Valsartan (LCZ696) and Statins Using a Physiologically Based Pharmacokinetic Model

Accepted Manuscript Evaluation of drug-drug interaction potential between sacubitril/valsartan (LCZ696) and statins using a physiologically-based pharmacokinetic model Wen Lin, Tao Ji, Heidi Einolf, Surya Ayalasomayajula, Tsu-Han Lin, Imad Hanna, Tycho Heimbach, Christopher Breen, Venkateswar Jarugula, Handan He PII:

S0022-3549(17)30010-2

DOI:

10.1016/j.xphs.2017.01.007

Reference:

XPHS 615

To appear in:

Journal of Pharmaceutical Sciences

Received Date: 8 September 2016 Revised Date:

23 December 2016

Accepted Date: 3 January 2017

Please cite this article as: Lin W, Ji T, Einolf H, Ayalasomayajula S, Lin TH, Hanna I, Heimbach T, Breen C, Jarugula V, He H, Evaluation of drug-drug interaction potential between sacubitril/valsartan (LCZ696) and statins using a physiologically-based pharmacokinetic model, Journal of Pharmaceutical Sciences (2017), doi: 10.1016/j.xphs.2017.01.007. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Evaluation of drug-drug interaction potential between sacubitril/valsartan (LCZ696) and statins using a physiologically-based pharmacokinetic model

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Wen Lin, Tao Ji, Heidi Einolf, Surya Ayalasomayajula, Tsu-Han Lin, Imad Hanna, Tycho Heimbach, Christopher Breen, Venkateswar Jarugula, Handan He

Drug Metabolism & Pharmacokinetics, Novartis Institutes for Biomedical Research, East

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Hanover, NJ 07936, USA

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Abstract Sacubitril/valsartan (LCZ696) has been approved for the treatment of heart failure. Sacubitril is an in vitro inhibitor of OATPs. In clinical studies, LCZ696 increased atorvastatin Cmax by 1.7-

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fold and AUC by 1.3-fold, but had little or no effect on simvastatin or simvastatin acid exposure. A PBPK modelling approach was applied to explore the underlying mechanisms behind the statin-specific LCZ696 drug interaction observations. The model incorporated OATP-mediated clearance (CLint,T) for simvastatin and simvastatin acid to successfully describe the PK profiles of either analyte in the absence or presence of LCZ696. Moreover, the model successfully

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described the clinically observed drug effect with atorvastatin. The simulations clarified the critical parameters responsible for the observation of a low, yet clinically relevant, DDI between

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sacubitril and atorvastatin and the lack of effect with simvastatin acid. Atorvastatin is administered in its active form and rapidly achieves Cmax that coincide with the low Cmax of sacubitril. In contrast, simvastatin requires a hydrolysis step to the acid form and therefore is not present at the site of interactions at sacubitril concentrations that are inhibitory. Similar models were used to evaluate the DDI risk for additional OATP-transported statins which predicted to

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maximally result in a 1.5-fold exposure increase.

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ARNI, angiotensin receptor neprilysin inhibitor; AUC, area under the plasma concentration-time curve; Cmax, maximum plasma concentration; DDI, drug-drug interaction; PBPK, Physiologically based pharmacokinetics; OATP, organic anion-transporting polypeptide; Ki, Inhibition constant;

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Clint,T, transporter-mediated intrinsic clearance (µL/min/cm2)

Key words:

Physiologically based pharmacokinetic modelling, transporters, statin, absorption, organic aniontransporting polypeptide, drug-drug interaction

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1. Introduction Sacubitril/valsartan (LCZ696) is an angiotensin receptor neprilysin inhibitor (ARNI) , which has been approved for the treatment of patients with chronic heart failure (New York Heart

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Association (NYHA) Class II–IV) and reduced ejection fraction[1, 2]. LCZ696 is a sodium salt complex and a combination drug which systemically delivers valsartan, an angiotensin receptor blocker (ARB), along with sacubitril following oral administration. Sacubitril is a prodrug that is metabolized to the active neprilysin inhibitor, sacubitrilat, by carboxyl esterase-1 (CES-1) but

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not CES2 [3]; however, the role of other carboxyl esterases is unknown. Neprilysin inhibition by sacubitrilat enhances the natriuretic peptide (NP) levels thereby promoting natriuresis, diuresis and vasodilatory effects via increasing cGMP [4]. Valsartan is known to be an angiotensin

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receptor blocker (ARB)[5, 6], which inhibits the deleterious cardiovascular and renal effects of angiotensin II and its effectors[5-8]. Following oral administration, LCZ696 provides systemic exposure to sacubitril, sacubitrilat, and valsartan (referred to as LCZ696 analytes). Therefore, LCZ696 exert its effects by simultaneous modulation of natriuretic peptides NP system and renin–angiotensin–aldosterone system (RAAS)[5, 6].

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Absorption of LCZ696 analytes is rapid following oral administration, with peak plasma concentrations of sacubitril, sacubitrilat, and valsartan observed within a median time of 0.5– 4.0 h [9]. Elimination of LCZ696 analytes occurs with terminal plasma half-lifes (T1/2) for valsartan of 9.9 h; sacubitril of 1.4 h; and sacubitrilat of 11.5 h [10]. Elimination of sacubitril is

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primarily via metabolism to sacubitrilat. Sacubitrilat is predominantly eliminated by the renal route [10, 11], while valsartan is primarily eliminated via the biliary route [12]. LCZ696 does not

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inhibit or induce liver cytochrome P450 (CYP) enzymes [10]. Consistent with this observation, no significant drug interactions were observed when LCZ696 was co-administered with digoxin, warfarin

[7],

omeprazole,

metformin,

levonorgestrel-ethinyl

estradiol[13],

carvedilol,

hydrochlorothiazide, or amlodipine [14]. In vitro studies identified sacubitril as an inhibitor of OATP1B1 and OATP1B3 [15], the principal transporters involved in the disposition certain statins. The results from two clinical studies were reported evaluating the impact of LCZ696 on atorvastatin [16] and simvastatin [15]. While up to about 2-fold increases in the Cmax of atorvastatin and its metabolites (ohydroxyatorvastatin and p-hydroxyatorvastatin) were observed, along with only a marginal 3

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increase in the AUC of atorvastatin and its metabolites (~1.3-fold), there were no significant changes in the Cmax and AUC of simvastatin and its principal metabolite, simvastatin acid. Since both atorvastatin and simvastatin acid have been identified as substrates of OATP1B1 and OATP1B3, the reasons for differences in the impact of LCZ696 were to be investigated, taking

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into account that simvastatin/simvastatin acid is known to be a more sensitive substrate of OATP compared to atorvastatin [17].

The objective of these analyses was to evaluate and describe the potential reasons for the observed differences between the results observed from atorvastatin and simvastatin studies

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using PBPK models. This was accomplished by developing and verifying PBPK models for sacubitril, simvastatin/simvastatin acid and atorvastatin using observed clinical data, followed by

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simulating drug interaction potential when simvastatin acid or atorvastatin were co-administered as victim drugs and sacubitril as the perpetrator. Underlying reasons for the distinctly different drug interactions results for simvastatin acid and atorvastatin were identified using this approach. These assessments can potentially be extended to anticipate the impact of LCZ696 on other OATP1B1 and OATP1B3 substrates. Methods

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2.1 Modelling and simulation software

The following software program was used:

Simcyp® Population-Based ADME Simulator

(Version 15 Release 1, Certara, Sheffield, UK) run on a Lenovo computer platform with Intel®

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52 Core i5 processor.

