C3TI Compass (Knowledge Repository)
CDER Center for Clinical Trial Innovation (C3TI)
C3TI Compass is a knowledge repository that connects users to FDA guidance documents, case studies, and resources that support innovative approaches to clinical trial design and conduct. Compass centralizes completed activities, ongoing efforts, and practical tools to support engagement with CDER's clinical trial innovation work and improve clinical trial efficiency.
Resources are organized by topic area and resource type. Use the filters below to search the repository.
| Title | Description | Topic Area | Resource Type |
|---|---|---|---|
| Use of Bayesian Methodology in Clinical Trials of Drug and Biological Products | This draft guidance provides guidance to sponsors and applicants submitting investigational new drug applications (INDs), new drug applications (NDAs), biologics licensing applications (BLAs), or supplemental applications on the appropriate use of Bayesian methods in clinical trials. Bayesian methods can be used in various ways in clinical trials. For example, Bayesian calculations can be used to govern the timing and adaptation rules for an interim analysis in an adaptive design, to inform design elements (e.g., dose selection) for subsequent clinical trials, or to support primary inference in a trial. The primary focus of this guidance is on the use of Bayesian methods to support primary inference in clinical trials intended to support the effectiveness and safety of drugs. | Bayesian Approaches | Guidance Documents |
| A Bayesian Approach in Design and Analysis of Pediatric Cancer Clinical Trials | A Bayesian approach can be a flexible tool to formally leverage prior knowledge of adult or external controls in pediatric cancer trials. In this FDA-authored publication, a case example is provided to demonstrate how Bayesian approaches can be used to design, monitor, and analyze pediatric trials. | Bayesian Approaches | Case Studies |
| Adaptive Design Clinical Trials for Drugs and Biologics: Final Guidance for Industry | This final guidance is provided for applicants submitting Investigational New Drug applications (INDs), New Drug Applications (NDAs), Biologics Licensing Applications (BLAs), or supplemental applications on the appropriate use of adaptive designs for clinical trials to provide evidence of the effectiveness and safety of a drug or biologic. It also advises sponsors on the types of information to submit to facilitate FDA evaluation of clinical trials with adaptive designs, including Bayesian adaptive and complex trials that rely on computer simulations for their design. | Complex Innovative Trial Design | Guidance Documents |
| Advancing Applications of Real-World Data (RWD) for Post-Marketing Safety Evaluations in the Sentinel System | This program aims to improve the quality and acceptability of Real-World Evidence (RWE)-based approaches in support of new intended labeling claims, including the approval of new indications for approved medical products or to satisfy post-approval study requirements. | Real World Evidence/Data | Programs |
| Advancing the Use of Complex Innovative Trial Designs (CIDs) in Clinical Trials: From Pilot to Practice Workshop (2024) | The purpose of this public workshop was to facilitate discussion on the use of external data sources, Bayesian statistical methods, and simulations in Complex Innovative Trial Design (CID) as well as trial implementation. This meeting met the performance goal of convening a public workshop on CIDs included in the seventh authorization of the Prescription Drug User Fee Act (PDUFA VII). Specific examples and slides are available on this page. | Complex Innovative Trial Design | Public Meeting or Workshop Content |
| Application of Machine Learning in Drug Development and Regulation: Current Status and Future Potential | In this FDA-authored landscape analysis, an overview is provided of a sample of Machine Learning (ML) algorithms and describe areas where ML has been used to support drug development and regulatory submissions to the US Food and Drug Administration (FDA), as well as to facilitate review and research. | Artificial Intelligence / Machine Learning | Journal Articles |
| Bayesian Methods in Human Drug and Biological Products Development in the Center for Drug Evaluation and Research (CDER) and Center for Biologics Evaluation and Research (CBER) | This FDA-authored article discusses the Bayesian framework for drug and biological product development, highlights key settings in which Bayesian approaches may be appropriate, and provides recent examples of the use of Bayesian approaches within the FDA’s CDER and CBER. | Bayesian Approaches | Case Studies |
| C3TI Bayesian Supplemental Analysis (BSA) Demonstration Project | C3TI aims to increase the use and understanding of Bayesian statistical methods in simple clinical trial settings across sponsors, CDER clinical reviewers, and CDER statisticians. The Bayesian Statistical Analysis (BSA) demonstration project supports this goal by providing a structured opportunity to use Bayesian approaches for the pre-specified primary analysis, supplementary analysis, or monitoring of simple trials (e.g., trials with a non-adaptive or straightforward sequential design). | Bayesian Approaches | Programs |
