Product Images Duloxetine

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Product Label Images

The following 9 images provide visual information about the product associated with Duloxetine NDC 68151-4727 by Carilion Materials Management, 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.

Label Image - 68151 4727

Label Image - 68151 4727

This is a prescription medication called Cymbalta, containing Duloxetine with a strength of 60 mg. It comes in a delayed-release capsule form manufactured by Lilly, and the batch number is C190849A. The medication has an expiration date of 07/14 and the product code is W0107143m2. The medication is classified as prescription-only and has a PDM number of 4727.*

Figure 1 - cag pi 1v1

Figure 1 - cag pi 1v1

Figure 2 - cag pi 2v1

Figure 2 - cag pi 2v1

Figure 3 - cag pi 3v1

Figure 3 - cag pi 3v1

Figure 4 - cag pi 4v1

Figure 4 - cag pi 4v1

Figure 5 - cag pi 5v1

Figure 5 - cag pi 5v1

Figure 6 - cag pi 6v1

Figure 6 - cag pi 6v1

The given text represents a graph/chart that shows the percentage of patients improved with DLX60 mg once daily compared to a Placebo. The horizontal axis displays the percent improvement in pain from baseline (BOCF), while the vertical axis represents the percentage of patients improved from 0 to 100. The DLX60 mg once daily is represented by a line above Placebo, indicating that it is more effective in improving patients' conditions.*

Figure 7 - cag pi 7v1

Figure 7 - cag pi 7v1

The text shows a graph displaying the percentage of patients who showed improvement in pain after taking either placebo or DLX60/120 mg once daily. The graph displays a range of percentage improvement from 0 to 100 plotted against time. There is no description available for the underlying study or condition it pertains to.*

Structural Formula - cag pi chemstruct v1

Structural Formula - cag pi chemstruct v1

* 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.