Findings
The traditional process of evaluating biomarkers within the context of a single drug development program is inefficient and creates uncertainty for sponsors. This case-by-case approach leads to redundant efforts, slows down the development of novel therapies, and hinders the broad adoption of promising scientific tools. There is a clear need for a centralized, collaborative pathway to formally validate biomarkers, which can de-risk drug development, encourage innovation, and make the process more predictable and cost-effective for all stakeholders.

Recommendations
Drug developers, academic researchers, and other stakeholders should proactively engage with the FDA through the formal Biomarker Qualification Program to validate biomarkers for specific contexts of use. It is recommended to form public-private partnerships and other collaborations to pool resources and data, which strengthens the evidence package for a biomarker's utility. Developers should use the qualification process to establish a biomarker's value early, making it a publicly available and reliable tool that can accelerate the development of multiple drug products.

Regulatory Considerations
The Biomarker Qualification Program provides a distinct regulatory pathway for establishing a biomarker's validity for a specific Context of Use (COU), separate from an individual Investigational New Drug (IND) or New Drug Application (NDA). The process involves a three-stage submission and review cycle: the Letter of Intent, the Qualification Plan, and the Full Qualification Package. Once qualified, a biomarker is publicly listed and can be incorporated into multiple drug development programs without the need for sponsors to re-submit and re-justify the validation data for that specific COU, streamlining subsequent regulatory reviews.

Findings
Establishing cross-sector consortia does not guarantee success without a unified objective and stakeholder buy-in. A neutral, independent facilitator is a key element for successful governance in many collaborative platforms. Many consortia lack consistent methods for storing critical data, meeting minutes, and regulatory briefing packages, which creates barriers after project completion. Regulatory success depends heavily on the early development of a strategy that defines the necessary evidence to validate innovative methodologies. Successful examples include the qualification of biomarkers for polycystic kidney disease and type 1 diabetes, as well as imaging measures for Alzheimer’s disease.

Recommendations
Consortium members should develop an initial regulatory strategy during the project scoping and planning phases. Teams must explicitly define the context of use for any proposed tool to articulate exactly what decisions the output will inform. A robust data strategy should be implemented early, including formal agreements for data use, standardization, and sharing that remain in place in perpetuity. Consortia must prioritize sustainability plans to ensure data and active databases remain available for research and regulatory use after funding expires. Projects should integrate regulatory science expertise from the start to cover both EU and US frameworks.

Regulatory Considerations
Regulators require individual patient-level data that is fully curated, standardized, and presented through formal submissions like qualification applications. Formal regulatory endorsement ensures a tool can be trusted for consistent interpretation in drug development and marketing authorization evaluations. Early engagement with agencies such as the FDA and EMA is essential to gain feedback on novel methodologies and align study designs with regulatory expectations. Specific pathways like the EMA Qualification of Novel Methodologies and the FDA Qualification Process for Drug Development Tools should be utilized. Regulatory qualification may require ongoing access to databases to support the long-term use of the methodology.

Key Principles (Findings)
The central principle of the FDA's program is that Digital Health Technologies (DHTs) offer significant potential to make clinical trials more efficient, patient-centric, and capable of capturing novel data. A key finding is that a collaborative, multifaceted approach is necessary to address the challenges of incorporating DHT-derived data into regulatory decision-making. The program acknowledges that ensuring data quality, validating new endpoints, and establishing clear regulatory expectations are critical for the successful adoption of these technologies in drug development.

Program Activities (Recommendations)
The FDA's activities in this area function as implicit recommendations for the industry. The agency is actively:
Developing a Framework: Creating and publishing a clear framework to guide the use of DHTs in drug and biological product development.
Engaging Stakeholders: Convening public meetings and workshops to foster collaboration and share learning among patients, biopharmaceutical companies, DHT manufacturers, and academia.
Supporting Demonstration Projects: Funding and overseeing research projects to address critical gaps and demonstrate the reliability and validity of specific digital measures.
Building Internal Expertise: Establishing a DHT Steering Committee and enhancing internal knowledge to ensure consistent and expert review of submissions containing DHT-derived data.