2.2 Modelling assumptions, limitations, and uncertainties

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It was assumed that, after absorption, the simvastatin lactone converted to the open acid form and was available immediately as a substrate for OATP1B1. In the simvastatin acid PBPK model, the intrinsic transport clearances (CLint,T) due to the hepatic uptake by OATP1B1 were estimated values from PBPK/DDI simulations of simvastatin acid and gemfibrozil[18]. Sacubitril is an inhibitor for OATP1B1 and OATP1B3. However its metabolite, Sacubitrilat is a weak inhibitor of OATP1B1 with an IC50 value of ~126 µM[15]. Based on the Cmax of sacubitrilat (43 µM) observed therapeutically, it is unlikely that sacubitrilat will increase the systemic exposure of OATP1B1 substrates. Consequently formation of sacubitrilat was not considered in the model

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as its DDI potential was considered to be low for OATP and/or cytochrome P450 (CYP) interactions. 2.3 Construction of the PBPK models for sacubitril, simvastatin acid and atorvastatin

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2.3.1 Simcyp input parameters The PBPK models for sacubitril and simvastatin acids were developed and verified using observed clinical data. A combined [19] “bottom-up” and “top-down” approach was applied. The Simcyp® input parameters for sacubitril, simvastatin, and simvastatin acid are summarized in

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Table 1, Table 2, and Table 3, respectively. The input parameters for atorvastatin were described previously in the literature [20]. Typically, 10 trials of 10 healthy subjects each (i.e. a total of

and Cmax were expressed as mean values.

2.3.2 Sacubitril 2.3.2.1 Estimation of oral absorption:

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100 subjects) were run and the proportion of female subjects was set at 0.5. The simulated AUC

The first-order absorption model was used where the user-defines and inputs fa, ka, Tlag. These

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values were optimized to fit the observed single and multiple dose sacubitril PK profiles from the clinical studies [15] [16]. In vitro Caco-2 apical to basolateral permeability data (passive plus active, in house data) was entered in the model and the human Peff was predicted by Simcyp

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(Table 1). QGut was predicted by Simcyp software. 2.3.2.2 Estimation of systemic PK parameters from human data

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The minimal PBPK model was used with user-defined inputs for Vss, Vsac, and Q to optimize predictions of sacubitril PK as above. The in vivo clearance model was used and the value was also optimized to predict the concentration-time profile of sacubitril. Renal clearance was considered to be negligible and a value of zero was entered. Unchanged sacubitril accounted for 0.8-2.8% of the dose in urine in human (in house data). There is negligible inhibition of CYP enzymes, particularly CYP3A; therefore no entries were made for CYP interactions. The IC50 values for OATP1B1 and OATP1B3 were entered in the model. The fraction of unbound sacubitril in the incubation (fu, inc) value was optimized to be 0.04 which provide the best match to the transporter mediated DDI with atorvastatin[16]. 5

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2.3.2.3 Sacubitril PBPK model verification using observed clinical data The plasma concentration-time profiles and PK parameters of sacubitril were simulated following single or multiple (BID) oral doses of 97 mg sacubitril (present in the 200 mg LCZ696

taking only LCZ696 in Part I and II from clinical studies [15, 16]. 2.3.3 Simvastatin and simvastatin acid 2.3.3.1 Estimation of oral absorption:

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dose). The simulated trials were conditioned according to the dosing regimen from the cohort

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Simvastatin is administered in the lactone form, which is highly permeable and is not a substrate of hepatic OATPs. Simvastatin acid, the active metabolite of simvastatin, is the substrate for

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OATPs. A simvastatin PBPK model incorporating simvastatin acid as the primary metabolite was developed. The absorption process was described for simvastatin as a first-order absorption model. The values for ka and Tlag were estimated via pharmacokinetics data from the clinical study [15]. Fitting the simvastatin pharmacokinetic profiles from the clinical study, fa value was optimized and entered in the absorption model. Peff was predicted by Simcyp and then QGut of simvastatin lactone were predicted by Simcyp (Table 2).

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2.3.3.2 Distribution model for simvastatin

Since simvastatin displayed a two-compartment model in humans after oral dosing, a minimal PBPK model in Simcyp was adopted accounting for distribution rate constant (kin and kout).

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Estimation of kin and kout values was obtained through the pharmacokinetic analysis of the plasma concentration-time data from the clinical study (15).

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2.3.3.3 Elimination model for simvastatin Simvastatin can be converted to simvastatin acid via hydrolysis by plasma paraoxonases and carboxylesterases (CES) primarily present in liver and small intestine [21-23]. The formation of simvastatin was described as a result of hydrolysis via CES1 in liver, small intestine and plasma. In addition, simvastatin undergoes oxidative metabolism by CYP3A4 and CYP2D6, CYP2A6, CYP2C8, CYP2C9, CYP2C19, CYP1A2, and CYP2E1 was found not to play significant roles in vitro. However, Backman’s study [18] found that gemfibrozil increased simvastatin exposure, which hinted that CYP2C8 was involved in the metabolism of simvastatin, given that 6

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gemfibrozil glucuronide was found to be a metabolism-dependent inhibitor of CYP2C8[24]. Therefore, the elimination model was characterized using CLint value of CES, CYP3A4 and CYP2C8, respectively, and the plasma T1/2 for plasma esterase. The values for the abovementioned parameters were optimized to fit the observed concentration-time profile for

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simvastatin and simvastatin acid from the clinical study (15) via the PE function within Simcyp Simulator. 2.3.3.4 PBPK model for simvastatin acid

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Simvastatin acid PBPK model was linked to simvastatin PBPK model as a primary metabolite to simvastatin. Parameters were entered in the distribution, elimination and transporter components

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within the PBPK model (Table 3). As a result, the PBPK model for simvastatin and simvastatin acid was developed which allowed for the simultaneous simulation of simvastatin and simvastatin acid.

The full PBPK model with default method 2 within Simcyp was used to predict Vss of simvastatin acid. The Kp scalar was optimized to adjust Vss to fit simvastatin acid PK profile from clinical studies [15]. The enzyme kinetic module was utilized in the elimination model in

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which the CLint value was optimized to predict the concentration-time profile of simvastatin acid. Renal clearance was considered to be negligible and a value of zero was entered. The in vitro simvastatin acid parameters for hepatic OATP1B1 are not translatable to CLint,T in the PBPK model. Using the literature reported DDI between simvastatin and gemfibrozil[18] and fixing all

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of the other parameters in the PBPK model, an iterative procedure in the Parameter Estimation (PE) model of the Simcyp Simulator was used to obtain the in vivo CLint,T value.

