EMERGING TECHNOLOGY AND MODERNIZING THE ARMY
THE KNOWN UNKNOWNS
Pacing biological threats with AI using a capabilities-based defense approach.
by Kelly Burkhalter T
he national COVID-19 pandemic response empha- sized the importance of
speed and agility in
combatting a biological threat. Te pandemic caused millions of deaths, illness and impacted day-to-day
life across the globe, emphasizing that despite the type of threat— natural, manmade or otherwise—biological threats can have significant and lasting consequences. DOD historically managed biological threats from a threats-based perspective, that is, creat- ing solutions for specific types of diseases or poisons. What was magnified during the COVID-19 response is that many threats are unpredictable, and the response to unforeseen threats still needs to be swift. Tis understanding initiated a new approach to biological defense. One that relies on having the best capa- bilities available to address any threat, rather than solving for a known handful.
A capabilities-based approach invests in platforms to have the infrastructure, processes and skillsets in place and ready to respond to whatever threat may materialize. A platform, like a monoclonal antibody, could be thought of like a founda- tional starting point that could be easily and quicky adapted to suit different needs. Think of a truck chassis. While there may be different types of cargo containers on the chassis, a new chassis does not need to be developed when it needs to transport different products. Te type of containers put on the chassis can change. Artificial intelligence and machine learning (AI/ML) are platform enablers that can support the derisking, sustainment and preparedness of medical countermeasures (MCM) by modeling the art of the possible before a significant investment is made to further develop an MCM.
Te Joint Program Executive Office for Chemical, Biological, Radiological and Nuclear Defense’s (JPEO-CBRND) Joint Proj- ect Lead for CBRND Enabling Biotechnologies (JPL CBRND EB) has invested in platform technology and is leveraging AI/ML
to support this capabilities-based defense approach. Te Gener- ative Unconstrained Intelligent Drug Engineering (GUIDE) program is at the forefront of examining how to use AI/ML to computationally design MCM candidates and test these candi- dates in dedicated labs. Doing so will accelerate the medical discovery phase and meet the joint force’s needs faster.
HARNESSING THE POWER OF COMPUTING TO STAY PREPARED Te GUIDE gives the medical community a rapid response tool that can increase preparedness posture by speeding up the discovery phase. Using high performance compute powered by capabilities made available to JPEO-CBRND from partners in the Department of Energy, GUIDE reads and analyzes various types of molecular data from studies conducted over the last 50 years and creates algorithms from this data as a prediction tool. AI/ML works exponentially faster than a human and its computa- tions allow it to “see” things on a molecular level that researchers cannot. GUIDE generates upwards of 300 various drug candidate computations that are then tested in a dedicated laboratory by experts and down selected to the best possible candidate. Using this method, GUIDE will identify potential drug candidates that have a high probability of success as measured by meeting four critical attributes (safety, manufacturability, efficacy, phar- macokinetics and pharmacodynamics). Meeting the four critical attributes de-risks the MCM candidate by the medical commu- nity by meeting the attributes more accurately, GUIDE provides an effective solution to de-risk an MCM candidate and fast track it into production.
Often, in the MCM development process, most of the cost comes from developing products that cannot be produced or fielded for reasons such as safety, efficacy or manufacturability. By using AI/ML to examine those features early, the MCM that comes out of the discovery phase is more likely to successfully reach
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