Physiologically based pharmacokinetic and pharmacodynamic modeling in cancer drug development: status, potential and gaps

Bibliography

• PHvan der Graaf, NBenson. Systems pharmacology: bridging systems biology and pharmacokinetics-pharmacodynamics (PKPD) in drug discovery and development. Pharm Res 2011;28(7):1460-4
   PubMed Web of Science ®Google Scholar
• PVicini, PHvan der Graaf. Systems pharmacology for drug discovery and development: paradigm shift or flash in the pan? Clin Pharmacol Ther 2013;93(5):379-81
   PubMed Web of Science ®Google Scholar
• SWillmann, CBecker, RBurghaus, et al. Development of a paediatric population-based model of the pharmacokinetics of rivaroxaban. Clin Pharmacokinet 2014;53(1):89-102
   PubMed Web of Science ®Google Scholar
• SWillmann. [The in silico Child. Can computer simulations replace clinical pharmacokinetic studies?]. Pharm Unserer Zeit 2009;38(1):62-7
   PubMedGoogle Scholar
• ANEdginton, WSchmitt, SWillmann. Development and evaluation of a generic physiologically based pharmacokinetic model for children. Clin Pharmacokinet 2006;45(10):1013-34
   PubMed Web of Science ®Google Scholar
• XLJiang, PZhao, JSBarrett, et al. Application of physiologically based pharmacokinetic modeling to predict acetaminophen metabolism and pharmacokinetics in children. CPT Pharmacometrics Syst Pharmacol 2013;2:e80
   PubMedGoogle Scholar
• JSBarrett, ODella Casa Alberighi, SLaer, BMeibohm. Physiologically based pharmacokinetic (PBPK) modeling in children. Clin Pharmacol Ther 2012;92(1):40-9
   PubMed Web of Science ®Google Scholar
• SBjorkman. Prediction of drug disposition in infants and children by means of physiologically based pharmacokinetic (PBPK) modelling: theophylline and midazolam as model drugs. Br J Clin Pharmacol 2005;59(6):691-704
   PubMed Web of Science ®Google Scholar
• EManolis, GPons. Proposals for model-based paediatric medicinal development within the current European Union regulatory framework. Br J Clin Pharmacol 2009;68(4):493-501
   PubMed Web of Science ®Google Scholar
• PZhao, MRowland, SMHuang. Best practice in the use of physiologically based pharmacokinetic modeling and simulation to address clinical pharmacology regulatory questions. Clin Pharmacol Ther 2012;92(1):17-20
   PubMed Web of Science ®Google Scholar
• PZhao, LZhang, JAGrillo, et al. Applications of physiologically based pharmacokinetic (PBPK) modeling and simulation during regulatory review. Clin Pharmacol Ther 2011;89(2):259-67
   PubMed Web of Science ®Google Scholar
• JZisowsky, AKrause, JDingemanse. Drug Development for Pediatric Populations: Regulatory Aspects. Pharmaceutics 2010;2(4):364-88
   PubMedGoogle Scholar
• ANEdginton, WSchmitt, SWillmann. Application of physiology-based pharmacokinetic and pharmacodynamic modeling to individualized target-controlled propofol infusions. Adv Ther 2006;23(1):143-58
   PubMed Web of Science ®Google Scholar
• ANEdginton, SWillmann. Physiology-based simulations of a pathological condition: prediction of pharmacokinetics in patients with liver cirrhosis. Clin Pharmacokinet 2008;47(11):743-52
   PubMed Web of Science ®Google Scholar
• TEissing, JLippert, SWillmann. Pharmacogenomics of codeine, morphine, and morphine-6-glucuronide: model-based analysis of the influence of CYP2D6 activity, UGT2B7 activity, renal impairment, and CYP3A4 inhibition. Mol Diagn Ther 2012;16(1):43-53
   PubMed Web of Science ®Google Scholar
• GFLi, KWang, RChen, et al. Simulation of the pharmacokinetics of bisoprolol in healthy adults and patients with impaired renal function using whole-body physiologically based pharmacokinetic modeling. Acta Pharmacol Sin 2012;33(11):1359-71
   PubMed Web of Science ®Google Scholar
• MLVieira, PZhao, EGBerglund, et al. Predicting drug interaction potential with a physiologically based pharmacokinetic model: a case study of telithromycin, a time-dependent CYP3A inhibitor. Clin Pharmacol Ther 2012;91(4):700-8
   PubMed Web of Science ®Google Scholar
• MVossen, MSevestre, CNiederalt, et al. Dynamically simulating the interaction of midazolam and the CYP3A4 inhibitor itraconazole using individual coupled whole-body physiologically-based pharmacokinetic (WB-PBPK) models. Theor Biol Med Model 2007;4:13
