Regulatory spotlight
We offer selected excerpts from relevant guidances and other FDA materials below, to help you get oriented and understand their significance. It is your responsibility to fully examine and interrogate FDA guidances in detail. Click through on individual resource links to be taken to the primary source material.
Remote data acquisition
Digital Health Technologies for Remote Data Acquisition in Clinical Investigations
There is a need for comprehensive validation and verification processes for DHTs.
Ensuring data security and privacy is a significant concern.
Usability issues for diverse populations need to be addressed.
There is a lack of clarity on whether certain DHTs meet the definition of a device under the FD&C Act.
The guidance does not establish legally enforceable responsibilities.
Recommendations
Ensure DHTs are fit-for-purpose for clinical investigations.
Implement robust data security measures to protect participant information.
Conduct usability evaluations to ensure DHTs can be used by intended populations.
Engage with FDA early to discuss the use of DHTs in clinical investigations.
Develop a risk management plan to address potential issues with DHT use.
Regulatory Considerations
Verification and validation should be addressed regardless of device classification.
Sponsors should ensure compliance with data protection and privacy regulations.
FDA evaluates DHT data based on endpoints, medical products, and patient populations. Sponsors can engage with FDA’s Q-Submission Program for feedback on DHT usage in clinical trials.
Sponsors should understand the legal implications of using DHTs in clinical investigations.
The guidance provides recommendations but does not establish legally enforceable responsibilities.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
“Context of use: A statement that fully and clearly describes the way the medical product development tool is to be used and the regulated product development and review-related purpose of the use.”
– Glossary, p. 27, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA).
“A precise definition of an endpoint typically specifies the type of assessment(s) made (e.g., activity level, average heart rate, sleep quantity and quality), the timing of those assessments, the tool(s) used for the assessment(s), and other details, as applicable, such as if (and if so, how) multiple assessments for a trial participant will be combined.”
– Section IV.D.1 (Defining the Endpoint), p. 15, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA).
“Verification and validation activities should consider all relevant functions of the DHT in the context of use in the clinical investigation.”
– Section IV.C (Verification, Validation, and Usability Evaluations of Digital Health Technologies), p. 12, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA).
“Depending on the DHT and its context of use, verification and validation may begin with benchtop studies, progress to testing in healthy volunteers, and continue in individuals representing the population to be studied in the clinical investigation. These studies should include demonstration that the clinical event or characteristic to be assessed (e.g., step count or heart rate) is consistently and appropriately measured in the population of interest.”
– Section IV.C (Verification, Validation, and Usability Evaluations of Digital Health Technologies), p. 12, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA).
“The same method for collection of data should be used in all study arms… The definition of the endpoints and the source data from which the endpoints are derived… should be prespecified in the statistical analysis plan… Use of a DHT to remotely acquire data… may impact the type and amount of missing data. Sponsors should have a plan in place to reduce the potential for missing data… and to address missing data and data quality issues.”
– Section IV.E (Statistical Analysis and Trial Design Considerations), p. 17–18, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA).
“Sponsors should provide detailed descriptions of the DHT and the context of use in their submission.”
– Appendix B (Example of Selecting a Digital Health Technology for a Clinical Investigation and Justifying the Endpoint for Which It Is Used), p. 30, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA).
Considerations for the use of AI
Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products, Draft, 2025 (FDA)
The document introduces a risk-based credibility assessment framework for establishing and evaluating the credibility of an Artificial Intelligence (AI) model’s output when used to support regulatory decisions regarding drug safety, effectiveness, or quality. The framework outlines a 7-step process beginning with defining the question of interest and the Context of Use (COU). Credibility is defined as trust, established through evidence, in the AI model’s performance for a particular COU. The credibility assessment is tailored to the AI model risk, which is a combination of model influence (the AI model’s evidence contribution relative to other evidence) and decision consequence (the significance of an adverse outcome from an incorrect decision). The document highlights challenges with AI use, including variability in development datasets (training/tuning), the need for methodological transparency due to model complexity, difficulty in quantifying and interpreting uncertainty in model output, and the potential for performance change over time (data drift), which necessitates life cycle maintenance.
Recommendations
Sponsors and interested parties should define the question of interest and clearly define the COU, detailing the AI model’s specific role and scope and whether other information will be used. They should assess the AI model risk (low, medium, or high) to ensure that subsequent credibility assessment activities (Step 4) are commensurate with that risk and tailored to the COU. For Step 4, the credibility assessment plan should include a description of the model, model development process (including inputs, architecture, feature selection, and rationale), and data used (training and tuning data). Development data must be deemed fit for use (relevant and reliable) to mitigate issues like algorithmic bias. The plan should also detail the model evaluation process using independent test data and include performance metrics with confidence intervals, an estimate of uncertainty, and a description of model limitations. Early engagement with the FDA is strongly encouraged to discuss model risk and the adequacy of the credibility assessment plan.
