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Population pharmacokinetic model of irinotecan and its metabolites in patients with metastatic colorectal cancer



Irinotecan (CPT-11) is a drug used against a wide range of tumor types. The individualized dosing of CPT-11 is essential to ensure optimal pharmacotherapy in cancer patients, given the wide interindividual pharmacokinetic variability of this drug and its active metabolite SN-38. Moreover, the reabsorption from SN-38-G to SN-38, by enterohepatic recirculation, is critical due to its influence in the treatment tolerance. The aim of this research was to build a joint population pharmacokinetic model for CPT-11 and its metabolites (SN-38, and its glucuronide, SN-38-G) that enabled an individualized posology adjustment.


We used data of 53 treatment cycles of FOLFIRINOX scheme corresponding to 20 patients with metastatic colorectal cancer. In order to build the population pharmacokinetic model, we implemented parametric and non-parametric methods using the Pmetrics library package for R. We also built multivariate regression models to predict the area under the curve and the maximum concentration using basal covariates.


The final model was a multicompartmental model which represented the transformations from CPT-11 to its active metabolite SN-38 and from SN-38 to inactive SN-38-G. Besides, the model also represented the extensive elimination of SN-38-G and the reconversion of the remaining SN-38-G to SN-38 by enterohepatic recirculation. We carried out internal validation with 1000 simulations. The regression models predicted the PK parameters with R squared adjusted up to 0.9499.


CPT-11, SN-38, and SN-38-G can be correctly described by the multicompartmental model presented in this work. As far as we know, it is the first time that a joint model for CPT-11, SN-38, and SN-38-G that includes the process of reconversion from SN-38-G to SN-38 is characterized.

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  1. Quetglas EG, Armuzzi A, Wigge S, Fiorino G, Barnscheid L, Froelich M, Danese S (2015) Review article: the pharmacokinetics and pharmacodynamics of drugs used in inflammatory bowel disease treatment. Eur J Clin Pharmacol 71:773–799.

    Article  CAS  PubMed  Google Scholar 

  2. Conroy T, Desseigne F, Ychou M, Bouché O, Guimbaud R, Bécourarn Y, Adenis A, Raoul J-L, Gourgou-Bourgade S, de la Fochardiere C, Bennouna J, Bachet J-B, Khemissa-Akouz F, Péré-Vergé D, Delbaldo C, Assenat E, Chauffert B, Michel P, Montoto-Grillot C, Ducreux M (2011) FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer. N Engl J Med 364:1817–1825

    Article  CAS  PubMed  Google Scholar 

  3. Muranaka T, Kuwatani M, Komatsu Y, Sawada K, Nakatsumi H, Kawamoto Y, Yuki S, Kubota Y, Kubo K, Kawahata S, Kawakubo K, Kawakami H, Sakamoto N (2017) Comparison of efficacy and toxicity of FOLFIRINOX and gemcitabine with nab-paclitaxel in unresectable pancreatic cancer. J Gastrointest Oncol 8:566–571.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Burris H, Fields S (1994) Topoisomerase I inhibitors. An overview of the camptothecin analogs. Hematol Oncol Clin North Am 8:333–355

    Article  PubMed  Google Scholar 

  5. Peterson C (2011) Drug therapy of cancer. Eur J Clin Pharmacol 67:437–447.

    Article  CAS  PubMed  Google Scholar 

  6. Berg AK, Buckner JC, Galanis E, Jaeckle KA, Ames MM, Reid JM (2015) Quantification of the impact of enzyme-inducing antiepileptic drugs on irinotecan pharmacokinetics and SN-38 exposure. J Clin Pharmacol 55:1303–1312.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Poujol S, Bressolle F, Duffour J, Abderrahim AG, Astre C, Ychou M, Pinguet F (2006) Pharmacokinetics and pharmacodynamics of irinotecan and its metabolites from plasma and saliva data in patients with metastatic digestive cancer receiving Folfiri regimen. Cancer Chemother Pharmacol 58:292–305.

    Article  CAS  PubMed  Google Scholar 

  8. Valenzuela Jiménez B, González Sales M, Escudero Ortiz V, Martínez Navarro E, Pérez Ruixo C, Rebollo Liceaga J, González Manzano R, Pérez Ruixo JJ (2013) Influencia de los polimorfismos genéticos en UGT1A1, UGT1A7 and UGT1A9 sobre la farmacocinética de irinotecan, SN-38 y SN-38G. Farm Hosp 37:111–127.

    Article  PubMed  Google Scholar 

  9. Wakefield J, Racine-Poon A (1995) An application of Bayesian population pharmacokinetic/pharmacodynamic models to dose recommendation. Stat Med 14:971–986.

