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Research ArticleDrug Discovery and Translational Medicine

Systematic Modeling and Design Evaluation of Unperturbed Tumor Dynamics in Xenografts

Zinnia P. Parra-Guillen, Victor Mangas-Sanjuan, Maria Garcia-Cremades, Iñaki F. Troconiz, Gary Mo, Celine Pitou, Philip W. Iversen and Johan E. Wallin
Journal of Pharmacology and Experimental Therapeutics July 2018, 366 (1) 96-104; DOI: https://doi.org/10.1124/jpet.118.248286
Zinnia P. Parra-Guillen
Pharmacometrics and Systems Pharmacology Research Unit, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Navarra Institute for Health Research, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Global PK/PD & Pharmacometrics (G.M., C.P., J.E.W.) and Lilly Research Laboratories (P.W.I.), Eli Lilly and Company, Indianapolis, USA Solna, Sweden
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Victor Mangas-Sanjuan
Pharmacometrics and Systems Pharmacology Research Unit, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Navarra Institute for Health Research, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Global PK/PD & Pharmacometrics (G.M., C.P., J.E.W.) and Lilly Research Laboratories (P.W.I.), Eli Lilly and Company, Indianapolis, USA Solna, Sweden
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Maria Garcia-Cremades
Pharmacometrics and Systems Pharmacology Research Unit, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Navarra Institute for Health Research, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Global PK/PD & Pharmacometrics (G.M., C.P., J.E.W.) and Lilly Research Laboratories (P.W.I.), Eli Lilly and Company, Indianapolis, USA Solna, Sweden
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Iñaki F. Troconiz
Pharmacometrics and Systems Pharmacology Research Unit, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Navarra Institute for Health Research, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Global PK/PD & Pharmacometrics (G.M., C.P., J.E.W.) and Lilly Research Laboratories (P.W.I.), Eli Lilly and Company, Indianapolis, USA Solna, Sweden
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Gary Mo
Pharmacometrics and Systems Pharmacology Research Unit, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Navarra Institute for Health Research, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Global PK/PD & Pharmacometrics (G.M., C.P., J.E.W.) and Lilly Research Laboratories (P.W.I.), Eli Lilly and Company, Indianapolis, USA Solna, Sweden
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Celine Pitou
Pharmacometrics and Systems Pharmacology Research Unit, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Navarra Institute for Health Research, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Global PK/PD & Pharmacometrics (G.M., C.P., J.E.W.) and Lilly Research Laboratories (P.W.I.), Eli Lilly and Company, Indianapolis, USA Solna, Sweden
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Philip W. Iversen
Pharmacometrics and Systems Pharmacology Research Unit, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Navarra Institute for Health Research, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Global PK/PD & Pharmacometrics (G.M., C.P., J.E.W.) and Lilly Research Laboratories (P.W.I.), Eli Lilly and Company, Indianapolis, USA Solna, Sweden
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Johan E. Wallin
Pharmacometrics and Systems Pharmacology Research Unit, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Navarra Institute for Health Research, Pamplona, Spain (Z.P.P.-G.,V.M.-S.,M.G.-C., I.F.T.); Global PK/PD & Pharmacometrics (G.M., C.P., J.E.W.) and Lilly Research Laboratories (P.W.I.), Eli Lilly and Company, Indianapolis, USA Solna, Sweden
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  • Fig. 1.
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    Fig. 1.

    Visual predictive checks of the unperturbed tumor growth model corresponding to lung tumor cell lines. Lines represent the 2.5th (dashed), 50th (solid), and 97.5th (dashed) percentiles of raw data. Gray areas correspond to the 95% confidence interval of the 2.5th, 50th, and 97.5th percentiles computed from 1000 simulated studies. Black lower marks indicate the used binning intervals for plotting.

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    Fig. 2.

    (A) Graphical representation of the parameter estimates for the different tumor cells lines categorized by the number of studies available and the associated variability magnitude. (B) Relation between the estimate of the different IAVs and the number of mice included in the analysis of each cell line.

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    Fig. 3.

    (A) Box plot of the relative S.E. (RSE) values for the structural and variability model parameters (parameter definition provided in Materials and Methods) of the different cell lines assuming samples taken every 2 days (Q2D). (B) Box plot representing the predicted precision when evaluating the sampling designs twice per week (BIW), once per week (QW), or the optimized sampling schema (OPT8) with respect to the Q2D reference design. The box represents the interquartile range (IQR) and the whiskers expand up to 1.5 times the IQR range. Dots represent outliers; RUV denotes residual unexplained variability.

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    Fig. 4.

    Relative S.E. values of the typical (upper panels) or IAV (lower panels) model parameters vs. the number of mice included in the analysis of the different cell lines. BIW, tumor size samples taken twice per week; Q2D, tumor size samples taken every two days; QW, tumor size samples taken every week.