Regulatory Considerations
The risk-based credibility assessment framework is intended to help organize and document information for regulatory submissions. The required stringency of assessment activities and the level of documentation should be commensurate with the AI model risk. For AI models whose performance can change over time (e.g., in pharmaceutical manufacturing or postmarketing), sponsors must implement life cycle maintenance plans to monitor performance and manage changes in a risk-based manner. Changes to AI models should be evaluated through the manufacturer’s change management system and may require re-execution of parts of the credibility assessment plan. Early engagement can be facilitated through formal meetings (e.g., Pre-IND) or other specialized programs listed in the guidance, such as the Center for Clinical Trial Innovation (C3TI), the Model-Informed Drug Development (MIDD) Paired Meeting Program, and the Emerging Technology Program (ETP) or Advanced Technologies Team (CATT).
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
“Whether, when, and where the plan will be submitted to FDA depends on how the sponsor engages with the Agency, and on the AI model and COU. For example, the plan could be described in a formal meeting package, or another appropriate engagement option (see section IV.C below). The risk-based credibility assessment framework envisions interactive feedback from FDA concerning the assessment of the AI model risk (step 3) as well as the adequacy of the credibility assessment plan (step 4) based on the model risk and the COU. Accordingly, FDA strongly encourages sponsors and other interested parties to engage early with FDA to discuss the AI model risk, the appropriate credibility assessment activities for the proposed model based on model risk and the COU. Although detailed information on all the credibility assessment activities described in subsections 4.a and 4.b may not be available or necessary to include at the time of early engagement with FDA, the proposed credibility assessment plan about which the sponsor engages with the Agency should, at a minimum, include the information described in steps 1, 2, and 3 (i.e., question of interest, COU, and model risk) and the proposed credibility assessment activities the sponsor plans to undertake based on the results of those steps.”
– Section IV.A.4 (Develop a Plan to Establish AI Model Credibility Within the Context of Use), p. 10, Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products, Draft, 2025 (FDA)
“This step involves executing the credibility assessment plan. As discussed in step 4, discussing the plan with FDA prior to execution may help (1) set expectations regarding the appropriate credibility assessment activities for the proposed model based on model risk and COU and (2) identify potential challenges and how such challenges can be addressed.”
– Section IV.A.5 (Execute the Plan), p. 15, Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products, Draft, 2025 (FDA)
“The results of the credibility assessment plan should be included in a report. For the purposes of this guidance, this report is referred to as a credibility assessment report. The credibility assessment report is intended to provide information that establishes the credibility of the AI model for the COU and should describe any deviations from the credibility assessment plan as outlined in step 4. During early consultation with FDA (described in step 4), the sponsor should discuss with FDA whether, when, and where to submit the credibility assessment report to the Agency. The credibility assessment report may, as applicable, be (1) a self-contained document included as part of a regulatory submission or in a meeting package, depending on the engagement option, or (2) held and made available to FDA on request (e.g., during an inspection). Submission of the credibility assessment report should be discussed with FDA.”
– Section IV.A.6 (Document the Results of the Credibility Assessment Plan and Discuss Deviations From the Plan), p. 15–16, Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products, Draft, 2025 (FDA)
“As noted previously, FDA strongly encourages sponsors and other interested parties to engage early with FDA to (1) set expectations regarding the appropriate credibility assessment activities for the proposed model based on model risk and COU and (2) help identify potential challenges and how such challenges may be addressed.”
– Section IV.C (Early Engagement), p. 17, Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products, Draft, 2025 (FDA)
Q-Submission Program
Requests for Feedback and Meetings for Medical Device Submissions: The Q-Submission Program
Pre-Submissions (Pre-Subs) allow submitters to obtain FDA feedback on specific questions before submitting formal IDEs, 510(k)s, PMAs, or other applications. Early feedback can improve submission quality and streamline the review process.
Submission Issue Requests (SIRs) provide a mechanism for addressing issues raised in FDA hold letters (e.g., 510(k) deficiencies) to help expedite resolutions.
Study Risk Determinations help sponsors clarify whether clinical studies are significant risk (SR), non-significant risk (NSR), or exempt from IDE regulations.
Informational Meetings are non-feedback sessions aimed at familiarizing FDA staff with new devices or sharing updates on ongoing development.