Regulatory Considerations
This webpage emphasizes the FDA's commitment to creating a clear regulatory framework for the use of DHTs in drug development. It highlights that while DHTs offer great promise, they also present new regulatory challenges related to data integrity, validation, and analysis. The FDA's approach involves a combination of issuing new regulatory guidance, promoting stakeholder collaboration, and advancing regulatory science. Sponsors are encouraged to engage with the FDA to discuss their use of DHTs in clinical trials to ensure alignment with the agency's expectations. The establishment of the CDRH Digital Health Center of Excellence provides a dedicated resource for such engagement.

Key Principles (Findings)
The central principle of the DDT Qualification Programs is to create a formal pathway for the FDA to conclude that a specific tool is well-suited for a particular Context of Use (COU) in drug development. A key finding, as reflected in the program's design, is that qualification de-risks drug development by allowing a tool to be used in any regulatory submission for its qualified COU without needing to be re-validated each time. The program is designed to foster stakeholder collaboration, encouraging the development of tools that can benefit the entire research community, thereby reducing the burden on individual sponsors.

Program Activities (Recommendations)
The structure of the DDT programs serves as a series of recommendations for tool developers:
Engage Early and Collaboratively: The programs are designed to provide a framework for early and ongoing scientific collaboration with the FDA to facilitate the development of new tools.
Follow a Staged Process: Developers are guided through a multi-stage process, typically involving a Letter of Intent, a Qualification Plan, and a Full Qualification Package, to systematically build the evidence needed for qualification.
Seek Public Qualification: The ultimate recommendation is to achieve public qualification for a DDT, which makes the tool available for broad use and integrates it into the regulatory review process, expediting future drug development.

Regulatory Considerations
The DDT Qualification Programs are a formal regulatory framework established under the 21st Century Cures Act. A "qualified" DDT has a specific regulatory status; it can be relied upon to have a specific interpretation and application in drug development and regulatory review for its stated Context of Use (COU). This qualification is publicly available and allows the tool to be included in Investigational New Drug (IND), New Drug Application (NDA), or Biologics License Application (BLA) submissions without the FDA needing to reconsider its suitability. This creates a more efficient and predictable regulatory compliance pathway for sponsors who use the qualified tool.

Findings
Long and Unpredictable Timelines: The COA Qualification Program is lengthy and unpredictable, with an average qualification time of six years. Nearly half of all submissions experience review times that exceed the FDA's own published targets.
Low Qualification and Uptake: As of October 2024, only seven COAs (8.1% of those listed) have been qualified, and only three of those have been used to support the benefit-risk assessment of new medicines. No COAs submitted after the passage of the 21st Century Cures Act in 2016 have been qualified.
Limited Regulatory Impact: Qualified COAs are consistently designated for "exploratory use" and have never been accepted as a primary endpoint in a clinical trial. In contrast, some non-qualified COAs have been used as key endpoints and included in drug labels, questioning the utility of the formal qualification pathway.
Discrepancy Between FDA Centers: There is a notable difference in how COAs are qualified between the drug (CDER/CBER) and device (CDRH) centers. The Kansas City Cardiomyopathy Questionnaire (KCCQ) was qualified by CDRH for use as a primary or secondary endpoint, while for drugs, it was only qualified as an "exploratory" measure.

Recommendations
Increase Transparency of Timelines: The FDA should publish its actual, historical review timelines for COA qualification so that drug developers can better plan and integrate these tools into their development programs.
Clarify the Use of Qualified COAs: The FDA should clearly articulate how and when qualified COAs can be used as primary or secondary endpoints to support regulatory decision-making and provide a clear pathway for updating a COA's status from "exploratory" to a key endpoint.
Publish Best Practices: Both sponsors and the FDA should be encouraged to publish their experiences with the qualification program to share best practices and learnings with the broader drug development community.
Create a List of Accepted Endpoints: The FDA should create and maintain a public list of qualified COAs that can be used as surrogate endpoints to support drug approval decisions, thereby increasing their utility and adoption.