   PubMed Web of Science ®Google Scholar
• SWillmann, ANEdginton, MKleine-Besten, et al. Whole-body physiologically based pharmacokinetic population modelling of oral drug administration: inter-individual variability of cimetidine absorption. J Pharm Pharmacol 2009;61(7):891-9
   PubMed Web of Science ®Google Scholar
• CDiestelhorst, JBoos, JSMcCune, et al. Predictive performance of a physiologically based pharmacokinetic model of busulfan in children. Pediatr Hematol Oncol 2014;31(8):731-42
   PubMed Web of Science ®Google Scholar
• OWeber, SWillmann, HBischoff, et al. Prediction of a potentially effective dose in humans for BAY 60-5521, a potent inhibitor of cholesteryl ester transfer protein (CETP) by allometric species scaling and combined pharmacodynamic and physiologically-based pharmacokinetic modelling. Br J Clin Pharmacol 2012;73(2):219-31
   PubMed Web of Science ®Google Scholar
• TRodgers, DLeahy, MRowland. Physiologically based pharmacokinetic modeling 1: predicting the tissue distribution of moderate-to-strong bases. J Pharm Sci 2005;94(6):1259-76
   PubMed Web of Science ®Google Scholar
• TRodgers, MRowland. Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. J Pharm Sci 2006;95(6):1238-57
   PubMed Web of Science ®Google Scholar
• WSchmitt, SWillmann. Physiology-based pharmacokinetic modeling: ready to be used. Drug Discov Today Technol 2004;1(4):449-56
   PubMedGoogle Scholar
• SWillmann, WSchmitt, JKeldenich, JBDressman. A physiologic model for simulating gastrointestinal flow and drug absorption in rats. Pharm Res 2003;20(11):1766-71
   PubMed Web of Science ®Google Scholar
• SWillmann, WSchmitt, JKeldenich, et al. A physiological model for the estimation of the fraction dose absorbed in humans. J Med Chem 2004;47(16):4022-31
   PubMed Web of Science ®Google Scholar
• LMBerezhkovskiy. Volume of distribution at steady state for a linear pharmacokinetic system with peripheral elimination. J Pharm Sci 2004;93(6):1628-40
   PubMed Web of Science ®Google Scholar
• WSchmitt. General approach for the calculation of tissue to plasma partition coefficients. Toxicol In Vitro 2008;22(2):457-67
   PubMed Web of Science ®Google Scholar
• PPoulin, FPTheil. Prediction of pharmacokinetics prior to in vivo studies. II. Generic physiologically based pharmacokinetic models of drug disposition. J Pharm Sci 2002;91(5):1358-70
   PubMed Web of Science ®Google Scholar
• PPoulin, FPTheil. Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution. J Pharm Sci 2002;91(1):129-56
   PubMed Web of Science ®Google Scholar
• KBeck, MBeedle, Avan Bennekum, et al. Manifesto for agile software development. 2001. Available from: http://www.agilemanifesto.org/
   Google Scholar
• CABoswell, EEMundo, RFirestein, et al. An integrated approach to identify normal tissue expression of targets for antibody-drug conjugates: case study of TENB2. Br J Pharmacol 2013;168(2):445-57
   PubMed Web of Science ®Google Scholar
• CABoswell, EEMundo, CZhang, et al. Differential effects of predosing on tumor and tissue uptake of an 111In-labeled anti-TENB2 antibody-drug conjugate. J Nucl Med 2012;53(9):1454-61
   PubMed Web of Science ®Google Scholar
• SCheeti, NRBudha, SRajan, et al. A physiologically based pharmacokinetic (PBPK) approach to evaluate pharmacokinetics in patients with cancer. Biopharm Drug Dispos 2013;34(3):141-54
   PubMed Web of Science ®Google Scholar
• KDickschen, TEissing, TMurdter, et al. Concomitant use of tamoxifen and endoxifen in postmenopausal early breast cancer: prediction of plasma levels by physiologically-based pharmacokinetic modeling. Springerplus 2014;3:285
   Google Scholar
• KDickschen, SWillmann, KThelen, et al. Physiologically based pharmacokinetic modeling of tamoxifen and its metabolites in women of different CYP2D6 phenotypes provides new insight into the tamoxifen mass balance. Front Pharmacol 2012;3:92
   Google Scholar
• TEissing, LKuepfer, CBecker, et al. A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks. Front Physiol 2011;2:4
   PubMed Web of Science ®Google Scholar
• JFoo, JChmielecki, WPao, FMichor. Effects of pharmacokinetic processes and varied dosing schedules on the dynamics of acquired resistance to erlotinib in EGFR-mutant lung cancer. J Thorac Oncol 2012;7(10):1583-93