The program encourages timely submissions, including supplements for ongoing discussions and amendments to update materials.
Recommendations
Clearly define the purpose and goals of the Q-Sub in the submission to facilitate effective FDA review.
Include specific, well-formulated questions that focus on a limited number of topics to ensure actionable feedback.
For Pre-Subs, align planned testing and submissions with FDA guidance and include detailed device descriptions, testing protocols, and relevant background information.
Use SIRs to discuss proposed solutions to deficiencies raised in FDA hold letters, focusing on timely resolution.
Draft and submit meeting minutes promptly (within 15 days of meetings) to ensure accurate documentation of FDA feedback.
Regulatory Considerations
Submitters should adhere to the timelines specified for different Q-Sub types, including 70 days for Pre-Sub feedback or 21 days for SIRs submitted promptly after a hold letter.
Q-Subs should include all relevant regulatory history and references to prior FDA communications to streamline the review process.
FDA feedback through the Q-Sub program is non-binding and based on the information available at the time; subsequent submissions must align with the provided feedback to maintain consistency.
Informational Meeting requests should clearly state that feedback is not expected and may be used to track interactions outside other formal Q-Sub types.
Confidentiality of Q-Subs is maintained in compliance with FDA’s disclosure regulations and the Freedom of Information Act (FOIA).
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
“Clinical Study Questions
Are the primary and secondary endpoint analyses appropriate for the proposed Indications for Use?”
– Appendix 2 (Example Pre-Sub Questions – Clinical Study Questions), p. 32, Requests for Feedback and Meetings for Medical Device Submissions: The Q-Submission Program, Final, May 29, 2025 (FDA).
Formal meetings with the FDA
Formal Meetings Between the FDA and Sponsors or Applicants of PDUFA Products Guidance for Industry
The guidance establishes a predictable and efficient framework for formal interactions between the FDA and sponsors. Its core principle is that timely, high-quality communication is critical to a streamlined drug development process. The document clarifies that different stages of development require different types of meetings (e.g., Type A, B, and C), each with specific timelines and objectives. A key principle is that productive meetings depend on the sponsor providing a comprehensive meeting package in advance, allowing the FDA to prepare and provide substantive feedback.
Recommendations for Sponsors
Sponsors are strongly recommended to engage with the FDA early and throughout the drug development process. To ensure a productive meeting, sponsors should clearly articulate the purpose of the meeting, provide specific questions, and submit a well-organized and complete meeting package by the specified deadline. It is recommended that sponsors carefully consider the type of meeting that is most appropriate for their stage of development and the nature of the questions they have. Following the meeting, sponsors should adhere to the timelines and procedures for submitting meeting minutes for the official record.
Regulatory Considerations
This guidance is a key component of the regulatory framework under the Prescription Drug User Fee Act (PDUFA). Adherence to the procedures outlined in this document is a matter of regulatory compliance. The formal meetings described are a critical part of the Investigational New Drug (IND) and Marketing Authorization Application processes. The meeting process is designed to provide regulatory clarity, reduce the risk of clinical holds or refuse-to-file actions, and ultimately support a more efficient and predictable path to drug approval. The written record of these meetings serves as an important part of the administrative file for a product’s development program.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
“A Type C meeting is any meeting other than a Type A, Type B, Type B (EOP), Type D, or INTERACT meeting regarding the development and review of a product, including meetings to facilitate early consultations on the use of a biomarker as a new surrogate endpoint that has never been previously used as the primary basis for product approval in the proposed context of use.”
– Section III.D (Type C Meeting), p. 4, Formal Meetings Between the FDA and Sponsors or Applicants of PDUFA Products, Draft Guidance, Procedural, Revision 1, September 2023 (FDA)
“The trial endpoints should be stated, as should whether endpoints were altered or analyses changed during the course of the trial.”
– Section VII.C (Meeting Package Content), p. 14, Formal Meetings Between the FDA and Sponsors or Applicants of PDUFA Products, Draft Guidance, Procedural, Revision 1, September 2023 (FDA)
“Known difficult design and questions about providing substantial evidence of effectiveness should be raised for discussion (e.g., use of a surrogate endpoint, reliance on a single study, use of a noninferiority design, adaptive designs).”
– Section VII.C (Meeting Package Content), p. 13–14, Formal Meetings Between the FDA and Sponsors or Applicants of PDUFA Products, Draft Guidance, Procedural, Revision 1, September 2023 (FDA)
PRO guidance
Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims
PRO instruments must demonstrate content validity through patient input and qualitative research, ensuring the instrument measures concepts relevant to the population and condition being studied.