Regulatory Considerations
"Qualified as a Measure" Ambiguity: The FDA's practice of qualifying COAs as "measures" for "exploratory use" creates regulatory uncertainty for sponsors, as it implies that significant additional evidence is still needed before the tool can be relied upon for a key endpoint.
Qualification is Not Required: The analysis shows that COAs can be accepted for regulatory decision-making and included in drug labels without going through the formal qualification program, suggesting that qualification is not a prerequisite for use as a reliable endpoint.
Unclear Path to Endpoint Progression: The current DDT guidance does not specify the process for upgrading a COA's qualification status (e.g., from exploratory to a primary endpoint) after additional data has been generated, which hinders its evolution and broader use.

Findings
The development and evaluation of medical devices require scientifically plausible and reliable tools for collecting data to support regulatory submissions. A lack of standardized, pre-vetted tools can lead to inefficiencies and unpredictability in the device development and review process. The qualification of development tools can be applied across a wide range of device areas, including cardiovascular, neurology, imaging, and cybersecurity. The evidence required for tool qualification must be robust enough to support its intended context of use.

Recommendations
Tool developers, medical device sponsors, research organizations, and academic institutions are encouraged to voluntarily submit proposals to the MDDT program to qualify their tools. Submissions should include a detailed description of the tool, a clearly defined context of use (COU), specific performance criteria, and a comprehensive plan for collecting evidence to validate the tool's performance and scientific plausibility. Collaboration in developing tools and supporting evidence is recommended to pool resources and increase the acceptance of qualified tools.

Regulatory Considerations
The MDDT program is a formal regulatory mechanism for the FDA to qualify tools that can be used to support assessments of medical device safety, effectiveness, or performance. Once a tool is qualified for a specific context of use, the FDA accepts assessments from that tool in support of regulatory submissions without needing to re-evaluate the tool's suitability. The program recognizes four main categories of tools: Non-clinical Assessment Models (NAM), Biomarker Tests (BT), Clinical Outcome Assessments (COA), and an "Other" category for tools that do not fit the primary classifications.

Findings
The use of Artificial Intelligence (AI) and Machine Learning (ML) is being applied to a broad range of drug development activities with the potential to accelerate the process and make clinical trials safer and more efficient. The inclusion of AI/ML is most common in the clinical development/research phase of regulatory submissions. Concerns exist that AI/ML algorithms could amplify errors and preexisting biases in underlying data sources, which raises issues related to generalizability and ethical considerations. Other challenges include limited explainability due to model complexity and proprietary reasons, as well as managing risks related to data quality, reliability, and representativeness. The FDA recognizes that a careful, risk-based assessment of the specific context of use (COU) is needed when evaluating AI/ML.

Recommendations
Stakeholders should adhere to practices in three key areas: human-led governance, accountability, and transparency; quality, reliability, and representativeness of data; and model development, performance, monitoring, and validation. A risk management plan should be applied to identify and mitigate risks based on the COU, guiding the level of documentation and transparency. Practices are needed to ensure the integrity of AI/ML and address issues like bias and missing data. For models, developers should use pre-specification steps and clear documentation for development and assessment criteria. Models must be monitored over time for reliability and consistency, and Real-World Data (RWD) performance can provide valuable feedback, including for potential re-training.

Regulatory Considerations
The FDA encourages early engagement through mechanisms like the Critical Path Innovation Meetings (CPIM), ISTAND Pilot Program, and Emerging Technology Program to discuss relevant AI/ML methodologies or technologies. The Verification and Validation (V&V 40) risk-informed credibility assessment framework and the principles for Good Machine Learning Practices (GMLP), while not specific to drug development, are helpful guides for evaluating models. The industry is exploring the use of a Predetermined Change Control Plan (PCCP) mechanism for AI/ML-based devices to proactively specify and manage modifications, enhancing adaptability. In general, a risk-based approach should guide the level of evidence and record keeping needed for the verification and validation of AI/ML models for a specific COU.

Findings
Diverse Pathways to Market Exist: The case studies demonstrate there is no single "right" way to approach the FDA; successful strategies are highly varied and include De Novo requests, 510(k) clearances, and leveraging established pathways for new indications.
Early FDA Engagement is Crucial: A consistent theme across the successful case studies is the value of engaging with the FDA early and often. This collaborative approach helps de-risk the development process, clarify evidentiary requirements, and build trust.
"Drug-like" Evidence Can Be a Differentiator: For novel software-based interventions, particularly digital therapeutics, generating a robust body of evidence similar to that of a pharmaceutical (i.e., randomized controlled trials) is a key strategy for gaining regulatory and commercial success.
Platform-Based Approaches are Emerging: Companies are finding success by moving from single-product solutions to integrated platforms that can monitor multiple health aspects, which requires a more holistic regulatory strategy.

