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

Ezuruike, Udoamaka; Humphries, Helen; Dickins, Maurice; Gardner, Iain; Yeo, Karen Rowland

doi:10.1002/cpt.1085

Cited by 9 publications

(15 citation statements)

References 49 publications

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“…With the availability of large DDI databases like the University of Washington DDI database and regulatory submissions, more information is now available on the CYP‐mediated interaction potential with COCs. A PBPK model has been developed to predict the impact of CYP3A modulation on EE PK 116 . A PBPK model was also used to evaluate the impact of efavirenz on LNG implants and determine the dose‐adjustment strategies due to the DDI 117 .…”

Section: Resultsmentioning

confidence: 99%

“…A PBPK model has been developed to predict the impact of CYP3A modulation on EE PK. 116 A PBPK model was also used to evaluate the impact of efavirenz on LNG implants and determine the doseadjustment strategies due to the DDI. 117 Such models can be used in the future to evaluate the need to conduct an in vivo clinical study.…”

Section: Oral Contraceptives As Ddi Perpetratorsmentioning

confidence: 99%

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Drug‐Drug Interaction Studies With Oral Contraceptives: Pharmacokinetic/Pharmacodynamic and Study Design Considerations

Sun

Sivasubramanian

Vaidya

et al. 2020

The Journal of Clinical Pharma

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Oral contraceptives (OCs) are the most widely used form of birth control among women of childbearing potential. Knowledge of potential drug‐drug interactions (DDIs) with OCs becomes imperative to provide information on the medication to women of childbearing potential and enable their inclusion in clinical trials, especially if the new molecular entity is a teratogen. Although a number of DDI guidance documents are available, they do not provide recommendations for the design and conduct of OC DDI studies. The evaluation of DDI potential of a new molecular entity and OCs is particularly challenging because of the availability of a wide variety of combinations of hormonal contraceptives, different doses of the ethinyl estradiol, and different metabolic profiles of the progestin component. The aim of this review is to comprehensively discuss factors to be considered such as pharmacokinetics (PK), pharmacodynamics (PD), choice of OC, and study population for the conduct of in vivo OC DDI studies. In this context, metabolic pathways of OCs, the effect of enzyme inhibitors and inducers, the role of sex hormone–binding globulin in the PK of progestins, current evidence on OC DDIs, and the interpretation of PD end points are reviewed. With the emergence of new tools like physiologically based PK modeling, the decision to conduct an in vivo study can be made with much more confidence. This review provides a comprehensive overview of various factors that need to be considered in designing OC DDI studies and recommends PK‐based DDI studies with PK end points as adequate measures to establish clinical drug interaction and measurement of PD end points when there is basis for PD interaction.

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Section: Resultsmentioning

confidence: 99%

Section: Oral Contraceptives As Ddi Perpetratorsmentioning

confidence: 99%

Drug‐Drug Interaction Studies With Oral Contraceptives: Pharmacokinetic/Pharmacodynamic and Study Design Considerations

Sun

Sivasubramanian

Vaidya

et al. 2020

The Journal of Clinical Pharma

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“…the well-stirred liver model and reported by Ezuruike et al, (2018) were converted to human liver cytosol (HLC) intrinsic clearance after the correction with relative ratio of healthy MPPGL to healthy CPPGL values.…”

