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 (CHCs) typically utilize a combination of a progestin and an estrogen. CHCs are potentially at risk to metabolic drug-drug interaction (DDI) via CYP3A4, the main enzyme involved in the oxidative metabolism of ethinyl estradiol and most progestins (e.g., levonorgestrel (LNG) and drospirenone (DRSP)). Recently, the US Food and Drug Administration (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 (fmCYP3A4 ), it would be of clinical benefit to determine if all progestins are at the same risk to CYP3A4-mediated metabolic DDIs. LNG and DRSP are commonly used progestins that are at the margins of the rifampicin induction effect observed in vivo because they have the relatively lowest and highest fmCYP3A4 among commonly used CHC formulations containing norgestimate, desogestrel, norgestrel, and norethindrone. Therefore, we applied a multi-pronged strategy (i.e., (i) development of the physiologically-based pharmacokinetic models; (ii) comparison of the effect of CYP3A inducers and inhibitors on DRSP vs. LNG; and (iii) providing the clinical-practice context based on real-world data, to explore the difference in DDI risk for oral CHCs.

A randomized, cross-over trial of metoprolol succinate formulations to evaluate PK and PD end points for therapeutic equivalence.

There are limited comparison data throughout the dosing interval for generic versus brand metoprolol extended-release (ER) tablets. We compared the pharmacokinetics (PKs) and pharmacodynamics of brand name versus two generic formulations (drugs 1 and 2) of metoprolol ER tablets with different time to maximum concentration (Tmax ) in adults with hypertension. Participants were randomized to equal drug doses (50-150 mg/day) administered in one of two sequences (brand-drug1-brand-drug2 or brand-drug2-brand-drug1) and completed 24-h PK, digital heart rate (HR), ambulatory blood pressure (BP), and HR studies after taking each formulation for greater than or equal to 7 days. Metoprolol concentrations were determined by liquid chromatography tandem mass spectrometry, with noncompartmental analysis performed to obtain PK parameters in Phoenix WinNonlin. Heart rate variability (HRV) low-to-high frequency ratio was determined per quartile over the 24-h period. Thirty-six participants completed studies with the brand name and at least one generic product. Among 30 participants on the 50 mg dose, the primary PK end points of area under the concentration-time curve and Cmax were similar between products; Tmax was 6.1 ± 3.6 for the brand versus 3.5 ± 4.9 for drug 1 (p = 0.019) and 9.6 ± 3.2 for drug 2 (p < 0.001). Among all 36 participants, 24-h BPs and HRs were similar between products. Mean 24-h HRV low-to-high ratio was also similar for drug 1 (2.04 ± 1.35), drug 2 (1.86 ± 1.35), and brand (2.04 ± 1.77), but was more sustained over time for the brand versus drug 1 (drug × quartile interaction p = 0.017). Differences in Tmax between metoprolol ER products following repeated doses may have implications for drug effects on autonomic balance over the dosing interval.

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.

Development of a model-based clinical trial simulation platform to optimize the design of clinical trials for Duchenne muscular dystrophy.

Early clinical trials of therapies to treat Duchenne muscular dystrophy (DMD), a fatal genetic X-linked pediatric disease, have been designed based on the limited understanding of natural disease progression and variability in clinical measures over different stages of the continuum of the disease. The objective was to inform the design of DMD clinical trials by developing a disease progression model-based clinical trial simulation (CTS) platform based on measures commonly used in DMD trials. Data were integrated from past studies through the Duchenne Regulatory Science Consortium founded by the Critical Path Institute (15 clinical trials and studies, 1505 subjects, 27,252 observations). Using a nonlinear mixed-effects modeling approach, longitudinal dynamics of five measures were modeled (NorthStar Ambulatory Assessment, forced vital capacity, and the velocities of the following three timed functional tests: time to stand from supine, time to climb 4 stairs, and 10 meter walk-run time). The models were validated on external data sets and captured longitudinal changes in the five measures well, including both early disease when function improves as a result of growth and development and the decline in function in later stages. The models can be used in the CTS platform to perform trial simulations to optimize the selection of inclusion/exclusion criteria, selection of measures, and other trial parameters. The data sets and models have been reviewed by the US Food and Drug Administration and the European Medicines Agency; have been accepted into the Fit-for-Purpose and Qualification for Novel Methodologies pathways, respectively; and will be submitted for potential endorsement by both agencies.