   PubMed Web of Science ®Google Scholar
• AGarg, JPBalthasar. Physiologically-based pharmacokinetic (PBPK) model to predict IgG tissue kinetics in wild-type and FcRn-knockout mice. J Pharmacokinet Pharmacodyn 2007;34(5):687-709
   PubMed Web of Science ®Google Scholar
• VSinha, PZhao, SMHuang, IZineh. Physiologically based pharmacokinetic modeling: from regulatory science to regulatory policy. Clin Pharmacol Ther 2014;95(5):478-80
   PubMed Web of Science ®Google Scholar
• YZhao, ZYHu. Physiologically based pharmacokinetic modelling and in vivo [I]/K(i) accurately predict P-glycoprotein-mediated drug-drug interactions with dabigatran etexilate. Br J Pharmacol 2014;171(4):1043-53
   PubMed Web of Science ®Google Scholar
• AStrougo, TEissing, AYassen, et al. First dose in children: physiological insights into pharmacokinetic scaling approaches and their implications in paediatric drug development. J Pharmacokinet Pharmacodyn 2012;39(2):195-203
   PubMed Web of Science ®Google Scholar
• SMHuang, MRowland. The role of physiologically based pharmacokinetic modeling in regulatory review. Clin Pharmacol Ther 2012;91(3):542-9
   PubMed Web of Science ®Google Scholar
• CWagner, YPan, VHsu, et al. Predicting the effect of cytochrome P450 inhibitors on substrate drugs: analysis of physiologically based pharmacokinetic modeling submissions to the US Food and Drug Administration. Clin Pharmacokinet 2015;54(1):117-27
   PubMed Web of Science ®Google Scholar
• PPoulin, CEHop, LSalphati, BMLiederer. Correlation of tissue-plasma partition coefficients between normal tissues and subcutaneous xenografts of human tumor cell lines in mouse as a prediction tool of drug penetration in tumors. J Pharm Sci 2013;102(4):1355-69
   PubMed Web of Science ®Google Scholar
• JLWilding, WFBodmer. Cancer cell lines for drug discovery and development. Cancer Res 2014;74(9):2377-84
   PubMed Web of Science ®Google Scholar
• BARuggeri, FCamp, SMiknyoczki. Animal models of disease: pre-clinical animal models of cancer and their applications and utility in drug discovery. Biochem Pharmacol 2014;87(1):150-61
   PubMed Web of Science ®Google Scholar
• FBilly, BRibba, OSaut, et al. A pharmacologically based multiscale mathematical model of angiogenesis and its use in investigating the efficacy of a new cancer treatment strategy. J Theor Biol 2009;260(4):545-62
   PubMed Web of Science ®Google Scholar
• HMByrne. Dissecting cancer through mathematics: from the cell to the animal model. Nat Rev Cancer 2010;10(3):221-30
   PubMed Web of Science ®Google Scholar
• MAChaplain, SRMcDougall, ARAnderson. Mathematical modeling of tumor-induced angiogenesis. Annu Rev Biomed Eng 2006;8:233-57
   PubMed Web of Science ®Google Scholar
• PMacklin, SMcDougall, ARAnderson, et al. Multiscale modelling and nonlinear simulation of vascular tumour growth. J Math Biol 2009;58(4-5):765-98
   PubMed Web of Science ®Google Scholar
• FDel Bene, MGermani, GDe Nicolao, et al. A model-based approach to the in vitro evaluation of anticancer activity. Cancer Chemother Pharmacol 2009;63(5):827-36
   PubMed Web of Science ®Google Scholar
• PMagni, MSimeoni, IPoggesi, et al. A mathematical model to study the effects of drugs administration on tumor growth dynamics. Math Biosci 2006;200(2):127-51
   PubMed Web of Science ®Google Scholar
• MRocchetti, FDel Bene, MGermani, et al. Testing additivity of anticancer agents in pre-clinical studies: a PK/PD modelling approach. Eur J Cancer 2009;45(18):3336-46
   PubMed Web of Science ®Google Scholar
• MRocchetti, MGermani, FDel Bene, et al. Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth after administration of an anti-angiogenic agent, bevacizumab, as single-agent and combination therapy in tumor xenografts. Cancer Chemother Pharmacol 2013;71(5):1147-57
   PubMed Web of Science ®Google Scholar
• MSimeoni, GDe Nicolao, PMagni, et al. Modeling of human tumor xenografts and dose rationale in oncology. Drug Discov Today Technol 2013;10(3):e365-72
   Google Scholar
• MSimeoni, PMagni, CCammia, et al. Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents. Cancer Res 2004;64(3):1094-101
   PubMed Web of Science ®Google Scholar
