Product Images Cvs Pharmacy Clinical Strength

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Product Label Images

The following 3 images provide visual information about the product associated with Cvs Pharmacy Clinical Strength NDC 59779-036 by Cvs Pharmacy, 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.

box - mid 505

box - mid 505

CVS/pharmacy Clinical Strength Antiperspirant & Deodorant is a prescription-strength protection against wetness, designed for people who suffer from excessive underarm perspiration. Its advanced formula fights odor while soothing the skin with conditioners, keeping you dry and fresh even after showering. The antiperspirant works with your body's nighttime temperature and for best results, it is recommended to apply it at night before bed. It contains 25% more than the national brand and has Aluminum zirconium trichlorohydrex gly as the active ingredient. Compare it to the active ingredient in Secret Clinical Strength Light and Fresh Scent antiperspirant.*

front label - mid 709

front label - mid 709

This product is a clinical strength deodorant with a light and fresh scent. It is similar to the active ingredient found in "Secret" Clinical Strength deodorant. The product has a net weight of 2 ounces (57.9 grams).*

back label - mid 710

back label - mid 710

This is a Drug Facts label for an antiperspirant product. It contains information on the active ingredient, directions, and warnings. It also states to ask a doctor before use if you have kidney disease, to keep the product out of reach of children, and to contact a Poison Control Center if swallowed. The inactive ingredients and the manufacturer are listed as well. The product is not manufactured or distributed by Procter & Gamble, owner of the registered trademark Secret. The product is distributed by CVS Pharmacy and there is a money-back guarantee.*

* 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.