Evidence map›Paper›PMID 38589695›Full record

ArticleThe AAPS journal2024

Leveraging Modeling and Simulation to Enhance the Efficiency of Bioequivalence Approaches for Generic Drugs: Highlights from the 2023 Generic Drug Science and Research Initiatives Public Workshop.

Arindom Pal, Fang Wu, Ross Walenga, Eleftheria Tsakalozou, Khondoker Alam, Yuqing Gong, Liang Zhao, Lanyan Fang

Abstract read
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Article in The AAPS journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Arindom PalDivision of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
Fang WuDivision of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
Ross WalengaDivision of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
Eleftheria TsakalozouDivision of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
Khondoker AlamDivision of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
Yuqing GongDivision of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
Liang ZhaoDivision of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
Lanyan FangDivision of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA. Lanyan.Fang@fda.hhs.gov.ORCID http://orcid.org/0000-0001-5378-2948

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The 2023 Generic Drug Science and Research Initiative Public Workshop organized by the U.S. Food and Drug Administration (FDA) discussed the research needs to improve and enhance bioequivalence (BE) approaches for generic drug development. FDA takes such research needs and panel discussions into account to develop its Generic Drug User Fee Amendments III (GDUFA III) Science and Research Initiatives specific to generics. During the five workshop sessions, presentations and panel discussions focused on identifying and addressing scientific gaps and research needs related to nitrosamine impurity issues, BE assessment for oral products, innovative BE approaches for long-acting injectable products, alternative BE approaches for orally inhaled products, and advanced BE methods for topical products. Specifically, this report highlights the discussions on how to improve BE assessment for developing generic drug products based on research priorities for leveraging quantitative methods and modeling, as well as artificial intelligence/machine learning (AI/ML).

Indexed as

Artificial IntelligenceDrugs, GenericDrug DevelopmentTherapeutic EquivalencyUnited StatesUnited States Food and Drug AdministrationDrugs, Genericbioequivalenceinhaled productslong-acting injectable productsmodeling and simulationorallytopical products

Identifiers

PMID38589695

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.