DIA 2025 Global Annual Meeting

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-527-L04-P; CME 1.00; RN 1.00

This forum will present: Health Authority (HA) expectations related to QbD, RBQM, and issue management. Specific examples of how study teams use technology to overcome challenges in achieving QbD and effective RBQM and issue management practices.

Learning Objectives

Discuss Health Authority (HA) expectations when designing and executing clinical studies, specifically for using risk proportionate approaches in Quality by Design (QbD), Risk Based Quality Management (RBQM) and issue management practices; Describe real life examples of technologies that facilitate the achievement of HA expectations for QbD, RBQM, and issue management.

Chair

Michael Torok, PhD

Speaker

Industry Perspective
Michael Torok, PhD

FDA Perspective
Emily Gebbia, JD

Industry Perspective
Kevin Richards, MSc

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-522-L04-P; CME 1.00; RN 1.00

This session will discuss regulatory guidance and frameworks on data quality and feasibility assessments of RWD sources to support RWE study proposals for health authority and HTA engagements. It will include information about how to leverage diverse sources of targeted RWD to support and improve the evolution of new therapies and indications for product safety, outcomes, reduction in label use, and help HC professionals and industry to create a knowledge base for use of RWD.

Learning Objectives

Examine the challenges and value of real-world data (RWD) in regulatory decision-making; Discuss the guidance on data quality and feasibility of RWD for real-world evidence (RWE) studies in health authority interactions; Recognize strategies to integrate RWD into dossier data for regulatory alignment; Identify challenges in standardizing data collection and sharing across industry, regulators, and academia for global acceptance.

Chair

Mehmet Burcu, PhD, MS, FISPE

Speaker

Data Quality Framework and EMA Regulatory Perspectives
Patrice Verpillat, DrMed, MD, PhD, MPH

Oncology Quality, Characterization, and Assessment of Real-world Data: US Regulatory Perspectives
Donna Rivera, PharmD, MSc, FISPE

Building Transparency and Reproducibility into the Practice of Real World Evidence Research
Shirley Wang, PhD, MSc, FISPE

Facilitating robust evidence: integrating digital health data into multi-modal datasets
Laura Lasiter, PhD

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Application UAN: 0286-0000-25-537-L04-P; CME 1.00; RN 1.00

Internal solutions for translation between regulatory frameworks to assure right use of control vocabularies to exchange data as structured components of submissions to Health Authorities, CMOs, CROs. Controlled vocabularies, ontologies. Presentation of AstraZeneca core data model to enable translation and mapping for data integration. Define use case for data exchange (with data products TLFs for CMC, iterative submissions by piloting the future representing data). vocabularies and business rules to enable data exchange. How everything connects to each Semantic Model Patient Centric vs. Study Centric for reuse scenarios - Amgen will present PQI Standards to build a regulatory filing. Map different regulatory standards frameworks to enable translation between frameworks (e.g., EMA and FDA) into messaging standards like HL7 FHIR. Reference Global Standard benefits (ICH M4Q, Q12, SPQS, IDMP and GSRS). A call for a solution – Interoperability as a Plan – open sources and sharing the standards – What would Dom do? Exchanging structured data everyone is using the same viewer in the Amgen use case. Regulatory Authorities and Industry must collaborate on solutions for development and application of standards to data. Platforms like Accumulus[2] to present the CMC data and translate from one set of standards to another for access for information. Finally we will discuss Digitial Equity and Transparency - one set of data, exposed in a variable output to HAs, supporting critical operations from source whilst maintaining the Digitial nature of data.

Learning Objectives

Analyze how regulatory digital data from divergent sources must meet varying ICH and Regulatory Authority standards; Identify gaps in applying external regulatory standards to internal industry data; Examine specific examples of gaps, such as enhanced granularity and differing regulatory frameworks (e.g., IDMP, XEVMPD, GSRS); Discuss AI and RWD Dimensions of interoperability.

Chair

David Sidney Ross, MBA, MSc, PMP, RAC

Speaker

Panelist
Mathew Woodwark, PhD

Panelist
Dominique Lagrave, PharmD

Panelist
Rita Algorri, PhD, MS

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-553-L04-P; CME 1.00; RN 1.00

Leveraging expertise from industry, this session will explore how responsible AI frameworks can maximize value in clinical research by ensuring ethical, transparent, and accountable AI use.