Recommendations
Leverage Pre-Submission (Pre-Sub) Meetings: Sponsors are strongly encouraged to use the Q-Submission program to gain valuable, early feedback from the FDA on their validation plans and overall regulatory strategy.
Build a Multi-faceted Commercialization Plan: Regulatory clearance is only one step. The case studies recommend developing a comprehensive strategy that considers market access, reimbursement, and payer engagement from the outset.
Address Underserved Markets: The examples highlight opportunities for innovation in underserved areas, such as pediatrics and behavioral health, where DHTs can fill significant gaps in care.
Innovate on Evidence Generation: Sponsors should be prepared to innovate not just in their technology, but also in their approach to clinical evidence, tailoring their trial designs to best demonstrate the unique value of their digital product.

Regulatory Considerations
Understand the Risk Classification: The regulatory pathway for a DHT is determined by its intended use and associated risk level. Sponsors must correctly classify their device to determine if a 510(k), De Novo, or other pathway is appropriate.
AI/ML Devices Have Unique Needs: For products incorporating artificial intelligence or machine learning, sponsors must address specific regulatory considerations, such as predetermined change control plans (PCCPs), to manage algorithm updates post-market.
Interoperability is a Key Factor: For devices intended to be part of a connected health ecosystem (e.g., automated insulin dosing systems), demonstrating interoperability and cybersecurity is a critical component of the regulatory submission.

Findings
Regulatory inconsistencies across different FDA divisions and international jurisdictions create inefficiencies in the approval process for digital health products.
Lack of alignment between regulatory approval and payer reimbursement requirements poses a significant barrier to commercialization and widespread adoption of digital health innovations.
There are limited regulatory pathways for novel digital health products, including AI-enabled solutions, requiring new frameworks to address iterative software development and real-world data integration.
Existing health technology assessment (HTA) models do not fully accommodate digital health technologies, limiting their inclusion in reimbursement decisions.
Industry stakeholders emphasize the need for clearer guidelines on cloud-based infrastructure, third-party AI model validation, and digital health interoperability.

Recommendations
FDA and international regulatory bodies should improve coordination to establish standardized approval processes and consistent clinical evidence requirements.
New regulatory pathways should be introduced for AI-driven and software-based digital health products, considering their unique lifecycle and iterative development models.
Greater transparency and communication between FDA divisions should be established to ensure consistent decision-making and regulatory interpretations across centers.
Policymakers should prioritize payer alignment strategies, incorporating real-world evidence (RWE) to streamline reimbursement and market access processes.
The digital health industry should collaborate with regulators to create standardized best practices for AI validation, cloud security, and digital biomarker evaluation.

Regulatory Considerations
FDA should clarify the evidentiary standards for AI-enabled medical devices and establish predefined change control plans for software updates.
Digital health products should adhere to globally recognized standards such as HL7 for interoperability and ISO regulations for data security.
Market access pathways must integrate pricing and reimbursement considerations to facilitate the commercial viability of digital health technologies.
The use of real-world data (RWD) should be expanded in regulatory decision-making, supporting the approval and post-market surveillance of digital health innovations.
Regulatory frameworks should be updated to accommodate cloud-based health platforms, addressing issues such as data privacy, operational security, and compliance with HIPAA and GDPR.

Findings
Analytical validation is critical for ensuring digital clinical measures align with regulatory and scientific expectations, particularly when no established reference measures exist.
Novel digital measures require flexible validation approaches, as traditional clinical reference measures often do not directly correspond to digital endpoints​
Statistical methodologies must be tailored to the nature of digital measures, using approaches such as factor analysis, regression modeling, and latent variable estimation​
Regulatory engagement is crucial early in the validation process to align evidentiary standards and facilitate market adoption​
The validation process must be context-specific, considering population characteristics, data collection settings, and sensor variability to ensure reliability across diverse applications​.

