Risk–Benefit Assessment of Ethinylestradiol Using a Physiologically Based Pharmacokinetic Modeling Approach

First‐pass metabolism of EE in the gut and liver

Although EE is well absorbed from the gut, its oral bioavailability is less than 50% due to extensive metabolism in the gut and/or liver during first pass.14, 15 In vitro incubation of EE in intestinal mucosal cells identified only conjugated metabolites, which constituted 38% of the incubated material. Approximately 90% of the metabolites were sulfo‐conjugates, while the rest were identified as glucuronides.16 Results of an in vivo mass balance study showed that 30% of the drug was recovered in the feces 2 weeks after oral administration, of which ∼80% was identified as unconjugated EE.8 The recovery of a high proportion of unconjugated EE in the feces does not necessarily contradict the in vitro study, given that deconjugation of previously conjugated drug by enterocytic microbes and shed enterocytes can also occur in the gut, especially when considering the duration of fecal collection.

A follow‐up in vivo study was performed to assess the extent of EE metabolism in the intestine vs. the liver during first pass by simultaneous sampling from the portal vein and the systemic circulation after oral administration. The results of the study confirmed that conjugation of EE in the gut wall plays a major role in its presystemic metabolism with a gut extraction ratio (E_G) of 0.44, while that in the liver (E_H) was 0.25.15 Although this study only measured conjugated metabolites, it was preferentially considered for estimating the fraction escaping gut metabolism (F_G) given that the afore‐mentioned in vitro study identified conjugated metabolites as the main product of intestinal metabolism. Based on these data, an F_G of 0.56 was assumed for EE with the estimated unbound intrinsic clearance for the gut back‐calculated using the “Q_Gut” model17; of which ∼70% was assigned to sulfation, ∼20% to hydroxylation, and the rest to glucuronidation (see details in the Methods).

Relative contributions of metabolic routes in the liver

A total intrinsic hepatic metabolic clearance (CL_int) of 275.49 μl/min/mg protein was back‐calculated from a mean intravenous (i.v.) clearance of 16.47 L/h18, 19, 20 via the well‐stirred liver model, using the retrograde calculator within the Simcyp simulator after subtracting renal clearance. Based on the study carried out by Reed et al.,8 the percentage of unchanged drug excreted in the urine is reported as 6% of an oral dose5; hence a value of ∼2.1 L/h was estimated for renal clearance from a mean oral clearance of 34.6 L/h.20, 21

A combination of bottom‐up (using scaled‐up in vitro data) and top‐down (using clinical data) approaches was used to assign the contributions of the respective enzymes to the systemic clearance of EE and hence obtain estimates of fm. Assignment of the scaled‐up hepatic metabolic CL_int of 66.77 μl/min/mg protein due to 2‐hydroxylation (which constitutes greater than 90% of the hydroxylated metabolites) of EE from the in vitro study carried out by Shiraga et al. in human liver microsomes (HLM),22 resulted in a contribution of ∼0.2 to the overall systemic clearance of EE (fm_CYP). Half of this (0.1) was assigned to CYP3A4, with the remainder apportioned to CYP2C9, 2C8, and 1A2, as indicated in the study with recombinant CYP enzymes.23 Based on the HLM study by Shiraga et al., the metabolic intrinsic clearance for the UGT1A1‐mediated metabolism of EE was calculated to be 21.26 μl/min/mg protein, resulting in an fm_UGT1A1 of about 0.05. It should be noted that sulfation and the minor 4‐hydroxylation pathway were not specifically accounted for in the model. Thus, the remaining unassigned hepatic metabolic CL_int of 187.46 μl/min/mg protein (275.49‐66.77‐21.26) representing an fm of about 0.57 was entered in the simulator as additional HLM clearance representing other EE hepatic metabolic pathways.

Although 9% of the drug has been reported as eliminated unchanged in the feces,5 due to the difficulty in separating out the amount of drug that remains unabsorbed from that which undergoes deconjugation after absorption and subsequent secretion/metabolism into the gastrointestinal tract, the exact amount of the parent drug eliminated via biliary clearance could not be estimated and is thus lumped with the non‐CYP clearance.

Simulated profiles using the PBPK model for EE

Simulated plasma concentration–time profiles using the developed model were able to reasonably recover observed data from both single doses of 50 μg EE administered i.v. and orally (Figure 2 a–d); as well as multiple doses of 35 μg EE administered orally (Figure 1 a). The predicted population mean (range of trial means) C_max and AUC_(0‐24) for the simulation of 100 individuals (10 trials × 10 female healthy volunteers (HV); 20–50 years) after multiple oral doses of 35 μg EE q.d. for 21 days (Figure 1 a) were 0.125 (0.08–0.154) ng/ml and 1.14 (0.989–1.308) ng/ml.h, respectively. Corresponding observed C_max and AUC_(0‐24) values on day 21 from different clinical studies were 0.087 ng/ml and 1.08 ng/ml.h24; 0.143 ng/ml and 1.199 ng/ml.h 25; and 0.117 ng/ml and 1.062 ng/ml.h.26