Physiologically-based pharmacokinetics modeling to investigate formulation factors influencing the generic substitution of dabigatran etexilate.

The exposure-response relationship of direct acting oral anti-coagulants (DOACs) for bleeding risk is steep relative to ischemic stroke reduction. As a result, small changes in exposure may lead to bleeding events. The overall goal of this project was to determine the effect of critical formulation parameters on the pharmacokinetics (PKs) and thus safety and efficacy of generic DOACs. In this first installment of our overall finding, we developed and verified a physiologically-based PK (PBPK) model for dabigatran etexilate (DABE) and its metabolites. The model was developed following a middle out approach leveraging available in vitro and in vivo data. External validity of the model was confirmed by overlapping predicted and observed PK profiles for DABE as well as free and total dabigatran for a dataset not used during model development. The verified model was applied to interrogate the impact of modulating the microenvironment pH on DABE systemic exposure. The PBPK exploratory analyses highlighted the high sensitivity of DABE exposure to supersaturation ratio and precipitation kinetics.

Quantitative Benefit-Risk Assessment of P-gp-Mediated Drug-Drug Interactions of Dabigatran Coadministered With Pharmacokinetic Enhancers in Patients With Renal Impairment.

Drug-drug interactions (DDIs) between dabigatran and ritonavir/cobicistat are of major concern in people living with HIV, particularly in those with impaired renal function, because they can result in increased dabigatran exposure and thus an increased risk of major bleeding events. However, the extent of this interaction and subsequent need for dose adjustment in subjects with varying degrees of renal function is currently not yet fully understood. To close this knowledge gap, we conducted an integrated population physiologically-based pharmacokinetic/pharmacodynamic analysis linking changes in dabigatran exposure due to DDIs and varying degrees of renal function to the probability of experiencing an ischemic stroke or major bleeding event within 1 year. The results of our analysis suggest that coadministration of dabigatran etexilate (dabigatran prodrug) and ritonavir/cobicistat should be avoided in subjects with severe renal impairment. A 2-hour dose separation or dabigatran etexilate dose reduction to 110 mg b.i.d. (twice daily) should be considered in subjects with moderate renal impairment when coadministered with ritonavir, while the dabigatran etexilate dose should be further reduced to 75 mg b.i.d. when coadministered with cobicistat. No dabigatran etexilate dose adjustment is needed in subjects with normal renal function receiving ritonavir, but dabigatran etexilate dose reduction to 110 mg b.i.d. should be considered when coadministered with cobicistat.

Development and Verification of a Body Weight-Directed Disease Trial Model for Glucose Homeostasis.

Weight loss has been associated with improvement in insulin sensitivity. It is consequently a cornerstone in the management of type 2 diabetes mellitus (T2DM). However, the strictly quantitative relationship between weight loss, insulin sensitivity, and clinically relevant glucose homeostasis biomarkers as well as changes therein as T2DM progresses is not yet fully understood. Therefore, the objective of our research was to establish a body weight-directed disease trial model for glucose homeostasis. To that end, we conducted a model-based meta-analysis using time course data of body weight loss (following lifestyle change or surgical procedure) and corresponding improvement of insulin sensitivity expressed as the Matsuda index. Changes in body weight were best described by a sigmoidal Emax model, whereas changes in the Matsuda index were best described by a linear model with a slope of 3.49. Once developed and verified, the model-based meta-analysis was linked to a disease-drug trial model for T2DM previously developed by our group to characterize and predict the impact of weight loss on clinically relevant glucose homeostasis biomarkers. The joint model was then used to conduct clinical trial simulations, which showed that weight loss can greatly improve clinically relevant glucose homeostasis biomarkers in T2DM patients.