| C3TI Streamlined Trials Embedded in clinical Practice (STEP) Demonstration Project | With the Streamlined Trials Embedded in clinical Practice (STEP) demonstration project, C3TI looks to engage with sponsors planning pragmatic/point-of-care trials to provide an opportunity to address and resolve issues (e.g., statistical analyses, incorporation of real-world data and evidence, trial endpoint selection, inspectional approaches) around trial design and conduct, the lessons learned from which will be made available broadly and used to inform updates to relevant CDER guidance. | Integrating Trials into Routine Clinical Practice | Programs |
| Complex Innovative Trial Design (CID) Guidance Podcast | The Guidance Recap Podcast provides highlights for FDA guidance documents straight from the authors. This episode features Dr. Greg Levin, who is the Deputy Director of the Division of Biometrics III in CDER’s Office of Biostatistics. Dr. Levin will be sharing some thoughts with us on the final guidance titled, “Interacting with the FDA on Complex Innovative Trial Designs for Drugs and Biological Products.” | Complex Innovative Trial Design | Guidance Documents |
| Complex Innovative Trial Design (CID) Case Study: A Study in Patients with Epilepsy with Myoclonic-Atonic Seizures (EMAS) | The proposed study is a multisite, double-blind, randomized, placebo-controlled, parallel group study in children and adolescents with Epilepsy with Myoclonic-Atonic Seizures (EMAS). The primary endpoint is EMAS-associated seizure frequency over the treatment period. | Complex Innovative Trial Design | Case Studies |
| Complex Innovative Trial Design (CID) Case Study: A Study in Patients with Systemic Lupus Erythematosus (SLE) | The proposed study is a randomized, double-blind, Phase 2 study in patients with Systemic Lupus Erythematosus (SLE), a rare disease with a high unmet need. Patients are to be randomized to one of four treatment groups: Three doses of Investigational Product (IP) or placebo. | Complex Innovative Trial Design | Case Studies |
| Complex Innovative Trial Design (CID) Case Study: A Study in Pediatric Patients with Multiple Sclerosis (MS) | The proposed study is a randomized, double-blind, Bayesian, group sequential, Non-Inferiority (NI) trial comparing an investigational treatment to an active control in pediatric patients with Multiple Sclerosis (MS), borrowing strength from external data in adults and children. The primary endpoint is the Annualized Relapse Rate (ARR). One interim analysis for efficacy is planned. | Complex Innovative Trial Design | Case Studies |
| Complex Innovative Trial Design (CID) Case Study: External Control in Diffuse Large B-Cell Lymphoma (DLBCL) | The proposed trial is a randomized, open-label, multicenter trial in patients with first-line Diffuse Large B-Cell Lymphoma (DLBCL). Patients are to be randomized 2:1 to treatment vs. control. The primary endpoint of the study is Investigator-assessed Progression-Free Survival (PFS), defined as the time from randomization to the first occurrence of progression or relapse, using the 2014 Lugano classification for Malignant Lymphoma (Cheson et al. 2014), or death from any cause, whichever occurs first. | Complex Innovative Trial Design | Case Studies |
| Complex Innovative Trial Design (CID) Case Study: Master Protocol to Study Chronic Pain | This proposal is for a master protocol designed to study chronic pain. This proposed master protocol permits multiple sub-studies to investigate proof of concept for several investigational products that may be intended to treat several types of chronic pain. | Complex Innovative Trial Design, Master Protocols | Case Studies |
| Complex Innovative Trial Design (CID) Guidance Snapshot | Guidance Snapshots are a communication tool that provide highlights from guidance documents using visuals and plain language. This guidance provides recommendations to sponsors and applicants on interacting with the FDA on Complex Innovative Trial Design (CID) proposals for drugs or biological products and describes the type of information FDA recommends submitting. | Complex Innovative Trial Design | Guidance Documents |
| Complex Innovative Trial Design (CID) Paired Meeting Program | This video shares information about the Complex Innovative Trial Design (CID) Paired Meeting Program’s process and what to expect. | Complex Innovative Trial Design | Supporting Materials |
| Complex Innovative Trial Design (CID) Pilot Meeting Program: Progress to Date | This FDA-authored publication highlights the five submitted meeting requests that have been selected for participation in the Complex Innovative Trial Design (CID) Pilot meeting program. The selected submissions, thus far, have all utilized a Bayesian framework. The reasons for the use of Bayesian approaches may be due to the flexibility provided, the ability to incorporate multiple sources of evidence, and a desire to better understand the FDA's perspective on such approaches. | Bayesian Approaches, Complex Innovative Trial Design | Journal Articles |
| Complex Innovative Trial Design (CID) Pilot Meeting Program: The Process - YouTube Video | This video provides an overview of the process for applying for the FDA’s Complex Innovative Trial Design (CID) Pilot Meeting Program. | Complex Innovative Trial Design | Trainings |
