The Advanced Simulation and Computing (ASC) program delivers leading-edge computer platforms, sophisticated physics and engineering codes, and uniquely qualified staff to support addressing a wide variety of stockpile issues for design, physics certification, engineering qualification, and production. The Laboratory Directed Research and Development (LDRD) and Site-Directed Research and Development (SDRD) programs fund leading-edge research and development central to the U.S. DOE national laboratories’ core missions.
Quarterly Highlights | Volume 5, Issue 2 | May 2022
In This Issue
ASC & LDRD Community—Upcoming Events (at time of publication)
- ASC Principal Investigators Meeting, Monterey, CA and LLNL; May 16-19
- Virtual Tri-lab Sustainable Scientific Software Conference (S3C); May 24-26
- Intl. Supercomputing Conference, Frankfurt, Germany; May 30-June 2
- NNSA-CEA Annual Computer Science Collaboration meeting in Paris, France; June 7-9 (invitation-only)
- CORAL-2 (Consortium of ORNL, ANL and LLNL) Quarterly Review in Austin, TX; June 14-16
- ECFM Ribbon Cutting Ceremony at LLNL; June 15
- DOE CSGF Annual Program Review, Crystal Gateway Marriott in Arlington, VA; July 17-21
- 2022 JOWOG-34 at LANL; July 18 week
- Production Simulation Initiative (PSI) Workshop at SNL-NM; July 18 week
- Crossroads QPR in Santa Fe; July 21
- Joint LLNL/LANL Predictive Science Panel (PSP) Meeting at LANL; week of August 8th
- CORAL-2 Quarterly Review at ORNL; August 29-30
Welcome to the second 2022 issue of the NA-114 newsletter - published quarterly to socialize the impactful work being performed by the NNSA laboratories and our other partners. This issue begins with a highlight from Sandia National Laboratories (SNL) where modeling and simulation experts created a hybrid model combining high-fidelity ASC SIERRA solid mechanics models with the fracture stress modeling code FRANC3D to help assess stronglink scratch locations that pose a significant risk of fracture, enabling new ceramic rotor acceptance criterion to be developed and intent stronglink part production to remain on schedule for the B61-12 (a notional stronglink example is shown in the banner image above). Other highlights include:
- Los Alamos National Laboratory’s (LANL’s) evaluation of high explosive (HE) reactive burn models and code improvements, moving toward improved evaluation of HE compositions in support of material production decisions.
- Lawrence Livermore National Laboratory’s (LLNL’s) study of plutonium void swelling - crucially important for plutonium aging.
- Kansas City National Security Campus (KCNSC) developed methods to simulate failure in welded assemblies.
- SNL’s accelerated delivery of new ASC mod-sim capabilities for the W80-4, W87-1, and W93 modernization programs through a 35% reduction in release time for the SIERRA engineering analysis suite.
Please join me in thanking the professionals who delivered the achievements highlighted in this newsletter, in support of our national security mission.
NA-114 Office Director
SNL modeling and simulation helps keep B61-12 intent stronglink parts production on schedule at KCNSC by assessing the impact of imperfections.
