789, 2015.PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Hanahan, D., and R. A. Weinberg. Hallmarks of cancer: the next generation. Cell 144:646–674, 2011.PubMedCrossRefGoogle Scholar
  46. 46.
    Heilbron, J. L. The affair of the countess Görlitz. Proc. Am. Philos. Soc. 138(2):284–316, 1994.Google Scholar
  47. 47.
    Hirt, M. N., A. Hansen, and T. Eschenhagen. Cardiac tissue engineering state of the art. Circulation research. 114(2):354–367, 2014.PubMedCrossRefGoogle Scholar
  48. 48.
    Hopkins, A. L. Network pharmacology: the next paradigm in drug discovery. Nature Chemical Biology. 4(11):682–690, 2008.PubMedCrossRefGoogle Scholar
  49. 49.
    Houk, B. E., C. L. Bello, B. Poland, L. S. Rosen, G. D. Demetri, and R. J. Motzer. Relationship between exposure to sunitinib and efficacy and tolerability endpoints in patients with cancer: results of a pharmacokinetic/pharmacodynamic meta-analysis. Cancer Chemother. Pharmacol. 66(2):357–371, 2010.PubMedCrossRefGoogle Scholar
  50. 50.
    Jang, G. R., R. Z. Harris, and D. T. Lau. Pharmacokinetics and its role in small molecule drug discovery research. Med. Res. Rev. 21:382–396, 2001.PubMedCrossRefGoogle Scholar
  51. 51.
    Jiang, C., C. Cui, L. Li, and Y. Shao. The anomalous diffusion of a tumor invading with different surrounding tissues. PLoS One. 9(10):e109784, 2014.PubMedPubMedCentralCrossRefGoogle Scholar
  52. 52.
    Joerger, M. Covariate pharmacokinetic model building in oncology and its potential clinical relevance. AAPS J. 14(1):119–132, 2012.PubMedPubMedCentralCrossRefGoogle Scholar
  53. 53.
    Jones, H. M., Y. Chen, C. Gibson, T. Heimbach, N. Parrott, S. A. Peters, J. Snoeys, V. V. Upreti, M. Zheng, and S. D. Hall. Physiologically based pharmacokinetic modeling in drug discovery and development: a pharmaceutical industry perspective. Clin. Pharmacol. Ther. 97:247–262, 2015.PubMedCrossRefGoogle Scholar
  54. 54.
    Kam, Y., K. A. Rejniak, and A. R. Anderson. Cellular modeling of cancer invasion: integration of in silico and in vitro approaches. J. Cell Physiol. 227(2):431–438, 2012.PubMedPubMedCentralCrossRefGoogle Scholar
  55. 55.
    Karthikeyan, M., R. Vyas, S. S. Tambe, D. Radhamohan, and B. D. Kulkarni. Role of chemical reactivity and transition state modeling for virtual screening. Comb. Chem. High Throughput Screen. 18(7):638–657, 2015.PubMedCrossRefGoogle Scholar
  56. 56.
    Kiang, T. K., C. M. Sherwin, M. G. Spigarelli, and M. H. Ensom. Fundamentals of population pharmacokinetic modelling: modelling and software. Clin. Pharmacokinet. 51(8):515–525, 2012.PubMedCrossRefGoogle Scholar
  57. 57.
    Kim, Y., G. Powathil, H. Kang, D. Trucu, H. Kim, S. Lawler, and M. Chaplain. Strategies of eradicating glioma cells: a multi-scale mathematical model with MiR-451-AMPK-mTOR control. PLoS One 10:e0114370, 2015.PubMedPubMedCentralCrossRefGoogle Scholar
  58. 58.
    Kirouac, D. C., J. Y. Du, J. Lahdenranta, R. Overland, D. Yarar, V. Paragas, E. Pace, C. F. McDonagh, U. B. Nielsen, and M. D. Onsum. Computational modeling of ERBB2-amplified breast cancer identifies combined ErbB2/3 blockade as superior to the combination of MEK and AKT inhibitors. Sci Signal. 6:ra68, 2013.PubMedCrossRefGoogle Scholar
  59. 59.
