Product Images Lisinopril

View Photos of Packaging, Labels & Appearance

Product Label Images

The following 3 images provide visual information about the product associated with Lisinopril NDC 10544-623 by Blenheim Pharmacal, 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.

Label - LABEL LISINOPRIL TABS 2 5MG BPI(10544 623 30) QUALITEST(0603 4209 28) REV1 Copy

Label - LABEL LISINOPRIL TABS 2 5MG BPI(10544 623 30) QUALITEST(0603 4209 28) REV1   Copy

This is a medication description for Lisinopril Tablets, USP 2.5mg with the NDC number 10544-623-30 and a batch number of BPOROOO000. The medication is kept in a container that resists light and moisture, and should be stored in a controlled room temperature (20-25°C). The package insert provides instructions on dosage. The tablets are obtainable only with a prescription, and the user should keep them out of reach of children. The expiry date is 00/00/0000. The text lists a few other batch numbers for this medication.*

Label - LABEL LISINOPRIL TABS 5MG BPI(10544 585 30) QUALITEST(0603 4210 32) REV1

Label - LABEL LISINOPRIL TABS 5MG BPI(10544 585 30) QUALITEST(0603 4210 32) REV1

This is a description of a drug called Lisinopril in tablet form. The drug comes in 5mg strength with 20 or 30 tablets in a package. The package should be stored in controlled temperature in order to retain its effectiveness. The drug is only available with a prescription and the correct dosage can be found in the package insert. The information includes production and expiration date along with manufacturing identification numbers and lot numbers. The text also includes warnings to keep the package out of children's reach and to protect tablets from heat, moisture, and freezing. The last line seems to be a code or identification number.*

Structure - lisin 1

Structure - lisin 1

* 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.