Chemical Structure (Entecavir 01)
Entecavir Tablet
Product Images NDC 42494-458
View Photos of Packaging, Regulatory Labels, and Product Appearance
Product Visual Gallery
This gallery contains 3 technical images submitted to the FDA as part of the official labeling for Entecavir (NDC 42494-458). Unlike standard consumer photos, these assets often include clinical data figures, molecular chemical structures, and official manufacturer packaging layouts.
As provided by Cameron Pharmaceuticals, these visuals offer a comprehensive scientific overview of the product's physical and chemical identity, aiding pharmacists and researchers in product verification and study.
Product Images & Figures Index
Principal Display Panel (0.5 mg Tablet Bottle Carton)
This is a description of Entecavir Tablets, USP, containing 0.5mg of entecavir per tablet. The tablets come in a package of 30 and are for prescription use only. The package insert provides information on indications and dosage. Manufactured for Cameron Pharmaceuticals in Louisville, Kentucky, USA, and made in China. The package is child-resistant and should be stored at 25°C with excursions permitted between 15-30°C. It is advised to keep the package out of reach of children, store in the outer carton to protect from light, and not use if the inner seal of the bottle is broken or missing. Rev. 04/2025.*
Principal Display Panel (1 mg Tablet Bottle Carton)
This is a description of a pharmaceutical product called Entecavir, which comes in the form of tablets with a strength of 1mg each. The package contains 30 tablets and includes information regarding usage, indications, and dosage. It is manufactured for Cameron Pharmaceuticals in Louisville, Kentucky, USA but made in China. The package is child-resistant and should be stored at 25°C with excursions permitted to 15-30°C. It is advised to store in the outer carton to protect from light and not to use if the inner seal is broken or missing. The revision date is April 2025.*
* These product label images have been analyzed using experimental machine learning. Please verify findings with the primary label text.
