Quantitative Assessment of Levonorgestrel Binding Partner Interplay and Drug-Drug Interactions Using Physiologically Based Pharmacokinetic Modeling

Physiologically based pharmacokinetic modeling in obesity: applications and challenges

INTRODUCTION

Rising global obesity rates pose a threat to people's health. Obesity causes a series of pathophysiologic changes, making the response of patients with obesity to drugs different from that of nonobese, thus affecting the treatment efficacy and even leading to adverse events. Therefore, understanding obesity's effects on pharmacokinetics is essential for the rational use of drugs in patients with obesity.

AREAS COVERED

Articles related to physiologically based pharmacokinetic (PBPK) modeling in patients with obesity from inception to October 2023 were searched in PubMed, Embase, Web of Science and the Cochrane Library. This review outlines PBPK modeling applications in exploring factors influencing obesity's effects on pharmacokinetics, guiding clinical drug development and evaluating and optimizing clinical use of drugs in patients with obesity.

EXPERT OPINION

Obesity-induced pathophysiologic alterations impact drug pharmacokinetics and drug-drug interactions (DDIs), altering drug exposure. However, there is a lack of universal body size indices or quantitative pharmacology models to predict the optimal for the patients with obesity. Therefore, dosage regimens for patients with obesity must consider individual physiological and biochemical information, and clinically individualize therapeutic drug monitoring for highly variable drugs to ensure effective drug dosing and avoid adverse effects.

Applying Physiologically Based Pharmacokinetic Modeling to Interpret Carbamazepine's Nonlinear Pharmacokinetics and Its Induction Potential on Cytochrome P450 3A4 and Cytochrome P450 2C9 Enzymes

Carbamazepine (CBZ) is commonly prescribed for epilepsy and frequently used in polypharmacy. However, concerns arise regarding its ability to induce the metabolism of other drugs, including itself, potentially leading to the undertreatment of co-administered drugs. Additionally, CBZ exhibits nonlinear pharmacokinetics (PK), but the root causes have not been fully studied. This study aims to investigate the mechanisms behind CBZ's nonlinear PK and its induction potential on CYP3A4 and CYP2C9 enzymes. To achieve this, we developed and validated a physiologically based pharmacokinetic (PBPK) parent-metabolite model of CBZ and its active metabolite Carbamazepine-10,11-epoxide in GastroPlus®. The model was utilized for Drug-Drug Interaction (DDI) prediction with CYP3A4 and CYP2C9 victim drugs and to further explore the underlying mechanisms behind CBZ's nonlinear PK. The model accurately recapitulated CBZ plasma PK. Good DDI performance was demonstrated by the prediction of CBZ DDIs with quinidine, dolutegravir, phenytoin, and tolbutamide; however, with midazolam, the predicted/observed DDI AUClast ratio was 0.49 (slightly outside of the two-fold range). CBZ's nonlinear PK can be attributed to its nonlinear metabolism caused by autoinduction, as well as nonlinear absorption due to poor solubility. In further applications, the model can help understand DDI potential when CBZ serves as a CYP3A4 and CYP2C9 inducer.

Physiologically-based pharmacokinetic modeling of prominent oral contraceptive agents and applications in drug-drug interactions

Considerable interest remains across the pharmaceutical industry and regulatory landscape in capabilities to model oral contraceptives (OCs), whether combined (COCs) with ethinyl estradiol (EE) or progestin-only pill. Acceptance of COC drug-drug interaction (DDI) assessment using physiologically-based pharmacokinetic (PBPK) is often limited to the estrogen component (EE), requiring further verification, with extrapolation from EE to progestins discouraged. There is a paucity of published progestin component PBPK models to support the regulatory DDI guidance for industry to evaluate a new chemical entity's (NCE's) DDI potential with COCs. Guidance recommends a clinical interaction study to be considered if an investigational drug is a weak or moderate inducer, or a moderate/strong inhibitor, of CYP3A4. Therefore, availability of validated OC PBPK models within one software platform, will be useful in predicting the DDI potential with NCEs earlier in the clinical development. Thus, this work was focused on developing and validating PBPK models for progestins, DNG, DRSP, LNG, and NET, within Simcyp, and assessing the DDI potential with known CYP3A4 inhibitors (e.g., ketoconazole) and inducers (e.g., rifampicin) with published clinical data. In addition, this work demonstrated confidence in the Simcyp EE model for regulatory and clinical applications by extensive verification in 70+ clinical PK and CYP3A4 interaction studies. The results provide greater capability to prospectively model clinical CYP3A4 DDI with COCs using Simcyp PBPK to interrogate the regulatory decision-tree to contextualize the potential interaction by known perpetrators and NCEs, enabling model-informed decision making, clinical study designs, and delivering potential alternative COC options for women of childbearing potential.