| Conducting Clinical Trials with Decentralized Elements: Final Guidance for Industry, Investigators, and Other Interested Parties | This final guidance provides recommendations regarding the implementation of decentralized elements in clinical trials. By enabling remote participation, decentralized clinical trials may enhance convenience for trial participants, reduce the burden on caregivers, expand access to more diverse patient populations, improve trial efficiencies, and facilitate research on rare diseases and diseases affecting populations with limited mobility. | Clinical Trials with Decentralized Elements | Guidance Documents |
| Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products | This guidance provides recommendations to sponsors and other interested parties on the use of Artificial Intelligence (AI) to produce information or data intended to support regulatory decision-making regarding safety, effectiveness, or quality for drugs. Specifically, this guidance provides a risk-based credibility assessment framework that may be used for establishing and evaluating the credibility of an AI model for a particular Context Of Use (COU). | Artificial Intelligence / Machine Learning | Guidance Documents |
| Considerations for the Use of Real-World Data and Real-World Evidence to Support Regulatory Decision-Making for Drug and Biological Products: Final Guidance for Industry | This final guidance discusses the applicability of FDA’s Investigational New Drug application (IND) regulations under part 312 to various clinical study designs that utilize Real-World Data (RWD), providing clarity on how RWD can be used in clinical studies. | Real World Evidence/Data | Guidance Documents |
| Developing a Composite Score with Actigraphy Physical Activity and Heart Rate Measurements as a Sensitive Study Endpoint to Facilitate the Use of Digital Health Technology in Pediatric Clinical Trials and Drug Development | This demonstration project is part of FDAs Digital Health Technologies (DHT) for Drug Development program. It proposes a novel composite score with the available actigraphy physical activity and Heart Rate (HR) measurement data by using an approach of computational modeling and simulation. This composite score could have a considerably better ability to detect treatment effect and to assess disease severity as well as to detect changes in disease progression in children with pediatric Pulmonary Arterial Hypertension (PAH). | Digital Health Technology | Programs |
| Digital Health Technologies (DHTs) for Drug Development: Demonstration Programs | To promote shared learning and understanding with external stakeholders, FDA continues to oversee and support numerous demonstration (i.e. research) projects on Digital Health Technologies (DHTs) through multiple venues and programs include FDA’s Broad Agency Announcement, Center's of Excellence in Regulatory Science and Innovation (CERSI) program, and other funding opportunities. This webpage lists current FDA-funded projects on DHTs. Projects include the following: - Measuring Digital Clinical Endpoints in Huntington's Disease (MEND-HD) - Improving the Integrity of Novel Digital Health Technology-derived Endpoints through Rigorous Simulation Studies of Multiple Imputation Techniques - Novel DHT-centric statistical methods for subject-level fingerprinting and handling missingness - Developing a Composite Score with Actigraphy Physical Activity and Heart Rate Measurements as a Sensitive Study Endpoint to Facilitate the Use of Digital Health Technology in Pediatric Clinical Trials and Drug Development - Developing an Objective and Quantitative Endpoint for Atopic Dermatitis in Pediatric and Adult Populations - Using mHealth to Measure Impact in Functionality Behavior, Activity, and Sleep Patterns in Children and Adolescents Treated with Psychotropics - Evaluation Mobile Health Tool Use for Capturing Patient-Centered Outcome Measures in Heart Failure Patients | Digital Health Technology | Programs |
| Digital Health Technologies (DHTs) for Remote Data Acquisition in Clinical Investigations: Final Guidance for Industry, Investigators, and Other Stakeholders | This final guidance supports the use of Digital Health Technology (DHT) in clinical trials to obtain physiological and other data directly from patients, enhancing the data collection process, and patient engagement in trials. | Digital Health Technology | Guidance Documents |
| Digital Health Technology (DHT) Guidance Podcast | The Guidance Recap Podcast provides highlights for FDA guidance documents straight from the authors. Through conversations with FDA staff, this podcast is intended to help communicate salient key points and background information on the use of Digital Health Technologies (DHTs) for remote data acquisition from participants in clinical investigations that evaluate medical products. The guidance focuses on recommendations for ensuring that a DHT is fit-for-purpose and that the level of validation associated with the DHT is sufficient to support the use, including the interpretability of its data, in the clinical investigation. This involves considerations of the DHT’s form (i.e., design) and function(s) (i.e., distinct purpose within an investigation). | Digital Health Technology | Guidance Documents |
