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

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 (T_max ) 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 C_max were similar between products; T_max 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 T_max between metoprolol ER products following repeated doses may have implications for drug effects on autonomic balance over the dosing interval.

Macrolide Treatment Failure due to Drug–Drug Interactions: Real-World Evidence to Evaluate a Pharmacological Hypothesis

Macrolide antibiotics have received criticism concerning their use and risk of treatment failure. Nevertheless, they are an important class of antibiotics and are frequently used in clinical practice for treating a variety of infections. This study sought to utilize pharmacoepidemiology methods and pharmacology principles to estimate the risk of macrolide treatment failure and quantify the influence of their pharmacokinetics on the risk of treatment failure, using clinically reported drug–drug interaction data. Using a large, commercial claims database (2006–2015), inclusion and exclusion criteria were applied to create a cohort of patients who received a macrolide for three common acute infections. Furthermore, an additional analysis examining only bacterial pneumonia events treated with macrolides was conducted. These criteria were formulated specifically to ensure treatment failure would not be expected nor influenced by intrinsic or extrinsic factors. Treatment failure rates were 6% within the common acute infections and 8% in the bacterial pneumonia populations. Regression results indicated that macrolide AUC changes greater than 50% had a significant effect on treatment failure risk, particularly for azithromycin. In fact, our results show that decreased or increased exposure change can influence failure risk, by 35% or 12%, respectively, for the acute infection scenarios. The bacterial pneumonia results were less significant with respect to the regression analyses. This integration of pharmacoepidemiology and clinical pharmacology provides a framework for utilizing real-world data to provide insight into pharmacokinetic mechanisms and support future study development related to antibiotic treatments.

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.

Physiologically Based Pharmacokinetic Modeling of Metoprolol Enantiomers and α-Hydroxymetoprolol to Describe CYP2D6 Drug-Gene Interactions

The beta-blocker metoprolol (the sixth most commonly prescribed drug in the USA in 2017) is subject to considerable drug–gene interaction (DGI) effects caused by genetic variations of the CYP2D6 gene. CYP2D6 poor metabolizers (5.7% of US population) show approximately five-fold higher metoprolol exposure compared to CYP2D6 normal metabolizers. This study aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model to predict CYP2D6 DGIs with metoprolol. The metoprolol (R)- and (S)-enantiomers as well as the active metabolite α-hydroxymetoprolol were implemented as model compounds, employing data of 48 different clinical studies (dosing range 5–200 mg). To mechanistically describe the effect of CYP2D6 polymorphisms, two separate metabolic CYP2D6 pathways (α-hydroxylation and O-demethylation) were incorporated for both metoprolol enantiomers. The good model performance is demonstrated in predicted plasma concentration–time profiles compared to observed data, goodness-of-fit plots, and low geometric mean fold errors of the predicted AUC_last (1.27) and C_max values (1.23) over all studies. For DGI predictions, 18 out of 18 DGI AUC_last ratios and 18 out of 18 DGI C_max ratios were within two-fold of the observed ratios. The newly developed and carefully validated model was applied to calculate dose recommendations for CYP2D6 polymorphic patients and will be freely available in the Open Systems Pharmacology repository.

Public Workshop Summary Report on Fiscal Year 2021 Generic Drug Regulatory Science Initiatives: Data Analysis and Model-Based Bioequivalence

On May 4, 2020, the US Food and Drug Administration (FDA) hosted an online public workshop titled "FY 2020 Generic Drug Regulatory Science Initiatives Public Workshop" to provide an overview of the status of the science and research priorities and to solicit input on the development of Generic Drug User Fee Amendments fiscal year 2021 priorities. This report summarizes the podium presentations and the outcome of discussions along with innovative ways to overcome challenges and significant opportunities related to model-based approaches in bioequivalence assessment for breakout session 4 titled, "Data analysis and model-based bioequivalence (BE)." This session focused on the application of model-based approaches in the generic drug development, with a vision of accelerating regulatory decision making for abbreviated new drug application assessments. The session included both podium presentations and panel discussions with three topics of interest: (i) in vitro study evaluation methods and their clinical relevance, (ii) challenges in model-based BE, (iii) emerging expertise and tools in implementing new BE approaches.

In Vitro Dissolution and in Silico Modeling Shortcuts in Bioequivalence Testing

Purpose: To review in vitro testing and simulation platforms that are in current use to predict in vivo performances of generic products as well as other situations to provide evidence for biowaiver and support drug formulations development. Methods: Pubmed and Google Scholar databases were used to review published literature over the past 10 years. The terms used were “simulation AND bioequivalence” and “modeling AND bioequivalence” in the title field of databases, followed by screening, and then reviewing. Results: A total of 22 research papers were reviewed. Computer simulation using software such as GastroPlus™, PK-Sim^® and SimCyp^® find applications in drug modeling. Considering the wide use of optimization for in silico predictions to fit observed data, a careful review of publications is required to validate the reliability of these platforms. For immediate release (IR) drug products belonging to the Biopharmaceutics Classification System (BCS) classes I and III, difference factor (ƒ₁) and similarity factor (ƒ₂) are calculated from the in vitro dissolution data of drug formulations to support biowaiver; however, this method can be more discriminatory and may not be useful for all dissolution profiles. Conclusions: Computer simulation platforms need to improve their mechanistic physiologically based pharmacokinetic (PBPK) modeling, and if prospectively validated within a small percentage of error from the observed clinical data, they can be valuable tools in bioequivalence (BE) testing and formulation development.

Pharmacometrics, Physiologically Based Pharmacokinetics, Quantitative Systems Pharmacology-What's Next?-Joining Mechanistic and Epidemiological Approaches

The application of modeling and simulation (M&S) tools to biological, physiological, and clinical data has great potential to enhance drug development and regulatory decision making. The strategic development of multidisciplinary projects aimed at integrating methodologies from different disciplines may bridge between preclinical and clinical drug development as well as between academic curiosity and clinical practice. Herein we review the history and present the state of M&S approaches as well as our vision for future challenges and applications.