Product Images Mycophenolate Mofetil

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Product Label Images

The following 3 images provide visual information about the product associated with Mycophenolate Mofetil NDC 0781-2067 by Sandoz 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.

Structure - Mycophenolate 01

Structure - Mycophenolate 01

250 mg capsule label - Mycophenolate 02

250 mg capsule label - Mycophenolate 02

Mycophenolate Mofetil Capsules, USP containing 250mg of the active ingredient. 100 capsules are provided with special handling and disposal instructions. Mycophenolate Mofetil is an immunosuppressant medication that is usually used to prevent rejection of organ transplants. It is manufactured by Sandoz Private Ltd. in India for Sandoz Inc. in Princeton, NJ, and comes in a tight, light-resistant container with a child-resistant cap. The medication guide is provided separately, and this drug should be stored between 20°C to 25°C and protected from moisture.*

500 mg Tablet label - Mycophenolate 03

500 mg Tablet label - Mycophenolate 03

Mycophenolate Mofetil Tablets contains 500mg of Mycophenolate Mofetil per tablet. It is used to prevent organ rejection in patients undergoing an organ transplant. The medication guide is provided separately, and pharmacists are requested to dispense it when giving out the medication. The tablets should be stored in a controlled room temperature of 20° to 25°C and protected from moisture. Also, they have to be dispensed in a tight, light-resistant container with a child-resistant cap. Children should not come into contact with the drug. Special handling and disposal instructions are provided in the insert to make sure the tablets are well disposed of. The product is manufactured by Sandoz Private Ltd. in India for Sandoz Inc. in Princeton, NJ 08540. It comes in a package of 100 tablets.*

* 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.