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2.3.3.5 Simvastatin PBPK model verification using observed clinical data The plasma concentration-time profile of simvastatin and simvastatin acid was simulated following a single oral dose of 40 mg simvastatin. The simulated trial was the control arm of the clinical study [15]. 2.3.4 Atorvastatin The Simcyp input parameters for the atorvastatin model used in this study were described in [20]. The plasma concentration-time profiles and PK parameters of atorvastatin were simulated following multiple oral doses of 80 mg atorvastatin based on previously reported data. [16] 7

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2.3.5 Pravastatin The Simcyp default PBPK model for pravastatin was used. The PK parameters of pravastatin were simulated following multiple oral doses of 200 mg LCZ696.

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2.4 DDI simulations Simulations of drug-drug interaction between sacubitril and atorvastatin

The dosing regimens of sacubitril and atorvastatin were set according to the clinical protocol [16]. The simulated mean concentration-time profile (with 5th and 95th percentile curves) was

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compared to the observed data.

Simulations of drug-drug interaction between sacubitril and simvastatin acid

2.5 Parameter sensitivity analyses

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The dosing regimens of sacubitril and simvastatin were set according to the clinical protocol [16].The simulated mean concentration-time profile (with 5th and 95th percentile curves) was compared to the observed data.

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Parameter sensitivity analysis was conducted within Simcyp. It is the measurement of the change in AUC with respect to the change in CLint,T for OATP1B1 for simvastatin acid or atorvastatin upon co-administration with LCZ696 (200 mg BID) in healthy subjects. Parameter sensitivity analyses were performed on CLint,T values in a range of 0 – 150 µL/min/million cells using sacubitril and simvastatin/simvastatin acid PBPK model or atorvastatin PBPK model. AUC and Cmax were chosen as output option.

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2.6 Simulations of drug-drug interaction between sacubitril and pravastatin The dosing regimens of sacubitril and pravastatin were set to be similar to the study design in [16]: LCZ696 200 mg was administered from Day 1 to 7 and a single-dose pravastatin 40 mg

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was given on Day 6. Results

3.1 Development and qualification of Sacubitril PBPK model The PBPK model was parameterized with sacubitril physiochemical and biopharmaceutical properties (Table 1). The observed and simulated PK parameters for sacubitril after a single 200 mg LCZ696 (97 mg sacubitril) dose or multiple BID 200 mg LCZ696 doses are summarized in Table 4 and Table 5, respectively. The observed sacubitril PK data were from a clinical cohort dosed only with LCZ696 in clinical studies [15] [16]. 8

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3.2 Qualification for atorvastatin Simcyp PBPK model

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According to the draft guideline[25] from European Medicines Agency (EMA), the quantitative predictive performance of any compound files used in a simulation needs to be confirmed. The PK parameters (AUC, Cmax, and Tmax) were predicted within 2-fold of the actual values. Following a single 200 mg LCZ696 dose, observed AUClast was 2420 ng·h/mL, which was similar to the simulated value (2084 ng·h/mL). The short Tmax of sacubitril was correctly simulated by PBPK model (Table 4). Table 5 summarizes the observed exposure parameters for sacubitril at steady state from three clinical studies and the simulated sacubitril exposure parameters at steady state. The simulated AUCtau was 2121 ng·h/mL which was in the observed range for mean AUCtau (1426 – 2320 ng·h/mL, Table 5). Figure 1 showed that the simulated concentration-time profiles for sacubitril can well fit the observed data after a single or multiple doses of 200 mg LCZ696.

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The plasma profiles (Figure 2) for atorvastatin with mean, 5th and 95th percentiles were simulated in Simcyp using physiochemical and biopharmaceutical parameters summarized in [20]. After 80 mg QD dosing of atorvastatin to healthy subjects, the observed AUCtau,ss for atorvastatin was 204 ± 81.2 ng·h/mL [16]. The predicted mean AUCtau,ss was 163 ng·h/mL, which was within 2fold of the observed AUC value (Table 6). The atorvastatin PBPK model predicted a mean Cmax of 27.7 ng/mL which was within 2-fold of the observed mean Cmax value (52.7 ± 24.8 ng/mL). The predicted median Tmax (0.90 h) was similar to the observed median Tmax (1.0 h) in healthy subjects.

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The developed PBPK model was further verified using the clinical data obtained after a 40 mg oral dose [20], from which the model originated, and was qualified within Simcyp, version 15. Mean Cmax and AUC values after a single 40 mg atorvastatin dose were predicted to be 10.9 ng/mL and 62.5 ng·h/mL, respectively (data not shown), similar to 13.4 ng/mL and 66.7 ng·h/mL predicted previously[20], within Simcyp® version 14. These results confirmed that the atorvastatin PBPK model was appropriately reproduced within Simcyp® version 15.

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3.3 Simulations of drug-drug interaction between sacubitril and atorvastatin Simulation of the clinical trial of LCZ696 and atorvastatin was performed to qualify the ability of the Simcyp model to predict the drug-interaction magnitude (geometric mean AUC and Cmax ratios) in the presence and absence of LCZ696 treatment. Table 6 summarizes the simulation results, in comparison with the actual clinical DDI observed from study [16] following multiple daily doses of 80 mg atorvastatin co-administered with LCZ696 (200 mg BID). The predicted geometric mean ratio for AUC and Cmax was 1.41 and 1.71, respectively (Table 6). The observed ratio was 1.34 for AUC and 1.74 for Cmax, which was consistent with the predicted DDI. The simulation results on DDI further validate the atorvastatin PBPK model. 3.4 Development and qualification of Simcyp PBPK model for simvastatin and simvastatin acid 9

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The PBPK model was parameterized with physiochemical and biopharmaceutical properties for simvastatin and simvastatin acid (Table 2 and Table 3). The esterase-mediated CL was predicted to represent about 80% of total CL, meanwhile, CYP3A4 and CYP2C8 was predicted to represent about 8-9% of total CL, respectively.

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The simvastatin acid CLint,T for OATP1B1 was estimated by simulating the observed DDI effect of gemfibrozil and simvastatin acid [18]. The default gemfibrozil PBPK model within Simcyp and the simvastatin-simvastatin acid linked PBPK model were utilized to simulate the DDI. The subjects received gemfibrozil 600 mg twice daily for 2 days. On day 3 the subjects ingested gemfibrozil 600 mg and simvastatin 40 mg one hour after gemfibrozil administration. The in vivo CLint,T value for OATP1B1-mediated elimination was obtained via the PE function within Simcyp. The PBPK model incorporating CLint,T value of 28 µl/min/million cells can well simulate the observed concentration-time profiles of simvastatin and simvastatin acid with or without gemfibrozil interaction as shown in Figure 4. The simulated AUC and Cmax ratio for simvastatin acid in the presence of gemfibrozil vs. simvastatin alone were 2.03 and 1.76 (Table 7), similar to the observed ratios of 2.49 for AUC and 2.18 for Cmax. Additionally, the slightly higher AUC ratio for simvastatin (AUC ratio = 1.12) in the presence of gemfibrozil was approximately reproduced by the PBPK modelling (Table 7), indicating the appropriate estimation for the CLint of CYP2C8.