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

Levonorgestrel (LNG) is the active moiety in many hormonal contraceptive formulations. It is typically coformulated with ethinyl estradiol (EE) to decrease intermenstrual bleeding. Due to its widespread use and CYP3A4-mediated metabolism, there is concern regarding drug-drug interactions (DDIs), particularly a suboptimal LNG exposure when co-administered with CYP3A4 inducers, potentially leading to unintended pregnancies. The goal of this analysis was to determine the impact of DDIs on the systemic exposure of LNG. To this end, we developed and verified a physiologically-based pharmacokinetic (PBPK) model for LNG in PK-Sim (version 8.0) accounting for the impact of EE and body mass index (BMI) on LNG's binding to sex-hormone binding globulin. Model parameters were optimized following intravenous and oral administration of 0.09 mg LNG. The combined LNG-EE PBPK model was verified regarding CYP3A4-mediated interaction by comparing to published clinical DDI study data with carbamazepine, rifampicin, and efavirenz (CYP3A4 inducers). Once verified, the model was applied to predict systemic LNG exposure in normal BMI and obese women (BMI ≥ 30 kg/m2 ) with and without co-administration of itraconazole (competitive CYP3A4 inhibitor) and clarithromycin (mechanism-based CYP3A4 inhibitor). Total and free LNG exposures, when co-administered with EE, decreased 2-fold in the presence of rifampin, whereas they increased 1.5-fold in the presence of itraconazole. Although changes in total and unbound exposure were decreased in obese women compared with normal BMI women, the relative impact of DDIs on LNG exposure was similar between both groups.

Examination of Metoprolol Pharmacokinetics and Pharmacodynamics Across CYP2D6 Genotype-Derived Activity Scores.

Recent CYP2D6 phenotype standardization efforts by CYP2D6 activity score (AS) are based on limited pharmacokinetic (PK) and pharmacodynamic (PD) data. Using data from two independent clinical trials of metoprolol, we compared metoprolol PK and PD across CYP2D6 AS with the goal of determining whether the PK and PD data support the new phenotype classification. S-metoprolol apparent oral clearance (CLo), adjusted for clinical factors, was correlated with CYP2D6 AS (P < 0.001). The natural log of CLo was lower with an AS of 1 (7.6 ± 0.4 mL/minute) vs. 2-2.25 (8.3 ± 0.6 mL/minute; P = 0.012), similar between an AS of 1 and 1.25-1.5 (7.8 ± 0.5 mL/minute; P = 0.702), and lower with an AS of 1.25-1.5 vs. 2-2.25 (P = 0.03). There was also a greater reduction in heart rate with metoprolol among study participants with AS of 1 (-10.8 ± 5.5) vs. 2-2.25 (-7.1 ± 5.6; P < 0.001) and no significant difference between those with an AS of 1 and 1.25-1.5 (-9.2 ± 4.7; P = 0.095). These data highlight linear trends among CYP2D6 AS and metoprolol PK and PD, but inconsistencies with the phenotypes assigned by AS based on the current standards. Overall, this case study with metoprolol suggests that utilizing CYP2D6 AS, instead of collapsing AS into phenotype categories, may be the most precise approach for utilizing CYP2D6 pharmacogenomics in clinical practice.

Predicting Cost-Effectiveness of Generic vs. Brand Dabigatran Using Pharmacometric Estimates Among Patients with Atrial Fibrillation in the United States.

Generic entry of newer anticoagulants is expected to decrease the costs of atrial fibrillation management. However, when making switches between brand and generic medications, bioequivalence concerns are possible. The objectives of this study were to predict and compare the lifetime cost-effectiveness of brand dabigatran with hypothetical future generics. Markov microsimulations were modified to predict the lifetime costs and quality-adjusted life years of patients on either brand or generic dabigatran from a US private payer perspective. Event rates for generics were predicted using previously developed pharmacokinetic-pharmacodynamic models. The analyses showed that generic dabigatran with lower-than-brand systemic exposure were dominant. Meanwhile, generic dabigatran with extremely high systemic exposure was not cost-effective compared with the brand reference. Cost-effectiveness of generic medications cannot always be assumed as shown in this example. Combined use of pharmacometric and pharmacoeconomic models can assist in decision making between brand and generic pharmacotherapies.