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    Fig. 5.

    TV profiles for three selected cell lines. Lines represent model predictions and dots represent the optimized sampling times.

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    TABLE 1

    Summary of the experimental data available for each of the tumor cell lines

    The Tmax value represents the latest sampling time available in the experiment; the Tmin value represents the time at which the tumor could start to be measured.

    TumorCellNumber of StudiesNumber of MiceNumber of SamplesNumber of Days
    TminTmax
    BreastMB-2311454231145
    ColonCOLO2055856686120
    ColonHCT11611025717
    GlioblastomaU-87 MG3100382763
    LeukemiaMV411233388773
    LungA549233389769
    LungCalu-6225288759
    LungDMS5316120798
    LungH1650221277762
    LungH1975361361741
    LungH2122892763750
    LungH358181601088
    LungH441172873391398
    LungH460113109847
    LungHCC82718991055
    LymphomaJEKO-1431312746
    LymphomaOCI-LY-19238178732
    LymphomaWILL-212480723
    MelanomaA2058220177735
    MelanomaA3758104840369
    MelanomaGAK215181856
    MelanomaSK-MEL-30224229045
    OvarianA27801868837
    OvarianSKVO-318104747
    PancreasMIA PaCa-218641141
    Renal786-o452740676
    RenalACHN350604760
    RenalCaki-11131161351
    • View popup
    TABLE 2

    Model parameter estimates corresponding to all the tumor cell lines analyzed

    The model parameters are defined in Materials and Methods.

    Tumor LineCell LineParameter
    λ0λ1TV0TVthIAV λ0IAV λ1IAV TV0RUV
    day−1mm3/daymm3mm3CV%CV%CV%Log mm3
    LungA5490.05158.548.2115019.8a29.5a0.205
    LungH4600.10423771.3228025.225.90.148
    LungHCC8270.10035.121.835116.90.194
    LungCalu-60.087512649.9144027.253.212.60.147
    LungDMS530.043827.616.063011.00.316
    LungH16500.057548.050.683520.112.80.201
    LungH19750.10418846.3181014.80.202
    LungH3580.020411588.6564030.00.129
    LungH4410.034275.069.6219068.640.10.223
    LungH21220.10342.660.241420.445.123.00.151
    RenalACHN0.024912.812351468.742.519.70.162
    RenalCAKI-10.026564.2102242014.30.239
    Renal786-o0.048929.582.460322.467.726.40.176
    MelanomaA20580.12955.964.74338.2039.60.154
    MelanomaA3750.094810169.3107039.867.236.30.193
    MelanomaGAK0.027719.088.868645.517.60.191
    MelanomaSK-MEL-300.064084.8148133045.923.70.200
    LymphomaJEKO-10.082521762.9263010.519.30.284
    LymphomaOCI-LY-190.13846430.033609.700.320
    LymphomaWILL-20.20335225.3173028.00.263
    ColonCOLO2050.10631.965.630125.188.161.50.211
    ColonHCT1160.12117667.5146019.30.409
    GlioblastomaU-87-MG0.054751.980.494938.929.00.400
    LeukemiaMV4110.069566.544.395725.4b72.6b34.70.194
    OvarianA27800.10325459.0366023.80.216
    OvarianSKVO-30.079855.461.469410.00.203
    BreastMB2310.500 FIX2.5881.95.0041.823.60.122
    PancreasMIA PaCa-20.12021.643.118058.215.40.114
    • RUV, residual unexplained variability.

    • ↵a Correlation between IAV λ0 and IAV TV0 of −90%.

    • ↵b Correlation between IAV λ0 and IAV λ1 of 100%.

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Journal of Pharmacology and Experimental Therapeutics: 366 (1)
Journal of Pharmacology and Experimental Therapeutics
Vol. 366, Issue 1
1 Jul 2018
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Research ArticleDrug Discovery and Translational Medicine

Unperturbed Tumor Growth Modeling and Design

Zinnia P. Parra-Guillen, Victor Mangas-Sanjuan, Maria Garcia-Cremades, Iñaki F. Troconiz, Gary Mo, Celine Pitou, Philip W. Iversen and Johan E. Wallin
Journal of Pharmacology and Experimental Therapeutics July 1, 2018, 366 (1) 96-104; DOI: https://doi.org/10.1124/jpet.118.248286

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Research ArticleDrug Discovery and Translational Medicine

Unperturbed Tumor Growth Modeling and Design

Zinnia P. Parra-Guillen, Victor Mangas-Sanjuan, Maria Garcia-Cremades, Iñaki F. Troconiz, Gary Mo, Celine Pitou, Philip W. Iversen and Johan E. Wallin
Journal of Pharmacology and Experimental Therapeutics July 1, 2018, 366 (1) 96-104; DOI: https://doi.org/10.1124/jpet.118.248286
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