Product Images Metoprolol Tartrate
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Product Label Images
The following 4 images provide visual information about the product associated with Metoprolol Tartrate NDC 57237-102 by Rising Pharma Holdings, Inc., such as packaging, labeling, and the appearance of the drug itself. This resource could be helpful for medical professionals, pharmacists, and patients seeking to verify medication information and ensure they have the correct product.
Rising Health's NDC 57237-100-01 is a film-coated tablet containing 25 mg of metoprolol tartrate, USP. The recommended storage temperature is 20 to 25°C with an allowed excursion to 15 to 30°C. The tablet should be protected from light and moisture and should be dispensed in a tight, light-resistant USP container with a child-resistant closure. The usual dosage and full prescribing information can be found in the package insert. The tablets are made in India and distributed by Rising Health in Saddle Brook, NJ. The code for the drug is TS/DRUGS/19/1993.*
This is a description for Rising® film-coated tablets. The tablets are distributed by Rising Health, LLC and are made in India. The usual dosage information is provided in the package insert, and the tablets should be stored tightly in a light-resistant container at 20° to 25°C (68° to 77°F). The container should also have a child-resistant closure. The tablets come in a bottle containing 100 tablets, and the product code is TS/DRUGS/19/1993.*
This is a description of Metoprolol tablets made in India by Rising Health, LLC. Each film-coated tablet contains 100mg of metoprolol tartrate, USP. The usual dosage and prescribing information can be found in the package insert. The tablets should be stored in a tight, light-resistant container at 20° to 25°C (68° to 77°F), with excursions permitted to 15° to 30°C (569° to 86°F) [see USP Controlled Room Temperature]. The tablets should be protected from light and moisture and dispensed using a child-resistant closure. The code for the tablets is TS/DRUGS/19/1993.*
* The product label images have been analyzed using a combination of traditional computing and machine learning techniques. It should be noted that the descriptions provided may not be entirely accurate as they are experimental in nature. Use the information in this page at your own discretion and risk.