Simulation of the clinical DDI with ketoconazole: refinement of fm_CYP3A4 for EE

The simulated increases in plasma concentration–time profiles of multiple doses of 20 μg EE after coadministration with ketoconazole, a potent CYP3A4 inhibitor, underpredicted the observed data by more than 2‐fold. The predicted increase in AUC was 18% vs. 40% in the clinical study, while the predicted increase in C_max was 12% vs. the observed value of 39%.12 The in vitro CYP3A4 metabolic CL_int was therefore optimized to recover the DDI with ketoconazole, resulting in an increase in fm_CYP3A4 from 0.11 to 0.22. By retaining the in vitro‐derived fm for the different CYP enzymes, the in vitro CL_int values for the other CYP enzymes were proportionally increased relative to that of CYP3A4 to give an increased fm_CYP of ∼0.4, while the fraction assigned to the additional HLM elimination pathway was reduced to account for the increased fm_CYP. It should be noted that despite the refinement of fm_CYP3A4 to the overall systemic clearance of EE, the simulated concentration–time profiles remained consistent with the observed data, as shown in Figure 2 a–d. A summary of the disposition of EE after oral administration, utilizing the available published data and the final mean contributions to the systemic clearance of EE in the optimized PBPK model are shown in Figure 3 a,b, respectively.

Simulation of DDIs with other CYP3A4 inhibitors/inducers: verification of the fmCYP3A4

Simulated and observed C_max and AUC ratios of EE following administration of two CYP3A4 inhibitors (fluconazole and voriconazole) as well as two CYP3A4 inducers (rifampicin and carbamazepine) were all reasonably consistent with observed data, as summarized in Table 1. Simulated plasma concentration profiles of multiple doses of 35 μg EE in the presence and absence of voriconazole and rifampicin are shown in Figure 1 b,c, respectively. Simulated plasma concentration profiles of EE in the presence and absence of fluconazole and carbamazepine are shown in Supplementary Figure S1A,B. The predictive ratios for both the C_max and AUC for all the DDI simulations were within 1.25‐fold of the observed data (Table 1). The simulated trial designs are summarized in Table S1.

Assessing the safety and/or toxicity of EE in COC formulations

With current EE formulations, there have been more reports of breakthrough bleeding, particularly when coadministered with CYP inducers due to the relatively low doses of EE, and no reports of CVD when EE is coadministered with potent CYP inhibitors. Despite this, not enough attention has been given to informing the choice and dosage of COC, especially in an era whereby drugs are commonly being coadministered with other medications. We therefore performed an evaluation of reported clinical studies carried out in HV women collated from the University of Washington drug interaction database, showing in vivo induction ≥20% in the pharmacokinetic parameters (AUC and C_max) of EE. The results of these clinical studies are summarized in Table 2.

In 8 out of 11 evaluated clinical studies comprising a total of 122 female HVs, there were reported increased incidences of breakthrough bleeding and/or higher levels of follicle stimulating hormone (FSH), luteinizing hormone (LH), or progesterone in the comedication arm compared to the control arm. In the remaining three clinical studies comprising 84 female HVs, no reports of breakthrough bleeding were mentioned, although two out of the three clinical studies had no specific pharmacodynamic (PD) evaluations of either efficacy or toxicity. In all but one of the clinical studies with reported incidences of breakthrough bleeding, coadministration of the perpetrator drug resulted in EE steady‐state mean AUC (AUC_ss) of less than 1,000 pg/ml.h in the comedication arm of the study.

Although the exact mechanism(s) for the CVD risk of EE is still unclear, most of the CVD effects have been attributed to metabolic changes caused by increased hormonal levels. There is, however, no information regarding what steady‐state systemic concentrations of EE are expected to result in undesirable side effects. Nonetheless, there have been specific bans and/or withdrawals of EE formulations containing >50 μg both in the UK and the US.2, 3 In order to make an assessment of adequate EE dosing required to minimize the risk of both contraceptive failure and CVD, we selected 1,000 pg/ml.h as a lower threshold, given that the AUC_ss for the comedication arm in the evaluated clinical studies with reported breakthrough bleeding were less than 1,000 pg/ml.h. The mean simulated concentration of 1,675 pg/ml.h observed for the 50 μg oral dose (Table 3) was chosen as the upper threshold associated with a risk for CVD based on the recommended ACOG guidelines. We thereafter determined how frequently within a simulated population of 100 subjects (10 trials of 10 HV women aged between 20 and 50 years), the AUC_ss for different doses of EE: 20 μg, 35 μg, and 50 μg alone and in the presence of moderate and strong CYP3A4 inhibitors and inducers was below 1,000 pg/ml.h, on the one hand, or above 1,675 pg/ml.h on the other hand.

