BAA Notice Final 10.29.26.docx

FDABAA-27-00123

Overview Information

Agency Name: Department of Health and Human Services (HHS), Food and Drug Administration (FDA), 10903 New Hampshire Avenue, Silver Spring, Maryland 20993

Issuing Office: Department of Health and Human Services, Food and Drug Administration, Office of Acquisitions & Grants Service, 4041 Powder Mill Rd. Beltsville, MD 20705

Research Opportunity Title: Food and Drug Administration Broad Agency Announcement for the Advanced Research and Development of Regulatory Science

Announcement Type: Broad Agency Announcement (BAA)

Eligible Applicants: This BAA is open to ALL responsible sources and Small Businesses are strongly encouraged to respond. Offerors may include single entities or teams from private sector organizations, Federally Funded Research and Development Centers (FFRDCs) (see page 5 for FFRDC eligibility requirements) and academic institutions.

Research Opportunity Description: The FDA solicits advanced research and development proposals to support regulatory science and innovation. The FDA anticipates that research and development activities awarded under this BAA will serve to advance scientific knowledge to accomplish its mission to protect and promote the health of our nation.

Types of instruments that may be awarded: Procurement Contracts Only [Not Grants]

Notes: Regarding Funding

To ensure sufficient time to conduct the two-tiered evaluation described in Section I and still be considered for an award within the current fiscal year, prospective Offerors are required to submit Stage One Submittal Packages with:

1. Checklist following required template (See attachment 3)

2. Freestanding Concept Paper following required template (See attachment 4)

3. Freestanding Full Proposal following required template (see attachment 5)

Freestanding Concept Paper and Full Proposal shall be submitted no later than 5:00 pm, Eastern Standard Time, Friday, January 22, 2027*, and earlier if possible. Early applications are highly encouraged. A submission will be considered incomplete if any of these critical elements are missing. Stage One Submittal Packages (Checklist, Freestanding Concept Paper, and Freestanding Full Proposal) received after that date will still be accepted, but due to lack of lead time, will not be considered for award in FY27.

Notice and Disclosure Regarding Incomplete/Nonconforming Stage One Submittal Packages: All Stage One Submittal Packages received in response to this announcement must be complete and comply with all instructions provided herein (Note: Part III: Proposal Preparation and Submission must be considered; it lays out the details of what is included in Stage I and Stage II Package). The FDA reserves the right to reject (or otherwise give no further consideration to) any Packages that are found to be (1) missing any pertinent information, (2) submitted in a nonconforming format, or (3) otherwise require exchanges with offerors for the FDA to complete its evaluation due to patent or latent ambiguities contained within the submittal.

Contents

Introduction Advancing Regulatory Science and Innovation

This Broad Agency Announcement (BAA), which sets forth research areas of interest for FDA, is issued under the Federal Acquisition Regulation (FAR) part 35.016(c). The purpose of this BAA is to provide a mechanism by which FDA can utilize industry and academia’s capabilities to advance the state of the art and achieve improvements in technology, materials, processes, methods, devices, or techniques in specific topics as described in this document. Proposals selected for award are the result of full and open competition and in full compliance with the provision of Public Law 98-369, "The Competition in Contracting Act of 1984" and subsequent amendments.

FDA is a science-based regulatory agency and a critical component to the success of the nation’s public health, health care systems, and economy. In the US, FDA-regulated products account for about 20 cents of every dollar spent by American consumers each year on products that touch the lives of every American daily. The FDA protects and promotes the health and safety of all Americans through enhancing the availability of safe medical products and foods and promoting innovation that addresses unmet medical and public health needs. FDA also protects and promotes the health and safety of animals through assuring the availability of safe animal drug products and food. Since 2009, FDA has worked to reduce the harm from all regulated tobacco products. At the same time, FDA helps consumers and health care providers get the accurate and science-based information they need to make the best possible decisions about their use of FDA regulated products. FDA must make decisions based on the best available scientific data and using the best tools and methods available to ensure products meet the highest quality standards for consumers, while at the same time fostering and advancing innovation in the products it regulates.

The core responsibility of FDA is to protect consumers by applying the best possible science to its regulatory activities, ranging from pre-market review of efficacy and safety of many of its regulated products to post-market product surveillance, review of product quality, regulation of product manufacture, and distribution and marketing of products. In the last few years, rapid advances in innovative science have provided new technologies to discover, manufacture, and assess novel medical products. In order to improve regulated product safety and quality, FDA must keep pace with and utilize these new scientific advances to accomplish its mission to protect and promote the health of our nation.