Evaluating the Clinical Impact of Formulation Variability: A Metoprolol Extended-Release Case Study.

The objective of this research was to evaluate the impact of changes in the formulation of metoprolol extended-release (ER) tablets on dissolution, pharmacokinetic, and exercise-induced heart rate (EIHR) using a combined physiologically based absorption pharmacokinetic, and population pharmacokinetic/pharmacodynamic modeling and simulation approach. Using a previously developed physiologically based absorption pharmacokinetic model in DDDPlus and GastroPlus, we simulated the changes in drug release and exposure as the result of quantitative changes in the release-controlling excipient, hydroxylpropylmethylcellulose, for 50 and 200 mg. The similarity of dissolution profiles was assessed using the f2 test, and bioequivalence was tested on the simulated pharmacokinetic profiles. We used the simulated concentration-time profiles following formulation changes as pharmacokinetic input into a population pharmacokinetic/pharmacodynamic model newly developed in NONMEM to determine if changes in pharmacokinetics lead to clinically significant changes in pharmacodynamics. Pharmacodynamic assessment was based on the percentage reduction in the EIHR from baseline. Therapeutic effect was considered similar when the model-predicted EIHR was within 50% to 85% of the average maximum EIHR of healthy 30-year-old subjects. A 40% or more increase in the release rate constant resulted in dissimilarity in dissolution profiles and bioINequivalence in pharmacokinetics for both 50 and 200 mg. Formulation-related differences in drug release of metoprolol ER tablets can lead to differences in pharmacokinetics. However, the evaluated pharmacokinetic differences do not lead to clinically meaningful differences in EIHR, suggesting that EIHR may not be sensitive enough to detect changes in pharmacokinetics of metoprolol ER products.

The role of multidrug resistance protein (MRP-1) as an active efflux transporter on blood-brain barrier (BBB) permeability.

Drugs acting on central nervous system (CNS) may take longer duration to reach the market as these compounds have a higher attrition rate in clinical trials due to the complexity of the brain, side effects, and poor blood-brain barrier (BBB) permeability compared to non-CNS-acting compounds. The roles of active efflux transporters with BBB are still unclear. The aim of the present work was to develop a predictive model for BBB permeability that includes the MRP-1 transporter, which is considered as an active efflux transporter. A support vector machine model was developed for the classification of MRP-1 substrates and non-substrates, which was validated with an external data set and Y-randomization method. An artificial neural network model has been developed to evaluate the role of MRP-1 on BBB permeation. A total of nine descriptors were selected, which included molecular weight, topological polar surface area, ClogP, number of hydrogen bond donors, number of hydrogen bond acceptors, number of rotatable bonds, P-gp, BCRP, and MRP-1 substrate probabilities for model development. We identified 5 molecules that fulfilled all criteria required for passive permeation of BBB, but they all have a low logBB value, which suggested that the molecules were effluxed by the MRP-1 transporter.

Classification of Breast Cancer Resistant Protein (BCRP) Inhibitors and Non-Inhibitors Using Machine Learning Approaches.

The breast cancer resistant protein (BCRP) is an important transporter and its inhibitors play an important role in cancer treatment by improving the oral bioavailability as well as blood brain barrier (BBB) permeability of anticancer drugs. In this work, a computational model was developed to predict the compounds as BCRP inhibitors or non-inhibitors. Various machine learning approaches like, support vector machine (SVM), k-nearest neighbor (k-NN) and artificial neural network (ANN) were used to develop the models. The Matthews correlation coefficients (MCC) of developed models using ANN, k-NN and SVM are 0.67, 0.71 and 0.77, and prediction accuracies are 85.2%, 88.3% and 90.8% respectively. The developed models were tested with a test set of 99 compounds and further validated with external set of 98 compounds. Distribution plot analysis and various machine learning models were also developed based on druglikeness descriptors. Applicability domain is used to check the prediction reliability of the new molecules.