GDUFA Research Outcomes
Quantitative Methods & Models
The Generic Drug User Fee Amendments (GDUFA) science and research program facilitates patient access to high-quality generic drugs by advancing research in areas where generic product development has been limited or prevented due to knowledge gaps about the kind of evidence needed to demonstrate that a generic product is the same as its brand name reference listed drug product. Leaders and experts across the generic industry collaborate to establish GDUFA research priorities for the most pressing scientific challenges they face with generic product development. Scientists and clinicians from industry, academia, and the U.S. Food and Drug Administration (FDA) strategically design research in these areas so that the outcomes help to build scientific bridges across the knowledge gaps, thereby facilitating pharmaceutical manufacturers to develop generic drugs that were previously challenging or unfeasible to develop.
A major GDUFA science and research priority is to facilitate the utility of model-integrated evidence (MIE) to support demonstrations of bioequivalence (BE). The advancement of research in this area focuses on developing tools and advancing approaches to integrate complementary in silico (modeling), in vivo, and in vitro evidence in ways that collectively mitigate the risk of failure modes for BE and support a framework for virtual BE studies. For example, while it may not be feasible to adequately characterize the long-term bioavailability of drugs from LAI products using in vivo or in vitro methods alone, it may be feasible to integrate limited in vivo and in vitro data with PBPK models that generate the remaining evidence needed to support a demonstration of BE. This area includes research on the use of MIE to evaluate failure modes for BE and to optimize the design of BE studies.
Outcomes including scientific publications, presentations, and posters arising from GDUFA-funded research in this priority area are available in this section.
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The Impact Of Actuation Force On Droplet Size Distribution And Spray Duration Of Three Commercially Available Nasal Sprays
Schroeter, Jeffry; Kimbell, Julia; Saluja, Bhawana; Delvadia, Renishkumar; Vallorz-III, Ernest; Sheth, Poonam
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Comparison of Robustness and Efficiency of Four Machine Learning Algorithms for Identification of Optimal Population Pharmacokinetic Models
Sale, Mark; Ismail, Mohamed; Wang, Fenggong; Feng, Kairui; Hu, Meng; Zhao, Liang; Bies, Robert
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Population Pharmacokinetic Analysis of Dabigatran in Bioequivalence Studies
Sales, Gonzalez; Fang, Lanyan; Zhao, Liang
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PoPKAT: A Framework for Bayesian Population PBPK Analysis
Reisfeld, Brad; Chiu, Weihsueh; Hsieh, Nanhung; Olschanowsky, Catherine; Bois, Frederic; Ghosh, Sudipto
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Clinical Ocular Exposure Extrapolation Using PBPK Modeling and Simulation: Gatifloxacin Solution Case Study
Qaraghuli, Farah Al; Tan, Ming Liang; Walenga, Ross; Babiskin, Andrew; Zhao, Liang; Lukacova, Viera; Lemerdy, Maxime
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Evaluation of Methamphetamine and Amphetamine Disposition Discrepancy upon Selegiline Transdermal patch Administration in Healthy volunteers versus Special populations using PBPK modelling
Puttrevu, Santoshkumar; Arora, Sumit; Polak, Sebastian; Patel, Nikunjkumar
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Qualitative Analysis of Generic and Reference LAI Drug PK Profiles Using Multivariate Score Space
Paul, Prokash; Handelman, Garry; Kim, Jaeyeon; Yoon, Seongkyu
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Predicting Diclofenac Systemic and Synovial Fluid Concentrations after Dermal Application using the Multi-Phase Multi-Layer MechDermA PBPK model
Polak, Sebastian; Patel, Nikunjkumar; Martins, Frederico; Salem, Farzaneh; Jamei, Masoud; Rostami-Hodjegan, Amin
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Development of the Dermal Absorption Model for the Ketoprofen Local and Systemic Exposure Prediction
Patel, Nikunjkumar
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Towards Mechanistic Simulation And Prediction Of Bioequivalence Studies Of Topical Formulations Case Study With Two Diclofenac Formulations
Polak, Sebastian; Patel, Nikunjkumar; Cristea, Sinziana; Rose, Rachel; Salem, Farzaneh; Abduljalil, Khaled; Johnson, Trevor; Raney, Sam; Zhang, Xinyuan; Lin, Hopi; Newman, Bryan; Chow, Edwin; Ghosh, Priyanka; Fan, Jianghong; Fang, Lanyan; Jamei, M