Learning Objectives

Recognize a set of responsible principles for AI in drug development; Demonstrate the ability to drive innovation and strategic advantage in AI-driven clinical trials; Propose how organizations can lead with integrity and trust in their AI applications.

Chair

Stephen Pyke, MSc

Speaker

Patient Perspectives on AI in Clinical Development
Alicia Staley, MBA, MS

Regulatory Perspectives on AI in Clinical Development
Tala Fakhouri, PhD, MPH

Industry Perspectives on AI in Clinical Development
Raja Shankar, MS

Component Type: Session
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-565-L04-P; CME 1.00; RN 1.00

This session will explore how Intelligent Automation (IA) and AI are transforming R&D and Regulatory Affairs by automating document-heavy processes and driving data-driven decisions. Through real-world use cases presented by industry sponsors, attendees will learn how IA and AI are being applied to improve efficiencies, reduce timelines, and optimize regulatory outcomes.

Learning Objectives

Discuss AI applications in drug discovery, clinical trials, regulatory, manufacturing, and pharmacovigilance; Explain practical use cases of AI in Regulatory Operations; Discuss advancing fit-for-purpose, risk-based frameworks for AI and global regulatory alignment.

Chair

Venkatraman Balasubramanian, PhD, MBA

Speaker

AI: Accelerating Clinical Trials Through Smarter Site Selection
Kevin Bugin, PhD, MS, RAC

Harnessing AI and Automation in Regulatory:  Insights from the Recent DIA RIM Intelligent Automation Survey
Cary Smithson, MBA

Industry Perspective
Cedric Berger, PhD, MBA

AI-Driven Drug Development: Transforming Life Sciences with Data and Innovation
Sridevi Nagarajan, PhD, MS, MSc

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-580-L04-P; CME 1.00; RN 1.00

As the potential of artificial intelligence (AI) is increasingly piquing the interest of researchers, it is critical to discuss how AI works by learning from the data, and how to leverage the results. Choosing the right approach for integrating AI into the research lifecycle can have a massive impact on likelihood of success. As one example, both generalized large language models (LLMs) and purpose build LLMs have their place in the toolkit needed to curate and analyze medical record and other patient data. This presentation will explore best practices/learnings for ensuring the right AI tool for the task, including LLMs tailored to the unique needs of pharmaceutical research; the impact for pharmaceutical research and for patients this can have; and imperatives for ascertaining the reliability and trustworthiness of AI solutions for medical research, as key to promoting adoption and responsible deployment in practice.

Learning Objectives

Identify key factors to consider when selecting AI strategies and methods for use in biomedical settings and pharmaceutical research; Describe the potential impact of AI strategies on research quality and patient outcomes, using real-world use cases and examples; Apply a framework to evaluate the suitability and readiness of AI technologies, such as large language models, for specific research or clinical applications.

Chair

Sarah Lyons, MSc

Speaker

Trust and adoption: ensuring the reliability of AI for Life Sciences applications
Patrick Michael Loerch, PhD

The right AI tool for the task: real world learnings and patient impacts from deploying LLMs in healthcare
Sarah Lyons, MSc

Deploying Large Language Models in Clinical Research: Practical Considerations from Real World Examples
Troy Astorino

Component Type: Session
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-596-L04-P; CME 1.00; RN 1.00

This panel will explore available regulatory guidance for Real-World Data (RWD) quality assessments and how available implementing tools and frameworks may be applied for different types of RWD and use cases to help drug developers meet regulator expectations.

Learning Objectives

Recognize expectations for real-world data (RWD) quality assessments in studies intended for regulatory submissions; Identify available tools and frameworks for implementing RWD quality assessments; Explain ways to incorporate RWD quality assessments into internal workflows using practical approaches.

