Metoprolol Succinate Tablet, Film Coated, Extended Release
Product Images NDC 72162-1858

Product Visual Gallery

This gallery contains 3 technical images submitted to the FDA as part of the official labeling for Metoprolol Succinate (NDC 72162-1858). Unlike standard consumer photos, these assets often include clinical data figures, molecular chemical structures, and official manufacturer packaging layouts.

As provided by Bryant Ranch Prepack, these visuals offer a comprehensive scientific overview of the product's physical and chemical identity, aiding pharmacists and researchers in product verification and study.

FDA Label Image

Label (Lbl721621858)

This is a description of a medication containing metoprolol succinate, USP in film-coated tablets. Each tablet contains 95 mg NDC 72162-1858-1 of metoprolol succinate, which is equivalent to 100 mg of metoprolol tartrate, USP. The warning advises to keep the extended-release tablets out of reach of children and store at 25°C. It also includes information for pharmacists to verify the product dispensed and where they were manufactured. The text also includes the website for more information and the caution to dispense the tablets to the patient in the patient package relabeled by the mentioned company.*

FDA Label Image

Metoprolol-structure (Metoprolol Structure)

FDA Label Image

Metoprolol-table1a (Metoprolol Table1a)

This text provides results on subgroups in the MERIT-HF study related to total mortality, all-cause hospitalization, and heart failure outcomes based on different factors like ejection fraction, ischemic or non-ischemic etiology, sex, race, and medical history. The data indicates the comparison between Metoprolol Succinate Extended-Release and Placebo tablets. The information includes relative risk and 95% confidence intervals. Relevant abbreviations such as NYHA (New York Heart Association), EF (ejection fraction), MI (myocardial infarction), and HR (heart rate) are also explained.*

* These product label images have been analyzed using experimental machine learning. Please verify findings with the primary label text.