Stronglinks are essential parts of a weapon’s nuclear safety theme, where they provide enhanced nuclear detonation safety. A stronglink is a mechanical safety device critical for the weapon’s proper function in various ways (see Figure 1). These devices are designed to be rugged and not readily circumvented by vibration, shock, fires, or electromagnetic fields. The intent stronglink (ISL) is a component designed to function only when use of the weapon has been formally authorized. When scratches that could lead to ceramic cracking in abnormal shock environments were observed on some finished contact assembly rotors produced for the B61-12 ISLs, the product realization team (PRT) requested development of a novel part acceptance criterion to avoid production delays at KCNSC. These rotors are small, precision machined ceramics that must not crack and scratches are known to significantly increase the probability of cracking in ceramics. In response to the PRT request, Sandia’s modeling and simulation experts created a hybrid model combining existing, validated, high-fidelity ASC SIERRA solid mechanics models with the fracture stress modeling code FRANC3D (F3D) to determine which scratch locations pose a significant risk of fracture. Using results from this modeling and simulation analysis, a new ceramic rotor acceptance criterion allowed the B61-12 Program to remove only at-risk parts, enabling ISL production to remain on schedule. The quantitative results from the hybrid modeling process indicate that only axially aligned scratches in high stress locations will experience critical stress intensities with a high probability of cracking. Key new features of the hybrid modeling process are: (1) identifying the rotor under the maximum principal stress, (2) creating a static F3D model for one rotor using higher order elements and methods of mapping dynamic SIERRA surface loads and solid body stress results to F3D conditions, (3) creating an automated process to properly insert surface scratches to the rotor’s mesh in each case for 120 combinations of scratch location and angle, and (4) analyzing F3D results to determine which scratch cases lead to critical stress intensities associated with ceramic cracking. Lessons learned from these ceramic-rotor scratch-induced cracking concerns include the importance of expert-based modeling and simulation resources. Resolving production issues in a timely manner requires a creative combination of hybrid models, expert analysts, and strategic planning to create high-fidelity, validated models before issues arise. (SAND2021-15950 O)
LANL is advancing the means to evaluate high explosive reactive burn models to better inform manufacturing decisions.
Plastic-bonded high explosives (HE) are important throughout the nuclear security enterprise. The process used to make these explosives results in the formation of small pores that can impact both performance and safety. Accurate predictions of these impacts are important for reducing the cost of manufacturing and informing material acceptance criteria.
Simulating what happens to HE when it is shocked is very difficult. Accurate predictions depend on how well the shock front is resolved in calculations, how it is detected, the calibration of the equation-of-state (EOS) for the active part of the HE, and the kinetics for the conversion of the HE reactants into detonation product. Pores can affect all of these considerations. Additionally, each of these processes can interact computationally with one another, complicating the range of applicability of prediction.
To enable LANL to better support production, a verification evaluation of reactive burn models in LANL ASC codes was conducted. It indicated that an improvement in the way a shock front was calculated and propagated through the HE would be beneficial. To this end, a modified shock fitting code was written and modularized. Modularizing the shock fitting code, rather than having the code embedded in the overall burn code, allows for isolation of the solution verification, as well as maintaining independence from details of calibration of the EOS of different HE formulations.
Figure 2 (on right) shows an application of this new shock fitting module applied to a simulation of a rate-stick experiment. Rate-sticks are workhorses for providing calibration tests of HE models. Calibration of reactive flow models, such as Arrhenius Wescott-Stewart-Davis (AWSD) and “Scaled Unified Reactive Front plus” (SURFplus), requires accurate computation of the evolving shock front, facilitated by the shock-fitting numerical method. The new method now allows for decoupling the shock propagation from the calibration. This computational advance is an important step in providing evaluation of HE of different compositions and densities, in support of material production decisions. (LA-UR-21-32292)
LLNL ASC Physics and Engineering Models ab initio calculations of void swelling bias in α- and δ-plutonium deepen understanding of plutonium aging for Stockpile Stewardship efforts.
Plutonium (Pu) metal transforms steadily over time due to self-irradiation. As Pu atoms spontaneously fission, they decay into uranium and helium (He), and the recoil of these fission products disrupts the Pu crystal by kicking numerous Pu atoms off their lattice sites, leaving vacancies behind. Over time, the interstitial Pu atoms and their associated vacancies migrate through the lattice, and along with the diffusing He, the material evolves into one rather different from its pristine starting state. One of the common radiation-induced aging effects in metals is void swelling. To first approximation, the swelling bias, defined as the difference in the magnitudes of the relaxation volumes of the vacancies and the interstitials, can be used as a measure of the propensity of a metal to swell under irradiation. The swelling bias of Pu is a crucially important topic for Pu aging. Experiments have revealed negligible swelling rates in the δ-phase of Pu. In LLNL’s published theoretical work from November 2021, the laboratory proposes a detailed explanation for this behavior which argues that a large swelling bias should be expected for α-phase Pu.