    Konukoglu, E., O. Clatz, P.-Y. Bondiau, H. Delingette, and A. Nicholas. Extrapolating glioma invasion margin in brain magnetic resonance images: suggesting new irradiation margins. Med. Image Anal. 14(2):111–125, 2010.PubMedCrossRefGoogle Scholar
  60. 60.
    Konukoglu, E., O. Clatz, H. Delingette, and N. Ayache. Personalization of reaction-diffusion tumor growth models in MR images: application to brain gliomas characterization and radiotherapy planning. In: Multi-scale cancer modeling, Chapman & Hall/CRC Mathematical and Computational Biology, edited by T. S. Deisboeck, and G. Stamatakos. Boca Raton: CRC Press, 2010.Google Scholar
  61. 61.
    Lalonde, R. L., K. G. Kowalski, M. M. Hutmacher, W. Ewy, D. J. Nichols, P. A. Milligan, B. W. Corrigan, P. A. Lockwood, S. A. Marshall, L. J. Benincosa, T. G. Tensfeldt, K. Parivar, M. Amantea, P. Glue, H. Koide, and R. Miller. Model-based drug development. Clin. Pharmacol. Ther. 82(1):21–32, 2007.PubMedCrossRefGoogle Scholar
  62. 62.
    Loewe, S. The problem of synergism and antagonism of combined drugs. Arzneimittelforschung. 3(6):285–290, 1953.PubMedGoogle Scholar
  63. 63.
    Lorz, A., T. Lorenzi, M. E. Hochberg, J. Clairambault, and B. Perthame. Populational adaptive evolution, chemotherapeutic resistance and multiple anti-cancer therapies. ESAIM Math. Model. Numer. Anal. 47:377–399, 2013.CrossRefGoogle Scholar
  64. 64.
    Luebeck, E., and S. Moolgavkar. Multistage carcinogenesis and the incidence of colorectal cancer. Proc. Natl. Acad. Sci. 99(23):15095, 2002.PubMedPubMedCentralCrossRefGoogle Scholar
  65. 65.
    Macklin, P., M. E. Edgerton, A. M. Thompson, and V. Cristini. Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): From microscopic measurements to macroscopic predictions of clinical progression. J. Theor. Biol. 301:122–140, 2012.PubMedPubMedCentralCrossRefGoogle Scholar
  66. 66.
    Mager, D. E., and W. J. Jusko. Development of translational pharmacokinetic-pharmacodynamic models. Clin Pharmacol Ther. 83:909–912, 2008.PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Mathijssen, R. H., A. Sparreboom, and J. Verweij. Determining the optimal dose in the development of anticancer agents. Nat. Rev. Clin. Oncol. 11(5):272–281, 2014.PubMedCrossRefGoogle Scholar
  68. 68.
    Meza, R. J., S. H. Jeon, and E. G. Moolgavkar. Luebeck. Age-specific incidence of cancer: Phases, transitions, and biological implications. Proc. Natl. Acad. Sci. 105(42):16284, 2008.PubMedPubMedCentralCrossRefGoogle Scholar
  69. 69.
    Moolgavkar, S. H., and A. G. Knudson. Mutation and cancer: a model for human carcinogenesis. J. Natl. Cancer Inst. 66:1037–1052, 1981.PubMedGoogle Scholar
  70. 70.
    Peng, H., T. Peng, J. Wen, D. A. Engler, R. K. Matsunami, J. Su, L. Zhang, C. C. Chang, and X. Zhou. Characterization of p38 MAPK isoforms for drug resistance study using systems biology approach. Bioinformatics. 30(13):1899–1907, 2014.PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Peng, H., J. G. Wen, H. W. Li, J. Chang, and X. Zhou. Drug inhibition profile prediction for NFκB pathway in multiple myeloma. PLoS One 6(3):e14750, 2011.PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    Peng, H., J. Wen, H. Li, L. Zhang, C. C. Chang, Y. Zu, and X. Zhou. A systematic modeling study on the pathogenic role of p38 MAPK activation in myelodysplastic syndromes. Mol. BioSyst. 8(4):1366–1374, 2012.PubMedCrossRefGoogle Scholar
  73. 73.