The predicted population mean AUC_ss for the 100 individuals at the different doses alone and in the presence of various degrees of CYP modulation are summarized in Table 3 and Figure S2 of the supplementary information. The AUC_ss of 86 simulated individuals was below 1,000 pg/ml.h for the 20 μg dose. In the presence of CYP3A4 or complete CYP inhibition, the concentration of EE increased as expected, with >50% of the virtual subjects having AUC_ss above the lower threshold. With the 35 μg dose, the AUC_ss of close to 50% of the virtual subjects was above the lower threshold. However, the presence of either moderate or strong CYP3A4 induction resulted in simulated concentrations below the lower threshold for up to 90% of the virtual subjects. In the presence of CYP3A4 or complete CYP inhibition, the simulated AUC_ss of close to 50% of the virtual subjects were above the population mean AUC_ss of 1,675 pg/ml.h. Finally, with the 50 μg dose, moderate and strong CYP3A4 induction resulted in ∼50% and 90% of the individual subjects respectively having AUC_ss below the threshold for efficacy.

PBPK model development

All PBPK simulations were carried out using the HV population within the Simcyp simulator v. 17 rel. 1 (Simcyp, Sheffield UK). The structural design and functional capabilities of the simulator as well as equations describing the genetic, physiological, and demographic variables for the population have been described previously.40 Prior metabolic, protein binding, and physicochemical data for EE were collated from the literature and incorporated into a minimal PBPK model with an additional single adjusting compartment (SAC) to recover the biphasic plasma concentration vs. time profile of EE. Input parameters for the SAC compartment were derived using the parameter estimation module in Simcyp and observed data from an i.v. clinical study.41 Oral absorption of the drug is described by a first‐order absorption process with fraction absorbed (fa) and ka predicted from polar surface area (PSA) and number of hydrogen bond donors for the compound.42

In vitro enzyme kinetic parameters for CYPs and UGTs were scaled up using physiological data as described previously.40 The unbound metabolic intrinsic clearance (CL_int) estimated from in vitro studies in recombinant CYP3A4, 2C9, 2C8, and 1A2 enzymes expressed in baculovirus‐infected SF21 cells amounted to 11.38 μl/min/mg protein.23 However, this CL_int estimate was much lower than that obtained when EE was directly incubated in human liver microsomes.22 Given that Shiraga et al.22 used pooled liver samples from 46 donors, the estimated unbound intrinsic clearance of 66.77 μl/min/mg protein for the 2‐hydroxylation pathway in this study was preferentially selected as input for the hydroxylation pathway in the PBPK simulation after applying the individual fm_CYP of 50.6%, 27.5%, 2.3%, and 19.6% for CYP3A4, 2C9, 2C8, and 1A2, respectively, obtained from the study with recombinant enzymes.23 For glucuronidation, the in vitro metabolic CL_int of 21.26 μl/min/mg protein derived from HLMs was used as the input and assigned to UGT1A1.22

In the absence of abundance data for SULTs in the simulator to do a direct IVIVE, the total intrinsic hepatic metabolic clearance of 275.49 μl/min/mg protein was back‐calculated from an i.v. clearance of 16.47 L/h via the well‐stirred liver model based on Eq. 2, below. The remaining hepatic intrinsic clearance was assigned as additional HLM clearance in the simulator to account for EE systemic clearance not mechanistically accounted for. A similar approach using the “Q_gut” model described in Eq. 2 below was used to apportion the total gut intrinsic clearance using an F_G of 0.56, after accounting for the intrinsic clearances due to the gut CYP enzymes (CYP2C9 and CYP3A4) and UGT1A1. The additional intrinsic gut clearance was assigned as intestinal cytosolic clearance in the simulator to represent sulfation.

Where CL_H is hepatic blood clearance, obtained by deducting renal clearance from i.v. clearance; Q_H is hepatic blood flow; fu_B is fraction of drug unbound in blood; fu_gut is fraction of unbound drug in the gut; F_G is the fraction of the drug escaping gut metabolism; and Q_gut is a composite term of the enterocytic blood flow and the permeability of the drug.17

PBPK model verification

Quantitative predictions of the clinical DDI between EE and ketoconazole, a known CYP3A4 inhibitor, was simulated using the initial model to verify the fm_CYP3A4. The automated sensitivity analysis module within the Simcyp simulator was thereafter used to refine the CYP3A4 CL_int value to recover the AUC ratio of 1.4 in the clinical DDI study with ketoconazole due to a greater than 2‐fold underprediction of both the AUC and C_max ratios. Further simulations with other CYP3A4 inhibitors (fluconazole and voriconazole) and inducers (carbamazepine and rifampicin) with the refined model were carried out based on published clinical studies.

The characteristics of the virtual subjects for all the simulations were matched closely with those of the clinical DDI studies: the number of subjects, age range, and gender ratios were replicated. Differential equations describing the kinetics of substrates, inhibitors, and inducers as well as enzyme dynamics with or without inhibition and/or induction have been described previously. The interaction parameters present in perpetrator models (inhibitors and inducers) against specific enzymes and/or transporters are only utilized in simulations when these enzymes/transporters are present in the substrate model.43, 44 Models for all of the compounds used in the simulations except voriconazole are available in the Simcyp v. 17 compound library. The final input parameters used for the EE model are shown in Table 4, while the input parameters used to simulate the kinetics of voriconazole are given in Table S3 of the supplementary information.