The BAA is open to all responsible sources. Offerors may include single entities or teams from private sector organizations, Federally Funded Research and Development Centers (FFRDCs), and academic institutions. Non-U.S. organizations and/or individuals may participate to the extent that such participants comply with any necessary nondisclosure agreements, security regulations, export control laws, and other governing statutes applicable under the circumstances. FDA also collaborates with other federal agencies to award research contracts.

Federally Funded Research and Development Centers (FFRDCs) and Government entities (e.g., Government/National laboratories, military educational institutions) are subject to applicable direct competition limitations and cannot propose to this BAA in any capacity unless they meet the following conditions:

1. Clearly demonstrate that the proposed work is not otherwise available from the private sector.

2. Provide a letter on official letterhead from their sponsoring organization citing the specific authority establishing their eligibility to propose Government solicitations and compete with industry, and their compliance with the associated sponsoring agreement and terms and conditions.

The purpose of this BAA is to solicit proposals that focus on one or more of the following research areas of interest as listed below in the regulatory science framework and further described in Part I of this announcement (Table 1). The goal of this regulatory science framework is to harness regulatory science research to accomplish the following four FDA public health pillars1 that directly align with FDA’s mission:

1. Innovation and Global Leadership

2. Advancing National Health Security

3. Increasing Access to Affordable Medicines & Medical Products

4. Preventing Chronic Disease and Promoting Wellness in America Multiple awards are anticipated. The amount of resources made available for individual contract awards under this BAA will depend on the quality of the proposals received and the availability of funds. All funding is subject to government discretion and availability. This BAA is available on www.sam.gov using Keyword Search “FDABAA-27-00123.”

This BAA is a continuously open announcement valid throughout the period from the date of issuance through the closing date specified in the http://www.sam.gov announcement. Amendments to this BAA, if necessary, will be posted on the same site when they occur. Interested parties are encouraged to periodically check the website for updates and potential amendments. Any amendments posted to this announcement will be highlighted. Additional information for FDA BAA Program is available at Regulatory Science Extramural Research and Development Projects | FDA.

The Government reserves the right to select for negotiation all, some, one, or none of the proposals received in response to this solicitation, and to make awards without discussions with proposers. The Government also reserves the right to conduct discussions if it is later determined to be necessary. If warranted, portions of resulting awards may be segregated into pre-priced/severable options. Additionally, FDA reserves the right to accept proposals in their entirety or to select only portions of proposals for award. In the event FDA desires to award only portions of a proposal, negotiations may be opened with that proposer. The Government reserves the right to fund proposals in phases with options for continued work at the end of one or more of the phases.

To be eligible for award, a prospective recipient must meet certain minimum standards pertaining to financial resources, ability to comply with the performance schedule, prior record of performance, integrity, organization, experience, operational controls, technical controls, technical skills, facilities, and equipment. The FDA will follow the direction in Executive Order 14402, allowing research contracts to use cost-reimbursement as a contract type. Research, by definition, does not always produce a deliverable whether due to the nature of the research or the results of the research that would facilitate a Firm Fixed Price contract. By proposing a severable, cost reimbursable contract, risk is reduced to both the government and the awardee.

Please note, the contract requirements differ between commercial and educational entities. It is HIGHLY recommended that all potential proposal submitters review Part 31- Contract Cost Principles and Procedures:

31.103 Contracts with commercial organizations.

31.104 Contracts with educational institutions.

Please also note that due to the nature of research contracts, the FDA will lean towards issuing most/all BAA research contracts as non-commercial as indicated by the market research conducted for each awarded contract.

Part I: Research Areas of Interest Through this BAA, FDA seeks to support advanced research and development strategies with potential for regulatory application in the following research areas of interest (See Table 1, which highlights the areas of regulatory science that are priorities for FDA for FY 27). This section presents the technical objectives that FDA seeks to achieve through this BAA. Offerors should propose a Statement of Work (SOW) that is consistent with research and development work as defined in FAR 35.001. Proposal preparation and submission instructions are contained in Part III. Because resources are limited, preference will be given to projects geared toward advancing regulatory science—the science of developing new tools, standards, and approaches to assess the safety, efficacy, quality, and performance of FDA-regulated products, which include medical devices, drugs, biologics, combination products, veterinary medicine, food, cosmetics, dietary supplements, and tobacco products—that will help advance FDA and HHS priorities.[footnoteRef:2] [2: https://www.whitehouse.gov/maha/, https://www.hhs.gov/about/priorities/index.html and https://www.fda.gov/about-fda/what-we-do/fdas-public-health-pillars

Table 1: Fiscal Year 2027 Priority Regulatory Science Research Areas of Interest

FDA-Regulated Areas

X

7. Medical Product Cybersecurity

X

11. Animal Health

X

X

X

19. Regenerative Medicine

X

X

22. Tobacco and Nicotine

X

FDA funded regulatory science researchers are encouraged to evaluate populations by including analyses according to sex, age, pregnancy, lactation status, and comorbidities, as appropriate. Investigators should consider sex as a biological variable regarding research aims, study designs, data collection and analyses, and reporting of research findings on cell lines, vertebrate animals, and humans.

