Product Images Fotivda
View Photos of Packaging, Labels & Appearance
Product Label Images
The following 4 images provide visual information about the product associated with Fotivda NDC 45629-134 by Aveo Pharmaceuticals, 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.
Chemical Structure - fotivda 01
Figure 1 - fotivda 02
This is a survival plot of two treatment groups "FOTIVDA" and "SORAFENIB" evaluated over 24 months. The plot shows the number of subjects at risk vs. survival time (in months) for each group. The survival time range is from 0 to 24 months. "FOTIVDA" treatment group has 173 subjects at risk and "SORAFENIB" has 175 subjects at risk. The data suggests that "FOTIVDA" may have a better survival rate than the "SORAFENIB" treatment group.*
PRINCIPAL DISPLAY PANEL - 0.89 mg Capsule Bottle Label - fotivda 03
Each capsule of FOTIVDA® contains 089 mg. The prescription information and storage details are provided by AVED Pharmaceuticals. The NDC code for this product is 45629-083-01. The storage temperature should be between 15°C to 30°C (59°F to 86°F) in a controlled room. Keep away from moisture and store at room temperature. Any other information regarding the usage and purpose of the product is not available.*
PRINCIPAL DISPLAY PANEL - 1.34 mg Capsule Bottle Label - fotivda 04
The text provides information for a drug called FOTIVDA, manufactured by Catalnt cTs I for AVEO Pharmaceutical, Boston. Each capsule contains 134 mg of tivozanib hydrochloride equivalent to 1.5mg of tivozanib. The recommended dosage and other prescription information is not available. The drug should be stored at a temperature between 20°C to 25°C; with excursions permitted at 15°C to 30°C. It should be kept out of reach of children. The package comes with only 21 capsules, and the National Drug Code is 45629-134-01.*
* 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.