| Digital Health Technology (DHT) Guidance Snapshot | Guidance Snapshots are a communication tool that provide highlights from guidance documents using visuals and plain language. This document is intended to provide highlights from the guidance. This final guidance provides recommendations for sponsors, investigators, and other interested parties on the use of Digital Health Technologies (DHTs) for remote data acquisition from participants in clinical investigations that evaluate medical products. The guidance focuses on recommendations for ensuring that a DHT is fit-for-purpose and that the level of validation associated with the DHT is sufficient to support the use, including the interpretability of its data, in the clinical investigation. This involves considerations of the DHT’s form (i.e., design) and function(s) (i.e., distinct purpose within an investigation). | Digital Health Technology | Guidance Documents |
| E19 A Selective Approach to Safety Data Collection in Specific Late-Stage Pre-Approval or Post-Approval Clinical Trials: Final Guidance for Industry | This final guidance is intended to provide internationally harmonized guidance on the use of Selective Safety Data Collection (SSDC) that may be applied in specific pre-approval or post-approval late-stage clinical trials. | Selective Safety Data Collection | Guidance Documents |
| E19 Guidance Snapshot | Guidance Snapshots are a communication tool that provide highlights from guidance documents using visuals and plain language. This guidance is intended to provide internationally harmonized guidance on the use of Selective Safety Data Collection (SSDC), that may be applied in specific pre-approval or post-approval late-stage clinical trials. This final guidance is intended to provide internationally harmonized guidance on the use of SSDC that may be applied in specific pre-approval or post-approval late-stage clinical trials. | Selective Safety Data Collection | Guidance Documents |
| E6(R3) Good Clinical Practice (GCP) Guidance | The FDA announced the availability of this final guidance for industry, entitled "E6(R3) Good Clinical Practice (GCP)." This revision incorporates flexible, risk-based approaches and embraces innovations in trial design, conduct, and technology. This important milestone marks a significant evolution in the global clinical trial landscape, aiming to modernize GCP principles in alignment with current scientific and technological advances while maintaining a strong focus on quality by design, participant protection, and the reliability of trial results. | Good Clinical Practice | Guidance Documents |
| E6R3 Step 4 Presentation | This guideline reached Step 2 (19 May 2023) and was issued by the International Council for Harmonization (ICH) Regulatory Members for public consultation. ICH E6(R3) Expert Working Group reviewed public consultation comments and revised the document as appropriate. This final document has been signed off as a Step 4 document (6 January 2025) to be implemented by the ICH Regulatory Members. This document was developed based on a Concept Paper (approved 18 November 2019) and a Business Plan (approved 18 November 2019). | Good Clinical Practice | Guidance Documents |
| Enhancing Adoption of Innovative Clinical Trial Approaches Workshop (2024) | The FDA and the Duke-Margolis Center for Health Policy convened a hybrid public workshop to discuss efforts to advance innovation of clinical trial design and conduct. The Center for Drug Evaluation and Research (CDER)'s portfolio of clinical trial innovation activities are wide ranging and span across drug development programs, therapeutic areas, and scientific disciplines. Stakeholders discussed barriers and challenges to incorporating successful or promising innovative clinical trial approaches in drug development. | Cross-Cutting Resources | Public Meeting or Workshop Content |
| Example Bayesian Statistical Plan for a Parallel-Group Trial with a Continuous Outcome | This is an example statistical analysis plan derived from the C3TI Bayesian Statistical Analysis (BSA) Demonstration Project. The hypothetical study design is a double-blind, parallel-group, two-treatment, randomized controlled trial of drug (group B) vs. placebo (group A) in acute hypertension in an emergency department setting. The primary analysis is intent-to-treat and the primary response variable is Systolic Blood Pressure (SBP) measured at 2 hours post randomization. | Bayesian Approaches | Case Studies |
| Example Statistical Analysis Plan for a Supplemental Bayesian Analysis: Unification of Evidence | This is an example statistical analysis plan derived from the C3TI Bayesian Statistical Analysis (BSA) Demonstration Project. The hypothetical study design is a double-blind, parallel-group, two-treatment, randomized controlled trial of drug (group B) vs. placebo (group A). The primary analysis is intent-to-treat and the outcomes are survival time (with follow-up up to three years), infection (within 90 days), and patient Performance Status (within 30 days; PS). The disease setting is a high mortality one, and infections and PS are frequently not assessable as a result. So, death is counted in both outcomes by making it the highest level of the ordinal outcome. The infection outcome thus has 3 levels (alive and infection-free for 90 days, alive and infection within 90 days, death), and PS has 6 levels. One could say that these outcomes are infection penalized for death and PS penalized for death. | Bayesian Approaches | Case Studies |
