Abstract
MBQ-167 is a dual inhibitor of the Rho GTPases Rac and Cdc42 that has shown promising results as an anti-cancer therapeutic at the preclinical stage. This drug has been tested in vitro and in vivo in metastatic breast cancer mouse models. The aim of this study is to develop a physiologically based pharmacokinetic/pharmacodynamic (PBPK-PD) model of MBQ-167 to predict tumor growth inhibition following intraperitoneal (IP) administration in mice bearing Triple Negative and HER2+ mammary tumors. PBPK and Simeoni tumor growth inhibition (TGI) models were developed using the Simcyp V19 Animal Simulator. Our developed PBPK framework adequately describes the time course of MBQ-167 in each of the mouse tissues (e.g., lungs, heart, liver, kidneys, spleen, plasma) and tumor, since the predicted results were consistent with the experimental data. The developed PBPK-PD model successfully predicts tumor shrinkage in HER2+ and triple-negative breast tumors after the intraperitoneal administration of 1 and 10 mg/kg body weight (BW) dose level of MBQ-167 three times a week. The findings from this study suggest that MBQ-167 has a higher net effect and potency inhibiting Triple Negative mammary tumor growth compared to HER2+ and that liver metabolism is the major route of elimination of this drug.
Highlights
Drug discovery and development represents an increasing economic and temporal cost for the pharmaceutical industry, which has not translated into significant increases in the number of approved active ingredients, especially in the oncology area [1,2]
Model predictions after a single IP administration of 10 mg/kg of MBQ-167 are shown in Figure 2; Figure 3, showing that the Physiologically- based pharmacokinetic (PBPK) model developed is able to capture the longitudinal MBQ-167 observations
These results agree with the numerical analysis (Table 3), as the fold error for Cmax is close to the unity in all tissues except from tumor, where a value of 0.8 arises
Summary
Drug discovery and development represents an increasing economic and temporal cost for the pharmaceutical industry, which has not translated into significant increases in the number of approved active ingredients, especially in the oncology area [1,2]. Physiologically- based pharmacokinetic (PBPK) modelling represents a mathematical framework that integrates physicochemical, physiological, and biochemical information to predict the concentration-time course at target tissues for a wide range of exposure conditions in animals or humans [4]. The tumor growth inhibition (TGI) model [10] constitutes a highly valuable preclinical methodology in oncology for the selection of therapeutic candidates and the design of optimal clinical evaluation strategies for the in vivo evaluation of anti-tumor effect [11,12,13,14,15,16]. The Simeoni TGI model has been widely implemented to characterize the pharmacological response of drug candidates in single-agent and combination experiments by linking drug concentration in the target tissue to the inhibition of tumor growth [17]
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