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Spotlight on CDER Science

Spotlight on CDER Science highlights the critical scientific research and advancements by the Center for Drug Evaluation and Research (CDER), showcasing the latest developments in drug evaluation and regulatory science, while offering insights into how science drives drug safety and effectiveness.


Latest CDER Science Spotlights

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A decorative image of a magnifying glass looking at pills

Determining Recommended Acceptable Intake Limits for N-nitrosamine Impurities in Pharmaceuticals: Development and Application of the Carcinogenic Potency Categorization Approach

FDA and international regulators developed a methodology, the Carcinogenic Potency Categorization Approach, that uses the chemical structure of a nitrosamine impurity to recommend AI limits by assignment to 1 of 5 predicted potency categories reflecting carcinogenic risk.

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Study explores providers’ use of prescribing information for lactating patients

Healthcare Providers’ Use of a Concise Summary to Prescribe for Lactating Patients

CDER collaborated with the Center for Biologics Evaluation and Research and outside researchers to conduct a study to explore if adding a concise summary of risk information would more succinctly communicate drug safety information to busy healthcare providers.

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Depiction of a group of molecules showing the connections between atoms

Bumetanide as a Model NDSRI Substrate: N-nitrosobumetanide Impurity Formation and its Inhibition in Bumetanide Tablets

CDER research provides manufacturers with potential approaches to drug formulation to mitigate nitrosamine formation.

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Prescriber Perceptions of Boxed Warnings Study

Prescriber Perceptions of Boxed Warnings: A Qualitative study

An important part of CDER’s work is communicating the most up-to-date safety information for medicines to help ensure that health care providers (HCPs) and their patients can make the most informed treatment decisions.

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Image representing AI (artificial intelligence) and SARS-CoV-2 virus particles (the virus that causes COVID-19)

Using Machine Learning to Identify a Suitable Patient Population for Anakinra for the Treatment of COVID-19

CDER review team discusses how machine learning was used to identify the most suitable COVID-19 patients for treatment with Anakinra under an Emergency Use Authorization (EUA), improving the precision of patient selection and potentially enhancing treatment outcomes.

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Group of people representing different ages and genders

Sociodemographic Characteristics of Adverse Event Reporting

Researchers in CDER’s Office of Surveillance and Epidemiology and other experts examine how sociodemographic factors like age, sex, and race influence adverse drug events reporting and the potential impacts on understanding drug safety across different populations.

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All CDER Science Spotlights

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