These conclusions result from a new and comprehensive first principles quantum mechanical study of the energies and structures of lattice defects in different crystal phases of Pu. LLNL predicts that in δ-phase Pu, a vacancy shrinks the lattice as much as an interstitial Pu atom expands it. For the low-symmetry α-phase Pu, vacancies are found to be associated with small lattice strains, while interstitials lead to significant lattice expansion. Consequently, the calculated void-swelling bias in α-Pu is found to be comparable in size to that of a simple close-packed metal such as copper (Cu), which is known to exhibit strong propensity to void swelling under irradiation (see the table in Figure 3). LLNL’s explanation of these findings relates to an important discovery: point defects in Pu crystals induce changes in electron correlations and bonding character of nearby lattice sites, which can significantly affect the associated defect formation volumes. Hence, the larger defect volumes in α-Pu stem from defect-induced weakening of the effective interatomic bonding of the neighboring lattice sites, while introduction of point defects in δ-Pu has the opposite effect.
To make these predictions, LLNL used a new theoretical and computational prescription for Pu, developed at the laboratory, in which standard density functional theory (DFT) is augmented with the means to account for relativistic as well as correlation effects among Pu’s outer f-electrons. In both δ- and α-Pu, this treatment results in formation of sizable f-electron spin and orbital moments within atomic spheres around the Pu nuclei, which greatly alters the bonding between atoms and helps bring the theoretical predictions of the phase energies, densities, and structures into good agreement with experiments.
In summary, LLNL’s ab initio results suggest that the classic void-swelling bias is far larger in α-Pu than in δ-Pu, thus swelling is more likely in the α-Pu phase. This illustrates a broader lesson: different crystal phases of the same material can behave very differently under irradiation.
However, it should be noted that conventional void swelling theory may not be reliable for low-symmetry phases such as α-Pu over long time periods. LLNL’s current ab-initio predictions of the density changes due to aging of α-Pu being much larger than δ-Pu can be directly verified experimentally with well-established techniques such as dilatometry. Preliminary dilatometry measurements in α-Pu suggest that the swelling rate may be a factor of 3x to 10x greater than δ-Pu. (LLNL-MI-835019)
Kansas City National Security Campus Advanced Engineering Simulation & Analysis (AESA) has deployed new methods to simulate failure in welded assemblies.
AESA can now perform “virtual cycles of learning” during weld process development and qualification to virtually test many trial welding procedures against strength design requirements, accelerating qualification schedules and reducing costs.
Accurate prediction of tearing in metals requires methods not previously used for simulation in AESA at KCNSC. These problems exhibit necking and failure behavior that cannot be characterized directly from experiments and must use iterative procedures to progressively match material models and simulations to experimental data. Adopting methods demonstrated in Sandia Fracture Challenge entries, a new automatic calibration code was developed in AESA using core routines shared by Sandia. This new code extends the old routines to also calibrate weld material behavior from coupon tensile tests on single tack welds performed at KCNSC as well as base metal behavior from tensile bar test data already available. An overview video of KCNSC AESA is also available at https://kcnsc.doe.gov. (NSC-614-4305 UUR)
SNL accelerated delivery of new ASC mod-sim capabilities for the W80-4, W87-1, and W93 modernization programs through a 35% reduction in release time for the SIERRA engineering analysis suite.
