TY - JOUR
T1 - Integrated PK-PD and agent-based modeling in oncology
AU - Wang, Zhihui
AU - Butner, Joseph D.
AU - Cristini, Vittorio
AU - Deisboeck, Thomas S.
N1 - Funding Information:
This work has been supported in part by the National Science Foundation (NSF) Grant DMS-1263742 (Z.W., V.C.), the National Institutes of Health Grant (NIH) 1U54CA149196, 1U54CA143837, 1U54CA151668, 1U54CA143907 (V.C.), King Abdulaziz University (KAU) Grant No. 54-130-35-HiCi (V.C.), the University of New Mexico Cancer Center Victor and Ruby Hansen Surface Professorship in Molecular Modeling of Cancer (V.C.), and the Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging and the Department of Radiology at Massachusetts General Hospital (T.S.D.). Finally, we apologize to those of our colleagues whose works could not be cited due to space limitations.
Publisher Copyright:
© 2015, Springer Science+Business Media New York.
PY - 2015/3/11
Y1 - 2015/3/11
N2 - 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.
AB - 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.
KW - Chemotherapy
KW - Computer simulation
KW - Mathematical modeling
KW - Multiscale
KW - Translational research
KW - Tumor growth and invasion
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U2 - 10.1007/s10928-015-9403-7
DO - 10.1007/s10928-015-9403-7
M3 - Review article
C2 - 25588379
AN - SCOPUS:84925539636
SN - 1567-567X
VL - 42
SP - 179
EP - 189
JO - Journal of Pharmacokinetics and Pharmacodynamics
JF - Journal of Pharmacokinetics and Pharmacodynamics
IS - 2
ER -