| Example Statistical Analysis Plan for Bayesian Subgroup Analysis: Sharing of Information Across Subgroups | This is an example statistical analysis plan derived from the C3TI Bayesian Statistical Analysis (BSA) Demonstration Project. The hypothetical study design is a double-blind, parallel-group, two-treatment, randomized, controlled trial of drug vs. control. The primary analysis evaluates a time-to-event endpoint using a hazard ratio. The trial is multiregional with sites in Asia, Europe, North America, and South America. There is interest in pre-specified supplemental statistical analyses that seek to understand heterogeneity in treatment effects across regions and estimate treatment effects within regions. | Bayesian Approaches | Case Studies |
| FDA Use of Real World Evidence (RWE) Webpage | The FDA's Use of Real World Evidence (RWE) webpage presents examples where CDER and CBER applied RWE in regulatory decision-making processes since 2011. These include, but are not limited to, product approvals, labeling changes, and assessments that determined no regulatory action was warranted. | Real World Evidence/Data | Case Studies |
| FDA use of Real-World Evidence (RWE) in Regulatory Decision Making | The Food and Drug Administration (FDA) has a long history of using Real-World Data (RWD) and Real-World Evidence (RWE) to monitor and evaluate the postmarket safety of medical products and is committed to realizing the full potential of fit-for-use RWD to generate RWE that can advance the development of medical products and strengthen their oversight. The studies presented in the accompanying tables exemplify instances in which the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER) applied RWE in regulatory decision-making processes since 2011. These include, but are not limited to, product approvals, labeling changes, and assessments that determined no regulatory action was warranted. This compilation forms part of a comprehensive landscape analysis to assess the scope and frequency of RWE use in regulatory determinations across the Agency. | Real World Evidence/Data | Supporting Materials |
| FDA, MHRA, and Health Canada Good Clinical Practice (GCP) Workshop: Global Clinical Trials - Considerations and Lessons Learned from the Changing Landscape (2022) | This workshop provided insight into key topics, compliance trends, and the opportunity to hear first-hand from regulators about lessons learned from the changing clinical trial landscape. Topics covered include: key aspects of building resilience in clinical trials, risk-based approach to sponsor oversight, use of Real-World Data/Real-World Evidence (RWD/RWE), updates regarding decentralized trials, changes in clinical trial activities and inspections, sponsor oversight of vendors, potential uses of artificial intelligence and machine learning in clinical trials, clinical and bioanalytical challenges in bioequivalence trials, and updates in guidance and inspection approaches. | Good Clinical Practice | Public Meeting or Workshop Content |
| FDA's Sentinel Initiative | Sentinel, the FDA’s national electronic system, continues to transform the way researchers monitor the safety of FDA-regulated medical products by enhancing participation to a wider array of scientific expertise, translating new technologies from emerging fields such as data science and big data, creating laboratories to develop new approaches to using Electronic Health Records (EHRs), and cultivating a robust scientific community to uncover novel ways to leverage the system’s core capabilities beyond drug safety. | Real World Evidence/Data | Programs |
| Guidance Recap Podcast | E19 A Selective Approach to Safety Data Collection in Specific Late-Stage Pre-approval or Post-approval Clinical Trials | The FDA recently published the Guidance Snapshot for E19 A Selective Approach to Safety Data Collection in Specific Late-Stage Pre-Approval or Post-Approval Clinical Trials. The Guidance Recap Podcast provides highlights from this FDA guidance document directly from the authors. | Selective Safety Data Collection | Guidance Documents |
| ICH E20: Adaptive Designs for Clinical Trials Step 2 Presentation | This document has been signed off as a Step 2a/b draft guideline (25 June 2025) to be issued by the ICH Regulatory Members for public consultation. The objective is to provide guidance on clinical trials with an adaptive design. The focus is on principles for the planning, conduct, analysis, and interpretation of confirmatory trials with an adaptive design. The emphasis is on principles that are critical to ensuring the trials produce reliable and interpretable information and that require specific considerations with use of an adaptive design. | Complex Innovative Trial Design | Supporting Materials |