    Pritchard, J. R., P. M. Bruno, L. A. Gilbert, K. L. Capron, D. A. Lauffenburger, and M. T. Hemann. Defining principles of combination drug mechanisms of action. Proc. Natl. Acad. Sci. USA 110(2):E170–E179, 2013.PubMedCrossRefGoogle Scholar
  74. 74.
    Qutub, A. A., F. Mac Gabhann, E. D. Karagiannis, P. Vempati, and A. S. Popel. Multi-scale models of angiogenesis. IEEE Eng. Med. Biol. Mag. 28:14–31, 2009.PubMedPubMedCentralCrossRefGoogle Scholar
  75. 75.
    Reardon, S. Organs-on-chips. Nature 423:266, 2015.CrossRefGoogle Scholar
  76. 76.
    Robertson-Tessi, M., R. J. Gillies, R. A. Gatenby, and A. R. Anderson. Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes. Cancer Res. 75:1567–1579, 2015.PubMedPubMedCentralCrossRefGoogle Scholar
  77. 77.
    Rubinacci, S., A. Graudenzi, G. Caravagna, G. Mauri, J. Osborne, J. Pitt-Francis, and M. Antoniotti. CoGNaC: a Chaste plugin for the multiscale simulation of gene regulatory networks driving the spatial dynamics of tissues and cancer. Cancer Inform. 14(Suppl 4):53–65, 2015.PubMedPubMedCentralGoogle Scholar
  78. 78.
    Sachs, J. R., K. Mayawala, S. Gadamsetty, S. P. Kang, and D. P. de Alwis. Optimal dosing for targeted therapies in oncology: drug development cases leading by example. Clin. Cancer Res. 22(6):1318–1324, 2016.PubMedCrossRefGoogle Scholar
  79. 79.
    Schoeberl, B., E. A. Pace, J. B. Fitzgerald, B. D. Harms, L. Xu, L. Nie, B. Linggi, A. Kalra, V. Paragas, R. Bukhalid, V. Grantcharova, N. Kohli, K. A. West, M. Leszczyniecka, M. J. Feldhaus, A. J. Kudla, and U. B. Nielsen. Therapeutically targeting ErbB3: a key node in ligand-induced activation of the ErbB receptor-PI3K axis. Sci. Signal. 2(77):ra31, 2009.PubMedCrossRefGoogle Scholar
  80. 80.
    Schoeberl, B., E. Pace, S. Howard, V. Garantcharova, A. Kudla, P. K. Sorger, and U. B. Nielsen. A data-driven computational model of the ErbB receptor signaling network. Conf. Proc. IEEE Eng. Med Biol. Soc. 1:53–54, 2006.PubMedGoogle Scholar
  81. 81.
    Shao, H. W., T. Peng, Z. Ji, and X. Zhou. Systematically studying kinase inhibitor induced signaling network signatures by integrating both therapeutic and side effects. PLoS One 8(12):e80832, 2013.PubMedPubMedCentralCrossRefGoogle Scholar
  82. 82.
    Sharan, S., and S. Woo. Systems pharmacology approaches for optimization of antiangiogenic therapies: challenges and opportunities. Front Pharmacol. 6:33, 2015.PubMedPubMedCentralCrossRefGoogle Scholar
  83. 83.
    Sherwin, C. M., T. K. Kiang, M. G. Spigarelli, and M. H. Ensom. Fundamentals of population pharmacokinetic modelling: validation methods. Clin. Pharmacokinet. 51(9):573–590, 2012.PubMedCrossRefGoogle Scholar
  84. 84.
