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Based on the feedback from stakeholders at the public meeting held on June 23, 2021 and the written feedback received on the 15 proposed research priorities announced at the meeting, the US FDA released specific generic drug research priorities for fiscal year 2022 Revised outline
Oral inhalation products also have various pharmacokinetic and pharmacodynamic modeling challenges.
The FDA pointed out that a very important scientific consideration related to harmful impurities such as nitrosamines is not specific to generic drugs, but given the number of generic drug prescriptions, this is a high priority for generic drug manufacturers
In addition, D2 also discusses other priorities related to pharmacokinetic and/or pharmacodynamic models to demonstrate the bioequivalence of topical products
Let's take a look at the FDA's research priorities for fiscal year 2022, which are divided into four main categories, each of which has many sub-categories:
A-Complex active ingredients, formulations or dosage forms
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B-Complex delivery route
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C-Complex medicine and device combination products
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D-BE and treatment equivalence assessment tools and methods
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Expand the scientific understanding of the role of excipients in generic drugs, whether it is related to the formation or mitigation of harmful impurities such as nitrosamines, or support the extension of the Biopharmaceutical Classification System (BCS) category 3 biological exemption to greater than the difference in formula Products with different formulations recommended in the current FDA guidelines
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Develop alternative BE methods to resolve emergencies, such as COVID-19-related research interruptions and program deviations
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Develop methods and integrated technical solutions that enable FDA to use artificial intelligence tools and large data sets (for example, develop models and data to support QSAR-based harmful impurities (such as nitrosamines), biological, etc.
Effectiveness method research declarations, electronic health records, substitution/use patterns, drug safety data, and drug quality data) to support regulatory decision-making and improve post-marketing supervision of generic drug substitution
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Author: Zhilin-Lanshan