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The developed PBPK model was further verified using the clinical data obtained after a single oral dose of 40 mg simvastatin to healthy subjects [15]. For simvastatin, the Simcyp PBPK model predicted mean Cmax (12.4 ± 16.8 ng/mL) was similar to the observed Cmax (11.5 ± 6.68 ng/mL). The AUClast, the predicted mean value was 30.3 ± 14.4 ng·h/mL, similar to the observed AUC value of 26.5 ± 16.4 ng·h/mL. The short Tmax (median value: 1.0 h) was captured by prediction (simulated median Tmax:1.4 h). Regarding simvastatin acid, the Simcyp PBPK model predicted mean Cmax (2.17 ± 2.44 ng/mL) was slightly underestimated as compared to the observed Cmax value (4.26 ± 3.48 ng/mL). Observed AUClast for simvastatin acid was 30.7 ± 29.3 (ng·h/mL), and AUC in the 20 – 50 years old population was predicted to be 22.0 ± 23.6 ng·h/mL. Of particular note, the predicted median Tmax was 3.4 h indicating that the delayed Tmax for simvastatin acid (observed median Tmax: 4 h) was reproduced by the simvastatin-simvastatin acid linked PBPK model. The agreement between the observed and predicted exposure and concentration versus time profile (Figure 3) suggests that the simvastatin-simvastatin acid linked PBPK model can be further utilized to perform DDI predictions. Note that the model is innately limited in liver and muscle tissue concentrations, which can’t be verified since measured concentrations are not available. Nevertheless, the Simcyp PBPK model can be readily applied for simulation on OATP1B1-mediated drug interaction. 3.5 Simulations of drug-drug interaction between sacubitril and simvastatin acid

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The DDI simulations for LCZ696 and simvastatin were performed to qualify the ability of Simcyp to correctly predict the drug-interaction magnitude (geometric mean AUC and Cmax ratios) for LCZ696 as a perpetrator. Table 8 summarizes the simulation results, in comparison with the actual data observed in a clinical study [15] following a single oral dose of 40 mg simvastatin with 200 mg QD of LCZ696. The predicted simvastatin acid-LCZ696 AUC and Cmax ratio remained unchanged after simvastatin was dosed 2 h or 1 h after LCZ696 dosing, respectively, which were consistent with the actual observed value in the same trial. After the coadministration of 40 mg simvastatin and 200 mg LCZ696 BID, the PBPK model predicted ~1.1fold AUC and Cmax ratio (Table 8). These results further suggested appropriateness of the simvastatin acid CLint,T values entered for OATP1B1 to represent the hepatic uptake of simvastatin acid in the model.

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3.6 Simulation of portal vein concentration profiles of sacubitril, atorvastatin and simvastatin acid The simulated sacubitril and atorvastatin concentrations in the portal vein are shown in Figure 5A. Portal vein concentration of sacubitril reached Tmax at ~0.5 h and declined rapidly to well below the IC50 value for OATP1B1 inhibition at ~2 h post-dose. For atorvastatin, the simulated portal vein Tmax was 1 h.

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Unlike atorvastatin, Cmax of simvastatin acid in the portal vein was delayed with a Tmax of ~3.5 h via simulation (Figure 5B). Portal vein Tmax of sacubitril was about 3 h shorter than that of simvastatin acid. Figure 5B showed that, at 2 h, sacubitril portal vein concentrations were below IC50 for OATP1B1. 3.7 Parameter sensitivity analysis

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Furthermore, a sensitivity analysis on hepatic OATP CLint,T was conducted to estimate the DDI potential for statins with a long Tmax (e.g. simvastatin acid) or a short Tmax (e.g. atorvastatin) as a result of the OATP1B1 inhibition by sacubitril.

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For statins with a short Tmax, nearly a 1.5-fold increase in AUC and about 2-fold increase in Cmax was anticipated (Figure 6A). These predictions are consistent with the observed about 1.7-fold higher Cmax and 1.3-fold higher AUC (1.34) ratio for atorvastatin co-administered with LCZ696, which was rapidly absorbed. Based on these predictions, we conclude that the Cmax of statins that are rapidly absorbed (Tmax: <1.5 h) are expected to increase to a maximal extent of 2-fold while AUC increase is predicted to be < 1.5-fold. For statins with a long Tmax , maximally a 1.2fold increase in statin exposure was predicted upon co-administration with LCZ696 (Figure 6B). 3.5 Simulations of drug-drug interactions between sacubitril and pravastatin The DDI simulations for LCZ696 and pravastatin were performed. Following a single oral dose of 40 mg pravastatin in the presence of 200 mg BID of LCZ696, the predicted geometric mean 11

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ratio for AUC was 1.27 ( 90% CI: 1.24 – 1.30); the predicted geometric mean ratio for Cmax was 1.54 ( 90% CI: 1.48 – 1.60). The predicted drug effect was similar to atorvastatin which likewise showed a short Tmax. 4 Discussion

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In recent years, modelling results have been submitted to health authorities to complement and rationalize the clinical trials [26-28]. Adequate model validation is important prior to decision making. The validation process with the observed clinical data can be particularly useful in identifying key parameter(s) which can lead to significant exposure change in the presence of a

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perpetrator or inhibitor.

LCZ696 is a combination drug with sacubitril/valsartan is a first in class ARNI inhibitor

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approved for the treatment of patients with heart failure with reduced ejection fraction. Many HF patients take statins as co-medication for the treatment of co-morbid conditions. Since sacubitril has been shown to have the potential to inhibit hepatic OATP-meditated transport which can be the rate limiting step in the disposition of many statins disposition, there is likelihood for interaction with statins. Atorvastatin Cmax and total exposure (AUC) were increased by 1.7- and 1.3-fold, respectively when co-administered with LCZ696. However there were no changes in

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either Cmax parameter for simvastatin/simvastatin acid. A PBPK model was developed in order to examine these statin-specific DDI observations. In this analysis, a strategy for the application of PBPK models to simulate the drug interaction potential between simvastatin acid and

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atorvastatin as a result of OATP inhibition has been developed and applied. Simvastatin is administered as a lactone prodrug which further converts to simvastatin acid via

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hydrolysis and carboxylesterases present in tissues especially in liver and small intestinal wall, as well as paraoxonases in plasma [23]. In addition, both lactone and simvastatin acid undergo CYP3A4/5 oxidative metabolism [21-23]. In order to accurately simulate the DDI effect for simvastatin acid, a simvastatin-simvastatin acid linked PBPK model was constructed. The key parameter, the OATP-mediated CLint,T, was estimated from the observed DDI effect of gemfibrozil using PBPK modelling (Table 6).