    Article  CAS  PubMed  Google Scholar 

  10. Asuphon O, Montakantikul P, Houngsaitong J, Kiratisin P, Sonthisombat P (2016) Optimizing intravenous fosfomycin dosing in combination with carbapenems for treatment of Pseudomonas aeruginosa infections in critically ill patients based on pharmacokinetic/pharmacodynamic (PK/PD) simulation. Int J Infect Dis 50:23–29.

    Article  CAS  PubMed  Google Scholar 

  11. Oteo I, Lukas JC, Leal N, Suarez E, Valdivieso A, Gastaca M, Ortiz De Urbina J, Calvo R (2013) Tacrolimus pharmacokinetics in the early post-liver transplantation period and clinical applicability via Bayesian prediction. Eur J Clin Pharmacol 69:65–74.

    Article  CAS  PubMed  Google Scholar 

  12. Usman M, Frey OR, Hempel G (2017) Population pharmacokinetics of meropenem in elderly patients: dosing simulations based on renal function. Eur J Clin Pharmacol 73:333–342.

    Article  CAS  PubMed  Google Scholar 

  13. Oken M, Creech R, Tormey D, Horton J, Davis T, McFadden E, Carbone P (1982) Toxicity and response criteria of the eastern cooperative oncology group. Am J Clin Oncol 5:649–656

    Article  CAS  Google Scholar 

  14. Castellanos Lácar MC (2003) Farmacocinética y farmacodinamia de irinotecan en pacientes con carcinoma colorrectal metastásico. Universidad de Navarra

  15. Escoriaza J, Aldaz A, Castellanos C, Calvo E, Giráldez J (2000) Simple and rapid determination of irinotecan and its metabolite SN-38 in plasma by high-performance liquid-chromatography: application to clinical pharmacokinetic studies. J Chromatogr B 740:159–168

    Article  CAS  Google Scholar 

  16. Jelliffe R, Schumitzky A, Van Guilder M, Wang X, Leary R (2001) Population pharmacokinetic models: parametric and nonparametric approaches In: 14th IEEE Symposium on Computer-Based Medical Systems (CBMS), pp 407–412

  17. Leary R, Jelliffe R, Schumitzky A, Van Guilder M (2001) An adaptive grid non-parametric approach to pharmacokinetic and dynamic (PK/PD) population models. In: IEEE Symposium on Computer-Based Medical Systems pp 389–394

  18. Neely MN, van Guilder MG, Yamada WM, Schumitzky A, Jelliffe RW (2012) Accurate detection of outliers and subpopulations with Pmetrics, a non-parametric and parametric pharmacometric modeling and simulation package for R. Ther Drug Monit 34:467–476.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Chen S, Yueh M-F, Bigo C, Barbier O, Wang K, Karin M, Nguyen N, Tukey RH (2013) Intestinal glucuronidation protects against chemotherapy-induced toxicity by irinotecan (CPT-11). Proc Natl Acad Sci 110:19143–19148.

    Article  CAS  PubMed  Google Scholar 

  20. Xie R, Mathijssen RHJ, Sparreboom A, Verweij J, Karlsson MO (2002) Clinical pharmacokinetics of irinotecan and its metabolites: a population analysis. J Clin Oncol 20:3293–3301.

    Article  CAS  PubMed  Google Scholar 

  21. Klein CE, Gupta E, Reid JM, Atherton PJ, Sloan JA, Pitot HC, Ratain MJ, Kastrissios H (2002) Population pharmacokinetic model for irinotecan and two of its metabolites, SN-38 and SN-38 glucuronide. Clin Pharmacol Ther 72:638–647.

    Article  CAS  PubMed  Google Scholar 

  22. Rosner GL, Panetta JC, Innocenti F, Ratain MJ (2008) Pharmacogenetic pathway analysis of irinotecan. Clin Pharmacol Ther 84:393–402.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Gupta E, Lestingi TM, Mick R, Ramirez J, Vokes EE, Ratain MJ (1994) Metabolic fate of irinotecan in humans: correlation of glucuronidation with diarrhea. Cancer Res 54:3723–3725

    CAS  PubMed  Google Scholar 

  24. Mathjissen RHJ, van Alphen RJ, Verweij J, Loos WJ, Nooter K, Stoter G, Sparreboom A (2001) Clinical pharmacokinetics and metabolism of irinotecan (CPT-11). Clin Cancer Res 7:2182–2194.

    Article  Google Scholar 

  25. Kodawara T, Higashi T, Negoro Y, Kamitani Y, Igarashi T, Watanabe K, Tsukamoto H, Yano R, Masada M, Iwasaki H, Nakamura T (2016) The inhibitory effect of ciprofloxacin on the β-Glucuronidase-mediated deconjugation of the irinotecan metabolite SN-38-G. Basic Clin Pharmacol Toxicol 118:333–337.