• NTerranova, MGermani, FDel Bene, PMagni. A predictive pharmacokinetic-pharmacodynamic model of tumor growth kinetics in xenograft mice after administration of anticancer agents given in combination. Cancer Chemother Pharmacol 2013;72(2):471-82
   PubMed Web of Science ®Google Scholar
• BRibba, NHHolford, PMagni, et al. A review of mixed-effects models of tumor growth and effects of anticancer drug treatment used in population analysis. CPT Pharmacometrics Syst Pharmacol 2014;3:e113
   PubMedGoogle Scholar
• MRocchetti, MSimeoni, EPesenti, et al. Predicting the active doses in humans from animal studies: a novel approach in oncology. Eur J Cancer 2007;43(12):1862-8
   PubMed Web of Science ®Google Scholar
• DEMager, EWyska, WJJusko. Diversity of mechanism-based pharmacodynamic models. Drug Metab Dispos 2003;31(5):510-18
   PubMed Web of Science ®Google Scholar
• MBlock, EScholl, DDrasdo. Classifying the expansion kinetics and critical surface dynamics of growing cell populations. Phys Rev Lett 2007;99(24):248101
   PubMedGoogle Scholar
• TSDeisboeck, ZWang, PMacklin, VCristini. Multiscale cancer modeling. Annu Rev Biomed Eng 2011;13:127-55
   PubMed Web of Science ®Google Scholar
• MRadszuweit, MBlock, JGHengstler, et al. Comparing the growth kinetics of cell populations in two and three dimensions. Phys Rev E Stat Nonlin Soft Matter Phys 2009;79(5 Pt 1):051907
   Google Scholar
• HByrne, DDrasdo. Individual-based and continuum models of growing cell populations: a comparison. J Math Biol 2009;58(4-5):657-87
   PubMed Web of Science ®Google Scholar
• IWMak, NEvaniew, MGhert. Lost in translation: animal models and clinical trials in cancer treatment. Am J Transl Res 2014;6(2):114-18
   PubMed Web of Science ®Google Scholar
• JFoo, FMichor. Evolution of resistance to anti-cancer therapy during general dosing schedules. J Theor Biol 2010;263(2):179-88
   PubMed Web of Science ®Google Scholar
• MVanneman, GDranoff. Combining immunotherapy and targeted therapies in cancer treatment. Nat Rev Cancer 2012;12(4):237-51
   PubMed Web of Science ®Google Scholar
• DMPardoll. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer 2012;12(4):252-64
   PubMed Web of Science ®Google Scholar
• GKAlderton, YBordon. Tumour immunotherapy--leukocytes take up the fight. Nat Rev Immunol 2012;12(4):237
   PubMed Web of Science ®Google Scholar
• KPWilkie, PHahnfeldt. Mathematical models of immune-induced cancer dormancy and the emergence of immune evasion. Interface Focus 2013;3(4):20130010
   PubMed Web of Science ®Google Scholar
• KPWilkie. A review of mathematical models of cancer-immune interactions in the context of tumor dormancy. Adv Exp Med Biol 2013;734:201-34
   PubMed Web of Science ®Google Scholar
• BZhao, MTHemann, DALauffenburger. Intratumor heterogeneity alters most effective drugs in designed combinations. Proc Natl Acad Sci USA 2014;111(29):10773-8
   PubMed Web of Science ®Google Scholar
• MGVander Heiden, LCCantley, CBThompson. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 2009;324(5930):1029-33
   PubMed Web of Science ®Google Scholar
• RJDeBerardinis, JJLum, GHatzivassiliou, CBThompson. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab 2008;7(1):11-20
   PubMed Web of Science ®Google Scholar
• PPHsu, DMSabatini. Cancer cell metabolism: Warburg and beyond. Cell 2008;134(5):703-7
   PubMed Web of Science ®Google Scholar
• MJang, SSKim, JLee. Cancer cell metabolism: implications for therapeutic targets. Exp Mol Med 2013;45:e45
   PubMed Web of Science ®Google Scholar
• NBenson, PHvan der Graaf. The rise of systems pharmacology in drug discovery and development. Future Med Chem 2014;6(16):1731-4
   PubMed Web of Science ®Google Scholar
• JPBai. Quantitative systems pharmacology for shifting the drug discovery and development paradigm. Biopharm Drug Dispos 2013;34(9):475-6
   PubMed Web of Science ®Google Scholar
• BMAgoram, ODemin. Integration not isolation: arguing the case for quantitative and systems pharmacology in drug discovery and development. Drug Discov Today 2011;16(23-24):1031-6
   PubMed Web of Science ®Google Scholar
• SRAllerheiligen. Next-generation model-based drug discovery and development: quantitative and systems pharmacology. Clin Pharmacol Ther 2010;88(1):135-7
   PubMed Web of Science ®Google Scholar