FDA funded regulatory science researchers are strongly encouraged to reduce the reliance on animal testing through the development and/or use of new approach methodologies (NAMS). Proposals that include the use of animals should also include considerations on the use of NAMS.

We have designated areas to target regulatory science efforts that support the FDA’s mission for each public heath pillar. The areas of regulatory science are designed to change with the evolution of science, technologies, and public health needs, and where possible, FDA-regulatory areas have been labeled. Through collaborative interactions with stakeholders, we aim to foster robust and innovative approaches to advance regulatory science through the following framework.

Areas of Regulatory Science

1. Augmenting Regulatory Review

2. Enhancing Nonclinical Development

3. Modernizing Clinical Trials

4. Onshoring Manufacturing

5. Antimicrobial Resistance

6. Medical Countermeasures

7. Medical Product Cybersecurity

8. Shortages and Supply Chain

9. Post-marketing Surveillance

10. Aging and Longevity

11. Animal Health

12. Digital Health

13. Generic Drugs

14. Mental Health

15. Neurological Disorders

16. Oncology

17. Pediatric Health

18. Rare Diseases

19. Regenerative Medicine

20. Women’s Health

21. Substance Use Disorders

22. Tobacco and Nicotine

Additional information regarding specific FDA initiatives, and/or center and office priorities can be found in the Part I Appendix.

1. Augmenting Regulatory Review

Examples: Use of super intelligence (SI)[footnoteRef:3] to augment the review process; real-world evidence (RWE); optimizing data infrastructure and data standards; global harmonization and optimization of technical consensus standards. [3: Per Executive Order 14434, federal agencies are now using "Super Intelligence" (SI) in place of "Artificial Intelligence" (AI). ]

1. Advance analytical and computational methodologies to generate clinical evidence using SI/ML sufficient to support regulatory use including:

i. Demonstrating how clinical evidence generated from the use of SI could inform clinical studies for regulatory use

ii. Developing and validating tools and models that assess fitness of SI/ML use to support regulatory decision making (e.g., using a placebo digital twin)

iii. Developing and validating methods to predict medical product performance using SI/ML

iv. Evaluating ML methods with a focus on identifying and/or addressing sources of methodological bias

v. Advancing causal SI inference

vi. Develop and validate methodologies for generating synthetic data that is demonstrably fit-for-use (i.e., relevant and reliable) for a specific context of use, in order to train, test, and assess the performance of SI/ML models, particularly against rare events or in scenarios with insufficient real-world data.

2. Develop and evaluate options to improve financing and reimbursement, and de-risk business investment early in the development process, to increase breakthrough medical device development and innovation

3. Leverage existing data to compare RWE generation with more traditional approaches for data collection and evidence generation

4. Develop methodological standards and frameworks for point-of-care (pragmatic) randomized trials that leverage real-world data from routine clinical encounters, EHR-captured endpoints, and broad patient populations to generate evidence sufficient to support regulatory submissions.

5. Develop and validate statistical and epidemiological methods to identify, quantify, and address confounding, selection bias, and other threats to causal inference when using RWD to generate RWE for regulatory decision-making, including methods to assess the robustness and reproducibility of findings across different data sources and analytical approaches.

6. Develop and validate methods for constructing and evaluating external control arms derived from RWD — including patient registries, EHRs, and claims data — and synthetic control arms derived from historical trial data or SI/ML-generated data, to support regulatory submissions particularly in settings where randomization is infeasible,

7. Develop standards for data quality and data sources that increase the quality, interoperability, and utility of RWD

8. Design and optimize data infrastructure to facilitate information exchange and data extraction

9. Develop, improve, and/or harmonize data standards applied for use in regulatory submissions including information supplied to FDA through the electronic Common Technical Document (eCTD) and/or other submission mechanisms used by FDA

10. Develop methods to incorporate data from DHTs not in EHRs into patient care beyond clinical trials

11. Develop and validate methods to assess algorithm performance, including techniques to manage methodological or statistical bias for SI/ML-enabled medical devices

