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 (DDI), 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 (v8.0) accounting for the impact of EE and BMI on LNG's binding to sex-hormone binding globulin (SHBG). 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≥30kg/m 2) with and without coadministration 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. While changes in total and unbound exposure were decreased in obese women compared to normal BMI women, the relative impact of DDIs on LNG exposure was similar between both groups.

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.

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 modelbased 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 mixedeffects 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.

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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/min) vs. 2-2.25 (8.3 ± 0.6 mL/min; p=0.012), similar between an AS of 1 and 1.25-1.5 (7.8 ± 0.5 mL/min; 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 HR 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 between 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.