Nintedanib Capsule
Product Images NDC 69097-641

View Photos of Packaging, Regulatory Labels, and Product Appearance

Product Visual Gallery

This gallery contains 12 technical images submitted to the FDA as part of the official labeling for Nintedanib (NDC 69097-641). Unlike standard consumer photos, these assets often include clinical data figures, molecular chemical structures, and official manufacturer packaging layouts.

As provided by Cipla Usa Inc., these visuals offer a comprehensive scientific overview of the product's physical and chemical identity, aiding pharmacists and researchers in product verification and study.

FDA Label Image

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FDA Label Image

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FDA Label Image

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This text provides information on survival estimates for Kapkan-Mhese, with a hazard ratio of 0.70. It also mentions the percentage of survival and the expected number of deaths over a 52-week period.*
FDA Label Image

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This is a statistical analysis report evaluating the impact of a drug called Nintedanib on the decline of Forced Vital Capacity (FVC) over 52 weeks compared to a placebo. The report includes estimates, population size, averages, confidence intervals, and comparisons of outcomes between the Nintedanib group and the placebo group. The data suggests that Nintedanib may have a beneficial effect on FVC decline compared to the placebo.*
FDA Label Image

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FDA Label Image

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FDA Label Image

Bottle-100mg-label-kkb (Bottle 100mg Label Kkb)

FDA Label Image

Container-150mg-label-kkb (Container 150mg Label Kkb)

Container-150mg-label-kkb (Container 150mg Label Kkb)
This text describes a product packaging for "Nintedanib 150mg Capsules" by Cipla. It states the quantity of capsules as 50 and mentions an area for batch overprinting during commercial packing, including information such as Serial Number, Expiry Date, and Lot Number.*
FDA Label Image

Figure-7 (Figure 7)

FDA Label Image

Figure-8 (Figure 8)

FDA Label Image

Figure-9 (Figure 9)

FDA Label Image

Structure (Structure)

* These product label images have been analyzed using experimental machine learning. Please verify findings with the primary label text.