Chair

Eleanor Caplan, PharmD, PhD

Speaker

Regulatory Perspective
Luis Pinheiro, PharmD, MSc

Balancing Patient Centricity with Data Quality Requirements and Standards
Jennifer Farmer, MS

Learnings About Meeting RWD Fitness Standards From FDA Use Cases
Ulka B Campbell, PhD

Can we Separate Relevance From Reliability in Data Quality Assessments? Quality-by-Design in Secondary use of Health Data
Pamela Dobay, PhD, MSc

Component Type: Session
Level: Basic
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-613-L04-P; CME 1.00; RN 1.00

Come learn about the exciting world of digital protocol and how industry innovators are taking the steps to transition from a document-centric protocol to a data-centric protocol, how digital protocol use cases can be enabled by AI and listen to how you and your company can prepare for the change required to adopt digital protocol. We will start with an update on current initiatives including the TransCelerate Digital Data Flow (DDF), CDISC Unified Study Definition Model (USDM), and ICH M11 Harmonized Protocol. Then we will explore the opportunities industry has to leverage structured digital protocol information to enable AI supporting innovation such as intelligent study design, automation of data transformation and analysis, and dynamic regulatory review. Finally, we will share our thoughts on how you and your company can get started on the digital protocol journey.

Learning Objectives

Recognize how various digital protocol collaborations are working together including ICH M11, TransCelerate/CDISC Digital Data Flow, and the HL7 Vulcan Unified Digital Protocol; Discuss possible AI use cases that can leverage standard digital protocol information; Describe ways to prepare for the implementation and change management required to realize the benefits of a digital protocol.

Chair

Chris Decker, MS

Speaker

Preparing for ICH M11 and Digital Protocols: Am I Ready?
William Illis, MPH

Industry Initiative Updates: How TransCelerate DDF, CDISC, USDM, and ICH M11 are Transforming Industry
Chris Decker, MS

Opportunities for Digital Protocol Standards Can Enable AI
Robert DiCicco, PharmD

Component Type: Session
Level: Basic
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-625-L04-P; CME 1.00; RN 1.00

Single Arm Trials (SATs) are an important tool for drug development. The session while focusing on EMA and FDA guidance documents on SATs, will discuss how external information can support the needs and expectations of regulators and HTA globally.

Learning Objectives

Identify the appropriate settings and considerations for using external evidence in designing single-arm trials; Discuss strategies for advancing global drug development with single-arm trials, addressing differing perspectives among regulators and between regulators and HTA representatives.

Chair

Solange Corriol-Rohou, DrMed, MD, PhD

Speaker

Panelist
Alex Bliu, PhD

Panelist
Andrew Thomson, PhD, MA, MS

Panelist
Anja Schiel, PhD

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-640-L04-P; CME 1.00; RN 1.00

As Asia’s post-marketing surveillance evolves with the use of RWD, aligning these changes with US and European frameworks is essential. This session will cover Asia’s regulatory shifts and provide insights for global harmonization efforts.

Learning Objectives

Discuss how the integration of RWD into post-marketing surveillance has improved safety monitoring and formulate strategies for applying these practices within your own organization's regulatory and safety frameworks; Analyze case studies on PMS in Asia to propose actionable approaches for implementing database-driven RMP systems in your organization, enhancing selective safety measures.

Chair

Judy Shin, PhD

Speaker

Evolving Post-Marketing Safety Management in Korea: Opportunities and Challenges in Integrating RMP
Min-Jung Lim, MPharm, RPh

Case study using Real-World Data on Post-Marketing Safety in Korea
Bonggi Kim, PhD

An Overview of RWD Utilization for Drug Safety Assessment in PMDA
Shinya Watanabe

Global Perspectives on Post-Marketing Safety: Leveraging RWD for Regulatory Alignment and Enhanced Safety Surveilance
Jeff Lange, PhD

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-641-L04-P; CME 1.00; RN 1.00

Industry leaders explore how biopharma is leveraging data foundations and technological advancements, including AI/ML, to improve access and representation in clinical trials, moving from DEI planning to tangible progress in clinical research.

Learning Objectives

Identify key areas where tech and analytics facilitate health equity advancement, focusing on achievable, tech-enabled outcomes; Illustrate concrete use cases where tech and data can support equitable clinical research, such as community outreach, recruitment, and measurement against diversity action plans; Discuss non-technical success factors needed to wholly support data-driven tech

Chair

Katrina Rice, MS

Speaker

From Data to Action: Driving Inclusive Clinical Trials
Del Smith, PhD, MS

Placing Patients First in the Era of Advanced Tech and AI
Patrick Gee, SR, PhD, MS

Responsible AI and Diversity in Clinical Trials
Nicole Ward, MBA, MS, PMP

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-639-L04-P; CME 1.00; RN 1.00

ICH is advancing a global technology platform for multi-region collaborative review for CMC Post Approval submissions and inspections. Industry and regulators will discuss the platform’s governance, security, financing, and operations and outlook.

