Already a DIA Member? Sign in. Not a member? Join.

Sign in

Forgot User ID? or Forgot Password?

Not a Member?

Create Account and Join

Menu Back to Data-in-Clinical-Development

Want to learn more?

If you or your organization want to learn more about DIA’s Research projects or Think Tanks please contact Science@DIAglobal.org.


Contact Us

Data in Clinical Development

Advancing the uses of AI in biopharmaceutical development

To further understand how Artificial Intelligence impacts therapeutic development, DIA is conducting research to demonstrate its potential.


Phase 1. 2017-2018. PharmaTech

In 2018 DIA embarked on Phase 1 of this project with 8 pharmaceutical partners and Tufts University with a goal to further understanding of current and future uses of AI in biopharmaceutical development. The study demonstrated that AI was being utilized in every major function across healthcare, with the highest use occurring in clinical operations functions, followed by pharmacovigilance, safety, and risk management functions.1 (Figure 4) The study also identified that one major hurdle to AI utilization is the lack of validation, which has led to skepticism of AI and hindered widespread adoption.

Figure 4. Phase 1 highlights: Most common uses of AI across Drug Development functions

Figure 4. Phase 1 highlights: Most common uses of AI across Drug Development functions

Phase 2. Currently Launching!

Artificial Intelligence for Adverse Events Prediction: DIA is currently seeking funding partners for this study.

Drug-associated adverse events add hundreds of billions to healthcare expenses every year. The FDA, EMA, and other regulatory agencies are requiring biopharmaceutical companies to implement signal detection systems that identify and analyze adverse events to identify potentially causal relationships between drugs and events. This requires companies to use a monitoring system that is comprehensive and systematic.

In this upcoming study, DIA aims to help further the adoption and implementation of AI in adverse event identification and signal detection in a safety organization. The proposed project aims to:

  • Develop a predictive model for Immune-related adverse events (irAEs) using electronic health records (EHRs) and machine learning.
  • Identify and predict Adverse Drug Reactions (AEs which are causally related to the administration of a drug) using AI/ML methods.
  • Test the viability of AI/ML solutions in the application of signal detection and drug safety monitoring by testing AI/ML algorithm(s) in the ability to extract critical information from source data.

The effort will leverage unique EHR data from MedStar Health and Hackensack Meridian Health as the data source for the study. The Georgetown-Lombardi Comprehensive Cancer Center will be participating as the Academic and Clinical partners in the effort.

Currently Launching!

Examining the Current Use and Return Of Investment of Artificial Intelligence Supporting Drug Development

Tufts CSDD and DIA are inviting organizations to join a working group study comprised of pharmaceutical and biotechnology companies and contract research organizations to examine and map the current use of and experience with artificial intelligence (AI) including machine learning and natural language processing (NLP) that support the continuum of drug development.  This study updates recent advances and opportunities that are being implemented since a 2019 Tufts CSDD – DIA published study and will help companies prioritize AI investment. The study will identify and benchmark applications of AI to areas within clinical operations and development including study design, site identification and patient recruitment, pharmacovigilance, quality assurance, and clinical monitoring and will explore peer company deployment. Tufts CSDD will also assess the impact of AI-uses on the expected net present value (ENPV) of a typical development program and the return on investment (ROI) of AI/ML use in Phase II and III clinical trials.

To learn more about DIA’s Research projects, contact science@diaglobal.org.

Think Tank: In the Shadow of Uncertainty for the Future of Generative AI in Medicines Development: A Collaboration to Illuminate the Way

DIA

In today's dynamic landscape of medical innovation, convening a diverse array of stakeholders is critical. The transformative potential of generative AI in medicine development is undeniable, but its responsible integration requires a collaborative effort like no other. From researchers and clinicians to regulators, tech innovators, and (ultimately) patients, the convergence of all these voices is the crucible in which we forge a future of healthcare that truly serves everyone. The challenges are myriad. But we can harness the diverse recommendations and best practices of generative AI to revolutionize diagnostics, drug discovery, and patient care through collective dialogue.

At the margins of the DIA Annual Meeting 2023, stakeholders from different regions came together to attend a DIAmond session and participate in a Solution Room focused on Generative AI to discuss and explore the requirements and best practices for implementing generative AI in the pursuit of patient benefit. Through their dynamic exchange of ideas, stakeholders revealed the importance of collaboration as a key to overcoming complex challenges. As we look to the future, one thing becomes abundantly clear: Continued collaborative conversations are not just desirable, they are essential. Through these collaborative forums, transparency is fortified, and partnerships are forged, ultimately laying the groundwork for identifying shared solutions that will shape the future of this groundbreaking field.

For DIAmond Session and Solution Room highlights, please refer to DIA Global Forum

To access the Outcomes report, click here

To learn more about DIA’s Research projects, contact science@diaglobal.org.


Be informed and stay engaged.

Don't miss an opportunity - join our mailing list to stay up to date on DIA insights and events.