Abstract 2952: Predicting concentration of PARP inhibitors in human tumor tissue using PBPK modeling
Authors: Rachel H. Rose, Kaiming Sun, Linzhong Li, Keyur Gada, Jing Yu Wang, Yongchang Qiu
Journal: Cancer Research (2019)

Abstract Poly (ADP-ribose) polymerase (PARP) inhibitors exert their effect intracellularly within tumor, thus sufficient tumor penetration is essential for a pharmacological response. Preclinical mouse xenograft data show a 3.3-fold higher tumor versus plasma exposure of niraparib, while for olaparib tumor exposure was less than plasma. This study aimed to build a physiologically-based pharmacokinetic (PBPK) model extended with a tissue composition-based permeability-limited tumor model to: (a) gain a mechanistic understanding of the differences in tumor exposure of niraparib and olaparib; and (b) to predict clinical tumor exposure in ovarian cancer patients at clinically relevant dosing regimens. A permeability-limited tumor model was developed that integrates data on tumor composition and drug physicochemical properties analogous to the established permeability-limited organ model available for the liver in the Simcyp Simulator [1,2]. The model assumes that unbound unionized drug is in equilibrium between the vascular and interstitial compartments and movement of the drug between the interstitial and intracellular space is via passive permeability. Total tumor concentration is dependent on passive permeability of the drug, drug binding to PARP and nonspecific binding to neutral lipids, neutral phospholipids, and acidic phospholipids in the intracellular space, albumin in the interstitial and pH of the tumor interstitial and intracellular spaces. Clinical and preclinical tumor physiological parameters such as volume, blood flow, and tissue composition are defined using published data. The model was developed using the Simcyp Simulator V17.1 and R [3]. Consistent with preclinical data, the model predicts a 5-7-fold higher tumor exposure relative to plasma, as measured by the AUC tumor to plasma ratio of niraparib compared with olaparib. Significant binding to acidic phospholipids contributes to the increased tumor exposure to niraparib, a basic drug, a mechanism that is not relevant to the neutral drug olaparib. Ongoing work aims to extrapolate the model to predict the clinical tumor concentration of olaparib and niraparib in ovarian cancer patients and to investigate the sensitivity of the model to key tumor attributes, including blood flow and interstitial pH that may contribute to variability in tumor drug exposure. A similar modeling approach may be used to predict the tumor exposure of other small molecule anticancer drugs from their plasma concentration and physicochemical properties in different types of solid tumor. References Jamei M et al, Clin Pharmacokinet. 2014;53:73-87 Poulin P et al, J Pharm Sci. 2015;104:1508-21 R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Citation Format: Rachel H. Rose, Kaiming Sun, Linzhong Li, Keyur Gada, Jing Yu Wang, Yongchang Qiu. Predicting concentration of PARP inhibitors in human tumor tissue using PBPK modeling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2952.