12. Develop tools or methodologies to evaluate the performance of LLMs/generative SI as they are applied to devices including methodologies that would enable robust post-market monitoring to ensure continued high-quality performance of LLM-enabled devices, including identifying and preventing data drift, and ensuring ongoing model accuracy

13. Examine the use of LLMs/generative SI to identify patient perspectives and/or to augment qualitative and mixed methods research into patient perspectives

14. Explore the value of innovators incorporating evidence generation targeting the needs of downstream stakeholders in the MedTech innovation space, (e.g., physician professional societies, payers, and patients into pivotal trial protocols to support commercialization and widespread availability of medical devices)

15. Develop and validate holistic frameworks, methodologies, and standards to ensure the safety, effectiveness, and quality of medical products and integrated systems intended for use in home and community settings. Research in this area should address:

i. Human-Centered Design and Usability: Evaluate and establish best practices for device and software design that account for the varied cognitive, physical, and environmental realities of home use by lay-users and their caregivers, including factors like caregiver burden and multi-generational household dynamics

ii. System-Level Interoperability and Safety: Develop methodologies to assess the safety and performance of multi-vendor medical device ecosystems within the home. This includes integration with non-medical consumer platforms (e.g., home automation standards) and creating frameworks for system-level risk assessment

iii. Real-World Performance and Evidence Generation: Create and validate novel methods for collecting, validating, and analyzing real-world data (RWD) and patient-generated health data from home environments to support regulatory review, accounting for challenges in data quality, patient adherence, and long-term engagement

16. Optimize and harmonize technical consensus standards and guidelines developed through recognized bodies such as ISO, IEC, and IMDRF so that they are more directly applicable to regulatory submissions and better aligned among global regulatory authorities. As a result, device manufacturers across jurisdictions can ultimately use a single, harmonized technical file that satisfies multiple jurisdictions simultaneously - eliminating redundant testing and duplicative submissions. Optimizing these standards to reflect the current state of device science and technology - and to incorporate clear conformity assessment methods and acceptance criteria - ensures a rigorous evidentiary bar and consistent testing. Collaborative frameworks such as MDSAP and bilateral recognition agreements further enable mutual reliance among global regulators, allowing one authority's thorough review to inform and expedite decisions by others. Together, these efforts create a more streamlined, predictable regulatory pathway that rewards innovation and delivers safe, effective medical devices to patients faster across global markets.

17. Address the limitations of existing RWD sources (lack of semantic interoperability and missing data) with development of coordinated registry networks, the model that has been documented as more efficient and producing more robust, regulatory grade data.

2. Enhancing Nonclinical Development

Examples: Model-informed product development (MIPD) including silico modeling and simulation (M&S) tools; new approach methodologies (NAMS) including organs-on-a-chip systems, micro physiological systems (MPS), complex in vitro models (CIVM); refined in vivo models; animal models.

1. Develop computer models of cells, organs, and systems (including the impact of hormones) to predict product risk, safety, and efficacy of FDA regulated products including potential interactions with drugs and dietary supplements

2. Develop computer models that integrate pharmacokinetic, pharmacodynamic, materials science, or mechanistic safety data to predict clinical risk in different patient populations

3. Develop and apply data mining, knowledge building, and data visualization tools to inform computer model development, clinical risk prediction, and regulatory decision-making

4. Develop novel methods to display model output in both graphical and numeric formats

5. Identify opportunities and develop computer simulation and modeling to streamline data analysis and model biological systems and their responses to agents of concern, such as toxins, toxic compounds, pathogens, and biomaterials

6. Integrate an understanding of product quality and safety based on novel genomic, proteomic, metabolomic, and other-OMIC technologies

7. Develop and use novel bench testing methods to support evaluation of medical products

8. Develop and use NAMS[footnoteRef:4] approaches such as MPS, CIVM, and computational modeling to improve the predictability of nonclinical testing and support the regulatory assessment of drug medical products, potentially address the 3Rs (replace, reduce, and refine) of animal use in product testing and scientific research, and support implementation of the Roadmap to Reduce Animal Testing in Preclinical Safety Studies [4: New Approach Methodologies (NAMs) | FDA, FDA Achieves Year 1 Goals in Reducing Animal Testing in Drug Development | FDA]

9. Develop refined in vivo methods that can reduce the reliance on animal testing while improving predictive power including:

i. Developing animal models that better mimic diseases to better understand the potential influence of disease progression and risk factors, including disease co-morbidities on the emergence of adverse events

ii. Evaluating the specificity and sensitivity with which animal models and in vitro assays predict potential human and animal risk, both overall and/or in sub-populations (e.g., pediatric populations)