| ICH Harmonised Guideline: Adaptive Designs for Clinical Trials E20 | This document provides guidance on confirmatory clinical trials with an adaptive design intended to evaluate a treatment for a given medical condition within the context of its overall development program. The focus of this guideline is on principles for the planning, conduct, analysis, and interpretation of trials with an adaptive design intended to confirm the efficacy and support the benefit-risk assessment of a treatment. The emphasis is on principles that are critical to ensuring the trials produce reliable and interpretable information and that require specific considerations with use of an adaptive design. This guideline does not discuss the use of specific statistical methods. Although the guideline primarily focuses on confirmatory clinical trials, the principles outlined are relevant to all phases of clinical development. | Complex Innovative Trial Design | Guidance Documents |
| Integrating Randomized Controlled Trials (RCTs) for Drug and Biological Products Into Routine Clinical Practice: Draft Guidance for Industry | This draft guidance is intended to support the conduct of randomized controlled drug trials with streamlined protocols and procedures that focus on essential data collection, allowing integration of research into routine clinical practice. This may improve convenience and accessibility for participants and allow for enrollment of more representative populations, resulting in more generalizable trial results. | Integrating Trials into Routine Clinical Practice | Guidance Documents |
| Interacting with the FDA on Complex Innovative Trial Designs (CIDs) for Drugs and Biological Products: Final Guidance for Industry | This final guidance discusses the use of novel trial designs in the development and regulatory review of drugs and biological products, how sponsors may obtain feedback on technical issues related to modeling and simulation, and the types of quantitative and qualitative information that should be submitted for review. | Bayesian Approaches, Complex Innovative Trial Design | Guidance Documents |
| Introduction to ICH E19: Selective Collection of Safety Data in Clinical Trials - YouTube Video | This video explains the internationally harmonized guidance on the use of Selective Safety Data Collection (SSDC) that may be applied in specific late-stage interventional clinical trials that are pre-approval or post-approval. | Selective Safety Data Collection | Supporting Materials |
| Landscape Analysis of the Application of Artificial Intelligence and Machine Learning (AI/ML) in Regulatory Submissions for Drug Development From 2016 to 2021 | This landscape analysis of regulatory submissions of drug and biological products to the FDA from 2016 to 2021 demonstrated that Artificial Intelligence and Machine Learning (AI/ML) has been used successfully to perform a variety of tasks, such as informing drug discovery/repurposing, enhancing clinical trial design elements, dose optimization, enhancing adherence to drug regimen, endpoint/biomarker assessment, and post- marketing surveillance. | Artificial Intelligence / Machine Learning | Journal Articles |
| Leveraging Artificial Intelligence (AI) in Drug and Biological Product Development: An FDA and Clinical Trial Transformation Initiative Workshop Report | Artificial Intelligence (AI) holds immense potential to transform drug development by improving the efficiency and accuracy of key processes across the drug product life cycle. However, the scalable adoption of this technology may be influenced by new and unique challenges. The U.S. Food and Drug Administration collaborated with the Clinical Trial Transformation Initiative to organize a public workshop on Artificial Intelligence in Drug and Biological Product Development in August 2024 with medical product sponsors, technology innovators, academicians, and regulators to discuss guiding principles for the use of AI in drug and biological product development in order to realize its transformative potential. This article synthesizes key insights from the workshop and discusses the emerging current need for policy development to enhance the integration of AI in drug and biological product development. | Artificial Intelligence / Machine Learning | Journal Articles |
| Master Protocols for Drug and Biological Product Development: Draft Guidance for Industry | This draft guidance provides recommendations on the design and analysis of trials conducted under a master protocol as well as guidance on the submission of documentation to support regulatory review. | Master Protocols | Guidance Documents |
| Master Protocols: Efficient Clinical Trial Design Strategies to Expedite Development of Oncology Drugs and Biologics: Final Guidance for Industry | This final guidance provides recommendations to sponsors of drugs or biologics for the treatment of cancer regarding the design and conduct of clinical trials intended to simultaneously evaluate more than one investigational drug and/or more than one cancer type within the same overall trial structure (master protocols) in adult and pediatric cancers. | Master Protocols | Guidance Documents |