In a series of genotype-panel studies, the effects of the Solute Carrier Organic Anion Transporter Family Member 1B1 (SLCO1B1) c.521TC single-nucleotide polymorphism (SNP) were 12

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examined on the pharmacokinetics of simvastatin, atorvastatin, and other statins in the same group of healthy young subjects [17, 29]. The largest effect was observed on simvastatin acid, the active form of simvastatin (3.2-fold increased mean AUC in c.521CC homozygotes). The SLCO1B1 genotype effect on atorvastatin was about 2-fold increase on AUC. Simvastatin acid

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appears to be a more sensitive and selective substrate for hepatic OATP1B1 than atorvastatin. The static model for transporter-mediated DDI simulation [30] predicted a maximum potential fold increase in exposure (R-value) of 1.32 or 1.29 due to the inhibition of OATP1B1 or OATP1B3, respectively, by sacubitril at clinically observed peak concentrations[15]. The

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limitation of a static model for DDI prediction is that the time function was not considered. Application of this static prediction may explain the minor changes in atorvastatin PK (within 2-

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fold) that were observed clinically. Both atorvastatin and sacubitril have similar Tmax, which simultaneously presents either compound at the site of interaction (i.e. hepatic inlet) at their highest respective concentration. On the other hand, application of the static model is likely to result in an inaccurate prediction of simvastatin acid DDI whose Tmax is delayed relative to that of sacubitril.

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In contrast to the static model, the mechanistic evaluation incorporating simulated concentration profiles as a function of time demonstrated that OATP inhibition by sacubitril leads to varied degrees of statin exposure. Sacubitril is a pro-drug with a good permeability; therefore it is rapidly absorbed in human with a short Tmax. After absorption, sacubitril is rapidly metabolized

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to the neprilysin inhibitor sacubitrilat in vivo, hence the T1/2 (1.4h [15]) was short. Figure 5 shows the sacubitril concentration in the portal vein was maintained above IC50 for OATP1B1

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inhibition from ~ 5 min to 1.8 h post dose. Tmax for simvastatin acid concentration in portal vein was delayed to about 2 h, at which time the sacubitril concentration in portal vein will have declined to below IC50 for OATP inhibition at the delayed Tmax (Figure 5B). Hence the likelihood for OATP-mediated DDI was low. In contrast, due to the relatively rapid Tmax of atorvastatin in portal vein, the time frame for the sacubitril concentration above IC50 for OATP1B1 inhibition in the presence of atorvastatin was about 2 h (Figure 5A). The cooccurrence of sacubitril concentration above the IC50 value of OATP inhibition and higher atorvastatin concentration in portal vein led to ~ 1.7 fold increases on atorvastatin Cmax and a smaller change in atorvastatin AUC (~ 1.3-fold increase). Further, the simulated sacubitril Cmax 13

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in portal vein was no more than 3-fold higher than IC50 for OATP1B1 inhibition (Figure 5A). Taken together, the short Tmax, short T1/2 and up to 3-fold higher Cmax than IC50 are key parameters governing the DDI potential for sacubitril with statins for which the rate-limiting step for DDI is the inhibition of hepatic OATP uptake. Apparently, for evaluation of transporter-

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mediated DDI, PBPK modelling can be reliably applied since it integrates information in drug absorption, disposition, DDI mechanism for both substrate and perpetrator drugs. Moreover, the static estimation (i.e. R-value) of the degree of interaction with the sensitive OATP1B substrates represents the worst-case scenario of prediction since PK characteristic of the victim, and the

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fractional contribution of the OATPs to the hepatic elimination of statin, are not considered. In addition the temporal changes in perpetrator concentrations are not considered in the static

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model.

Many known OATP inhibitors are found to display a long T1/2 in human. The T1/2 for ezetimibe was~ 22 h in humans[31]; 23h for fenofibrate/fenofibric acid [32] and 17 – 19 h for velpatasvir[33]. Contrasting to these OATP inhibitors, sacubitril exhibited a very short T1/2 about 1.4 h [10] in humans since it was rapidly converted to sacubitrilat in vivo. Conceivably the short Tmax together with its short T1/2 leads to short residence time for sacubitril in vivo. Thus, for DDI assessment.

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statins, the relative time to reach Tmax in comparison to sacubitril Tmax is a key parameter for the

Another key parameter for sacubitril DDI assessment is the CLint,T value of statins due to their

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hepatic OATP uptake. Extrapolating from the pharmacokinetic effects of SLCO1B1 variants on statins in more than 20 clinical studies [17], it is evident that drug interactions affecting

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OATP1B1 activity is an important determinant of systemic exposure. To evaluate the potential DDI effect for sacubitril on other statins which are OATP substrate, we investigated the impact of CLint,T for hepatic OATP uptake on DDI effect. A parameter sensitivity analysis was performed using simvastatin-simvastatin acid linked PBPK model or atorvastatin PBPK model, to represent the fast and delayed absorbed statins, respectively. For statins which displayed a delayed Tmax, the DDI effect was maximally not more than 1.2-fold increase on exposure (Figure 6B). In the clinical DDI study, the observed results showed no DDI effect for simvastatin acid. For statins which yielded a short Tmax, the maximal Cmax ratio was about 2 and the maximal AUC ratio was about 1.5 (Figure 6A). The parameter sensitivity analysis results revealed that, when 14

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co-administered with LCZ696, the DDI risk for statins was maximally mild to moderate. Sacubitril exhibited short Tmax and T1/2, and the clinically relevant Cmax,ss (5.89 µM) was no more than 3-fold higher than the IC50 for OATP1B1. It appears conceivable that sacubitril was a mild

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to moderate OATP inhibitor in vivo The DDI risk evaluation for sacubitril was further conducted based on the Tmax difference between sacubitril and other statins (Table 9). Like simvastatin, the structurally similar statin lovastatin, is administered in it inactive lactone form which is converted to the active acid form

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[34]. Consequently, the apparent Tmax for lovastatin acid in vivo is prolonged thereby limiting the potential interaction with sacubitril due to OATP inhibition. Likewise, sacubitril is unlikely to affect the pharmacokinetics of rosuvastatin due to its delayed absorption resulting in a Tmax

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between 3 – 5 h after administration. Therefore a DDI effect with rosuvastatin is unlikely to occur upon co-administration with sacubitril (Table 9). The systemic exposure to atorvastatin and rosuvastatin has been shown to be dependent on the OATP1B genotype [29]. However, unlike atorvastatin, the absorption of rosuvastatin is appreciably slower which averts a DDI with sacubitril. Although fluvastatin is a substrate for hepatic OATP1B1/1B3, the SLCO1B1 genotype had no significant effect on fluvastatin exposure [17] confirming that CYP2C9-

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metabolism is the rate limiting step in its disposition in vivo. Sacubitril and LBQ657 do not inhibit CYP2C9 and valsartan only marginally inhibits CYP2C9 [35] suggesting anticipated to be low. Similar to atorvastatin (Tmax 1 h), pitavastatin ( Tmax 1- 2 h) and pravastatin (Tmax 1 – 1.5 h) are also rapidly absorbed, and therefore may also be subject to DDI when co-administered

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with sacubitril. The DDI simulation results for pravastatin co-administered with LCZ696 predicted only an AUC ratio of 1.26 and Cmax ratio of 1.53, which confirmed the postulation of a

