Development of a physiological-based pharmacokinetic model for sacubitril and its metabolite
Tham Thi Bui(1), Khanh Linh Duong(1), Lien Thi Ngo(1), Quyen Thi Tran(1), Hwi-yeol Yun(1,2*), Jung-woo Chae(1,2*) *Those authors contributed equally as correspondence
(1)College of Pharmacy, Chungnam National University, South Korea, (2)Bio-AI Convergence Research Center, Chungnam National University, South Korea
Objectives: Sacubitril/valsartan, a novel angiotensin receptor neprilysin inhibitor, has been approved for chronic heart failure treatment to reduce the risk of cardiovascular death and hospitalization in adult patients. Sacubitril inhibits neprilysin via LBQ657, the major active metabolite of prodrug sacubitril[1]. Sacubitril is metabolized by carboxylesterase 1 (CES1) to LBQ657. Then, LBQ657 was excreted in the unchanged form via urine and feces, with renal elimination accounting for the majority (51.7– 67.8%)[2]. It also reported that sacubitril is an in vitro inhibitor of organic anion transporter 3 (OAT3) and organic anion transporting polypeptides 1B1/3 (OATP1B1/3), whereas LBQ657 is a substrate of that transporters[3]. In a clinical study, sacubitril/valsartan increased the area under the curve (AUC) and the peak plasma concentration (Cmax) of atorvastatin, an OATP1B1/3 substrate, by 34% and 74%, respectively[4]. In the literature, a physiological-based pharmacokinetic (PBPK) model for sacubitril had been developed using Simcyp Simulator software and applied to predict sacubitril – statins drug interaction[5]. However, the metabolite LBQ657 was not included in the model. Considering LBQ657 is an active compound, and a substrate of transporters, a PBPK model for sacubitril, including its metabolite LBQ657 is necessary. First, the developed model could be applied to evaluate the drug interaction of sacubitril as an inhibitor, and LBQ657 as a substrate of transporters of OAT3 and OATP1B1/3, respectively. Second, LBQ657 is an active compound, so, the model for LBQ657 can be further used to develop the PBPK-pharmacodynamic model (PBPK-PD) to simulate the neprilysin inhibition activity of sacubitril treatment. Therefore, our study aims to develop a physiological-based pharmacokinetic (PBPK) model for sacubitril and its active metabolite.
Methods: A whole-body PBPK model of sacubitril and its metabolite were developed and evaluated in PK-Sim® software. Drug-dependent parameters (such as physiochemical and ADME properties) and 42 existing clinical pharmacokinetic profiles of sacubitril and LBQ657 were extracted from the literature and used for model development. These clinical pharmacokinetic profiles were orally administered with single- and multiple- doses in a wide range of doses (from 9.7 to 437mg). CES1 exclusively metabolized Sacubitril to LBQ657 following Michaelis–Menten kinetics. Similarly, OAT3, OATP1B1/3, and multidrug resistance-associated protein 2 (MRP2) transporters facilitated urinary and hepatobiliary excretion of LBQ657 as saturate process following Michaelis–Menten kinetics; physiological-dependent parameters were set at the default values on PK-Sim® software. Input parameters not found in the literature were optimized by fitting the model to the observed dataset. For model evaluation, plasma concentration-time profiles, AUC, and Cmax were predicted and compared to the observed data.
Results: A PBPK model for sacubitril and its metabolite was successfully developed. For sacubitril, the fraction unbound (3%) and the solubility (50 mg/ml) were fixed at the values picked up from the literature. The parameter identification with the clinical dataset was used to optimize the lipophilicity (logP 3.09), specific intestinal permeability (2.71E-6 cm/s), and the catalytic rate constant of CES1 (1543 1/min). For LBQ657, the kinetic of MRP2 transporter, and the catalytic rate constant of OAT3 (24.94 1/min) and OATP1B3 (17.12 1/min) were identified by optimization. The developed model accurately predicts the disposition of sacubitril and LBQ657 of 42 clinical pharmacokinetic profiles, with all predicted AUC and Cmax ratios within 2-fold and 86.5% of the predicted concentrations lying between the 2-fold border of the observed concentrations.
Conclusion: A whole-body PBPK model of sacubitril and its metabolite were built and evaluated. Then, the developed model could be a useful tool to support the investigation of transporter-mediated drug-drug interaction and further be used to develop the PBPK-PD model for pharmacodynamic assessment of sacubitril treatment.
References:
[1] Yamamoto K, Rakugi H. Angiotensin receptor-neprilysin inhibitors: Comprehensive review and implications in hypertension treatment. Hypertens Res. 2021;44(10):1239-1250. doi:10.1038/S41440-021-00706-1
[2] Flarakos J, Du Y, Bedman T, et al. Disposition and metabolism of [14C] Sacubitril/Valsartan (formerly LCZ696) an angiotensin receptor neprilysin inhibitor, in healthy subjects. Xenobiotica. 2016;46(11):986-1000. doi:10.3109/00498254.2015.1014944
[3] Hanna I, Alexander N, Crouthamel MH, et al. Transport properties of valsartan, sacubitril and its active metabolite (LBQ657) as determinants of disposition. Xenobiotica. 2018;48(3):300-313. doi:10.1080/00498254.2017.1295171
[4] Ayalasomayajula S, Pan W, Han Y, et al. Assessment of Drug-Drug Interaction Potential Between Atorvastatin and LCZ696, A Novel Angiotensin Receptor Neprilysin Inhibitor, in Healthy Chinese Male Subjects. Eur J Drug Metab Pharmacokinet. 2017;42(2):309-318. doi:10.1007/S13318-016-0349-Y
[5] Lin W, Ji T, Einolf H, et al. Evaluation of Drug-Drug Interaction Potential Between Sacubitril/Valsartan (LCZ696) and Statins Using a Physiologically Based Pharmacokinetic Model. J Pharm Sci. 2017;106(5):1439-1451. doi:10.1016/j.xphs.2017.01.007