    Article  CAS  PubMed  Google Scholar 

  26. Czejka M, Gruenberger B, Kiss A, Farkouh A, Schueller J (2010) Pharmacokinetics of irinotecan in combination with biweekly cetuximab in patients with advanced colorectal cancer. Anticancer Res 30:2355–2360

    CAS  PubMed  Google Scholar 

  27. Satoh T, Yasui H, Muro K, Komatsu Y, Sameshima S, Yamaguchi K, Sugihara K (2013) Pharmacokinetic assessment of irinotecan, SN-38, and SN-38-glucuronide: a substudy of the FIRIS study. Anticancer Res 33:3845–3854

    CAS  PubMed  Google Scholar 

  28. Innocenti F, Iyer L, Ratain MJ (2001) Pharmacogenetics of anticancer agents: lessons from amonafide and irinotecan. Drug Metab Dispos 29:596–600

    CAS  PubMed  Google Scholar 

  29. Raymond E, Boige V, Faivre S, Sanderink GJ, Rixe O, Vernillet L, Jacques C, Gatineau M, Ducreux M, Armand JP (2002) Dosage adjustment and pharmacokinetic profile of irinotecan in cancer patients with hepatic dysfunction. J Clin Oncol 20:4303–4312.

    Article  CAS  PubMed  Google Scholar 

  30. Rouits E, Charasson V, Pétain A, Boisdron-Celle M, Delord JP, Fonck M, Laurand A, Poirier AL, Morel A, Chatelut E, Robert J, Gamelin E (2008) Pharmacokinetic and pharmacogenetic determinants of the activity and toxicity of irinotecan in metastatic colorectal cancer patients. Br J Cancer 99:1239–1245.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Saltz LB, Cox JV, Blanke C, Rosen LS, Fehrenbacher L, Moore MJ, Maroun JA, Ackland SP, Locker PK, Pirotta N, Elfring GL, Miller LL (2000) Irinotecan plus fluorouracil and leucovorin for metastatic colorectal cancer. N Engl J Med 343:905–914

    Article  CAS  PubMed  Google Scholar 

  32. Saltz LB, Douillard J-Y, Pirotta N, Alakl M, Gruia G, Awad L, Elfring GL, Locker PK, Miller LL (2001) Irinotecan plus fluorouracil/Leucovorin for metastatic colorectal cancer: a new survival standard. Oncologist 6:81–91

    Article  CAS  PubMed  Google Scholar 

  33. Woillard JB, Debord J, Monchaud C, Saint-Marcoux F, Marquet P (2017) Population pharmacokinetics and Bayesian estimators for refined dose adjustment of a new tacrolimus formulation in kidney and liver transplant patients. Clin Pharmacokinet 56:1491–1498.

    Article  CAS  PubMed  Google Scholar 

  34. Flint RB, ter Heine R, Spaans E, Burger DM, de Klerk JCA, Allegaert K, Knibbe CAJ, Simons SHP (2018) Simulation-based suggestions to improve ibuprofen dosing for patent ductus arteriosus in preterm newborns. Eur J Clin Pharmacol 74:1585–1591.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kweekel D, Guchelaar HJ, Gelderblom H (2008) Clinical and pharmacogenetic factors associated with irinotecan toxicity. Cancer Treat Rev 34:656–669.

    Article  CAS  PubMed  Google Scholar 

  36. Bozdogan H (1987) Model selection and Akaike’s Information Criterion (AIC): the general theory and its analytical extensions. Psychometrika 52:345–370.

    Article  Google Scholar 

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This work is partially supported by the “Ayuda para Doctorados Industriales del Ministerio de Economía, Industria y Competitividad” (Ref. DI-15-07511).

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Authors and Affiliations



AA conceived the study and contributed towards study design. EOI and AI analyzed the data. All authors were involved in the interpretation of data. EOI drafted the manuscript. OS and AA were involved in critical revision of the manuscript, with all study authors approving the final version for submission.

Corresponding author

Correspondence to Esther Oyaga-Iriarte.

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This observational study was approved by the University Clinic of Navarre.

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The authors declare that they have no conflicts of interest.

