A groundbreaking initiative aims to manufacture medications on-site, bypassing traditional production processes
The EQUIP-A-Pharma program, a collaboration between DARPA and the HHS Administration for Strategic Preparedness and Response (ASPR), is setting out to revolutionise the pharmaceutical industry in the United States. The initiative aims to establish a qualification process for agile pharmaceutical manufacturing, ensuring a resilient and flexible supply chain of essential medicines.
Dr. Sushma Savarala, a program manager at DARPA and a scientist with the U.S. Department of Health and Human Services, oversees the EQUIP-A-Pharma program. The program seeks to address U.S.-based manufacturing of at least eight Active Pharmaceutical Ingredients (APIs) and unfinished drug formulations of various forms, including injectables and oral medications.
The key purpose of EQUIP-A-Pharma is to create a structured qualification framework that enables faster regulatory approval and more agile production capabilities. By doing so, the program aims to mitigate vulnerabilities in medicine supply caused by traditional manufacturing constraints and supply chain disruptions.
EQUIP-A-Pharma is not just about speed, however. It also focuses on point-of-need manufacturing at hospitals, military bases, and disaster zones to ensure rapid drug availability during emergencies. The program will utilise artificial intelligence, machine learning, and metrology to ensure FDA approval, producing drugs from domestic raw materials, targeting those on the FDA's drug shortage-list medicines.
The program is also designed to make quick decisions and ensure a secure supply of essential medicines. It will do this by using a real-time digital regulatory approval framework for multiple finished drug products produced in a single, reprogrammable hardware platform.
EQUIP-A-Pharma is a two-year program, with the first three to six months dedicated to figuring out collaborations. The following 12 to 18 months will be spent manufacturing APIs and finished drug formulations. The last three to six months will be used to integrate new technology and artificial intelligence into the manufacturing process.
Key performers in the private sector are involved in the program, with Brightpath Labs, Mark Cuban Cosplay Structs, Rutgers, and Battelle designing, building, and operating the agile manufacturing platforms. The program is also intended to get into the ASPR IBMSC supply chain portfolio for supply chain resilience, reducing reliance on foreign suppliers, and mitigating geopolitical disruptions.
Moreover, EQUIP-A-Pharma aims to use at least 80% of the in-process data for quality attributes, as opposed to the traditional less than 1% usage. The FDA is collaborating with EQUIP-A-Pharma performers and government agencies to use this data for FDA approval decisions.
In summary, EQUIP-A-Pharma serves as a strategic federal program to streamline pharmaceutical manufacturing qualification, enhancing agility and security in medicine supply chains across the U.S. The program leverages collaborative innovation to address current and future public health challenges, moving away from traditional manufacturing towards agile, modular manufacturing to adapt to changing demands.
- By utilizing technology such as artificial intelligence and machine learning, the EQUIP-A-Pharma program aims to reimagine the federal workforce by integrating new technology and artificial intelligence into the pharmaceutical manufacturing process.
- In addition to speed, the EQUIP-A-Pharma program focuses on health-and-wellness by producing drugs from domestic raw materials, targeting those on the FDA's drug shortage-list medicines and ensuring rapid drug availability during emergencies.
- The EQUIP-A-Pharma program seeks to revolutionize the pharmaceutical industry by creating a qualification framework for agile pharmaceutical manufacturing, including science-based approaches like point-of-need manufacturing at hospitals, military bases, and disaster zones, and the implementation of therapies-and-treatments using at least 80% of the in-process data for quality attributes.
