Pharmacokinetic and pharmacogenetic markers of irinotecan toxicity.

BACKGROUND: Irinotecan (IRI) is a widely used chemotherapeutic drug, mostly used for first-line treatment of colorectal and pancreatic cancer. IRI doses are usually established based on patient's body surface area, an approach associated with large inter-individual variability in drug exposure and high incidence of severe toxicity. Toxic and therapeutic effects of IRI are also due to its active metabolite SN-38, reported to be up to 100 times more cytotoxic than IRI. SN-38 is detoxified by the formation of SN-38 glucuronide, through UGT1A1. Genetic polymorphisms in the UGT1A1 gene are associated to higher exposures to SN-38 and severe toxicity. Pharmacokinetic models to describe IRI and SN-38 kinetic profiles are available, with few studies exploring pharmacokinetic and pharmacogenetic-based dose individualization. The aim of this manuscript is to review the available evidence supporting pharmacogenetic and pharmacokinetic dose individualization of IRI in order to reduce the occurrence of severe toxicity during cancer treatment.

METHODS: The PubMed database was searched, considering papers published in the period from 1995-2017, using the keywords irinotecan, pharmacogenetics, metabolic genotyping, dose individualization, therapeutic drug monitoring, pharmacokinetics and pharmacodynamics, either alone or in combination, with original papers being selected based on the presence of relevant data.

CONCLUSIONS: The findings of this review confirm the importance of considering individual patient characteristics to select IRI doses. Currently, the most straightforward approach for IRI dose individualization is UGT1A1 genotyping. However, this strategy is sub-optimal due to several other genetic and environmental contributions to the variable pharmacokinetics of IRI and its active metabolite. The use of dried blood spot sampling could allow the clinical application of complex sampling for the clinical use of limited sampling and population pharmacokinetic models for IRI doses individualization.