Product Images Leader Maximum Strength Invisible Acne

View Photos of Packaging, Labels & Appearance

Product Label Images

The following 2 images provide visual information about the product associated with Leader Maximum Strength Invisible Acne NDC 37205-279 by Cardinal Health, 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.

image of carton - Acne carton

image of carton - Acne carton

This is a product advertisement for a brand called "Leader". The product is an "Invisible Acne Cream" that contains "10% Benzoyl Peroxide" and comes in a 10 oz (28g) container. The description mentions that the cream is an Acne Medication and is equivalent to the active ingredient in "Clearasil" Vanishing Cream. The packaging lists a "NDC: 37205-279-10" number and a "Cat. No. F0463-000" distributed by "Cardinal Health, Dublin, Ohio 43017". The text also lists some additional information like guarantees and satisfaction policies. There is some gibberish text at the end of the description that appears to be a random mix of letters and numbers making it unreadable.*

image of tube - Acne tube

image of tube - Acne tube

This document contains information about a drug which uses Benzoyl Peroxide 10% as an active ingredient. It is designed to treat acne and dry up pimples while also helping prevent new ones from forming. The product comes in cream form and should only be used externally. Those with broken skin or sensitive skin should avoid use. Recommended use is to cleanse the skin before applying the cream 1 to 3 times a day as needed. Excessive use can lead to skin irritation. The product can bleach hair and dyed fabrics and should be kept out of the reach of children. The document contains a logo for Pantone in red, black, and blue. The length of 107.95mm and 3mm are mentioned, but their relevance is unclear.*

* 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.