Model-Based Analysis of In Vivo Release Data of Levonorgestrel Implants: Projecting Long-Term Systemic Exposure

Levonorgestrel (LNG) is a progestin used in many contraceptive formulations, including subcutaneous implants. There is an unmet need for developing long-acting formulations for LNG. To develop long-acting formulations, release functions need to be investigated for LNG implant. Therefore, a release model was developed and integrated into an LNG physiologically-based pharmacokinetic (PBPK) model. Utilizing a previously developed LNG PBPK model, subcutaneous administration of 150 mg LNG was implemented into the modeling framework. To mimic LNG release, ten functions incorporating formulation-specific mechanisms were explored. Release kinetic parameters and bioavailability were optimized using Jadelle^® clinical trial data (n = 321) and verified using two additional clinical trials (n = 216). The First-order release and Biexponential release models showed the best fit with observed data, the adjusted R-squared (R²) value is 0.9170. The maximum released amount is approximately 50% of the loaded dose and the release rate is 0.0009 per day. The Biexponential model also showed good agreement with the data (adjusted R² = 0.9113). Both models could recapitulate observed plasma concentrations after integration into the PBPK simulations. First-order and Biexponential release functionality may be useful in modeling subcutaneous LNG implants. The developed model captures central tendency of the observed data as well as variability of release kinetics. Future work focuses on incorporating various clinical scenarios into model simulations, including drug-drug interactions and a range of BMIs.

Development of a Translational Exposure-Bracketing Approach to Streamline the Development of Hormonal Contraceptive Drug Products

Worldwide, 922 million women of reproductive age (or their partners) use some sort of contraception to prevent pregnancy. Oral combined hormonal contraceptives (CHC) typically utilize a combination of a progestin and an estrogen. CHC are potentially at risk to metabolic drug-drug interaction (DDI) via CYP3A4, the main enzyme involved in the oxidative metabolism of ethinyl estradiol (EE) and most progestins, e.g. levonorgestrel (LNG) and drospirenone (DRSP). Recently, the FDA issued a guidance addressing metabolic DDIs in the realm of CHC, establishing an overall class-based recommendation with respect to avoidance of CYP3A4 induction interactions. Given that different progestins have varying magnitudes of fraction metabolized by CYP3A4 (fm_CYP3A4 ), it would be of clinical benefit to determine if all progestins are at the same risk to CYP3A4-mediated metabolic DDI. LNG and DRSP are commonly used progestins that are at the margins of the rifampicin induction effect observed in vivo since they have the relatively lowest and highest fm_CYP3A4 among commonly used CHC formulations containing norgestimate, desogestrel, norgestrel, and norethindrone. Therefore, we applied a multi-pronged strategy, i.e. (1) development of the PBPK models, (2) comparison of the effect of CYP3A inducers and inhibitors on DRSP vs. LNG, and (3) providing the clinical-practice context based on real world data, to explore the difference in DDI risk for oral CHCs.

Key Factors in Effective Patient-Tailored Dosing of Fluoroquinolones in Urological Infections: Interindividual Pharmacokinetic and Pharmacodynamic Variability

Fluoroquinolones (FQs) are a critical group of antimicrobials prescribed in urological infections as they have a broad antimicrobial spectrum of activity and a favorable tissue penetration at the site of infection. However, their clinical practice is not problem-free of treatment failure, risk of emergence of resistance, and rare but important adverse effects. Due to their critical role in clinical improvement, understanding the dose-response relation is necessary to optimize the effectiveness of FQs therapy, as it is essential to select the right antibiotic at the right dose for the right duration in urological infections. The aim of this study was to review the published literature about inter-individual variability in pharmacological processes that can be responsible for the clinical response after empiric dose for the most commonly prescribed urological FQs: ciprofloxacin, levofloxacin, and moxifloxacin. Interindividual pharmacokinetic (PK) variability, particularly in elimination, may contribute to treatment failure. Clearance related to creatinine clearance should be specifically considered for ciprofloxacin and levofloxacin. Likewise, today, undesired interregional variability in FQs antimicrobial activity against certain microorganisms exists. FQs pharmacology, patient-specific characteristics, and the identity of the local infecting organism are key factors in determining clinical outcomes in FQs use.

Antifungal Drugs TDM: Trends and Update

PURPOSE

The increasing burden of invasive fungal infections results in growing challenges to antifungal (AF) therapeutic drug monitoring (TDM). This review aims to provide an overview of recent advances in AF TDM.

METHODS

We conducted a PubMed search for articles during 2016-2020 using "TDM" or "pharmacokinetics" or "drug-drug-interaction" with "antifungal," consolidated for each AF. Selection was limited to English language articles with human data on drug exposure.

RESULTS

More than 1000 articles matched the search terms. We selected 566 publications. The latest findings tend to confirm previous observations in real-life clinical settings. The pharmacokinetic variability related to special populations is not specific but must be considered. AF benefit-to-risk ratio, drug-drug interaction (DDI) profiles, and minimal inhibitory concentrations for pathogens must be known to manage at-risk situations and patients. Itraconazole has replaced ketoconazole in healthy volunteers DDI studies. Physiologically based pharmacokinetic modeling is widely used to assess metabolic azole DDI. AF prophylactic use was studied more for Aspergillus spp. and Mucorales in oncohematology and solid organ transplantation than for Candida (already studied). Emergence of central nervous system infection and severe infections in immunocompetent individuals both merit special attention. TDM is more challenging for azoles than amphotericin B and echinocandins. Fewer TDM requirements exist for fluconazole and isavuconazole (ISZ); however, ISZ is frequently used in clinical situations in which TDM is recommended. Voriconazole remains the most challenging of the AF, with toxicity limiting high-dose treatments. Moreover, alternative treatments (posaconazole tablets, ISZ) are now available.

