Literature DB >> 31087554

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

Sarah Kim1, Vishnu D Sharma1, Karthik Lingineni1, Nashid Farhan1, Lanyan Fang2, Liang Zhao2, Joshua D Brown3, Rodrigo Cristofoletti1,4, Valvanera Vozmediano1, Sihem Ait-Oudhia1, Lawrence J Lesko1, Mirjam N Trame1, Stephan Schmidt1.   

Abstract

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.
© 2019, The American College of Clinical Pharmacology.

Entities:  

Keywords:  bioequivalence; extended-release metoprolol; formulation variability; physiologically based modeling and simulation

Year:  2019        PMID: 31087554     DOI: 10.1002/jcph.1433

Source DB:  PubMed          Journal:  J Clin Pharmacol        ISSN: 0091-2700            Impact factor:   3.126


  6 in total

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

Authors:  Scott A Mosley; Sarah Kim; Stephan Schmidt; Larisa H Cavallari; Nihal El Rouby; Karthik Lingineni; Valvanera Vozmediano Esteban; Yan Gong; Yiqing Chen; David Estores; Kairui Feng; Hyewon Kim; Minori Kinjo; Taimour Langaee; Zhichuan Li; Siegfried O F Schmidt; Julie A Johnson; Reginald F Frye; Lanyan Lucy Fang; Liang Zhao; Philip F Binkley
Journal:  Clin Transl Sci       Date:  2022-05-21       Impact factor: 4.438

Review 2.  Pharmacometrics, Physiologically Based Pharmacokinetics, Quantitative Systems Pharmacology-What's Next?-Joining Mechanistic and Epidemiological Approaches.

Authors:  Stephan Schmidt; Sarah Kim; Valvanera Vozmediano; Rodrigo Cristofoletti; Almut G Winterstein; Joshua D Brown
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2019-06-09

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

Authors:  Nashid Farhan; Rodrigo Cristofoletti; Sumit Basu; Sarah Kim; Karthik Lingineni; Sibo Jiang; Joshua D Brown; Lanyan Lucy Fang; Lawrence J Lesko; Stephan Schmidt
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2021-02-10

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

Authors:  Simeon Rüdesheim; Jan-Georg Wojtyniak; Dominik Selzer; Nina Hanke; Felix Mahfoud; Matthias Schwab; Thorsten Lehr
Journal:  Pharmaceutics       Date:  2020-12-11       Impact factor: 6.321

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

Authors:  Brian Cicali; Stephan Schmidt; Markus Zeitlinger; Joshua D Brown
Journal:  Pharmaceutics       Date:  2022-03-25       Impact factor: 6.525

Review 6.  In Vitro Dissolution and in Silico Modeling Shortcuts in Bioequivalence Testing.

Authors:  Moawia M Al-Tabakha; Muaed J Alomar
Journal:  Pharmaceutics       Date:  2020-01-04       Impact factor: 6.321

  6 in total

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