The SIERRA multi-physics engineering analysis suite is a vital tool in design and qualification work for the W80-4, W87-1, and W93 modernization programs. SIERRA has historically released versions with new modeling capabilities on a semi-annual schedule. However, the strategic push to significantly reduce modernization timelines is driving the SIERRA team to deliver new mod/sim capabilities to SNL Nuclear Deterrence (ND) analysts more quickly. As a first step, the team is moving to a quarterly release schedule, with intent to release even more frequently. To support this accelerated delivery schedule, the SIERRA Development and Operations (DevOps) team modernized the SIERRA code checkout, build, test, and packaging automation to enable more agile scheduling and versioning. This modernized system was used to successfully deliver the SIERRA 5.2.1 release about 35% faster than the average over the past two years. Furthermore, a 5.2.2 release was delivered mid-week in one business day from the decision to create the release. Historically, this process took several weeks due to a rigid testing schedule, and process and system issues. The ability to release software faster and more often directly impacts how quickly analysts for the modernization programs can leverage new capabilities in their design and qualification activities. (SAND2022-1938 O)
LANL provides an unexpected insight about inertial confinement fusion fill tubes.
Ignition capsules at the National Ignition Facility (NIF) are exquisitely sensitive to small perturbations that can degrade compression and integrity of the fusion fuel. The fill tubes used for putting deuterium and tritium into the center of the capsule are a particularly important source of asymmetry. Generally speaking, smaller fill tubes are more desirable than larger ones. For this reason, an enormous effort has gone into developing fill tubes as small as 2 microns (more ten times smaller than the width of a human hair). By using the unique capabilities of xRAGE, the LANL team was able to show that while – all things being equal – smaller tubes are better than larger ones, other details of the fill tube are also critical. Figure 5 shows calculations for two high yield shots. The shot with the smaller fill tube (on the right) is calculated to have a larger disruption than the shot with the larger fill tube. This occurred because the smaller fill tube was set more deeply into the capsule, and this shot had a shorter coast time, which increases the amount of time the laser is at full power pushing jetted material towards the center. These types of detailed quantitative insights, uniquely possible with xRAGE, are being used to guide progress toward repeatable ignition. (LA-UR-22-20174)
LLNL Exascale Computing Facility Modernization Project reaches major milestones in preparations to support El Capitan for the ASC program.
The Exascale Computing Facility Modernization (ECFM) project has achieved major milestones in the first half of 2022, as the project approaches successful completion. On February 4, 2022, the U450 electrical power distribution sub-station yard was energized by the Western Area Power Administration (WAPA) utility and remains in service. The work included 115 kilovolt transmission lines, air switches, substation transformers, switch gear, relay control enclosures, and feeders to secondary substations in LLNL building 453. A second major milestone was achieved when Beneficial Occupancy of the facility was completed on February 15, 2022. Punchlist activities proceed, with a ribbon cutting ceremony now scheduled for June 15, 2022. The project is on schedule to soon achieve CD-4, despite the added challenges of the COVID-19 pandemic and related supply chain disruptions over the past two years.
ECFM ensures that Livermore Computing facilities and infrastructure are capable, available, flexible, and adaptable to site future generations of high-performance computing systems for NNSA mission activities. The leadership machine room facility housed in building 453 will have 85 mega-watts of electric power, and 28,000 tons of cooling capacity. This will be enough to ensure the capability to operate the El Capitan exascale system for NNSA, while maintaining concurrent operation of the current 125-PetaFLOPS Sierra system, allowing for uninterrupted continuation of the NNSA stockpile stewardship modeling and simulation activities. (LLNL-MI-835012)
SNL advances fundamental understanding of turbulence – for atmospheric reentry and beyond.