CONCLUSIONS

TDM seems to be crucial for curative and/or long-term maintenance treatment in highly variable patients. TDM poses fewer cost issues than the drugs themselves or subsequent treatment issues. The integration of clinical pharmacology into multidisciplinary management is now increasingly seen as a part of patient care.

Application of exposure bracketing to streamline the development of contraceptive products

Developing new long-acting products of well-characterized contraceptive drugs is one way to address some of the reasons for unmet need for modern methods of family planning among women in low- and middle-income countries. Development and approval of such products traditionally follow a conventional paradigm that includes large Phase 3 clinical trials to evaluate efficacy (pregnancy prevention) and safety of the investigational product. Exposure-bracketing is a concept that applies known pharmacokinetics and pharmacodynamics of a drug substance to inform its safe and efficacious use in humans. Several therapeutic areas have applied this concept by leveraging established drug concentration-response relationships for approved products to expedite development and shorten the timeline for the approval of an investigational product containing the same drug substance. Based on discussions at a workshop hosted by the Bill & Melinda Gates Foundation in December 2020, it appears feasible to apply exposure-bracketing to develop novel contraceptive products using well-characterized drugs.

Determining the Exposure Threshold for Levonorgestrel Efficacy Using an Integrated Model Based Meta-Analysis Approach

Combined oral contraceptive pills are the most commonly used hormonal contraceptives for the prevention of unintended pregnancies in United States. They consist of a progestin (e.g., levonorgestrel (LNG)) and an estrogen component, typically ethinyl estradiol (EE). In addition to adherence issues, drug-drug interactions (DDIs) and obesity (women with body mass index (BMI) ≥ 30 kg/m² ) are prime suspects for decreased LNG efficacy. Therefore, we developed an integrated physiologically-based pharmacokinetic modeling and model-based meta-analysis approach to determine LNG's efficacy threshold concentrations and to evaluate the impact of DDIs and obesity on the efficacy of LNG-containing hormonal contraceptives (HCs). Based on this approach, co-administration of strong CYP3A4 inducers and LNG-containing HCs (LNG150: LNG 150 µg + EE 30 µg and LNG100: LNG 100 µg + EE 20 µg) resulted in a predicted clinically relevant decrease of LNG plasma exposure (women with BMI < 25 kg/m² : 50-65%; obese women: 70-75%). Following administration of LNG150 or LNG100 in the presence of a CYP3A4 inducer, there was an increase in mean Pearl Index of 1.2-1.30 and 1.80-2.10, respectively, in women with BMI < 25 kg/m² (incidence rate ratios (IRRs): 1.7-2.2), whereas it ranged from 1.6-1.80 and 2.40-2.85 in obese women (IRR: 2.2-3.0), respectively. Our results suggest that the use of backup or alternate methods of contraception is not necessarily required for oral LNG + EE formulations except within circumstances of both obesity and strong CYP3A4 inducer concomitance following administration of LNG100.

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

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.

Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its Drug-Drug Interaction Potential: A Physiologically Based Pharmacokinetic Modeling Approach

The anticonvulsant carbamazepine is frequently used in the long-term therapy of epilepsy and is a known substrate and inducer of cytochrome P450 (CYP) 3A4 and CYP2B6. Carbamazepine induces the metabolism of various drugs (including its own); on the other hand, its metabolism can be affected by various CYP inhibitors and inducers. The aim of this work was to develop a physiologically based pharmacokinetic (PBPK) parent-metabolite model of carbamazepine and its metabolite carbamazepine-10,11-epoxide, including carbamazepine autoinduction, to be applied for drug-drug interaction (DDI) prediction. The model was developed in PK-Sim, using a total of 92 plasma concentration-time profiles (dosing range 50-800 mg), as well as fractions excreted unchanged in urine measurements. The carbamazepine model applies metabolism by CYP3A4 and CYP2C8 to produce carbamazepine-10,11-epoxide, metabolism by CYP2B6 and UDP-glucuronosyltransferase (UGT) 2B7 and glomerular filtration. The carbamazepine-10,11-epoxide model applies metabolism by epoxide hydroxylase 1 (EPHX1) and glomerular filtration. Good DDI performance was demonstrated by the prediction of carbamazepine DDIs with alprazolam, bupropion, erythromycin, efavirenz and simvastatin, where 14/15 DDI AUClast ratios and 11/15 DDI Cmax ratios were within the prediction success limits proposed by Guest et al. The thoroughly evaluated model will be freely available in the Open Systems Pharmacology model repository.