    Sorger, P. K., S. R. B. Allerheiligen, D. R. Abernethy, R. B. Altman, K. L. R. Brouwer, A. Califano, D. Z. D’Argenio, R. Iyengar, W. J. Jusko, R. Lalonde, D. A. Lauffenburger, B. Shoichet, J. L. Stevens, S. Subramaniam, P. Van der Graaf, P. Vicini, and R. War. Quantitative and systems pharmacology in the post-genomic era: new approaches to discovering drugs and understanding therapeutic mechanisms: An NIH white paper by the QSP workshop group. Bethesda: NIH, 2011. http://www.nigms.nih.gov/News/reports/Documents/SystemsPharmaWPSorger2011.pdf
  85. 85.
    Su, J., L. Zhang, W. Zhang, D. S. Choi, J. Wen, B. Jiang, C. C. Chang, and X. Zhou. Targeting the biophysical properties of the myeloma initiating cell niches: a pharmaceutical synergism analysis using multi-scale agent-based modeling. PLoS One. 9(1):e85059, 2014.PubMedPubMedCentralCrossRefGoogle Scholar
  86. 86.
    Sun, X., J. Bao, K. C. Nelson, K. C. Li, G. Kulik, and X. Zhou. Systems modeling of anti-apoptotic pathways in prostate cancer: psychological stress triggers a synergism pattern switch in drug combination therapy. PLoS Comput. Biol. 9(12):e1003358, 2013.PubMedPubMedCentralCrossRefGoogle Scholar
  87. 87.
    Sun, X., J. Su, J. Bao, T. Peng, L. Zhang, Y. Zhang, and X. Zhou. Cytokine combination therapy prediction for bone remodeling in tissue engineering based on the intracellular signaling pathway. Biomaterials. 33(33):8265–8276, 2012.PubMedPubMedCentralCrossRefGoogle Scholar
  88. 88.
    Swanson, K. R., R. C. Rostomily, and E. C. Alvord. A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle. Br. J. Cancer. 98(1):113–119, 2008.PubMedCrossRefGoogle Scholar
  89. 89.
    Tang, J., and T. Aittokallio. Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles. Curr. Pharm. Des. 20(1):23–36, 2014.PubMedCrossRefGoogle Scholar
  90. 90.
    Tang, L., J. Su, D.-S. Huang, D. Y. Lee, K. C. Li, and X. Zhou. An integrated multiscale mechanistic model for cancer drug therapy. ISRN Biomath. 2:1–12, 2012.CrossRefGoogle Scholar
  91. 91.
    Tatonetti, N. P., T. Y. Liu, and R. B. Altman. Predicting drug side-effects by chemical systems biology. Genome Biol. 10(9):238, 2009.PubMedPubMedCentralCrossRefGoogle Scholar
  92. 92.
    Upton, R. N., and D. R. Mould. Basic concepts in population modeling, simulation, and model-based drug development: part 3-introduction to pharmacodynamic modeling methods. CPT Pharmacomet. Syst. Pharmacol. 3:e88, 2014.CrossRefGoogle Scholar
  93. 93.
    Venkatakrishnan, K., L. E. Friberg, D. Ouellet, J. T. Mettetal, A. Stein, I. F. Trocóniz, R. Bruno, N. Mehrotra, J. Gobburu, and D. R. Mould. Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities. Clin. Pharmacol. Ther. 97(1):37–54, 2015.PubMedCrossRefGoogle Scholar
  94. 94.
    Visser, S. A., D. P. de Alwis, T. Kerbusch, J. A. Stone, and S. R. Allerheiligen. Implementation of quantitative and systems pharmacology in large pharma. CPT Pharmacomet. Syst Pharmacol. 3:e142, 2014.CrossRefGoogle Scholar
  95. 95.
    Wang, Z., J. D. Butner, R. Kerketta, V. Cristini, and T. S. Deisboeck. Simulating cancer growth with multiscale agent-based modeling. Semin. Cancer Biol. 30:70–78, 2015.PubMedCrossRefGoogle Scholar
  96. 96.
    Weis, J. A., M. I. Miga, L. R. Arlinghaus, X. Li, A. B. Chakravarthy, V. Abramson, J. Farley, and T. E. Yankeelov. A mechanically coupled reaction-diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy. Phys. Med. Biol. 58(17):5851–5866, 2013.PubMedPubMedCentralCrossRefGoogle Scholar
  97. 97.