10. Promote a better understanding of toxicity mechanisms by evaluating safety assessment data at multiple levels of biological organization including genes, proteins, pathways, and cell/organ function

11. Develop toxicologic and pharmacologic tools to assess and characterize molecular targets and host genetic and inflammatory factors that may be associated with rare and unexpected adverse events (i.e., “off-target” drug effects)

12. Develop and use MPS, CIVM, and modeling approaches to predict toxicity

13. Explore the combined use of human factor engineering principles, patient science methods and patient engagement in device product design to improve usability of devices

14. Develop methods to identify and mitigate usability challenges related to sex-based differences and support innovation in sex-conscious engineering design and development of medical devices

15. Identify user interface differences that will likely lead to clinically meaningful differences in use error rates or use success rates. (For example, conducting a meta-analysis of comparative use human factor studies (CUHF) using any available data sets and/ or study reports from marketing applications to FDA to inform whether differences in the activation steps of autoinjectors result in differences in success rates)

3. Modernizing Clinical Trials

Examples: Advancing the use of innovating clinical trial designs including decentralized trials, use of real-world data (RWD) and super intelligence (SI)[footnoteRef:5]; modernizing statistical methods for clinical trial evaluation, improving the utility of clinical outcome assessments (COA) [5: Per Executive Order 14434, federal agencies are now using "Super Intelligence" (SI) in place of "Artificial Intelligence" (AI)]

1. Develop clinical trial simulation models that can reveal interactions between drug effects, patient characteristics, and disease variables influencing outcomes

2. Explore methods for patient input (e.g., through patient-generated data) into the development of analytical and computational methods related to medical devices.

3. Advance methodologies for qualification of biomarkers and to support analytical and clinical validation of biomarkers that can provide objective measures to replace or complement subjective classifications of disease diagnosis or progression

4. Advance biomarker and clinical tool validation to reduce disease heterogeneity in clinical trials by refining clinical disease classification or inform reproducible patient stratification based on risk factors in therapeutic areas with unmet need

5. Assess concordance between animal and human biomarkers of toxicity and determine how the performance of these biomarkers and their interpretation may vary across different organ systems and human populations

6. Demonstrate how terminology for encoding biomarkers in real-world data can integrate with patient care data encoding standards recommended by the Office of the National Coordinator (e.g. SNOMED CT, LOINC, CVX and RxNorm)

7. Identify and evaluate improved clinical endpoints and related biomarkers for trials in areas where optimal endpoints are lacking (e.g., efficacy and safety endpoints for osteoarthritis in humans and animals, for gene therapy, for transplant-related studies (endpoints and duration), for tumor vaccines, and for stem cell-derived therapies)

8. Modify or adapt existing COA instruments for a representative population, regulatory use, intended use, or context of use, including at home, and collect evidence to support the modification or adaptation

9. Explore combined use and respective performance of COAs, including those digitally derived from DHTs (e.g. patient-generated health data) as study endpoints, components of clinical or post-market datasets and/or super intelligence studies

10. Identify and if possible, quantify, known biases (e.g., open-label bias, placebo effect, sex bias) for patient-reported outcomes

11. Explore methods for reducing the burden of conducting patient preference information studies early in the total product life cycle (e.g., use of case examples)

12. Conduct studies among representative populations that investigate patient preferences in benefit-risk assessments, proposed endpoints, or potential device features to advance understanding and aid regulatory decisions

13. Develop clinical trial innovation tools such as moving clinical studies from clinical sites to decentralized trials including identifying aspects that are best suited for decentralized vs centralized clinical sites

14. Develop readiness models for both decentralized and clinical site studies in the areas of informed consent, data collection, and post-trial communication

15. Develop novel study designs to capture patient perspectives and patient preference information

16. Explore patient engagement methods to collect and use patient input in regulatory decision-making, spur patient-centric medical product development, and to inform patient-centric regulation

17. Develop and validate methods for collecting and analyzing patient experience data (inclusive of qualitative, quantitative and mixed methods)

18. Correlate patients experience data to product quality management maturity at the firm level

19. Investigate and develop evidence-based educational resources and training methodologies to improve stakeholder understanding and implementation of FDA's Patient-Focused Drug Development (PFDD) guidance series for enhanced collection and submission of patient experience data in medical product development and regulatory decision-making.

