Peribañez-Dominguez, Sara (1,2); Parra-Guillen, Zinnia P (1,2); Pascoal, Susana (3); DÃez-Punzano, Rubén (3); Troconiz, Iñaki F (1,2,4)
(1) Department of Pharmaceutical Science, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain; (2) Navarra Institute for Health Research (IdiSNA), Pamplona, Spain; (3) IKAN BIOTECH, Plaza CEIN, 5-A4, Noáin, Navarra; (4) Navarra Institute of Data Science and Artificial Intelligence, DATAI, University of Navarra, Pamplona, Spain
Introduction/Objectives: Colorectal cancer holds the third position among cancers affecting men and the second position among cancers affecting women, being one of the most prevalent. Frequently, first line treatment for mCRC consists on the intravenous administration of 5-fluorouracil and leucovorin in combination with oxaliplatin or irinotecan, regimens commonly referred to as FOLFOX and FOLFIRI respectively. Despite several studies have described the pharmacokinetics of these drugs [1–4], less is known about their disposition in different body organs. Gaining insight into how anticancer drugs are distributed throughout the body, particularly in organs hosting the tumor growth, is relevant for effective treatment. Physiologically-based pharmacokinetic models (PBPK) aim to mechanistically incorporate body physiology and drug physicochemical attributes enabling the description of both, systemic and tissue drug exposure based on the treatment specificities [5]. This bottom-up approach technique presents an opportunity to personalize treatment based on individual patient characteristics, thereby minimizing the therapeutic risk/benefit ratio. This project aims to characterize the systemic exposure and predict tissue levels in humans of four different anti-cancer drugs (irinotecan, 5-fluorouracil, oxaliplatin, leucovorin) commonly used in mCRC through the building of PBPK platform using literature data.
Methods: . A literature search was performed to collect, for each of the aforementioned drugs, clinical data represented preferably by systemic concentration versus time profiles. Data were scanned using WebplotDigitalizer tool. Each dataset was built and explored using R version 4.0.5 through RStudio interface version 1.4.1106. Collected data were divided at random into training (80%) and test (20%) datasets. Physicochemical features were obtained from the literature for each drug, as well as parameters associated to metabolization and excretion processes. PBPK models were built using PK-Sim®(Open Systems Pharmacology Suite11) software. Models underwent calibration and validation by comparing model predictions with observations from the training and test datasets, respectively. Simulations were conducted to assess the extent of drug exposure under different treatment scenarios, including those organs related to the mCRC.
Results: Data from 54 clinical studies (11,18, 8, and 17 studies for oxaliplatin, 5-fluorouracil, leucovorin, and irinotecan, respectively) were extracted and combined in one single dataset for each drug. Oxaliplatin model incorporated an unspecific hepatic clearance elimination process (2.5 L/h). Irinotecan elimination comprised CYP3A4 mediated metabolism (501.2 mL/min/Kg), biliar(142.9 mL/min/Kg) and renal excretion (86.68 mL/min/Kg). Elimination of 5-fluorouracil, and leucovorin was characterized by metabolism mediated by the enzymes dihydropyrimidine-dehydrogenase (Km=3.17 nmol/mL; Vmax=22.5 mL/min/g tissue), 5,10-methylenetetrahydrofolate-reductase (enzymatic clearance=297 mL/min), respectively. For the case of leucovorin, renal excretion was also considered (83 mL/min). All models developed described successfully the exposure vs time profiles with respect to both, the typical tendency and dispersion shown by the literature data. The 70.8, 65.9, 93.6 and 78.67% of the data was within +/- 2 SD for oxaliplatin, 5-fluorouracil, leucovorin, and irinotecan, respectively. The ratios obtained from AUC0-tend measured in colon and the AUC0-tend measured in plasma were 0.40, 0.20, 0.7 and 8.47 for oxaliplatin, 5-fluorouracil, leucovorin, and irinotecan, respectively.
Conclusions: The successful description of exposure-versus-time profiles for four anti-cancer drugs has been achieved through the building of four PBPK models. Understanding exposure in target organs and establishing the link between in vitro or ex vivo studies are crucial for enabling a more personalized approach to treatment. This model serves as the initial step towards incorporating a dedicated tumor compartment.
References:
[1] Townsend, D. (2007) ‘Oxaliplatin’, xPharm: The Comprehensive Pharmacology Reference, 38 (1), pp. 1–4
[2] Chabot GG.Clinical pharmacokinetics of irinotecan. Clin Pharmacokinet.1997; 33 (4) :245–59.
[3] Zittoun J,Tonelli AP, Marquet J, De Gialluly E, Hancock C, Yacobi A ,et al.Pharmacokinetic comparison of leucovorin and levoleucovorin. Eur-J Clin Pharmacol. 1993; 44 (6):569–73.
[4] Diasio RB, Harris BE. Clinical Pharmacology of 5-Fluorouracil. Clin-Pharmacokinet. 1989; 16 (4): 215–37.
[5] Kuepfer L, Niederalt C, Wendl T, Schlender JF, Willmann S, Lippert J, et al. Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.CPT-Pharmacometrics-Syst Pharmacol. 2016; 5(10): 516–31.
Reference: PAGE 32 (2024) Abstr 10862 [www.page-meeting.org/?abstract=10862]
Poster: Drug/Disease Modelling - Absorption & PBPK