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Methodology of non-compartmental analysis

A non-compartmental analysis was performed using the software WinNonlin version 8 with best fit approximation to the selection of the optimal regression line (which maximizes the coefficient of determination of linear regression) to estimate the elimination constant. We studied the following parameters:

  • AUCCPT-11: Area under the curve of CPT-11

  • AUCSN-38: Area under the curve of SN-38

  • AUCSN-38-G: Area under the curve of SN-38-G

  • C-MAXCPT-11: Maximum serum concentration that CPT-11 achieves

  • C-MAXSN-38: Maximum serum concentration that SN-38 achieves

  • C-MAXSN-38-G: Maximum serum concentration that SN-38-G achieves

  • T-MAXCPT-11: Time at which the C-MAXCPT-11 is observed

  • T-MAXSN-38: Time at which the C-MAXSN-38 is observed

  • T-MAXSN-38-G: Time at which the C-MAXSN-38-G is observed

  • IB: Biliary index [23],

$$ IB=\frac{AUC_{CPT-11}.\kern0.5em {AUC}_{SN-38}}{AUC_{SN-38-G}}. $$
  • IM: Metabolization index,

$$ IM=\frac{AUC_{SN-38}+{AUC}_{SN-38-G}}{AUC_{CPT-11}}. $$

We examined the relationship between covariates and PK parameters resulting from the non-compartmental pharmacokinetic analysis using SPSS version 20 in order to delve into the repercussion of the physiopathological state in the pharmacokinetics of the treatment.

To that end, we used the Pearson or Spearman tests and scatter plots for continuous variables and Student’s t or Mann–Whitney U tests and boxplot diagrams for the categorical covariate (gender). The Levene test was used to check normality criteria. The significance level for all tests was set to 0.05 (P < 0.05).

We implemented, by means of a stepwise backward exclusion based on AIC [36], a multivariate regression for each PK parameter using those covariates that resulted statistically significant by the previous tests and the interactions among them. To select the model that best fitted the data, ANOVA test was used.

Non-compartmental pharmacokinetic results

The descriptive results of the parameters of non-compartmental pharmacokinetic analysis can be seen in Table 3. Within these PK parameters, some followed a normal distribution and others a non-normal distribution. AUCCPT-11, C-MAXCPT-11, C-MAXSN-38, C-MAXSN-38-G, and T-MAXCPT-11 followed a normal distribution and AUCSN-38, AUCSN-38-G, T-MAXSN-38, T-MAXSN.38-G, IB, and IM followed a non-normal distribution. Out of the 11 previous PK parameters, six showed correlations with the covariates presented in Table 1 and the dose. These are reflected in Table 4. In Figs. 5 and 6, different scatter plots with the PK individual parameters (AUC and C-MAX, respectively) are shown. The PK parameters are represented in the abscissa axis and the values of the covariates (in the cases where the correlation between them is significant at least at 0.05 level) are represented in the ordinate axis. With respect to the unique categorical covariate, there were no statistically significant differences in the mean/median between genders (Student’s t and Mann–Whitney U tests). In Fig. 7, we show boxplots of the PK parameters for each gender. The results of the multivariate regression analysis studying the relationships between covariates and PK parameters are shown in Table 5 for AUC values and in Table 6 for C-MAX values. The R squared adjusted (R2adj) value of the AUCCPT-11 regression was 0.6646 with 4.391 residual standard error (RSE), and the covariate dose and the interaction between ALP and neutrophils were statistically significant (P < 0.001 and P < 0.05, respectively). For the parameter AUCSN-38, R2adj was 0.2427 and RSE 0.1616, and in this case, the dose and LDH were statistically significant (P < 0.01). For AUCSN-38-G, R2adj was 0.9499 and RSE 0.4, and several covariates and interactions were statistically significant (see Table 5). For C-MAXCPT-11, R2adj was 0.5287 and RSE 0.6348, and dose and ALP were statistically significant (P < 0.001 and P < 0.01, respectively). For C-MAXSN-38, R2adj was 0.2862 and RSE 0.0197, and dose, TBil, and neutrophils were statistically significant (P < 0.05). Lastly, for C-MAXSN-38-G, R2adj was 0.4574 and RSE 0.0329, and dose, ALP, and the interaction between them were statistically significant (P < 0.001, P < 0.01, and P < 0.05, respectively).

Table 3 Means (deviations) of the population PK parameters
Table 4 Significant Pearson and Spearman correlation results with population PK parameters of non-compartmental analysis
Fig. 5
figure 5

Scatter plots of significant correlations for AUC values

Fig. 6
figure 6

Scatter plots of significant correlations for C-MAX values

Table 5 Multivariate regression for AUC values with covariates and their interactions
Table 6 Multivariate regression for C-MAX values with covariates and their interactions
Fig. 7
figure 7

Distribution of non-compartmental PK parameters by gender

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Oyaga-Iriarte, E., Insausti, A., Sayar, O. et al. Population pharmacokinetic model of irinotecan and its metabolites in patients with metastatic colorectal cancer. Eur J Clin Pharmacol 75, 529–542 (2019).

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  • Irinotecan
  • Population pharmacokinetic model
  • Enterohepatic recirculation
  • Parametric method
  • Non-parametric method