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DDI effect mediated via a Tmax difference. The exposure to pitavastatin has been shown to be sensitive to OATP1B1 inhibition where its AUC was increased by 3.5 fold and 4.7 fold when coadministered either with cyclosporine A or rifampicin [36, 37]. Additionally, pitavastatin was found to yield significantly higher AUC ratio (AUCR) after co-administration with IV-infused rifampicin (600 mg) compared to the same dose given PO [37]. Rifampicin concentrations exhibited similar terminal phases after PO or IV administration except that Cmax was ~ 2 fold higher following IV than PO dosing. Notably rifampicin Tmax was 0.5 h and 1.5 h following IV and PO dosing, respectively and the Tmax for pitavastatin (0.5 – 1 h) was similar to that for intravenously dosed rifampicin. The finding of a greater impact of IV than PO rifampicin 15

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suggests the additional contributory factor of a short Tmax resulting from IV infused rifampicin. Indeed the static model utilizing the observed Cmax after IV or PO dosing and the default absorption rate constant (ka = 0.1 min-1) could not predict the differing DDI effects (data not shown). The result from this DDI study has afforded support to the mechanism for the differing

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DDI effect on atorvastatin and simvastatin acid by sacubitril. Additionally, LCZ696 is expected to interact with pitavastatin in a manner similar to that observed with atorvastatin resulting in clinically insignificant changes in exposure (<1.5-fold).

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Conclusion

In the present study, a sacubitril PBPK model and a simvastatin-simvastatin acid linked PBPK

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model including OATP-mediated CLint,T , was constructed, which allowed the simulation of the pharmacokinetics of sacubitril, simvastatin and simvastatin acid in humans, respectively. The PBPK model adequately described the lack of a DDI effect in the presence of sacubitril, dosed as LCZ696. Similarly, the mild to moderate DDI effect on atorvastatin as a result of OATP inhibition was successfully simulated with PBPK modelling. The PBPK model allowed the mechanistic evaluation of observed DDIs on statins ranging from no effect to mild or moderate

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to various statins.

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effects. More importantly, this evaluation extends the assessment of sacubitril interaction effect

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References Entresto Prescribing Information. Highlights of Prescribing information. US FDA, 2015. https://www.pharma.us.novartis.com/sites/www.pharma.us.novartis.com/files/entres to.pdf (Jan 12, 2016).

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Ayalasomayajula S, J.P., MBChB2, Pal P, PChandra P, Albrecht D, Langenickel T, Rajman I and Sunkara G, Assessment of Drug Interaction Potential between LCZ696, an Angiotensin Receptor Neprilysin Inhibitor, and Digoxin or Warfa rin. Clin Pharmacol Biopharm, 2015. 27(4).

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McMurray, J.J., et al., Angiotensin-neprilysin inhibition versus enalapril in heart failure. N Engl J Med, 2014. 371(11): p. 993-1004.

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Ayalasomayajula, S., et al., Assessment of drug interaction potential between LCZ696, an angiotensin receptor neprilysin inhibitor, and digoxin or warfarin. Clin Pharmacol Biopharm, 2015. 4.

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Flarakos, J., et al., Disposition and metabolism of [(14)C] Sacubitril/Valsartan (formerly LCZ696) an angiotensin receptor neprilysin inhibitor, in healthy subjects. Xenobiotica, 2016. 46(11): p. 986-1000.

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Ayalasomayajula, S.P., et al., Effect of renal function on the pharmacokinetics of LCZ696 (sacubitril/valsartan), an angiotensin receptor neprilysin inhibitor. Eur J Clin Pharmacol, 2016.

Brookman, L.J., et al., Pharmacokinetics of valsartan in patients with liver disease. Clin Pharmacol Ther, 1997. 62(3): p. 272-8.

Gan, L., et al., Pharmacokinetic drug-drug interaction assessment of LCZ696 (an angiotensin receptor neprilysin inhibitor) with omeprazole, metformin or levonorgestrelethinyl estradiol in healthy subjects. Clin Pharmacol Drug Dev, 2016. 5(1): p. 27-39.

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Hsiao, H.L., et al., Pharmacokinetic drug-drug interaction assessment between LCZ696, an angiotensin receptor neprilysin inhibitor, and hydrochlorothiazide, amlodipine, or carvedilol. Clin Pharmacol Drug Dev, 2015. 4(6): p. 407-17.

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Ayalasomayajula, S., et al., In vitro and clinical evaluation of OATP-mediated drug interaction potential of sacubitril/valsartan (LCZ696). J Clin Pharm Ther, 2016. 41(4): p. 424-31.

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Ayalasomayajula, S., 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, 2016.

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Niemi, M., M.K. Pasanen, and P.J. Neuvonen, Organic anion transporting polypeptide 1B1: a genetically polymorphic transporter of major importance for hepatic drug uptake. Pharmacol Rev, 2011. 63(1): p. 157-81.

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Backman, J.T., et al., Plasma concentrations of active simvastatin acid are increased by gemfibrozil. Clin Pharmacol Ther, 2000. 68(2): p. 122-9.

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Tsamandouras, N., A. Rostami-Hodjegan, and L. Aarons, Combining the 'bottom up' and 'top down' approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data. Br J Clin Pharmacol, 2015. 79(1): p. 48-55.

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Zhang, T., Physiologically based pharmacokinetic modeling of disposition and drug-drug interactions for atorvastatin and its metabolites. Eur J Pharm Sci, 2015. 77: p. 216-29.

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Prueksaritanont, T., et al., In vitro metabolism of simvastatin in humans [SBT]identification of metabolizing enzymes and effect of the drug on hepatic P450s. Drug Metab Dispos, 1997. 25(10): p. 1191-9.

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Prueksaritanont, T., B. Ma, and N. Yu, The human hepatic metabolism of simvastatin hydroxy acid is mediated primarily by CYP3A, and not CYP2D6. Br J Clin Pharmacol, 2003. 56(1): p. 120-4.

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Prueksaritanont, T., et al., Glucuronidation of statins in animals and humans: a novel mechanism of statin lactonization. Drug Metab Dispos, 2002. 30(5): p. 505-12.

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Ogilvie, B.W., et al., Glucuronidation converts gemfibrozil to a potent, metabolismdependent inhibitor of CYP2C8: implications for drug-drug interactions. Drug Metab Dispos, 2006. 34(1): p. 191-7.

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Agency, E.M., Guideline on the qualification and reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation. 2016. Grillo, J.A., et al., Utility of a physiologically-based pharmacokinetic (PBPK) modeling approach to quantitatively predict a complex drug-drug-disease interaction scenario for rivaroxaban during the drug review process: implications for clinical practice. Biopharm Drug Dispos, 2012. 33(2): p. 99-110.

Zhao, P., et al., Applications of physiologically based pharmacokinetic (PBPK) modeling and simulation during regulatory review. Clin Pharmacol Ther, 2011. 89(2): p. 259-67.

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Wagner, C., et al., Application of Physiologically Based Pharmacokinetic (PBPK) Modeling to Support Dose Selection: Report of an FDA Public Workshop on PBPK. CPT Pharmacometrics Syst Pharmacol, 2015. 4(4): p. 226-30.

29.

Pasanen, M.K., et al., Frequencies of single nucleotide polymorphisms and haplotypes of organic anion transporting polypeptide 1B1 SLCO1B1 gene in a Finnish population. Eur J Clin Pharmacol, 2006. 62(6): p. 409-15.

