DIA Global Forum Driving Insights to Action typography logo
DIA Global Forum Driving Insights to Action typography logo
May 2026

Subscribe

Love Global Forum’s online format? Subscribe today and never miss an issue.

Editorial Board

Content stream editors

Translational science
Gary Kelloff US National Institutes of Health
Ilan Kirsch Adaptive Biotechnologies Corp.

regulatory science
Isaac Rodriguez-Chavez 4Biosolutions Consulting

Patient engagement
Stacy Hurt Parexel
Richie Kahn Canary Advisors

Data and Digital
Lisa Barbadora Barbadora INK

VALUE AND ACCESS
Wyatt Gotbetter Cytel, Inc.

Editorial Staff

Alberto Grignolo, Editor-in-Chief

Sandra Blumenrath, Executive Editor, Scientific Publications & Senior Scientific Program Manager, DIA Scientific Communications

Chris M. Slawecki, Managing Editor, Global Forum DIA Scientific Communications

Linda Felaco, Copy Editor and Proofreader

Regional Editors

AFRICA
Lorraine Danks The Gates Foundation

ASEAN
Helene Sou Takeda

AUSTRALIA/NEW ZEALAND
Richard Day University of New South Wales, Medicine, St. Vincent’s Hospital

CHINA
Li Wang Eli Lilly China

EUROPE
Emma Du Four Independent R&D/Regulatory Policy Professional
Isabelle Stoeckert Independent Regulatory Science Expert

INDIA
J. Vijay Venkatraman Oviya MedSafe

JAPAN
Toshiyoshi Tominaga SunFlare

LATIN AMERICA
Cammilla Gomes Roche

DIA Membership

Bringing together stakeholders for the betterment of global healthcare.

White Paper

Cover of Docuvera: AI + Structured Content

White Paper

Enhancing Chemical Entity Recognition and Contextual Understanding from Unstructured Text Using Regular Expressions and Large Language Models Based Multi-Model Pipelines
This paper demonstrates how a pre-trained named entity recognition model, regular expression techniques, and a large language model can be integrated and leveraged to identify and extract key information—such as starting materials, suppliers, and supplier-related details—without the need for extensive ground truth data sets. This framework bypasses other methods like fine-tuning, retrieval augmented generation, or rule-based extraction, significantly enhancing both the efficiency and comprehensiveness of the extraction process while reducing costs and the reliance on large-scale labeled data.

White Paper

Cover of Docuvera: AI + Structured Content

White Paper

Self-Driving CAR: The Promise of In Vivo CAR-T Therapy for Hematologic Malignancies
In vivo CAR-T therapies represent a paradigm shift in immuno-oncology, offering a promising approach toward scalable, accessible, cost-effective treatment for hematologic malignancies. In vivo CAR-T platforms eliminate the need for complex cell harvesting and manufacturing, potentially broadening patient access and reducing logistical burdens. As these therapies advance, their transformative potential in hematologic malignancies becomes increasingly evident, with early data suggesting robust efficacy and safety.
Thanks for reading our May 2026 Issue!
Views and opinions expressed in Global Forum are those of the authors alone and do not necessarily represent those of DIA or any other agency, organization, employer, or company. DIA does not guarantee the accuracy or completeness of any information published in Global Forum and will not be responsible for any errors, omissions, or claims for damages, including exemplary damages, arising out of use, inability to use, or with regard to the accuracy or sufficiency of the information contained in Global Forum.