20. Investigate the aggregation of multiple patient preference studies for the assessment of new medical devices (specifically, estimate and characterize the distribution of patients’ maximum acceptable risk of harms or minimum acceptable benefits based on publicly available information for patients with a condition(s) commonly managed with medical devices and for which there are a variety of device types as alternatives with some common attributes)

21. Investigate thematic elements of patient perspectives across publicly available device-related workshops and meetings to understand key factors that influence patient and consumer decision-making including but not limited to benefit/risk, outcomes of interest, and clinical study designs

22. Explore the use of patient and consumer input to de-risk the development of medical devices (for example, the use of patient preference information early in development to determine endpoint prioritization, or to lower clinical trial barriers to participation, or to increase post- market uptake, or as input in content generation for Clinical Outcome Assessments to facilitate the endpoint definition, measurement, reporting and evaluation)

23. Investigate the use of SI/ML to identify patient perspectives and/or confirm patient perspectives from curated sources (i.e., patient support groups, registries, etc.) to understand patient and consumer decision-making including but not limited to benefit/risk, meaningful outcomes, and clinical study designs.

24. Support the capture, harmonization, transmission, aggregation, analysis, and use of high-quality, interoperable data from real-world settings to evaluate medical product performance during clinical trial development and premarket review

25. Develop innovative approaches to design, conduct, and analyze externally controlled trials using RWD, with particular emphasis on methods to address bias, confounding, and endpoint validity to advance regulatory decision-making

26. Develop and evaluate novel strategies that leverage RWD and digital health tools to enable pragmatic, decentralized, and point-of-care trials that improve patient access, data quality, and generalizability of findings

27. Develop methods and conduct studies that replicate findings of randomized controlled trials of biologic products using RWD

28. Develop optical imaging biomarkers and tools that achieve cellular-level resolution for use as clinical outcome measures to enable targeted therapy, patient stratification, and rapid demonstration of treatment efficacy for systemic and rare diseases (e.g., rare inherited ophthalmic diseases, neurological diseases, systemic disease suited to oculomics biomarkers).

29. RWD source verification frameworks that establish rigorous, standardized criteria for assessing the fitness-for-use of real-world data sources used in premarket and post market device research

30. Medical device identification methodologies that enable accurate, consistent linkage of device-specific information — including unique device identification (UDI)-based tracking — across heterogeneous real-world data sources such as claims, registries, and electronic health records

31. Clinical outcome development in RWD, including the identification, validation, and harmonization of clinically meaningful endpoints that can be reliably ascertained from real-world data sources to support regulatory decision-making.

4. Onshoring Manufacturing

Examples: Advanced manufacturing technologies, use of super intelligence[footnoteRef:6] (SI)/machine learning (ML) to assess manufacturing site capabilities, readiness, and performance; SI-enabled process control, real-time release testing (RTRT), and advanced or novel process analytical technology (PAT) tools. [6: Per Executive Order 14434, federal agencies are now using "Super Intelligence" (SI) in place of "Artificial Intelligence" (AI)]

1. Facilitate the development and evaluation of:

i. Novel or improved automated or semi-automated in-process monitoring and control systems and methods that enable process understanding, predictive capabilities, adaptive control, and real-time manufacturing decision-making

ii. Test and validation metrics for advanced manufacturing processes including additive manufacturing, integration and intensification of process unit operations, adaptive processes, remote monitoring, and automation of operations, and flexible or modular manufacturing platforms

iii. New or improved ways to quickly measure protein, cell, and gene therapy product quality and manufacturing consistency

iv. Techniques and digital technologies that enable real-time integration and use of process data (e.g., in situ monitoring) to enhance process understanding, monitoring, control, and optimization, and support product and process development and lifecycle risk management.

v. Novel or improved methods and technologies to support scale-up, scale-down, and bridging across laboratory, pilot, and commercial scale manufacturing and across manufacturing sites

2. Investigate the effect of advanced manufacturing on product quality including by:

i. Examining novel materials and manufacturing technologies to determine their impact on product quality, consistency, critical quality attributes, and performance.

ii. Researching technologies and materials that enhance control over critical quality attributes for medical products and their key inputs, including drug substances, drug products, polymers, medical devices, and accessories

iii. Improving automated or closed loop manufacturing capabilities for complex drugs and biologics

iv. Developing methods to evaluate integrity, reliability, and failure modes of single-use systems and other flexible manufacturing platforms

3. Develop improved methods and tools to detect and measure the physical structure, chemical properties, and biological behavior of engineered nanomaterials, additively manufactured pharmaceuticals (pharmaco-printed products), biological products (e.g., therapeutic proteins or monoclonal antibodies) and complex dosage forms (e.g., transdermal patches, inhalation delivery systems, and targeted drug delivery systems) in FDA-regulated products