Sponsors must confirm the reliability, construct validity, and ability to detect change for the PRO instrument before use in confirmatory clinical trials.
Statistical analysis plans should address multiplicity, handling of missing data, and cumulative distribution function comparisons to interpret clinical trial results.
Modifications to PRO instruments (e.g., format changes, population adaptations) require evidence that measurement properties are preserved.
Electronic PRO systems must comply with regulatory requirements for data integrity, security, and investigator access.
Recommendations
Develop and validate PRO instruments early in the clinical development process, ensuring alignment with the clinical trial’s endpoint model.
Document all stages of instrument development, including qualitative input from patients, pilot testing, and cognitive interviews.
Use clear and consistent administration procedures, whether paper-based or electronic, to minimize variability and missing data.
Define responder thresholds using anchor-based methods and consider presenting cumulative distribution functions to interpret treatment benefits.
Address cultural and linguistic adaptation of PRO instruments by ensuring equivalent content validity and measurement properties across versions.
Regulatory Considerations
Include detailed descriptions of the PRO instrument, its conceptual framework, and scoring algorithms in regulatory submissions.
Ensure PRO instruments used in clinical trials comply with FDA requirements for record-keeping, data security, and source data accessibility.
Plan for the FDA to review all modifications to PRO instruments, including changes in administration mode or population.
Address missing data in clinical trial protocols and statistical analysis plans, ensuring prespecified handling rules.
Provide evidence that PRO instruments reliably measure the intended concepts across all study populations and data collection methods.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
“Sponsors should define the role a PRO endpoint is intended to play in the clinical trial (i.e., a primary, key secondary, or exploratory endpoint) so that the instrument development and performance can be reviewed in the context of the intended role, and appropriate statistical methods can be planned and applied. It is critical to plan these approaches in what can be called an endpoint model.”
– Section III (Evaluation of a PRO Instrument), p. 3, Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims, Final, 2009 (FDA)
PFDD 1: Comprehensive and representative input
Patient-Focused Drug Development: Collecting Comprehensive and Representative Input
Patient experience data encompass a range of inputs, including symptom burdens, treatment impacts, patient preferences, and views on unmet medical needs.
These data inform all stages of medical product development, from discovery to post-market use.
Quantitative methods (e.g., surveys) provide numerical insights, while qualitative methods (e.g., interviews) offer in-depth understanding. Mixed methods combine both for a fuller perspective.Social media and verified patient communities present novel data collection opportunities but require consideration of verification and representativeness challenges.
Probability sampling (e.g., stratified random sampling) is emphasized for generalizability, while non-probability methods (e.g., convenience sampling) are useful for exploratory research. Representativeness ensures that patient input reflects the diversity and heterogeneity of the target population.
Data collection should adhere to good clinical practices and regulatory standards.
Research protocols should address missing data, quality assurance, and confidentiality.
Early collaboration with the FDA is recommended to align on study designs and regulatory requirements.
Recommendations
Define clear research objectives and determine specific research questions before selecting data collection methods.
Use probability sampling methods whenever feasible to ensure representativeness of the target population.
Address data quality through rigorous planning, data management, and adherence to FDA-supported standards.
Incorporate diverse perspectives by including underrepresented patient populations, tailoring methods to specific subgroups as needed.
Leverage existing data sources, such as patient registries and literature, to complement primary data collection efforts.
Regulatory Considerations
Data submitted to FDA should include clear documentation of the study protocol, intended use, and data collection methodologies.
Researchers must comply with human subject protection regulations (e.g., 21 CFR Parts 50 and 56) and good clinical practice guidelines.
For data intended to support regulatory submissions, adherence to FDA-supported data standards (e.g., CDISC) is strongly encouraged.
Missing data should be addressed through pre-planned strategies and summarized in the study report.
Patient experience data must meet methodological rigor to ensure their reliability and relevance for regulatory decision-making.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
“Clinical Relevance: The extent to which an endpoint can capture and measure an aspect of a potential clinical benefit (improvement in how the patient feels, functions, or survives) or other change in health status that is clinically meaningful.”
