Product Images Kybella
View Photos of Packaging, Labels & Appearance
- Figure 1. Avoid the Marginal Mandibular Nerve Area - kybella 01
- Figure 2. Sagittal View of Platysma Area - kybella 02
- Figure 3. Injection Pattern - kybella 03
- The chemical structure of deoxycholic acid. - kybella 04
- Figure 5. ≥ 2-Grade and ≥ 1-Grade Composite Clinician and Patient Response - kybella 05
- Principal Display Panelkybella™(deoxycholic acid) injection 10 mg/mL20 mg/2 mL(10 mg/mL)for subcutaneous use onlyRx ONLY - kybella 07
- kybella 08
Product Label Images
The following 7 images provide visual information about the product associated with Kybella NDC 61168-101 by Kythera Biopharmaceuticals 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.
Figure 1. Avoid the Marginal Mandibular Nerve Area - kybella 01
Figure 2. Sagittal View of Platysma Area - kybella 02
Platysma Muscle is a thin, flat muscle located in the neck region. It extends from the collarbone to the jawbone and plays an important role in facial expressions such as grimacing and expressing pain or surprise. Pre-platysmal fat refers to fat deposits located just beneath the platysma muscle. These deposits can contribute to the appearance of a double chin or sagging skin in the neck area.*
Figure 3. Injection Pattern - kybella 03
The chemical structure of deoxycholic acid. - kybella 04
Figure 5. ≥ 2-Grade and ≥ 1-Grade Composite Clinician and Patient Response - kybella 05
Principal Display Panelkybella™(deoxycholic acid) injection 10 mg/mL20 mg/2 mL(10 mg/mL)for subcutaneous use onlyRx ONLY - kybella 07
kybella 08
This is the label of a drug called Kybella which is used for subcutaneous injection. It contains 10mg/mL of deoxycholic acid and is stored at controlled room temperature. The label has a product code: CAS2612 and a license number NOC 61168-101-04.*
* 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.