| Mitigating Limited Data Challenges to Improve Artificial Intelligence (AI) Integration in Rare Disease Drug Development | The Orphan Drug Act defines a rare disease as a condition affecting fewer than 200,000 people in the United States. However, most rare diseases are categorized as ultrarare or hyper-rare, impacting fewer than 100 individuals worldwide. Developing drugs for these conditions involves multiple challenges, such as geographically dispersed and small patient populations, limited natural history data, and poor disease characterization. Issues related to small patient numbers, scarce natural history information, and clinical heterogeneity within rare diseases can be addressed by various strategies, including using Artificial Intelligence (AI) and advanced analytical methods, leveraging detailed individual-level data, and exploring synthetic data generation to overcome the limitations of small datasets. Moreover, establishing centralized databases and promoting public–private partnerships can help build a more comprehensive repository of available data. | Artificial Intelligence / Machine Learning | Journal Articles |
| Modernizing Research and Evidence (MoRE) Consensus Definitions: An FDA-NIH Collaboration | This document is intended to facilitate communication within the clinical research community by helping establish a common vocabulary to more uniformly characterize clinical research. This includes innovative trial designs and studies using real-world data, that support scientific, clinical, and/or regulatory decision-making. By referencing this consensus terminology, the research community may be better situated to evaluate potential strengths and limitations of individual studies, and to convey clinical research findings in a meaningful way to funders, reviewers, and the public. | Cross-Cutting Resources | Supporting Materials |
| Policy for Device Software Functions and Mobile Medical Applications (MMAs) | FDA is issuing this guidance to communicate how the Agency intends to apply its regulatory oversight to certain software, including device software functions and Mobile Medical Applications (MMAs) intended for use on mobile platforms or on general-purpose computing platforms. FDA intends to apply its regulatory oversight to those device software functions that meet the definition of a medical device and whose functionality could pose a risk to a patient’s safety if the device were not to function as intended. This guidance describes FDA’s policy for device software functions and MMAs that meet the device definition, including some that are the focus of FDA’s regulatory oversight and some for which FDA does not intend to enforce requirements under the Federal Food, Drug, and Cosmetic Act (FD&C Act). This guidance also provides information on some software functions that do not meet the device definition, and as such, are software functions that are not subject to applicable FDA regulatory requirements. | Digital Health Technology | Guidance Documents |
| Project Pragmatica | Project Pragmatica seeks to introduce functional efficiencies and enhance patient centricity by integrating aspects of clinical trials with real-world routine clinical practice through the appropriate use of pragmatic design elements, aiming to make clinical trials more relevant and integrated into everyday healthcare practices. | Integrating Trials into Routine Clinical Practice | Programs |
| Public Workshop: Understanding Priorities for the Development of Digital Health Technologies (DHTs) to Support Clinical Trials for Drug Development and Review (2023) | The FDA and the Duke-Robert J. Margolis, MD Center for Health Policy hosted a virtual public workshop to understand the priorities for the development of Digital Health Technologies (DHTs) to support clinical drug trials, including accessibility and clinical outcomes measures using DHTs. | Digital Health Technology | Public Meeting or Workshop Content |
| Rare Diseases: Considerations for the Development of Drugs and Biological Products: Final Guidance for Industry | This final guidance is intended to assist sponsors of drugs and biological products for treatment of rare diseases in conducting efficient and successful drug development programs through a discussion of selected issues commonly encountered in rare disease drug development. | Cross-Cutting Resources | Guidance Documents |
| Real-World Data (RWD) and Real-World Evidence (RWE)-focused Demonstration Projects | To promote shared learning and understanding with external stakeholders, FDA continues to oversee and support numerous demonstration (i.e. research) projects through multiple venues and programs including the broad agency announcements, funding opportunity announcements, Centers of Excellence in Regulatory Science and Innovation (CERSI) grants, and inter-agency agreements. This webpage lists ongoing and completed demonstration projects focused on Real-World Data (RWD) and Real-World Evidence (RWE). | Real World Evidence/Data | Programs |
| Real-World Data (RWD) as External Controls Case Study: BALVERSA (erdafitinib) | Erdafitinib is a New Molecular Entity (NME) approved for the treatment of adult patients with locally advanced or metastatic Urothelial Carcinoma (UC) that included patient-level Real-World Data (RWD) as an external control. However, the FDA review concluded that the RWD submitted were uninterpretable due to data quality and completeness issues (such as the lack of standardized approach in assessing tumor response, a small sample size, differential selection of comparison groups, and missing data) and deemed the results inconclusive. Therefore, there is no discussion of the Real-World Evidence (RWE) in the BALVERSA US Prescribing Information. These conclusions raise awareness around challenges and issues when using external control data to potentially aid in making submissions using RWD of better quality (pp. 1-27). | Real World Evidence/Data | Case Studies |
