Collaborative Accord

A Shared Commitment to Onshoring Critical Medicine Manufacturing to Secure America’s Health and Prosperity

The undersigned Original Signatories, as the foremost leaders in artificial intelligence, medicine manufacturing, and public policy in the United States, express their shared commitment to onshoring critical medicine manufacturing to secure America’s health and prosperity through this Collaborative Accord.

The United States faces an unprecedented opportunity to rapidly expand its capabilities in AI-driven manufacturing, thereby increasing its national security and the reliability of its healthcare delivery systems by bringing a greater portion of its pharmaceutical supply chain into the United States’ domestic manufacturing environment. By leveraging cutting-edge artificial intelligence and advanced manufacturing technologies, the United States can enter a period of manufacturing renaissance to return essential medicine production to the United States through development and implementation of AI-driven processes. This opportunity will dramatically lower costs while improving manufacturing efficiency and quality control and reduce production bottlenecks.

To address the national opportunity of expanding American drug manufacturing through the use of AI-enabled pharmaceutical innovation, the Original Signatories have come together at the Making Medicines in America: How Congress Can Help America’s AI, Biopharma, and Manufacturing Industries Make It Happen to confirm their shared commitment and harness their collective expertise to accelerate AI-based drug manufacturing in the United States

Given this shared vision, the Original Signatories express their shared commitment through a set of Shared Objectives to enhance national security, provide for economic revitalization of American manufacturing communities, establish supply chain resilience, accelerate innovation, ensure technological leadership and workforce development, and reduce costs of essential medicines for American families.

Shared objectives

In furtherance of the commitment to explore the intersection of AI and medicine manufacturing to advance American manufacturing capabilities, the Original Signatories agree to collaborate to achieve these Shared Objectives:

Objective 1

Development of new and innovative AI-enabled, U.S.-based medicine manufacturing platforms that dramatically reduce production costs and recall-related expenses.

Objective 2

Creation of flexible, distributed medicine manufacturing networks across America to prevent geographic concentration vulnerabilities.

Objective 3

Implementation of real-time quality control, predictive maintenance, and agile supply chain management systems within the United States.

Objective 4

Establishment of innovative training programs that prepare 100,000+ Americans over the coming decade for advanced careers in medicine manufacturing, quality control, and process optimization.

Objective 5

Acceleration of U.S. regulatory pathways for innovative manufacturing technologies, while ensuring safety.

Objective 6

Enhancing America’s rich natural environment through clean chemistry innovations, optimizing manufacturing processes and reducing waste.

Signatories

• Blackstone Life Sciences
• Eli Lilly and Company
• Google
• Phlow
• Purdue University
• Texas A&M Health
• Alliance for Building Better Medicine
• Commonwealth Center for Advanced Manufacturing
• DIANT Pharma
• Evonik Corporation
• Global Health Sciences and Biotech
• Information Technology and Innovation Foundation
• International Pharma Quality
• Long Island University
• National Institute for Pharmaceutical Technology and Education
• Occam Systems
• On Demand Pharmaceuticals
• Quality Co
• Skellig Automation
• Stamato Solutions LLC
• Trident Agricultural Solutions
• Uniformed Services University
• University of Connecticut
• University of Maryland
• Virginia Commonwealth University
• University of Minnesota
• Sentio BioSciences LLC

Additional Signatories who are committed to these Objectives may be added to the Accord from time-to-time.