Product Images Escitalopram

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Product Label Images

The following 6 images provide visual information about the product associated with Escitalopram NDC 67296-1731 by Redpharm Drug, 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.

00093585101

00093585101

LABEL - 67296 1731

LABEL - 67296 1731

This is a prescription medication called Escitalopram. The NDC number is 67296-1731-3, and the lot number is A90439X2. The medication contains 10mg of the drug, and there are 30 tablets in the container. The usual adult dosage is found in the package insert. The medication should be stored at a controlled room temperature of 20-25 degrees Celsius. The medication is manufactured by Mig Cipla LTD in Kurkumbh India and is distributed by Redpharm Drug in Eden Prairie, MN, USA. This medication requires a prescription and should only be used under a doctor's supervision. The expiration date is 12/20.*

structural formula - image 1

structural formula - image 1

This text is not available for description as it does not provide sufficient information for me to work with. It consists of just two letters - "OH".*

Escitalopram Tablets USP 5 mg 100s Label - image 2

Escitalopram Tablets USP 5 mg 100s Label - image 2

This is a description for a medication guide for Escitalopram tablets USP. It is recommended to see the package for the usual dosage of the medication. The tablets contain Escitalopram olats, USP, equivalent to 5mg of Escitalopram. The guide also includes a warning to keep the medication away from children. The manufacturer is MHDRUGS/PDI46, located in India. The Rev. D date is 12/2015.*

Escitalopram Tablets USP 10 mg 100s Label - image 3

Escitalopram Tablets USP 10 mg 100s Label - image 3

Escitalopram Tablets USP 20 mg 100s Label - image 4

Escitalopram Tablets USP 20 mg 100s Label - image 4

* 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.