| Real-World Data (RWD) as External Controls Case Study: BRINEURA (cerliponase alfa) | Cerliponase alfa is a biologics New Molecular Entity (NME) approved for the treatment of neuronal Ceroid Lipofuscinosis type 2 (CLN2 disease) that included patient-level Real-World Data (RWD) as an external control. The FDA requested multiple comparative analyses between the clinical trial and the historical control. Despite finding some differences in patient characteristics between the two cohorts and the use of a different version of the clinician-reported outcomes (ClinROs) than the one used in the clinical trial, the BRINEURA US Prescribing Information includes the comparative analysis results on the motor domain of the CLN2 rating scale. These conclusions raise awareness around challenges and issues when using external control data to potentially aid in making submissions using RWD of better quality (pp. 9-27). | Real World Evidence/Data | Case Studies |
| Real-World Data (RWD) as External Controls Case Study: XPOVIO (selinexor) | Selinexor is a is a New Molecular Entity (NME) approved to treat patients with relapsed refractory multiple myeloma that included patient-level Real-World Data (RWD) as an external control. However, the FDA review concluded that the evidence generated from the RWD was inadequate to provide context or comparison for the overall survival observed in the single-arm trial. Therefore, there is no discussion of the Real-World Evidence (RWE) in the XPOVIO US Prescribing Information. These conclusions raise awareness around challenges and issues when using external control data to potentially aid in making submissions using RWD of better quality (pp. 83-91). | Real World Evidence/Data | Case Studies |
| Real-World Data (RWD): Assessing Electronic Health Records (EHRs) and Medical Claims Data to Support Regulatory Decision-Making for Drug and Biological Products: Final Guidance for Industry | This final guidance is intended to provide sponsors and other interested parties with considerations when proposing to use Electronic Health Records (EHRs) or medical claims data in clinical studies to support a regulatory decision for effectiveness or safety. | Real World Evidence/Data | Guidance Documents |
| Real-World Data (RWD): Assessing Registries to Support Regulatory Decision-Making for Drug and Biological Products: Final Guidance for Industry | This final guidance provides comprehensive recommendations on designing a registry or using a pre-existing one to support regulatory decision-making, offering a detailed approach to utilizing registries in the regulatory process. | Real World Evidence/Data | Guidance Documents |
| Real-World Evidence (RWE) Submissions to FDA’s Center for Drug Evaluation and Research (CDER) | As part of the reauthorization of the Prescription Drug User Fee Act (PDUFA VII), FDA committed to reporting aggregate and anonymized information on submissions to CDER that contain Real-World Evidence (RWE). This webpage describes submissions to CDER containing RWE that meet specified reporting criteria. This report is not intended to include all submissions to CDER containing analyses of Real-World Data (RWD). Columns will be added annually to represent submissions by Fiscal Year (FY) from FYs 2023 through 2027. | Real World Evidence/Data | Supporting Materials |
| Selective Safety Data Collection (SSDC) White Paper | This white paper describes the International Council for Harmonization’s (ICH) E19 guidance on Selective Safety Data Collection (SSDC); discusses trials that have incorporated SSDC in the oncology and cardiometabolic spaces; and explores the potential future use of SSDC. | Selective Safety Data Collection | Supporting Materials |
| The Role of Artificial Intelligence (AI) in Clinical Trial Design and Research Q&A with FDA Podcast | Artificial Intelligence (AI) and Machine Learning (ML) are gaining traction in clinical research, changing the clinical trial landscape, and is increasingly being targeted in areas where FDA is actively engaged, including clinical trial design, Digital Health Technologies (DHTs), and Real World Data (RWD) analytics. This podcast discusses recent advances and the use of technology in clinical trial design. | Artificial Intelligence / Machine Learning | Supporting Materials |
| Use of Electronic Health Record (EHR) Data in Clinical Investigations: Final Guidance for Industry | This final guidance is intended to assist sponsors, clinical investigators, Contract Research Organizations (CROs), Institutional Review Boards (IRBs), and other interested parties on the use of Electronic Health Record (EHR) data in FDA-regulated clinical investigators. | Digital Health Technology | Guidance Documents |
| Using Bayesian Statistical Approaches to Advance our Ability to Evaluate Drug Products Q&A with Podcast | In this podcast, Jennifer Clark, a Lead Mathematical Statistician in the Center for Drug Evaluation and Research (CDER) at the FDA, discussed the recent changes in the use of Bayesian statistics and the therapeutic areas in which Bayesian statistics can be particularly helpful. | Bayesian Approaches | Supporting Materials |