– Appendix 2 (Glossary), p. 36, Patient-Focused Drug Development: Collecting Comprehensive and Representative Input, Final, 2020 (FDA)
PFDD 3: Fit-for-purpose clinical outcome assessments (COAs)
Patient-Focused Drug Development: Selecting, Developing, or Modifying Fit-for-Purpose Clinical Outcome Assessments
The guidance applies to four types of Clinical Outcome Assessments (COAs): Patient-Reported Outcomes (PROs), Observer-Reported Outcomes (ObsROs), Clinician-Reported Outcomes (ClinROs), and Performance Outcomes (PerfOs). A COA is considered fit-for-purpose when the validation evidence is sufficient to support its context of use (COU). To determine if a COA is fit-for-purpose, sponsors must clearly describe the Concept of Interest (COI) and the COU, and present sufficient evidence to support a clear rationale for the COA’s proposed interpretation and use. The rationale for using a COA should include up to eight components, such as justification for the COA type, capturing the important parts of the COI, appropriate administration and scoring, minimal influence from irrelevant factors or measurement error, and correspondence with the Meaningful Aspect of Health (MAH). The most direct assessment of how a patient feels or functions (MAH) should be used as the COI whenever possible.
Recommendations
Sponsors should use the Roadmap to Patient-Focused Outcome Measurement to guide the selection, modification, or development of a COA. The process begins with understanding the disease/condition (including patient perspectives) and conceptualizing clinical benefits and risks (defining the MAH, COI, and COU). When feasible, existing COAs are generally preferred, especially for well-established COIs, as this approach is often the least burdensome. If an existing COA is modified or used in a different context, additional evidence (e.g., cognitive interviews, psychometric studies) must be collected to justify its fitness for the new context of use. For new COA development, sponsors should involve patients, document all steps, and generally avoid using the new COA for the first time in a registration (pivotal) trial; a standalone observational study or early phase trial is recommended for evaluation.
Regulatory Considerations
Sponsors are encouraged to interact early and throughout medical product development with the relevant FDA review division to ensure COAs are appropriate for the intended COU. Sponsors should communicate their proposed COA-based endpoint approach, including the MAH, COI, COA type/name/score, and the final COA-based endpoint, ideally using the suggested format. The type and amount of evidence required to support the rationale for a COA’s use is weighed against the degree of uncertainty regarding that part of the rationale. For ClinROs, it is recommended to use an assessor masked to treatment assignment and study visit for primary endpoints, if feasible. FDA strongly discourages proxy-reported measures for concepts known only to the patient (e.g., pain) and recommends using an ObsRO to measure observable behaviors instead when the patient cannot self-report.
Recommendations
Clearly define the concept of interest and its context of use to ensure COAs align with trial objectives.
Use conceptual and measurement frameworks to communicate how COAs measure patient experiences and generate interpretable scores.
Leverage existing COAs where possible, modifying them only when justified, and document all modifications rigorously.
Ensure COAs are accessible and inclusive, incorporating features like large fonts, touch interfaces, or audio assistance for diverse populations.
Conduct early engagement with FDA to discuss COA selection, development, and validation plans.
Regulatory Considerations
Fit-for-purpose validation requires evidence of conceptual alignment, scoring reliability, and sensitivity to clinically meaningful changes.
Digital health technologies used for COAs must comply with FDA’s guidance on data integrity, usability, and technical performance.
COAs intended for regulatory submissions must be developed and validated before pivotal trials to avoid jeopardizing trial outcomes.
Modifications to COAs or scoring methods during trials necessitate justification and revalidation.
Sponsors should submit comprehensive documentation on COA development, including scoring algorithms and item tracking matrices.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
“A COA is a measure that is intended to describe or reflect how a patient feels or functions. COA scores can be used to support effectiveness, dose optimization, safety, and tolerability in the context of a clinical trial to determine the clinical benefit(s) and risks(s) of a medical product. There are four types of COAs and choosing which type(s) of COA to use is driven by the concept(s) of interest to be measured, the best source of that measurement (e.g., self-report, clinician report/rating), and the context in which it will be applied (the context of use). More than one type of COA can be used in a clinical trial to capture the patient experience and the status of the patient’s disease or condition.”
– Section II.A (Types of COAs), p. 5, Patient-Focused Drug Development: Selecting, Developing, or Modifying Fit-for-Purpose Clinical Outcome Assessments, Draft, 2022 (FDA)
“The context of use should clearly specify the way COA scores will be used as the basis for an endpoint intended to reflect a specific MAH. The appropriateness of a COA is evaluated within the proposed context of use. During the course of a development program, some elements of the context of use will be established early on, such as the target population, and others (e.g., trial design, timing of assessments) might evolve, for example, through discussions with FDA and/or as different COAs are considered.”
– Section II.B.2 (The Context of Use), p. 8, Patient-Focused Drug Development: Selecting, Developing, or Modifying Fit-for-Purpose Clinical Outcome Assessments, Draft, 2022 (FDA)
“Context of use considerations may include the following:
– Target Population: Including a definition of the disease or condition; participant selection criteria for clinical trials (e.g., baseline symptom severity, comorbidities, patient demographics and cultures); and expected patient experiences or events during the trial (e.g., that some patients will require assistive devices).
