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

  • Pharmacokinetics and Disposition
  • Published:
European Journal of Clinical Pharmacology Aims and scope Submit manuscript

Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusion

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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Funding

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

  1. Pharmamodelling S.L., Pamplona, Spain

    Esther Oyaga-Iriarte, Asier Insausti & Onintza Sayar

  2. Department of Hospital Pharmacy, Clínica Universidad de Navarra, Pío XII 36, Pamplona, Spain

    Azucena Aldaz

Authors
  1. Esther Oyaga-Iriarte
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  2. Asier Insausti
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  4. Azucena Aldaz
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Contributions

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.

Ethics declarations

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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Appendix

Appendix

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
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Table 4 Significant Pearson and Spearman correlation results with population PK parameters of non-compartmental analysis
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Fig. 5
figure 5

Scatter plots of significant correlations for AUC values

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Fig. 6
figure 6

Scatter plots of significant correlations for C-MAX values

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Table 5 Multivariate regression for AUC values with covariates and their interactions
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Table 6 Multivariate regression for C-MAX values with covariates and their interactions
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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). https://doi.org/10.1007/s00228-018-02609-6

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