Quantifying random vibrations of weapon components during atmospheric reentry of nuclear delivery systems requires a deep understanding of turbulence during expected flight conditions. For decades, researchers and engineers have relied on turbulence models to make flow simulations practical by approximating the effects of small length-scale turbulent motions. The very smallest turbulent length-scales are called the “dissipation range” and accurate measurements of these length scales remain beyond the reach of state-of-the-art diagnostics due to the extreme spatial and temporal resolutions required. Thus, turbulence models have been primarily informed using solutions to the classic Navier-Stokes equations which models dissipation-scales using a second-order viscous diffusion term. Recently, large-scale simulations on the LANL Trinity and LLNL Sierra high-performance computers using the ASC molecular gas dynamics code SPARTA revealed the details of dynamics in the dissipation range of turbulence and their dominance by thermal molecular fluctuations - effects the Navier-Stokes equations are unable to capture. This result provides an avenue for directly assessing the importance of molecular-level effects on the energy-containing scales of turbulence and lays the groundwork for future modeling and simulation efforts that may provide improved understanding and more accurate engineering predictions for turbulent flows. This capability may also benefit modeling of other technologically relevant processes, such as turbulent mixing and chemically-reacting turbulent flows. (SAND2022-1103 O)
LANL hydrocode xRAGE performs the first integrated capsule implosion simulations supporting success of the MARBLE campaign, including implosion of foam-filled capsules with complex engineered pore structures.
The LANL hydrocode xRAGE uses Eulerian hydrodynamics and adaptive mesh refinement to perform numerous ignition simulations that are very difficult to perform in other codes. For example, LANL scientists have now used xRAGE to perform the first integrated (capsule and hohlraum) implosion simulations that include a capsule support tent. These simulations show a significant distortion of the radiation field near the capsule at early times caused by the presence of the tent. This impacts the implosion shape and contributes to discrepancies that have been observed between implosion shapes predicted in simulations that do not include the support tent and those observed in experiment. Another example is the critical role of 3D xRAGE simulations in the success of LANL’s MARBLE campaign, which involves the implosion of foam-filled capsules with complex engineered pore structures. In these implosions, the different fusion reactants reside in different materials, the materials heat to different temperatures, and do not achieve thermal equilibrium during the experiment. xRAGE is the only code capable of resolving the complex foam geometry necessary to observe this effect in simulation. Overall, xRAGE has been proven invaluable for the design and evaluation of these detailed ignition studies. (LA-UR-22-20174)
Enhancements to LLNL’s ASC-funded Kull code for non-local thermal equilibrium effects enable designers to better match experimental diagnostics at NIF.
Recent advances in Kull have helped LLNL High Energy Density (HED) designers gain a better understanding of experimental data through the ability to better match experimental diagnostics for the Sonoma, Espada, and Hohlraum Wall Heating campaigns. These experiments rely on the Dante detectors, which are time-resolved soft x-ray spectrometers used to infer the x-ray spectrum and hohlraum radiation temperature. Accurately matching the Dante results requires high-fidelity non-local thermal equilibrium (NLTE) physics to model the laser-produced plasmas inside NIF hohlraums. To this end, the Kull code has recently been enhanced to support NLTE material models. Kull now supports both inline NLTE kinetics (via interfacing with the Minikin app) and tabular NLTE properties (via the Opacity client library). The latter approach is computationally inexpensive and allows designers to assess the impact of NLTE physics with relatively rapid turnaround during design iteration studies. These models are currently in active use for designing HED experiments on NIF to validate hohlraum physics, radiation flow, and
general turbulence questions. The NLTE material models are incorporated into Kull in a modular fashion, which will enable rapid adoption of new approaches. One exciting possibility is the use of machine learning to predict NLTE material properties, which is under development at LLNL by the HERMIT project. Such high-fidelity numerical modelling capabilities are expected to grow in importance as the success of the NIF 210808 experiment achieving near-ignition fusion yields opens up ambitious new possibilities for HED experiment designers. (LLNL-MI-834975)
NNSS researcher wins 2022 Sidney D. Drell Science and Technology Award.
Dr. Marylesa Howard, a scientist and mathematician in the Physical Sciences group at the NNSS, has won the 2022 Sidney D. Drell Science and Technology Award from the Intelligence and National Security Alliance (INSA) Achievement Awards Committee. The annual award recognizes one individual who exemplifies excellence in the intelligence, homeland security, and national security communities.
