Product Images Eliquis
View Photos of Packaging, Labels & Appearance
Product Label Images
The following 7 images provide visual information about the product associated with Eliquis NDC 63629-8432 by Bryant Ranch Prepack, 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.
Apixaban Chemical Structure - apixaban struct
ARISTOTLE Forest Plot - eliquis clin forest fig5
ARISTOTLE Major Bleeding Forest Plot - eliquis forest fig1
This is a medical data table containing information on various subgroups of patients and their response to different treatments (Apixaban and Warfarin). The table shows the number of events, number of patients (% per year) in each subgroup, and the hazard ratio for each group. The subgroups include age, gender, weight, previous stroke or TIA, diabetes mellitus status, CHADS2 score, creatinine clearance, and geographic region. The table also mentions the apixaban and warfarin outcomes.*
ARISTOTLE Kaplan-Meier Curve - eliquis km fig4
Effect of Coadministered Drugs on Pharmacokinetics of Apixaban - eliquis pkplot drugs fig2
This text provides a table showing the interaction of various drugs with P-gp and strong CYP3A4 inhibitors or inducers. The table outlines the fold change and 90% confidence interval in drug PK (pharmacokinetics) values, particularly AUC (area under the curve), caused by these interactions. The drugs included in the study are ketoconazole, glarithromycin, diltiazem, naproxen, and rifampin.*
O:\E-Submissions\PBO\PRODUCTS\Eliquis 202155\08. PAS ESRD Dosing\01. Orig Submission\Source\eliquis-pkplot-pop-fig3.jpg - eliquis pkplot pop fig3
Label - lbl636298432
The text appears to be a label of the medicine named "Eliquis 2.5mg". The label provides information on the appearance of the tablet, storage temperature, expiry date and the National Drug Code (NDC).*
* 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.