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ZENODO
Article . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
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Impact of USFDA 483s on Warning Letters

Authors: Yogesh Madhukar Binnar; Dr Vinod Bhalla;

Impact of USFDA 483s on Warning Letters

Abstract

Objectives: This review looks at how United State Food and Drug Administration (USFDA) Form 483 observations are linked to warning letters, especially for Indian pharmaceutical companies. Methodology: The study focuses on drug manufacturers and uses data from inspections and warning letters between January 2023 and November 2024. It also compares some older data from 2018 to 2020 to see changes over time. The researcher studied several sources, including regulatory guidelines like USFDA, World Heald Organization (WHO), and European Union good manufacturing practices (EU GMP), to understand current good manufacturing practices (cGMP) and why companies fail to meet them. Results: The findings show that if company gets a Form 483 during an inspection, there is a more than 50% chance it may get warning letter later, especially if the company has failed to reply properly. Over the past two years, 20 Indian companies obtained letters. Out of these, 10 already had 483 observations earlier, while others failed their first inspection due to serious good manufacturing practices (GMP) problems. The review concludes that many warning letters happen because of poor GMP compliance and weak follow-up after past inspections. Future research can help companies avoid such issues by improving quality systems. This can lead to better product safety and help companies save time and money

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green