Responding to the award, Howard said, “If I’ve learned one thing on my journey, it’s to apply yourself and dare to dream, even if your dreams lead you to underground tunnels with hard hats and steel-toed boots. Maybe even especially then! My colleagues are a joy, the work is challenging, and I enjoy knowing I’m contributing to our national security. I love my job, and I’m honored to be recognized with this award.”
“I heartily congratulate Marylesa on this well-deserved recognition,” said Mark W. Martinez, NNSS President. “She is an outstanding scientist and a leader in her field, and has made great contributions to the incredible, cutting-edge work happening at the NNSS. We are proud to have her on our team.”
Over the eight years at the NNSS, Howard has contributed significantly to applied mathematics and physics within the Stockpile Stewardship Program. She currently leads the Signal Processing and Applied Mathematics team while maintaining a large scientific role in research and development. Her innovative approach to image segmentation has enabled the weapons laboratories to achieve deliverables not previously achieved with image processing.
Quantitative image analysis goes beyond the more common qualitative approach and enables a new class of information to be extracted. In dynamic experiments, images can be used for recovering information, such as material location, density, and/or velocity, which can then be used in simulation for comparision with current physics models of such processes. The challenge lies within extracting this information from images.
To this end, Howard led a Site-Directed Research and Development (SDRD) project over the course of three years to develop an image segmentation technique for material location identification, assuming that a given material may have different statistical image properties at different spatial locations within an image. She leveraged her machine learning techniques to develop a spatially-aware method for computer-detected material location. Her work has resulted in publications in peer-reviewed journals. The tool developed within this project also has been successfully deployed within the nuclear security enterprise, and the NNSS has licensed it to several national laboratories and to the Massachusetts Institute of Technology (MIT) for use in their image analysis endeavors.
The 2022 INSA Achievement Awards Ceremony took place on February 16, 2022, in Arlington, VA, and included keynote speaker Lt. Gen. Scott D. Berrier, Director of the Defense Intelligence Agency. See a video of Howard’s award acceptance speech. The INSA Achievement Awards annually welcome nearly 250 senior intelligence community leaders from across government, industry, and academia. (Per NNSS classification, this article is confirmed to be unclassified and approved for public release)
LANL’s progress in algorithms makes small, noisy quantum computers viable.
As reported in a new article in Nature Reviews Physics, instead of waiting for fully mature quantum computers to emerge, LANL and other leading institutions have developed hybrid classical/quantum algorithms to extract the most performance - and potentially quantum advantage - from today’s noisy, error-prone hardware. Known as variational quantum algorithms, they use the quantum boxes to manipulate quantum systems while shifting much of the workload to classical computers to let them do what they currently do best: solve optimization problems.
“Quantum computers have the promise to outperform classical computers for certain tasks, but on currently available quantum hardware they can’t run long algorithms. They have too much noise as they interact with the environment, which corrupts the information being processed,” said Marco Cerezo, a physicist specializing in quantum computing, quantum machine learning, and quantum information at LANL and a lead author of the paper. “With variational quantum algorithms, we get the best of both worlds. We can harness the power of quantum computers for tasks that classical computers can’t do easily, then use classical computers to compliment the computational power of quantum devices.”
Current noisy, intermediate scale quantum computers have between 50 and 100 qubits, lose their “quantumness” quickly, and lack error correction, which means requiring more qubits to make up the loss. Since the late 1990s, however, theoreticians have been developing algorithms designed to run on an idealized large, error-correcting, fault-tolerant quantum computer.
“We can’t implement these algorithms yet because they give nonsense results or they require too many qubits. So people realized we needed an approach that adapts to the constraints of the hardware we have - an optimization problem,” said Patrick Coles, a theoretical physicist developing algorithms at LANL and the senior lead author of the paper. “We found we could turn all the problems of interest into optimization problems, potentially with quantum advantage, meaning the quantum computer beats a classical computer at the task,” Coles said. Those problems include simulations for material science and quantum chemistry, factoring numbers, big-data analysis, and virtually every application that has been proposed for quantum computers.