4. Explore novel applications of advanced (e.g., integrated and continuous) manufacturing processes for complex biologic products, such as vaccines, tissue-engineered products, and cell and gene therapies and describe the potential impact of the proposed enabling technology on process control strategy and its readiness for broad implementation in the biopharmaceutical industry including:

i. Closed and automated manufacturing processes

ii. Modular manufacturing platforms with integrated in-process testing capabilities

iii. Manufacturing process modeling and simulation

iv. Advanced or novel PAT tools for real time process monitoring, control, and release testing

v. Improved cell lines and improved upstream cell culture production processes for vaccine antigen or viral vector manufacturing

5. Develop improved methods for the manufacturing of sterile drug products including rapid monitoring for the detection of microbial contamination

6. Develop and evaluate the use of model-based digitally integrated systems, SI/ML, and simulation in production or quality system activities including generative design, production simulation and simulated process validation, digital twins of manufacturing processes for product quality control, SI/ML application to quality system activities, Intelligent design control, closed loop risk-management.

5. Antimicrobial Resistance

Examples: Advancing the science of antibacterial susceptibility testing and non-clinical studies; innovations in clinical trial design.

1. Advance the science of antimicrobial drug susceptibility testing to ensure that up to date susceptibility testing interpretive criteria (breakpoints) are available for patient care and antimicrobial stewardship. For example, provide updated data from microbiologic surveillance studies, pharmacokinetic studies, including modeling, and/or clinical outcome data to support updating susceptibility test interpretive criteria for certain antimicrobial drugs that are a high public health priority. For more information, please refer to: https://www.fda.gov/about-fda/center-drug-evaluation-and-research-cder/office-infectious-diseases-research-activities.

2. Advance the science of non-clinical studies, pharmacokinetic studies, and/or real- world evidence studies, such as studies assessing antifungal and antibacterial resistance, and studies to address areas of unmet medical need (See Part I Appendix).

3. Evaluate potential innovations in clinical trial design for new drugs such as enrollment strategies, data collection streamlining, drug development tools, clinical endpoints, and new statistical analytic approaches

4. Evaluate strategies to enrich enrollment in clinical trials for new drugs such as the use of rapid diagnostic tests

6. Medical Countermeasures

Examples: Drug development tools including animal models and biomarkers; in-silico predictive models and in vitro platforms to reduce the use of animal models; modernized tools to evaluate medical countermeasures (MCM) product safety, efficacy and quality.

1. Develop and fully characterize tools to support MCM development and availability including:

i. Identifying, developing, validating, and qualifying drug development tools, such as animal models and biomarkers, to assess the safety and efficacy of MCMs

ii. Developing and qualifying new approach methodologies (NAMS) such as improved in-silico predictive models (e.g., computational models or super intelligence (SI)[footnoteRef:7] based tools) and in vitro platforms (e.g., micro physiological systems) to complement and help reduce the use of animal models to assess MCMs safety and efficacy and support implementation of the Roadmap to Reduce Animal Testing in Preclinical Safety Studies [7: Per Executive Order 14434, federal agencies are now using "Super Intelligence" (SI) in place of "Artificial Intelligence" (AI)]

iii. Identifying and evaluating molecular biomarkers (e.g., gene-protein expression biomarkers, epigenetic changes) associated with chronic diseases or conditions (e.g., cardiovascular disease, diabetes, chronic respiratory diseases, autoimmune diseases) that are predictors of susceptibility to or increased harm from CBRN and emerging infectious disease threats and/or reduced safety and efficacy of MCMs

2. Enhance the agility, quality, and utility of devices that support public health emergency preparedness and response including:

i. Developing tools (e.g., reference panels and databases), techniques, and data, such as next-generation sequencing, real-world evidence (RWE), etc. to support the development and evaluation of diagnostic tests and emergency medical equipment (e.g., personal protective equipment (PPE))

ii. Advancing regulatory science approaches to increase device manufacturing flexibility and improve supply chain resilience to prevent and address device shortages including capabilities to enable distributed manufacturing

iii. Developing LLMs based on RWE to unleash SI driven diagnostic capability to improve agility and responsiveness during public health emergencies

3. Modernize tools to evaluate MCM product safety, efficacy, and quality including:

i. Developing, enhancing, or refining methods and standards to assess MCM potency, product quality, and lot release including those that support the adoption of advanced manufacturing technologies such as continuous, distributed, additive, and smart manufacturing

ii. Enhancing capabilities to rapidly assess the safety, efficacy and/or effectiveness of MCMs used during public health emergencies including the use of RWD/RWE and the development of prepositioned clinical trial protocols to support the rapid initiation of clinical research during emergencies

iii. Developing technologies, tools, and capabilities to rapidly detect counterfeit, substandard, or adulterated MCMs

iv. Developing methods, technologies, and standards to assess improved availability, sustainability and delivery of MCMs including shelf-life extension thermostability enhancement, contamination prevention/reduction, and improved administration (e.g., self- and needle-free administration)