Learning Objectives

Explain the drivers, purpose, and plan for a global regulator-industry collaboration platform for PQKM; Describe the platform operating framework including governance and operation implications for regulators and industry participants; Discuss the initiative’s lessons learned and expectations by industry and regulators.

Chair

David C. Isom

Speaker

Industry Perspective
David Sidney Ross, MBA, MSc, PMP, RAC

EMA Perspective
Hilmar Hamann, PhD

Health Canada Perspective
Marcin Boruk, MBA, MSc

Component Type: Session
Level: Basic
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-650-L04-P; CME 1.00; RN 1.00

In the rapidly evolving landscape of medical product development, the utilization of synthetic data and digital twins has gained increasing interest as a transformative approach to accelerate the development and approval of medical products. This session aims to explore the potential and innovative applications of these technologies in the realm of drug development, featuring speakers from health authorities, academia and industry. Dr. Khaled El Emam, Professor at the University of Ottawa, will present his latest research on generating synthetic data to augment clinical trials. Additionally, he will explore practical applications of synthetic data generation using real-world data. Dr. Arman Sabbaghi, Associate Professor at Purdue University, with prior experience at Unlearn, will present AI-Generated Digital Twins to deliver more efficient clinical trials. He will discuss the innovative statistical methodologies and novel trial designs that combine historical data, artificial intelligence (AI), and randomization to deliver smaller, faster, and more powerful RCTs that are built with regulatory guidance in mind. Dr. Ye Li, Mathematical Statistician, CDER, FDA will present statistical challenges leveraging machine learning in clinical trials. Several key factors that may influence the application of ML and AI algorithms in analyzing efficacy data will be discussed, including estimand and type I error rate control, as well as the importance of interpretability and reproducibility of ML/AI models to ensure robust and reliable results. Furthermore, she will address corresponding considerations related to these factors when using Digital Twins.

Learning Objectives

Describe what synthetic data and digital twins are; Discuss ways to apply AI to generate synthetic data and digital twin; Illustrate the potential of synthetic data and digital twins in medical product development.

Chair

Di Zhang, PhD

Speaker

Applications of Synthetic Data in Clinical Trials and Real-World Studies
Khaled El Emam, PhD

Statistical Methods for Unleashing AI-Generated Digital Twins to Deliver More Efficient Randomized Controlled Trials
Arman Sabbaghi, PhD, MA

Leveraging Machine Learning in Clinical Trials: Statistical Challenges
Ye Li, PhD

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-654-L04-P; CME 1.00; RN 1.00

Data privacy has been a long-standing challenge for RWD sharing and evidence generation across geography. This session will discuss privacy-preserving approaches that enable data access, sharing and collaboration in industry, academia and regulatory

Learning Objectives

Identify privacy-preserving approaches for accessing and sharing real-world data (RWD); Discuss use cases leveraging various applications (e.g., federated learning networks, synthetic data) in industry, academia and regulatory for RWE generation; Examine policy and regulatory perspectives, methodological challenges and identify future development needs.

Chair

Echo Wang, DrPH, MPH

Speaker

Global Privacy and Regulatory Considerations for Synthetic Data
Rachele Hendricks-Sturrup, DrSc, MA, MSc

Real-World Applications of Distributed Data Analytic Solutions in Real-World Data Networks
Darren Toh, DrSc, FISPE

Privacy-Preserving Solutions for RWD Access and Collaboration: A Life Science Industry Perspective on Challenges & Opportunities
Mehmet Burcu, PhD, MS, FISPE

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-670-L04-P; CME 1.00; RN 1.00

The use of generative AI (genAI) is already shown to be useful in content generation. The use of genAI to accelerate development and maintainance of data standards and data transformation is rapidly evolving as it will be shown in this session.

Learning Objectives

Describe the current landscape of AI applications for developing, maintaining, and utilizing data standards to accelerate access to source data in electronic health records (EHRs) and registries; Evaluate how AI is currently shaping data standardization and transformation and anticipate its future impact on the landscape.

Chair

Jesper Kjær, MS

Speaker

Industry Feedback to AI in Data Standards
Robert DiCicco, PharmD

OHDSI OMOP AI and open science
Patrick Ryan

CDISC and AI
Chris Decker, MS

HL7 and the AI Train
Charles Jaffe, MD, PhD, FACMI, FACP