– Use of the COA: Clinical trial objectives and how the COA will be used to support a COA-based endpoint intended to reflect a specific MAH (e.g., computing the mean COA score at 12 weeks).
– COA Implementation: Including the location where the COA is collected (e.g., inpatient hospital, outpatient clinic, home); how the COA will be collected (i.e., mode of administration, such as electronic data capture, paper form); and by whom (e.g., patient, study coordinator, investigator, parent/caregiver).”
– Section II.B.2 (The Context of Use), p. 8, Patient-Focused Drug Development: Selecting, Developing, or Modifying Fit-for-Purpose Clinical Outcome Assessments, Draft, 2022 (FDA)
“Clinical benefit is defined as “a positive effect on how an individual feels, functions, or survives”. To provide clinical benefit, a medical product should affect a meaningful aspect of health (MAH), i.e., some aspect of feeling or functioning in daily life that is important to patients (Walton et al. 2015). In a clinical trial, we study the effect of a medical product on a MAH by estimating a treatment effect on an endpoint that is thought to reflect the MAH. To precisely describe the role of a COA in a clinical study, sponsors should propose to FDA how they intend to interpret scores from a COA (i.e., what they believe the score measures), how scores will be used to reflect the MAH (e.g., to construct an endpoint), and the context in which scores will be used. In other words, the sponsor’s proposal should explicitly reference the MAH, the concept of interest (COI; see section II.B.2) and the context of use (COU; see section II.B.3). For COAs with multiple domains and related scores, the domain of interest (and particular score) should be clearly stated. Having established the COA measures something meaningful, the construction of the COA-based endpoint should preserve the meaningfulness. Using COAs to construct trial endpoints is discussed in PFDD Guidance 4, Incorporating Clinical Outcome Assessments Into Endpoints for Regulatory Decision-Making (April 2023).”
– Section II.B (The Role of COAs in Evaluating Clinical Benefit for a Medical Product), p. 7, Patient-Focused Drug Development: Selecting, Developing, or Modifying Fit-for-Purpose Clinical Outcome Assessments, Draft, 2022 (FDA)
“FDA recommends conducting a search to identify whether a COA already exists that measures the concept of interest in the intended context of use and is available for use. Existing COA measures for which there is already experience in the relevant context of use are generally preferred, particularly when measuring well-established concepts of interest (e.g., pain intensity). Sponsors can identify potential measures by searching the scientific literature; repositories of measures, including item banks26 comprising previously developed and tested items; and clinicaltrials.gov, summaries of prior FDA decisions, and other resources (FDA COA Qualification Program; FDA Medical Device Development Tools [MDDT]). Ultimately, Sponsors should ensure that there is sufficient evidence to support the use of such COAs within the intended context of use in the planned clinical trial. Sufficient evidence may vary where there is residual regulatory uncertainty such as in the context of a rare disease.”
– Section III.C (Selecting/Developing the Outcome Measure), p. 13, Patient-Focused Drug Development: Selecting, Developing, or Modifying Fit-for-Purpose Clinical Outcome Assessments, Draft, 2022 (FDA)
“A COA is considered fit-for-purpose when “the level of validation associated with a medical product development tool is sufficient to support its context of use”. Whether a COA is fit-for-purpose is determined by the strength of the evidence in support of interpreting the COA scores as reflecting the concept of interest within the context of use. It is expected that both qualitative and quantitative sources of evidence may be needed to support a determination that a COA is fit-for-purpose.”
– Section II.C (Deciding Whether a COA Is Fit-for-Purpose), p. 9, Patient-Focused Drug Development: Selecting, Developing, or Modifying Fit-for-Purpose Clinical Outcome Assessments, Draft, 2022 (FDA)
PFDD 4: Incorporating COAs into endpoints
Patient-Focused Drug Development: Incorporating Clinical Outcome Assessments Into Endpoints for Regulatory Decision-Making
COA-based endpoints should reflect meaningful patient health aspects and support clear treatment effect inferences.
Selection of endpoints requires a well-supported rationale, including evidence of their importance to patients.
Use of MSD and MSR approaches enhances the interpretation of treatment effects by linking COA scores to meaningful patient experiences. Proper anchors (e.g., global impression of severity) are essential for validating these approaches.
Frequency and timing of COA data collection must align with disease characteristics and study objectives.
Adjustments for potential practice effects and assistive device use are critical for robust outcome measurement.