Recommendations
Developers should follow a stepwise approach in designing validation studies, incorporating existing reference measures, novel comparators, and statistical validation techniques.
Regulatory authorities should provide clearer guidance on acceptable validation methodologies, particularly for novel digital endpoints.
Analytical validation must be tailored to the intended use environment, ensuring that sensor-based measures capture meaningful health outcomes in real-world settings​.
Multi-stakeholder collaboration (regulators, payers, researchers, and patients) should be prioritized to create consensus on validation strategies and market access pathways.
Machine learning and AI models used for digital clinical measures should undergo rigorous evaluation to mitigate bias and improve interpretability in healthcare decision-making​.

Regulatory Considerations
Digital endpoint validation must incorporate both traditional statistical measures and novel validation frameworks, ensuring credibility in regulatory submissions.
FDA and international regulators encourage early engagement to discuss validation plans, data requirements, and evidentiary thresholds for digital measures​.
Real-world evidence (RWE) and real-world data (RWD) should be leveraged to support regulatory submissions and post-market surveillance of digital health innovations​.
Validation studies should align with global regulatory standards, such as ISO, FDA’s digital health guidance, and European Medical Device Regulations (MDR).
Data privacy, security, and compliance with regulations like HIPAA and GDPR are critical considerations when deploying and validating digital clinical measures​

Findings
Evaluating patient outcomes on a case-by-case basis within individual drug programs is an inefficient use of resources and creates regulatory unpredictability. This approach frequently leads to redundant efforts to validate the same assessment tools across different development programs. The lack of a standardized, transparent process for accepting Clinical Outcome Assessments (COAs) hinders the development and use of novel, patient-centric endpoints, ultimately slowing the delivery of therapies that address outcomes that matter most to patients.

Recommendations
Developers of COAs, including patient groups, academic researchers, and pharmaceutical sponsors, are encouraged to collaborate with the FDA through the qualification program. This engagement should occur early to ensure that the measures are developed with sufficient rigor to meet regulatory standards. Stakeholders should leverage the program to validate a wide range of COAs, particularly Patient-Reported Outcomes (PROs), making them publicly available to advance patient-focused drug development across the entire industry and reduce redundant validation work.

Regulatory Considerations
The COA Qualification Program offers a formal regulatory pathway for the FDA to review and accept a COA for a specific Context of Use (COU). This qualification is separate from the review of an individual drug application, making the validated tool accessible for any sponsor to use in their clinical trials without re-adjudicating the COA's fitness for that purpose. Qualification requires a comprehensive submission demonstrating the measure is well-defined and reliable, ensuring that it appropriately captures the patient's experience or functional status.

Findings
Regulatory inconsistencies across FDA divisions create uncertainty and inefficiencies in the approval process for digital health products.
Misalignment between FDA regulatory requirements and payer expectations hinders the commercialization and adoption of digital health innovations.
The absence of clear alternative regulatory pathways for novel digital health products discourages investment and innovation.
The lack of standardized regulatory frameworks for AI-driven healthcare technologies, including large language models (LLMs), poses challenges for industry adoption.
Limited international harmonization in digital health regulation makes it difficult for companies to scale innovations globally.

Recommendations
FDA should improve communication and coordination across divisions to ensure consistent regulatory interpretations and processes.
Regulatory pathways for novel digital health products should be modernized, including the introduction of alternative approval mechanisms tailored to iterative software development and AI-enabled devices.
A regulatory framework for third-party large language models (LLMs) should be developed to support their integration into digital health applications.
Greater alignment between FDA and payer decision-makers is needed to streamline market access and ensure reimbursement for digital health products.
International regulatory harmonization efforts should be expanded to facilitate global adoption of digital health technologies.

Regulatory Considerations
The FDA should clarify and refine regulatory requirements for AI-driven digital health products, including predefined change control plans for software updates.
Cloud-based health platforms require clear regulatory guidance on security, data ownership, and compliance with HIPAA and international privacy laws.
Real-world evidence (RWE) should be incorporated into regulatory decision-making to facilitate faster approvals and post-market surveillance of digital health products.
Standardized regulatory frameworks for digital biomarkers and digital drug development tools (DDDTs) should be developed to support clinical research applications.
Policymakers should collaborate with industry stakeholders to establish education and training programs on digital health innovation and regulatory science.