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FDA Draft DDI Guidance 2012. US FDA, 2012. http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/gui dances/ucm292362.pdf.

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ZETIA® (ezetimibe) Product Label. US FDA, 2016. http://www.accessdata.fda.gov/drugsatfda_docs/label/2007/021445s018lbl.pdf.

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FENOGLIDE® Prescirbing Information. Highlights of Prescribing Information. US FDA, 1993. http://www.accessdata.fda.gov/drugsatfda_docs/label/2012/022118s005lbl.pdf.

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EPCLUSA® (sofosbuvir and velpatasvir) Prescribing Information. Highlights of Prescribing Information. US FDA, 2016. http://www.accessdata.fda.gov/drugsatfda_docs/label/2016/208341s000lbl.pdf.

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Kunze A, H.J., Camenisch G, Poller B, Prediction of OATP1B1 and OATP1B3 mediated hepatic uptake of statins based on transporter protein expression and activity data Drug Metab Dispos, 2014. 42(9): p. 1514-21.

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Taavitsainen, P., K. Kiukaanniemi, and O. Pelkonen, In vitro inhibition screening of human hepatic P450 enzymes by five angiotensin-II receptor antagonists. Eur J Clin Pharmacol, 2000. 56(2): p. 135-40.

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Pitavastatin (Livalo®) Prescribing Information. Highlights of Prescribing Information. US FDA, 2009. http://www.accessdata.fda.gov/drugsatfda_docs/nda/2009/022363s000_Lbl.pdf.

37.

Prueksaritanont, T., et al., Pitavastatin is a more sensitive and selective organic aniontransporting polypeptide 1B clinical probe than rosuvastatin. Br J Clin Pharmacol, 2014. 78(3): p. 587-98.

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Figure Captions

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Figure 1. Construction and qualification of the sacubitril PBPK model. Simulated versus observed mean plasma concentrations of sacubitril in healthy subjects. The solid line represents the mean simulated plasma concentrations of sacubitril (10 trials of 10 subjects in each trial) with 5th and 95th percentile profile represented by the grey lines. The symbols and error bars represent the mean (and SD for trials with observed plasma concentrations profiles from studies Ayalasomayajula S, et. al. [16] (Part I and Part II) and Ayalasomayajula S, et. al. [15].

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Figure 2. Simulated versus observed mean plasma concentrations of atorvastatin after 80 mg QD in the presence or absence of sacubitril (dosed as 200 mg BID LCZ696). The solid and dashed black lines represent the mean simulated plasma concentrations of atorvastatin in the absence and presence of sacubitril. The dashed grey lines are the respective upper 95th and lower 5th percentiles. The symbols and error bars represent the observed plasma concentrations (mean and SD) for atorvastatin alone (open circles) and in the presence of sacubitril (solid circles) from study Ayalasomayajula S, et. al. [16].

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Figure 3. Construction and qualification of the simvastatin-simvastatin acid linked PBPK model. 3A) Simulated versus observed mean plasma concentration of simvastatin after a single 40 mg oral dose of simvastatin in healthy subjects; 3B) Simulated versus observed mean plasma concentration of simvastatin acid after a single 40 mg oral dose of simvastatin in healthy subjects. The solid line represents the mean simulated plasma concentrations of simvastatin acid and the dashed lines represent the upper 95th and lower 5th percentiles, respectively. The symbols and error bars represent the observed plasma concentrations (mean and SD) of simvastatin or simvastatin acid when 40 mg simvastatin was given without LCZ696 coadministration from study Ayalasomayajula S, et. al. [15].

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Figure 4. Qualification of hepatic OATP1B1 CLint, T for simvastatin acid. Predicted versus observed mean plasma concentrations of simvastatin acid after a single oral dose of 40 mg simvastatin in the presence or absence of gemfibrozil (600 mg BID). The solid black line represents the mean simulated plasma concentration of 100 subjects (10 trials of 10 subjects in each trial) without gemfibrozil. The observed concentrations from Backman J, et. al. [18] are represented as solid triangles or squares. The dashed black line represents the mean simulated plasma concentration of 100 subjects (10 trials of 10 subjects in each trial) in the presence of gemfibrozil. The observed concentrations are represented as solid circles.

Figure 5. Simulation of the mean portal vein concentrations vs time profiles for atorvastatin, simvastatin acid and sacubitril in healthy subjects. 5A) Simulated mean portal vein concentrations vs time profiles for atorvastatin and sacubitril; 5B) Simulated mean portal vein concentrations vs time profiles for simvastatin acid and sacubitril. 20

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Figure 6. Parameter sensitivity analysis for hepatic OATP CLint,T on exposure parameters of statins in healthy subjects upon co-administration with LCZ696 (200 mg BID). 6A) Simulated AUC or Cmax ratio for atorvastatin versus CLint,T ; 6B) Simulated AUC or Cmax ratio for simvastatin acid versus CLint,T.

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Table 1. Simcyp Input Parameters for Sacubitril Value 411.5 4.469 0.583 0.033

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In silico calculated LogP In house data In house data

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First-order absorption model In house data User defined User defined Simcyp predicted Simcyp default value Passive + active, in house data Simcyp predicted

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1 0.14 1.3 7.48 1 3.87 1.43

Comment/Reference

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Minimal PBPK 0.253 0.2 1.49

In vivo clearance 27 0

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Parameter M.W. (g/mol) LogPo:w B/P fu in plasma Absorption Model used fa Tlag (h) ka (h-1) Qgut (L/h) fu,gut Papp (cm/s x 106) Peff,man (10-4 cm/s) Distribution Model used Vss (L/kg) Vsac (L/kg) Q (L/h) Elimination Model used CLiv (L/h) Renal clearance, CLR (L/h)

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Interaction SLCO1B1 (OATP1B1) Ki (µM) and fu,inc

1.91 and 0.04

User defined User defined User defined

User defined Unchanged sacubitril accounted for 0.8-2.8% of dose in urine described by Flarakos J, et. al.[10]

IC50 values from Ayalasomayajula S, et.al. [15] and fu,inc was user defined to predict atorvastatin DDI a User defined values were optimized to fit sacubitril PK and DDI

1

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Table 2. Simcyp Input Parameters for Simvastatin Reference In silico predicted In silico predicted In silico predicted

0.09 0.8 0.25

First-order absorption model User defineda User defineda User defined

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Value 418.6 4.98 0.75 0.028

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Parameter M.W. (g/mol) LogPo:w B/P fu in plasma Absorption Model used fa ka (h-1) Tlag (h)

