Jeongmin Ha (1), Hyunjung Lee (2), Seongwon Park (1), Soyoung Lee* (1), Lien Thi Ngo* (1), Hwi-yeol Yun* (1,2), Jung-woo CHAE* (1,2)
(1) College of Pharmacy, Chungnam National University, Korea. (2) Bio-AI Convergence Research Center, Chungnam National University, Korea * Those authors contributed equally as correspondence
Objectives: Enzalutamide (ENZ) is a second-generation androgen receptor inhibitor used in the treatment of castration-resistant prostate cancer (CRPC). The clinical pharmacokinetics (PK) of ENZ depend on rapid absorption and dose-proportional kinetics, with ENZ and N-desmethyl enzalutamide (NDE) having a long half-life of 5 and 7 days, compared to other drugs. ENZ is mainly metabolized by cytochrome P450 (CYP450) 2C8 and 3A4, leading to the biotransformation of an active compound, NDE. Exposure to NDE is considered clinically important [1]. Because NDE has similar potency to enzalutamide in all primary and secondary pharmacodynamic endpoints [2]. Most prostate cancer patients have higher PSA levels than normal with greater than 4.0ng/mL. Currently, PSA is a biomarker for the diagnosis and screening of prostate cancer, and it was the first cancer biomarker approved by the Food and Drug Administration [3]. ENZ and NDE can reduce abnormally elevated PSA levels by targeting the androgen receptor signaling pathway with a once-daily regimen of registered dose of ENZ 160mg. However, due to the occurrence of adverse reactions, there are several opinions to reduce the dose from 160mg to 120mg. [4],[5]. The aim of study was to develop a physiologically based pharmacokinetic (PBPK) model to characterize the PKs of human ENZ and its major active metabolite,NDE. Accordingly, we aimed to develop the pharmacodynamic (PD) and link it to PBPK to elucidate the dose-dependent effects of ENZ and NDE on PSA levels.
Methods: The PBPK model for ENZ and NDE was developed using PK-Sim® modeling software. To establish the model, an extensive literature search was conducted to gather drug-dependent parameters, including physicochemical properties and PK parameters, along with clinical PK datasets for these 2 compounds. Physiological-dependent variables were used at default values provided within the software. For model development, parameters (AUClast, Cmax) were estimated by fitting simulated PK profiles to observed clinical PK data. The predicted and observed PK parameters (AUClast, AUC from zero to last sampling time, and Cmax, maximum concentration) of the 2 compounds were calculated for the model evaluation. The geometric mean fold error (GMFE) for AUClast and Cmax was calculated as a quantitative measure to assess model accuracy. Sequentially, a PD model was developed to predict PSA reduction by ENZ using an indirect-response model with Berkeley Madonna software.
Results: The clinical data for single-dose study and multi-dose studies were collected from 7 clinical trials data and 2 published paper.The developed PBPK model successfully predicted the PK profiles of ENZ and NDE in a combined population of healthy male volunteers and prostate cancer patients with dose range of 30 to 360 mg/day. Specifically, the predicted PK parameters (AUClast and Cmax) for ENZ were within a 1.5-fold dimension error, and for NDE, they were within a 2-fold dimension error. Accordingly, the GMFE values for the AUClast and Cmax of ENZ were 1.3 and 1.16, respectively, and for NDE, corresponding values were 1.43 and 1.57, respectively. The mean Cmax and 5th to 95th percentile range of Cmax were chosen as parameters affecting PD, depending on the dosing regimen. With this, a PD model simulation predicted a PSA decline of 55.7% for the total observed data and 78.1% for the weekly observe data average values, categorized by study. This model showed PSA response did not differ between both simulations of 120mg to 160mg (P>0.22).
Conclusions: Our PBPK model well characterized the PK properties of ENZ and NDE inhealthy subjects and patients with prostate cancer. Additionally, the PD model predicted PSA levels according to the dosage regimen of ENZ. The developed PBPK-PD models could serve for references to adjust the dose regimen for ENZ in human populations.
References:
[1] Gibbons, J.A. et al (2015). Clinical pharmacokinetic studies of enzalutamide. 54(10), 1043-1055
[2] Gibbons, J.A. et al (2015). Pharmacokinetic Drug Interaction Studies with Enzalutamide. 54(10), 1057-1069
[3] Eleftherios P Diamandis (2000), “Prostate-specific Antigen: A Cancer Fighter and a Valuable Messenger?”, Clinical Chemistry, Volume 46, Issue 7, Pages 896–900
[4] Emmy, Boerrigter et al (2024). “A Prospective Randomised Trial to Determine the Effect of a Reduced Versus Standard Dose of Enzalutamide on Side Effects in Frail Patients with Prostate Cancer, European Urology Oncology.”
[5] Joanneke Overbeek et al (2023). “The effect of a reduced dose of enzalutamide on fatigue and cognition.” JCO 41, 5051-5051.
Reference: PAGE 32 (2024) Abstr 11165 [www.page-meeting.org/?abstract=11165]
Poster: Drug/Disease Modelling - Absorption & PBPK