Proper handling of missing data and sensitivity analyses ensure valid conclusions from COA-based endpoints.
Continuous, ordinal, and dichotomized endpoints require tailored statistical methods for analysis.
Early engagement with the FDA is crucial for aligning study designs and COA approaches with regulatory expectations.
Recommendations
Engage patients and caregivers early to identify meaningful endpoints and assess potential barriers to COA use.
Use anchor-based methods to validate COA scores and define meaningful thresholds for interpretation.
Develop and pilot test study protocols to ensure COA reliability, usability, and alignment with regulatory requirements.
Implement strategies to reduce participant burden, such as concise COA instruments and patient-friendly data collection methods.
Submit comprehensive documentation, including endpoint justification and scoring rationale, to FDA for feedback before trial initiation.
Regulatory Considerations
Endpoints must be supported by evidence of their fit-for-purpose status and alignment with the trial’s objectives.
COAs used in digital or adaptive formats must meet FDA’s standards for usability and data integrity.
Trials with nonrandomized designs require robust measures to mitigate bias in COA-based endpoint analysis.
Thresholds for MSD or MSR must be prespecified and justified with empirical evidence.
Sponsors must follow FDA guidance for submitting COA-based data, ensuring compliance with electronic data standards.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
“This guidance focuses on COA issues associated with clinical trial (study) endpoints, design, conduct, and analysis and will be of most relevance for those designing and conducting trials using COAs as well as analyzing and interpreting the trial data. This guidance builds on Guidance 3 by focusing on endpoints constructed from fit-for-purpose COAs which are intended to reflect, directly or indirectly, how patients feel, function, or survive. Some COAs provide direct insight on how patients feel or function (e.g., a patient-reported outcome (PRO) instrument measuring pain intensity). Other COAs, however, may provide more indirect information to evaluate clinical benefit (e.g., clinician-reported outcome (ClinRO) instruments measuring extent or activity of disease such as psoriasis area and severity). In these situations, it is important to understand how the COA-based endpoint corresponds to changes relevant to patients (e.g., the type and extent of change that is meaningful to patients).”
– Section I.B (Purpose and scope), p. 3, Patient-Focused Drug Development: Incorporating Clinical Outcome Assessments Into Endpoints for Regulatory Decision-Making, Draft, 2023 (FDA)
“Generally, endpoints that are based on COAs should (1) reflect an aspect of the patient’s health that is meaningful; and (2) be capable of supporting an inference of treatment effect within the context of the planned clinical trial.”
– Section II.A.1 (Selecting and Justifying Endpoints), p. 4, Patient-Focused Drug Development: Incorporating Clinical Outcome Assessments Into Endpoints for Regulatory Decision-Making, Draft, 2023 (FDA)
“An endpoint’s use in another trial evaluating a different product may not be adequate support for the use of the same endpoint for a trial under consideration, because the context of use can vary in important ways from trial to trial and science and/or policy might have evolved since the endpoint was last used.”
– Section II.A.1 (Selecting and Justifying Endpoints), p. 6, Patient-Focused Drug Development: Incorporating Clinical Outcome Assessments Into Endpoints for Regulatory Decision-Making, Draft, 2023 (FDA)
“A COA is considered fit-for-purpose when the level of validation is sufficient to support its context of use. Note that having a fit-for-purpose COA is necessary for a strong endpoint rationale, but it is not sufficient. For example, a COA that is considered fit-for-purpose for assessing symptom intensity might be used for an endpoint based on the average symptom intensity score across 7 days. However, if worst intensity were identified as the most relevant patient experience for improvement based on patient input and the product’s mechanism of action, the rationale for using an endpoint of average symptom intensity would be very weak—despite being based on a fit-for-purpose COA.”
– Section I.B (Purpose and scope), p. 3, Patient-Focused Drug Development: Incorporating Clinical Outcome Assessments Into Endpoints for Regulatory Decision-Making, Draft, 2023 (FDA)
“Sponsors should first review any existing evidence in support of the interpretability of the COA scores used to construct the endpoints. If the body of evidence supporting the interpretability of COA scores (e.g., from existing literature) is not sufficient, FDA recommends conducting empirical studies to support interpretability of COA scores prior to conducting a registration trial.”
– Section III.B (Approaches for Collecting Evidence to Support Interpretability of COA-Based Endpoints), p. 20, Patient-Focused Drug Development: Incorporating Clinical Outcome Assessments Into Endpoints for Regulatory Decision-Making, Draft, 2023 (FDA)
Convergence of DHTs and biomarkers
Digital biomarkers: Convergence of digital health technologies and biomarkers
Digital biomarkers should align with the FDA-NIH definition of biomarkers as indicators of biological processes, avoiding conflation with COAs, which measure patient-reported or observed outcomes.