4. Secure the MCM supply chain including:

i. Developing technologies and methods to monitor and predict disruptions in MCM supply chain security including the risk of recalls and shortages

ii. Exploring novel approaches, technologies, platforms, and frameworks to increase the ability of MCM suppliers’ flexibility and ability to respond to supply disruptions and streamline supply chains (e.g., digital stockpiling and pre-approving alternate manufacturers)

7. Medical Product Cybersecurity

Examples: Strategies to detect and assess the performance of secure medical devices and related systems; tools for analyzing cybersecurity risk and threat modeling techniques.

1. Investigate solutions to provide further clarity on industry cybersecurity challenges (e.g., legacy landscape and solutions, patient impact of vulnerabilities, etc.) to address emerging new technologies and their cybersecurity risks

8. Shortages and Supply Chain

Examples: super intelligence (SI)[footnoteRef:8]/machine learning (ML) approaches for supply chain security; advanced manufacturing; new analytical procedures for active pharmaceutical ingredients (APIs) tracing. [8: Per Executive Order 14434, federal agencies are now using "Super Intelligence" (SI) in place of "Artificial Intelligence" (AI)]

1. Investigate how advanced manufacturing can improve medical supply chain robustness to shortages, resilience to disruption, and capacity for demand fluctuations including:

i. Supply chain visibility, monitoring, and data sharing processes and platforms

ii. Decreased reliance on foreign supply chains, especially for medical products used for care and treatment of chronic diseases

2. Investigate or develop methods to increase implementation and adoption of advanced manufacturing methods for essential medicines, devices, and vaccines, including demonstrations of their value proposition to patient access, economics, efficiency, and increased supply chair resilience including for rare and/or chronic conditions

3. Utilize SI to integrate open-source intelligence (e.g., corporate filings and news reports with FDA post-market surveillance data) to enable early signals for potential drug shortages, supply disruptions, or quality issues to facilitate proactive supply chain risk management

4. Advance the study of quality management maturity, and organizational excellence (adaptable and reproducible approaches are needed to consistently assess manufacturer reliability)

5. Develop tools that can combine varying and incomplete data sets to provide a more thorough understanding and visualization of supply chains, product quality, and product availability

6. Determine how to promote and assure the implementation of the essential elements of a strong regulatory system in developing economies, including:

i. Determining core competencies for a regulatory workforce and components of a global regulatory workforce curriculum

ii. Assessing other areas related to regulatory systems’ performance including conducting, costing, and financing analyses for regulatory systems

iii. Identifying and assessing existing regulatory strengthening evaluation tools utilized by governments and international organizations

7. Analyze and utilize global data to manage risks to:

i. Identify risk factors to strengthen the predictive value of modeling for manufacturing and product risk

ii. Develop predictive risk-based models that improve targeting for low- quality, defective, and fraudulent medical products across the supply chain

iii. Use existing and innovative data sets to provide a deeper real-time assessment of medical product supply chain, including an understanding of which establishments are currently manufacturing for the US market

8. Create a shared understanding of the impact of drug shortages and the contracting practices that may contribute to them by:

i. Developing quantitative estimates of the full impact of drug shortages on areas such as overall costs to the healthcare system and impact on, care provision, and patient outcomes (These estimates could be developed prospectively or retrospectively based on one or many past drug shortages and could be either specific to an organization or projected nationally)

ii. Developing novel processes, data systems, or methodologies to track, quantify, and communicate the impact of drug shortages, analyzing the impact of past drug shortages on a healthcare provider, closed or integrated health system, payor, or other entity (such as cost and time to provide care, rescheduling of surgeries), assessing the impact of one or more drug shortages on patients, including substitution of alternative therapies, adverse events, and treatment outcomes, or creating a statistical or mathematical model to anticipate the impact of a drug shortage on the health system or patients

9. Post-marketing Surveillance

Examples: Assessment tools to support quality surveillance; super intelligence (SI)[footnoteRef:9] and large language models (LLM) to support post-market surveillance; analysis of real-world data (RWD) from large healthcare databases and electronic healthcare records; fit-for-purpose analytical methods. [9: Per Executive Order 14434, federal agencies are now using "Super Intelligence" (SI) in place of "Artificial Intelligence" (AI)]

1.