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GUIDE Program Prepares DOD to Defend Against the Unknown

FEBRUARY 28, 2024 – Significant biological events can take different shapes and sizes, from disease to intentional poisons and anywhere in between. COVID-19 taught the world firsthand that we likely will not know what the next threat is, but that any biological event can cause great harm to society and the world. To meet this reality, Joint Program Executive Office for Chemical, Biological, Radiological and Nuclear Defense (JPEO-CBRND) Joint Project Lead for CBRND Enabling Biotechnologies (JPL EB) submitted concepts and was ultimately funded by the Department of Defense Chemical and Biological Defense Program (CBDP) to launch the Generative Unconstrained Intelligent Drug Engineering (GUIDE) program.

The Defense Department (DOD) Chemical and Biological Defense Program (CBDP) is adapting to this new reality by developing capabilities that could rapidly produce medical countermeasures (MCM) and other solutions to address and combat the threat regardless of the specific threat source and creating MCMs that are less risky altogether. The National Biodefense Strategy outlined the importance of staying ready for any biological incident through investing in capabilities such as manufacturing, developing prototypes, analyzing existing data that could be useful in developing future countermeasures and standardizing clinical trials. The new way forward is to invest in capabilities that can prepare the nation to tackle any challenge rather than working towards a specific threat. While this has always been an important principle of the Chemical and Biological defense enterprise, it is easier to achieve now due to advances in computational and biological technologies, which have made artificial intelligence and machine learning capabilities “smarter,” faster and more accessible.

JPL EB is responsible for accelerating the medical development timeline by creating enablers that allow the medical community to get MCMs to the Warfighter quickly. JPL EB was established in 2019 with a strategic framework that allows for this type of investment into a capabilities-based portfolio. JPL EB continues to invest in new technologies within the Advanced Technology Platforms and Clinical Studies Directorate which focus on rapid solutions, resiliency to withstand iterative improvements to solutions, and supporting operationally relevant delivery of medical countermeasures that are applicable to each scenario or event. One of its current programs that reflect a capabilities-forward strategy is the Generative Unconstrained Intelligent Drug Engineering (GUIDE) program.

GUIDE* is an interagency program between JPL EB, the Department of Energy, other interagency, academic, and industry partners. GUIDE’s mission is to leverage its integrated computational and experimental capabilities to accelerate drug development for the warfighter by harnessing the power of advanced simulation and machine learning. Ultimately GUIDE will yield reduced developmental risk, time, and cost. Traditional drug development requires a “try and fail” process to evaluate effectiveness, safety, and ultimately manufacturing at scale. Using robust integration of computational and experimental design, GUIDE evaluates these variabilities and potential liabilities upfront, prior to extensive (and expensive) product development. Within the systems-of-systems in which GUIDE falls, the moonshot goal of GUIDE is to do this evaluation (up to Phase 1 clinical trials) in 100 days or less: a seismic departure from the traditional timeline and cost which is usually estimated to be ~$1.5B and 10 years of product development per drug candidate. GUIDE ultimately reduces timelines and cost while providing an increased chance of effectiveness, safety, and scale.

In the current wave of advancement in artificial intelligence and machine learning the computational prowess of GUIDE’s high-performance computing (HPC) capabilities may seem like the program’s standout capability, however the real power of GUIDE is the tight integration with experimental design and validation. GUIDE’s integration of HPC capabilities with experimental validation allows for an iterative cycle of computational design-to-experimental validation to quickly generate a plethora of highly effective drug candidate designs under an aggressive timeline.

“The difference with GUIDE and other programs that utilize computational design is the purposeful integration of computational design with rapid experimental methods so that the system can be operationally relevant to Warfighter needs,” said Chris Earnhart, Ph.D. Chief Technology Officer for JPL EB. “Computational drugs can be produced and tested in a dedicated rapid response lab and then manufactured in a facility that gives the government priority access. This program is incredibly robust and enables us to move fast and honor our commitment to rapid response.”

That integration is made possible through the many partnerships formed between interagency, academia, DOD and the DOE. Each entity brings a critical asset to the table to create a process that challenges the norm of drug development to create better solutions faster.

“GUIDE takes the approach of innovation driven through collaboration,” said Rachel Campbell de Bautista, M.S. Program Officer for JPL EB GUIDE. “That is the real power of this program: is its partners and collaborators. The world-renown capabilities and infrastructure our National Laboratories bring are an unquestionable asset, but our people are its core.”

GUIDE is just one element of a larger strategic plan to develop rapid response capabilities within EB and accelerate the medical development timeline. Other projects in that strategic plan include the Accelerated Antibodies program and the Rapid Access to Products in Development (RAPID) program. While GUIDE is just in its second year as a program, it’s demonstrating promising impact to the future of MCM development and CBRN defense.

Story by Kelly Burkhalter
Joint Program Executive Office for Chemical, Biological, Radiological and Nuclear Defense (JPEO-CBRND)

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