Digital biomarkers can consolidate data from multiple DHTs to derive context-rich health indicators, enhancing population baselines and patient-specific insights.
Applications include detecting atrial fibrillation via wearable sensors, monitoring tremors in Parkinson’s patients, and assessing gait in Huntington’s disease, each emphasizing specific biomarker categories (e.g., diagnostic or monitoring).
Inconsistent use of the term “digital biomarker” may impede communication between developers and regulators, complicating evidence requirements for medical product evaluation.
External factors, such as pollen counts for asthma or UV exposure for photosensitivity, can complement digital biomarkers, offering comprehensive health insights.
Recommendations
Standardize the term “digital biomarker” within the healthcare and regulatory communities to improve consistency in research and medical product evaluations.
Foster collaboration across the healthcare ecosystem to ensure DHTs are integrated effectively into clinical workflows and regulatory frameworks.
Explore opportunities to combine digital biomarkers with environmental data to enhance predictive and preventative healthcare applications.
Encourage ongoing validation of digital biomarkers through robust analytical and clinical studies to build confidence in their utility and regulatory acceptance.
Incorporate patient-centric design principles into DHTs to ensure usability and relevance across diverse patient populations.
Regulatory Considerations
Align digital biomarker definitions with FDA guidance to ensure clarity in regulatory submissions and evaluations.
Validate digital biomarkers with evidence that demonstrates analytical validity, clinical validity, and clinical utility for their intended use.
Include considerations for patient privacy and data security, especially when integrating external environmental data into digital biomarker systems.
Develop frameworks for evidence generation that address both individual patient and population-level health insights, enabling broad regulatory and clinical applications.
Establish clear pathways for incorporating digital biomarkers into the regulatory review process, including guidance on how to demonstrate reliability and relevance.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
“Increasing digitization across the healthcare continuum has revolutionized medical research, diagnostics, and therapeutics. This digitization has led to rapid advancements in the development and adoption of Digital Health Technologies (DHT) by the healthcare ecosystem. With the proliferation of DHTs, the term ‘digital biomarker’ has been increasingly used to describe a broad array of measurements. Our objectives are to align the meaning of ‘digital biomarker’ with established biomarker terminology and to highlight opportunities to enable consistency in evidence generation and evaluation, improving the assessment of scientific evidence for future digital biomarkers.”
– Abstract, Vasudevan S, Saha A, Tarver ME, Patel B. Digital biomarkers: Convergence of digital health technologies and biomarkers. NPJ Digit Med. 2022 Mar 25;5(1):36.
“There are currently multiple definitions of the term digital biomarker reported in the scientific literature, and some seem to conflate established definitions of a biomarker and a clinical outcomes assessment (COA). Biomarkers and clinical outcome assessments measure different concepts and both could be useful in understanding the impact of a condition on patients. For example, an investigational product used to treat patients with heart failure could be assessed by measuring a biomarker of the heart’s output (left ventricular ejection fraction) as well as through a COA, a subjective measure of how the patient feels (the Kansas City Cardiomyopathy Questionnaire). Conflating the terms can hamper communication and evidence expectations between medical product developers and regulators. Therefore, a clear definition of the term digital biomarker could potentially facilitate the effective use of a DHT in the evaluation of a medical product, potentially increasing patient access to safe and effective medical products.”
– Introduction, Vasudevan S, Saha A, Tarver ME, Patel B. Digital biomarkers: Convergence of digital health technologies and biomarkers. NPJ Digit Med. 2022 Mar 25;5(1):36.
Scope of PFDD guidances
The FDA’s Patient-Focused Drug Development (PFDD) Guidance Series “is intended to facilitate the advancement and use of systematic approaches to collect and use robust and meaningful patient and caregiver input that can better inform medical product development and regulatory decision making.”
While the PFDD series provides this key framework, different FDA centers emphasize distinct guidances in their assessments. For instance, the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER) currently utilize PFDD 1 and 2. In contrast, the Center for Devices and Radiological Health (CDRH) uses Principles for Selecting, Developing, Modifying, and Adapting Patient Reported Outcome Instruments for Use in Medical Device Evaluation, for patient-reported outcomes. All centers are also preparing for the implementation of PFDD 3 (finalized Oct 2025) and the forthcoming PFDD 4, which will together replace the older 2009 guidance on Patient-Reported Outcome Measures.
Once you’ve read the guidances, explore these best practices from the field:
Industry spotlight
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