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fu,gut 0.028 fup value Qgut (L/h) 12.1 Simcyp predicted 3.84 In silico predicted Effective human permeability (10-4 cm/s) PSA (A2) 50 In silico predicted Distribution Model used minimal PBPK Vss (L/kg) 2.3 Obtained from PK analysis Vsac (L/kg) 1.47 Obtained from PK analysis Kin (1/h) 0.155 Obtained from PK analysis Kout (1/h) 0.262 Obtained from PK analysis Elimination Model used Enzyme kinetics CYP3A4 100 User defined CLint (µL/min/pmol of isoform) CYP2C8 100 User defined CLint (µL/min/pmol of isoform) CES1 250 User defined; primary metabolite is simvastatin acid CLint (µL/min/mg protein) Plasma esterase 8 User defined; primary metabolite is simvastatin acid Plasma t1/2, min Renal clearance, CLR 0.47 From Simcyp default compound file for (L/h) simvastatin a User defined values were optimized to fit simvastatin and simvastatin acid PK, and DDI effect for simvastatin acid 2

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Table 3. Simcyp input parameters for simvastatin acid Value 436.6 1.88 1 0.055

Reference In silico predicted User defineda http://www.drugbank.ca/drugs/DB00641

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Parameter M.W. (g/mol) LogPo:w B/P fu in plasma Distribution Model used Vss (L/kg)

Full PBPK 0.95

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Simcyp predicted, method 2, Kp scalar = 15 to fit observed data

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Elimination Model used Enzyme kinetics CYP3A4 15 Estimated using retrograde model in Simcyp® to fit observed data CLint (µL/min/pmol of isoform) Renal clearance, CLR 0 (L/h) Transport Intrinsic clearance, Uptake 28 Matching DDI simulation result to the SLCO1B1(OATP1B1) published DDI result between simvastatin and CLint,T (µL/min/million gemfibrozil [18] cells) RAF/REF 1 User defineda a User defined values were optimized to fit simvastatin acid PK and DDI

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Table 4. Simcyp PBPK simulated versus observed sacubitril exposure parameters [mean (SD)] after a single 200 mg LCZ696 (97 mg sacubitril) oral dose in healthy subjects Simcyp predicted

0.50 (0.50, 3.0)

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Cmax, ng/mL 1512 (783) AUClast (0-24h), 2084 (1118) ng·h/mL 0.48 (0.30, 1.05) Tmax, h Tmax is represented as median (range)

Observed from Ayalasomayajula S, et. al.[16]– Period I 2030 (1270) 2420 (658)

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PK parameter

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Table 5. Sacubitril PBPK simulated versus observed exposure parameters [mean (SD)] after 200 mg LCZ696 (97 mg sacubitril) BID in healthy subjects

Cmax, ng/mL AUCtau, ng·h/mL Tmax, h

Simcyp predicted

Observed from Ayalasomayajula S, et. al.[16]

Observed from

Observed from

Ayalasomayajula S, et. al. [15]

Ayalasomayajula S, et. al. [11]

1523 (786) 2121 (1154)

2320 (1300) 2620 (728)

1760 (812) 2240 (715)

1426 (661) 1864 (787)

1.50 (1.00, 4.00)

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0.50 (0.33, 0.500 (0.500, 3.98) 1.05) Tmax is represented as median (range)

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PK parameter

5

0.5 (0.50, 4.0)

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Table 6. Atorvastatin PBPK simulated exposure parameters [mean (SD)] after 80 mg QD with or without 200 mg LCZ696 BID in healthy subjects PK parameter

Simcyp predicted

Observed from Ayalasomayajula S, et. al.

52.7 (24.8) 204 (81.2) 1.0 (0.50, 6.0) 98.2 (60.5) 282 (133) 1.0 (0.50, 4.0) 1.74 (1.49, 2.02)

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27.7 (18.0) 163 (115) 1.1 (0.70, 1.7) 45.1 (27.2) 225 (151) 0.90 (0.55, 1.55) 1.71 (1.59, 1.74) 1.41 (1.35, 1.44)

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Cmax,ss ng/mL AUCtau,ss ng·h/mL Tmax,h 200 (97) BID Cmax,ss ng/mL AUCtau,ss ng·h/mL Tmax,h Geometric mean Cmax ratio (90%CI)a Geometric mean AUC ratio (90%CI)a Tmax is represented as median (range)

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[16]

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LCZ696 dose (sacubitril dose) (mg) 0

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1.34 (1.23, 1.45)

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Table 7. Simvastatin PBPK simulated Cmax and AUC ratio versus observed ratio for simvastatin or simvastatin acid after a single 40 mg simvastatin oral dose with or without gemfibrozil (600 mg BID)

Simvastatin AUC ratio (95% CI) 1.09 (1.08 – 1.10 ) Cmax Ratio (95% CI) 1.07 (1.06 – 1.07) 1 (0.5-1) Tmax in the absence of gemfibrozil Tmax in the presence of 1.4 (0.4 – 2.3) gemfibrozil Simvastatin acid AUC ratio (95% CI) 2.03 (1.92 – 2.16) 1.76 (1.68 – 1.85) Cmax Ratio (95% CI) Tmax in the absence of 3.4 (3-8) gemfibrozil Tmax in the presence of 5.0 (2 – 9.2) gemfibrozil a Observed data from Backman J, et. al.[18] Tmax is represented as median (range)

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Simulated

1.12 (0.88-1.42) 0.91 (0.60-1.37) 1.3 (0.4- 2.3)

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1 (0.5-4)

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2.49 (1.74, 3.56) 2.18 (1.36, 3.52) 3.2 (1.1-5.7)

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Table 8. Simvastatin /simvastatin acid-linked PBPK simulated Cmax and AUC ratio versus observed ratio for simvastatin acid after a single 40 mg simvastatin oral dose with or without 200 mg LCZ696 BID AUC ratio Simcyp predictedb

a

Observeda

1.16 (1.00, 1.35) 0.96 (0.82, 1.08 0.90 (0.78, 1.03) 1.04 1.12) 0.87 (0.75, Co-administration 1.10 0.89 (0.77, 1.02) 1.09 1.01) a Clinical observed values from Ayalasomayajula S, et. al. [15], data is presented as geometric mean ratio (90% Confidence Interval) b Data is presented as geometric mean ratio 1.01 (0.88, 1.17)

1.02

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simvastatin dosed 2 h after LCZ696 simvastatin dosed 1 h after LCZ696

Observed

Cmax Ratio Simcyp predictedb

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Simvastatin acid

Table 9. Anticipated likelihood of sacubitril impact on co-administered statins

Fluvastatin Lovastatin (lactone) Simvastatin (lactone)

Tmax (h)

Comments

1

Observed Cmax; 1.7-fold increase Observed AUC: 1.3-fold increase from Ayalasomayajula S, et. al. [16]

OATP mediated uptake [34, 37] OATP mediated uptake [35] OATP and NTCP mediated uptake/CYP2C9 metabolism [27] CYP2C9 metabolism [36] Hydrolysis of lactone/OATP for lovastatin acid [35]

1-2 1 to 1.5 3–5

Predicted DDI < 1.5 fold Predicted DDI < 1.5 fold DDI unlikely

1 4

DDI unlikely DDI unlikely

Hydrolysis of lactone/OATP for simvastatin acid [21-23]

4

Observed no interaction from

AC C

Pitavastatin Pravastatin Rosuvastatin

TE D

Atorvastatin

Rate limiting step relevant to the interaction with sacubitril OATP mediated uptake [16, 20]

EP

Statin

8

Ayalasomayajula S, et al [15]

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT