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Genetics and Genomics

Integration of DNA sequencing with population pharmacokinetics to improve the prediction of irinotecan exposure in cancer patients

British Journal of Cancer volume 126, pages 640–651 (2022)Cite this article

Subjects

Abstract

Background

Irinotecan (CPT-11) is an anticancer agent widely used to treat adult solid tumours. Large interindividual variability in the clearance of irinotecan and SN-38, its active and toxic metabolite, results in highly unpredictable toxicity.

Methods

In 217 cancer patients treated with intravenous irinotecan single agent or in combination, germline DNA was used to interrogate the variation in 84 genes by next-generation sequencing. A stepwise analytical framework including a population pharmacokinetic model with SNP- and gene-based testing was used to identify demographic/clinical/genetic factors that influence the clearance of irinotecan and SN-38.

Results

Irinotecan clearance was influenced by rs4149057 in SLCO1B1, body surface area, and co-administration of 5-fluorouracil/leucovorin/bevacizumab. SN-38 clearance was influenced by rs887829 in UGT1A1, pre-treatment total bilirubin, and EGFR rare variant burden. Within each UGT1A1 genotype group, elevated pre-treatment total bilirubin and/or presence of at least one rare variant in EGFR resulted in significantly lower SN-38 clearance. The model reduced the interindividual variability in irinotecan clearance from 38 to 34% and SN-38 clearance from 49 to 32%.

Conclusions

This new model significantly reduced the interindividual variability in the clearance of irinotecan and SN-38. New genetic factors of variability in clearance have been identified.

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Fig. 1: Stepwise analysis workflow.
Fig. 2: Significant patient characteristics and genetic variants associated with irinotecan clearance and SN-38 clearance.
Fig. 3: Associations of ηCL4 with pre-treatment bilirubin and rs887829 in UGT1A1.
Fig. 4: The combined influence of the significant factors on the irinotecan clearance (a) and SN-38 clearance (b).

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Data availability

The data are not publicly available due to patient confidentiality. The data that support the findings of the current study are available within the article or upon request. Supplementary information is available at the British Journal of Cancer’s website.

Code availability

The Mlxtran code for population pharmacokinetic modelling and the R code for SNP- and gene-based analyses are available upon request.

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Acknowledgements

The authors would like to thank Lixoft for the expert advice on Monolix and the Information Technology Services Research Computing group at the University of North Carolina at Chapel Hill for their technical support.

Funding

This work was supported by discretionary funding from FI and the Division of Pharmacology and Experimental Therapeutics of the UNC Eshelman School of Pharmacy (to FI).

Author information

Author notes

  1. Deceased: Alan Forrest.

Authors and Affiliations

  1. Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Spinel Karas, Amy S. Etheridge, Alan Forrest & Federico Innocenti

  2. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Deborah A. Nickerson

  3. Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA

    Nancy J. Cox

  4. Department of Genetics, University of North Carolina, Chapel Hill, NC, USA

    Karen L. Mohlke

  5. Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, Italy

    Erika Cecchin & Giuseppe Toffoli

  6. Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands

    Ron H. J. Mathijssen

  7. Department of Pharmaceutical Sciences, University at Buffalo School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY, USA

    Robert R. Bies

  8. Computational and Data Enabled Sciences and Engineering Program, University at Buffalo, State University of New York at Buffalo, Buffalo, NY, USA

    Robert R. Bies

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Contributions

FI, RRB, and SK developed conception and design. FI, DAN, RHJM, GT, EC, NJC, and ASE collected and assembled the data. SK, RRB, AF, and FI developed methodology. SK, RRB, FI, AF, KLM, and ASE performed data analysis and interpretation. SK, FI, and RRB conducted manuscript writing. All authors discussed the results and contributed to the final manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Federico Innocenti.

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Competing interests

RRB has received funding from the US Department of Defense. The remaining authors state no conflict of interest.

Ethics approval and consent to participate

All patients provided written informed consent to this study, and all trials were approved by the local institutional review boards (IRB). This study was conducted according to the guidelines provided by the Declaration of Helsinki.

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All patients provided written informed consent for their data to be used for publication.

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Karas, S., Etheridge, A.S., Nickerson, D.A. et al. Integration of DNA sequencing with population pharmacokinetics to improve the prediction of irinotecan exposure in cancer patients. Br J Cancer 126, 640–651 (2022). https://doi.org/10.1038/s41416-021-01589-2

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