USFDA Announces FY 2027 Generic Drug Science and Research Priorities
The US Food and Drug Administration (FDA) has announced its Fiscal Year (FY) 2027 Generic Drug User Fee Amendments (GDUFA) Science and Research Priority Initiatives. The guidance outlines eight research areas intended to address scientific challenges in generic drug development, improve the efficiency of bioequivalence assessments, and accelerate patient access to generic medicines.
The priorities were identified through collaboration with industry and public stakeholders, including feedback from the FY 2026 Generic Drug Science and Research Initiatives Public Workshop held on 8 and 9 June 2026. The research programme focuses on complex generic products, innovative scientific methods, data science, modelling and tools to support more efficient and globally harmonised approaches to generic drug development.
1. Methods to Address Impurities, Including Nitrosamines
The FDA prioritises research into the formation, identification and control of potentially harmful impurities, including nitrosamine drug substance-related impurities (NDSRIs). The aim is to help generic drug applicants evaluate impurity risks and develop efficient approaches to address them.
Research includes modelling and simulation approaches to reformulate certain Biopharmaceutics Classification System (BCS) Class IV drugs, analytical methods to identify and quantify nitrosamines and their precursors, and investigations into the mechanisms of nitrosamine formation in active pharmaceutical ingredients (APIs), excipients and packaging. The priorities also include developing and validating methods to establish acceptable intake limits using mutagenicity assessments, metabolism studies and computational approaches.
2. Equivalence Approaches for Complex Active Ingredients
The FDA aims to improve the characterisation of complex active ingredients, including their chemical composition, molecular structure and impurity profiles. These improvements are intended to support pharmaceutical equivalence (PE) and bioequivalence (BE) assessments.
Research priorities include developing and standardising analytical methods to characterise oligonucleotides and their impurities, as well as improving in vitro methods to assess the immunogenicity risks of peptide and oligonucleotide products manufactured using synthetic or recombinant active ingredients.
3. Bioequivalence Approaches for Complex Dosage Forms and Formulations
This research area focuses on improving in vitro characterisation methods for complex dosage forms by identifying relevant critical quality attributes (CQAs) and suitable analytical techniques.
The FDA prioritises research into drug-release mechanisms and characterisation methods for complex injectable products and implants. This includes exploring alternative BE approaches using in vitro, integrated in vitro/in vivo or in silico methods, as well as digital twin reference listed drugs to support generic product development and assessment. Research will also improve characterisation tools for polymeric ingredients and complex formulations to assess compositional sameness and acceptable formulation design space.
4. Bioequivalence Approaches for Complex Routes of Delivery
The FDA intends to improve understanding of how formulation ingredients influence drug absorption through complex routes of delivery and to develop predictive models for assessing bioequivalence.
For inhalation products, priorities include improving analytical characterisation, modelling and recommendations for dissolution and drug-release testing. For topical products, research will focus on efficient BE methods that can accommodate certain compositional differences from the reference product. Improving comparative in vitro permeation testing (IVPT) study designs and data analysis is also a priority.
5. Equivalence Approaches for Complex Drug-Device Combination Products
The FDA will explore how differences in device design, user interfaces, hardware, software and propellants may affect medication errors, bioequivalence, therapeutic equivalence and postmarketing safety.
Research priorities include improving comparative risk assessments and human factors study approaches, developing better criteria for comparative device performance, and identifying circumstances in which in vitro methods could support BE demonstrations without certain in vivo studies. The FDA will also explore efficient and economically feasible approaches for generic products to transition to more environmentally friendly propellants and device components.
6. Bioequivalence Approaches for Oral and Parenteral Generic Products
The FDA aims to improve BE methods for oral and parenteral products by advancing dissolution testing, in vitro–in vivo correlations (IVIVCs) and computational models. These approaches may support broader use of BCS-based biowaivers, efficient evaluation of additional oral product strengths and greater global harmonisation.
Research includes using dissolution tools and physiologically based pharmacokinetic (PBPK) modelling to evaluate food effects and formulation-dependent drug interactions. Additional priorities address high-risk immediate-release and modified-release oral products, including orally administered GLP-1 peptides, as well as efficient BE approaches for certain parenteral and ophthalmic solutions with compositional differences from reference listed drugs.
The programme also seeks to improve understanding of subject-safety considerations and potential BE challenges in special populations, including paediatric and geriatric patients.
7. Model-Integrated Evidence to Support Bioequivalence
Model-integrated evidence (MIE) combines complementary in vitro, in vivo and in silico evidence to support BE assessments and address potential reasons for BE study failure.
The FDA prioritises approaches that integrate experimental data with predictive models, including PBPK models, to support efficient BE demonstrations for complex delivery routes and long-acting injectable (LAI) products. Research will also establish best practices for model standardisation, validation, acceptance and sharing to improve the reproducibility and reuse of quantitative pharmacology information.
Additional priorities include innovative pharmacokinetic BE study designs in patients, such as reduced or sparse sampling for oncology products and adaptive study designs.
8. Artificial Intelligence and Machine Learning
The FDA plans to expand the use of artificial intelligence (AI) and machine learning (ML) tools to improve the efficiency, consistency and quality of generic drug regulatory assessments.
Priorities include using large language models to support the preparation of information for product-specific guidances and Abbreviated New Drug Application (ANDA) assessments, and streamlining routine workflows such as Pre-ANDA meetings and controlled correspondence.
The programme will also explore real-world evidence for postmarketing surveillance of generic drug substitution and its public health impact. Additional research will integrate AI/ML tools with FDA data to support quantitative analysis and modelling, improve the reliability of model outputs, and streamline regulatory activities involving resources such as the inactive ingredient database and maximum daily dose determination.
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