I-038 Myriam Briki

Model-based meta-analysis of individual patient data from three independent datasets for the characterization of intravenous 5-fluorouracil population pharmacokinetics

Myriam Briki (1,2,3), Paul Thoueille (1,2), Markus Joerger (4), Etienne Chatelut (5), Marylise Sterle (6), Laurent A. Decosterd (2), François R. Girardin (1,2), Thierry Buclin (1), Sandro Carrara (3), Monia Guidi (1,7,8)

(1) Service of Clinical Pharmacology, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland. (2) Laboratory of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland. (3) Bio/CMOS Interfaces Laboratory, Department of Engineering, Swiss Federal Institute of Technology in Lausanne - EPFL, Switzerland. (4) Department of Oncology and Hematology, Cantonal Hospital St. Gallen, St. Gallen, Switzerland. (5) Oncopole Claudius-Regaud, Institut Universitaire du Cancer de Toulouse–Oncopole, Toulouse, France. (6) Pharmacy Department, Centre Georges-François Leclerc, Dijon – INSERM U1231, University of Burgundy Franche-Comté, Dijon, France. (7) Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, University of Lausanne, Geneva, Switzerland. (8) Centre for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.

Introduction: For certain old, widely used therapies, a plethora of non-compartmental analyses and population pharmacokinetic (popPK) studies are available in the literature: results from such analyses fit a particular set of data, which can naturally result in discrepancies between models1. An interesting approach for reconciling popPK studies is to summarize the available information using a model-based meta-analysis (MBMA), preferably on individual-patient data2. This method allows grouping datasets into a homogeneous meta-database and fit an overall model, while keeping the original studies identifiable and accounting for differences between them.

The cytotoxic chemotherapy 5-fluorouracil (5FU) is widely used in the treatment of digestive, breast and head and neck cancers, and many PK studies are available in the literature. The rationale for accurately predicting patients’ exposure to 5FU lies in the fact that exposure-response/toxicity relationships are known, making therapeutic drug monitoring (TDM) advised3. Pre-treatment investigation of the metabolic profile of the dihydropyrimidine dehydrogenase (DPD) enzyme by genotyping or phenotyping is also recommended to prevent 5FU overexposure and toxicity4.

This study aims at characterizing the PK of intravenous (iv) 5FU through a MBMA of individual data.

Methods: A literature search identified relevant popPK studies on 5-FU, whose authors were asked to share data. PopPK analysis was performed (NONMEM) following a stepwise procedure testing linear and saturable elimination, and multi-compartment disposition. Error models were first evaluated separately for each study; second, a nested analysis was performed (using $LEVEL blocks) to assess inter-study and inter-individual variability (ISV, IIV) on PK parameters. Sex, bodyweight (BW), age, and DPD metabolic profile were tested as covariates. For the latter, information on genotype/phenotype was delicate to handle, as no common metric was available throughout all studies (DPD activity, uracil concentration, genotype) and the occurrence of information for this potential covariate was inconsistent in one dataset. We classified the patients into binary categories, namely potentially slow metabolizers and others, with a cutoff of 150 pmol/min/mg5 for DPD activity and 16 ng/mL6 for uracil concentration.

Results: A total of 2344 5FU plasma concentrations, including both detailed PK profiles and sparse samples, were measured in 727 individuals from three independent datasets (i.e., Terret et al. (2000)7, Mueller et al. (2013)8, and the Georges-François Leclerc Center (Dijon clinical cancer center)). A two-compartment model with non-linear elimination best described 5FU PK. Proportional error models were used for the residual errors of the first two studies and a mixed one for the third study. The nested hierarchical approach did not provide a better data description and was therefore abandoned. The base model parameter estimates (with IIV %CV) were 1900 mg/h (20.6%) and 7.97 mg/L (18%) for the maximum reaction rate (Vmax) and the Michaelis constant (Km), respectively, 13.2L (84.9%) for the central volume of distribution, 15.9L (28%) for the peripheral one, and 129 L/h for the intercompartmental clearance (Q). Significant covariates included sex on drug elimination, with a 28.3% increase in Vmax and 11.1% increase in Km for males compared to females, consistent with previously reported sex-related differences8. An impact of BW on Km and of age on Vmax was also found: compared to a reference BW of 70kg, a patient weighing 95kg would have a 10.5% lower Km, and a young patient of 40 years would have a Vmax 11.2% higher than a median patient of 65 years. Slow DPD metabolism as classified here was described in 10% of individuals and could not be identified as significant on 5FU PK.

Conclusion: Using different error models for handling our combined database with data collected in different settings allowed us to describe the PK of 5FU, revealing non-linear elimination and significant impacts of sex, age and BW on drug disposition. Slow DPD metabolism, although not significant in our analysis, needs further attention due to the small fraction of identified slow metabolizers and the strict categorization. Both sex and DPD metabolic profile are known to impact treatment outcomes and should be considered in dosage individualization9,10. TDM and precision dosage, informed by a model such as ours, deserve consideration for 5FU.

References:

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Reference: PAGE 32 (2024) Abstr 10998 [www.page-meeting.org/?abstract=10998]

Poster: Drug/Disease Modelling - Oncology