Section: Methods 3 (Eflv; Empirical Functional Liver Volume + Scalars mentioning

confidence: 99%

Scaling Factors for Clearance in Adult Liver Cirrhosis

et al. 2020

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In-vitro in-vivo extrapolation (IVIVE) enables prediction of in vivo clinical outcomes related to drug exposure in various populations from in vitro data. Prudent IVIVE requires scalars specific to the biological characteristics of the system in each population. This study determined experimentally, for the first time, scalars in liver samples from patients with varying degrees of cirrhosis. Microsomal and cytosolic fractions were extracted from 13 noncirrhotic and 32 cirrhotic livers (6 mild, 13 moderate, and 13 severe, based on Child-Pugh Score). Fractional protein content was determined and cytochrome P450 reductase activity was used to correct for microsomal protein loss. Although the median microsomal protein per gram liver (MPPGL) in mild, moderate, and severe cirrhosis (26.2, 32.4, and 30.8 mg.g-1 , respectively) seemed lower than control livers (36.6 mg.g-1), differences were not statistically significant (Kruskal-Wallis test, p>0.05). Corresponding values for cytosolic protein per gram liver (CPPGL) were 88.2, 67.9, 62.2 and 75.4 (mg.g-1) for mild, moderate, sever cirrhosis and control livers, respectively, with statistically lower values for server vs controls (Mann-Whitney p=0.006). Cirrhosis associated with cancer showed lower MPPGL (24.8 mg.g-1) than cirrhosis associated with cholestasis (38.3 mg.g-1 , p=0.003). Physiologicallybased pharmacokinetic simulations with disease-specific scalars captured cirrhosis impact on exposure to afentanil, metoprolol, midazolam, and ethinylestradiol. These experimentallydetermined scalars should alleviate the need for indirect scaling using functional liver volume. Scaling factors in cirrhosis might be a reflection of the etiology rather than the disease severity. Hence, bundling various cirrhotic conditions under the same umbrella when predicting hepatic impairment impact should be revisited. Significance statement: Cirrhosis-specific scalars required for extrapolation from microsomal or cytosolic in vitro systems to liver tissue are lacking. These scalars can help in This article has not been copyedited and formatted. The final version may differ from this version.

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“…In this regard, CYP1A2 is especially perplexing as it is the most impacted CYP in vivo and weakly inhibited in vitro . In particular, relatively high magnitude DDIs with classical victims, such as tizanidine, melatonin (AUCR ~ 4 to 5), and especially selegiline (AUCR > 17), are problematic and will pose challenges when developing PBPK models requiring relevant input data to recapitulate the observed AUCR not involving CYP3A 15,83–85 . If such DDI victim drugs cannot be described, then what chance is there to model DDI victim NMEs with confidence?…”

Section: Discussionmentioning

confidence: 99%

Drug Interactions Involving 17α‐Ethinylestradiol: Considerations Beyond Cytochrome P450 3A Induction and Inhibition

Rodrigues

2021

Clin Pharma and Therapeutics

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It is widely acknowledged that drug‐drug interactions (DDIs) involving estrogen (17α‐ethinylestradiol (EE))‐containing oral contraceptives (OCs) are important. Consequently, sponsors of new molecular entities (NMEs) often conduct clinical studies with priority given to OCs as victims of cytochrome P450 (CYP) 3A (CYP3A) induction and inhibition. Such scenarios are reflected in the US Food and Drug Administration‐issued guidance documentation related to OC DDI studies. Although CYP3A is important, OCs such as EE are metabolized by sulfotransferase 1E1 and UDP‐glucuronosyltransferase (UGT) 1A1, expressed in the gut and liver, and so both can also serve as loci of victim OC DDI. Therefore, for any NME, one should carefully consider its induction and inhibition profile involving CYP3A4/5, UGT1A1, and SULT1E1. As DDI perpetrators, available clinical DDI data indicate that EE‐containing OCs can induce (e.g., UGT1A4 and CYP2A6) and inhibit (CYP1A2 ≥ CYP2C19 > CYP3A4/5 > CYP2C8, CYP2B6, CYP2D6, and CYP2C9) various CYP forms. Although available in vitro CYP inhibition data do not explain such a graded inhibitory effect in vivo, it is hypothesized that EE differentially modulates CYP expression via potent agonism of the estrogen receptor expressed in the gut and liver. From the standpoint of the NME as potential OC DDI victim, therefore, it is important to assess its projected (pre‐phase I) or known therapeutic index and pharmacokinetic profile (fraction absorbed, absolute oral bioavailability, clearance/extraction class, fraction metabolized by CYP1A2, CYP2C19, CYP2A6, and UGT1A4). Such information can enable the prioritization, design, and interpretation of NME‐OC DDI studies.

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

Cited by 9 publications

References 49 publications

Drug‐Drug Interaction Studies With Oral Contraceptives: Pharmacokinetic/Pharmacodynamic and Study Design Considerations

Drug‐Drug Interaction Studies With Oral Contraceptives: Pharmacokinetic/Pharmacodynamic and Study Design Considerations

Scaling Factors for Clearance in Adult Liver Cirrhosis

Drug Interactions Involving 17α‐Ethinylestradiol: Considerations Beyond Cytochrome P450 3A Induction and Inhibition

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