    Wirtz, D., K. Konstantopoulos, and P. C. Searson. The physics of cancer: the role of physical interactions and mechanical forces in metastasis. Nat. Rev. Cancer 11(7):512–522, 2011.PubMedPubMedCentralCrossRefGoogle Scholar
  98. 98.
    Wu, M., M. Sirota, A. J. Butte, and B. Chen. Characteristics of drug combination therapy in oncology by analyzing clinical trial data on ClinicalTrials.gov. Pac. Symp. Biocomput. 2015:68–79, 2015.Google Scholar
  99. 99.
    Xie, L., J. Li, and P. E. Bourne. Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of CETP inhibitors. PLoS Comput. Biol. 5(5):e1000387, 2009.PubMedPubMedCentralCrossRefGoogle Scholar
  100. 100.
    Xu, F., J. Celli, I. Rizvi, S. Moon, T. Hasan, and U. Demirci. A three-dimensional in vitro ovarian cancer coculture model using a high-throughput cell patterning platform. Biotechnol. J. 6:204–212, 2011.PubMedPubMedCentralCrossRefGoogle Scholar
  101. 101.
    Yankeelov, T. E., R. G. Abramson, and C. C. Quarles. Quantitative multimodality imaging in cancer research and therapy. Nat. Rev. Clin. Oncol. 11(11):670–680, 2014.PubMedPubMedCentralCrossRefGoogle Scholar
  102. 102.
    Yankeelov, T. E., N. Atuegwu, D. Hormuth, J. A. Weis, S. L. Barnes, M. I. Miga, E. C. Rericha, and V. Quaranta. Clinically relevant modeling of tumor growth and treatment response. Sci. Transl. Med. 5(187):187ps9, 2013.PubMedPubMedCentralCrossRefGoogle Scholar
  103. 103.
    Yankeelov, T. E., V. Quaranta, K. J. Evans, and E. C. Rericha. Toward a science of tumor forecasting for clinical oncology. Cancer Res. 75(6):918–923, 2015.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2016

Authors and Affiliations

  • Thomas E. Yankeelov
    • 1
  • Gary An
    • 2
  • Oliver Saut
    • 3
  • E. Georg Luebeck
    • 4
  • Aleksander S. Popel
    • 5
  • Benjamin Ribba
    • 6
  • Paolo Vicini
    • 7
  • Xiaobo Zhou
    • 8
  • Jared A. Weis
    • 9
  • Kaiming Ye
    • 10
  • Guy M. Genin
    • 11
  1. 1.Departments of Biomedical Engineering and Internal Medicine, Institute for Computational and Engineering SciencesCockrell School of Engineering, The University of Texas at AustinAustinUSA
  2. 2.Department of Surgery and Computation InstituteThe University of ChicagoChicagoUSA
  3. 3.Institut de Mathématiques de Bordeaux, Université de Bordeaux and INRIABordeauxFrance
  4. 4.Program in Computational BiologyFred Hutchinson Cancer Research CenterSeattleUSA
  5. 5.Departments of Biomedical Engineering and OncologyJohns Hopkins University School of MedicineBaltimoreUSA
  6. 6.Pharma Research and Early Development, Clinical PharmacologyF. Hoffmann-La Roche LtdBaselSwitzerland
  7. 7.Clinical Pharmacology and DMPKMedImmuneGaithersburgUSA
  8. 8.Center for Bioinformatics and Systems Biology, RadiologyWake Forest University School of MedicineWinston-SalemUSA
  9. 9.Department of Biomedical EngineeringVanderbilt UniversityNashvilleUSA
  10. 10.Department of Biomedical EngineeringWatson School of Engineering and Applied Science, Binghamton University, State University of New YorkBinghamtonUSA
  11. 11.Departments of Mechanical Engineering and Materials Science, and Neurological SurgeryWashington University in St. LouisSt. LouisUSA

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