The algorithms are called variational because the optimization process varies the algorithm on the fly, as a kind of machine learning. It changes parameters and logic gates to minimize a cost function, which is a mathematical expression that measures how well the algorithm has performed the task. The problem is solved when the cost function reaches its lowest possible value. In an iterative function in the variational quantum algorithm, the quantum computer estimates the cost function, then passes that result back to the classical computer. The classical computer then adjusts the input parameters and sends them to the quantum computer, which runs the optimization again. Read the full article here. This work is partially funded by the LDRD program. (LA-UR-21-28052)
Neutralizing antibodies for emerging viruses: SNL research defends against COVID-19, preparing for future pandemics.
Researchers at SNL funded by LDRD have created a platform for discovering, designing, and engineering novel antibody countermeasures for emerging viruses. This new process of screening for nanobodies that “neutralize” or disable the virus represents a faster, more effective approach to developing nanobody therapies that prevent or treat viral infection. Traditionally used to treat a variety of conditions, including cancer, autoimmune, and inflammatory diseases, nanobodies are smaller components of conventional antibodies - a vital element of the body’s immune system that defends against disease-causing viruses or bacteria. After screening a large, diverse library of synthetic nanobodies, SNL researchers identified and evaluated several potent nanobodies that can protect against COVID-19. The scientists now aim to replicate this method to defend against current and future biological threats.
“The coronavirus pandemic has made evident the need for a broad range of preventive and therapeutic strategies to control diseases associated with novel viruses,” said Craig Tewell, director of SNL’s Chemical, Biological, Radiological, and Nuclear Defense and Energy Technologies Center. “With a deep understanding of how infectious disease develops and spreads, as well as how the immune system defends from infection,” Tewell said, “our researchers are in a unique position to advance the creation of a wide array of disease-fighting tools, including nanobodies.”
Virologist Brooke Harmon leads SNL’s nanobody research, a new and growing area of bioscience. “Vaccines are very good at preventing infection, but they can take a long time to be developed and move through the regulatory process,” Harmon said. “We saw a critical need to create effective therapies that can be rapidly developed and deployed.”
Once the protein sequence, or genetic coding, of a virus has been identified, SNL researchers have shown they can produce a nanobody-based countermeasure within 90 days. The method has not yet been tested on humans. Speeding up the discovery of neutralizing antibodies could reduce the impact of future viral outbreaks.
“Under current practice, virologists rely upon patients’ blood samples to build an antibody library that we can then screen for potential treatments. This means we have to wait, either for people to become infected or for those who are vaccinated to build an immune response,” Harmon said. “Sandia’s new method is more forward thinking. Because we have already built a highly diverse, proprietary library, we can begin to screen for extremely potent neutralizing nanobodies as soon as the genetic coding of a virus has been identified."
Neutralizing nanobodies represent an attractive strategy, Harmon said, due to their ability to work effectively against an entire family of viruses or variants. “We can take advantage of the fact that virus families tend to interact with immune response in the same way,” Harmon said. “This makes our treatments rapidly adaptable to all variants of a virus.”
Nanobodies are modular, meaning they can be combined with other nanobodies to increase their ability to bind to the virus or target specific tissues. Nanobodies can also be produced as smaller versions of conventional antibodies with the ability to engage the immune response.
Additionally, due to the small size of the nanobodies, they can be released into the blood and penetrate tissues more thoroughly than conventional antibodies, they can target an infection site directly (decreasing the dose needed and increasing efficacy), and can be administered via aerosol orally or in an inhalable form. “All of these qualities and features of nanobodies make nanobody therapies more effective than current solutions. These treatments are also easier and cheaper to manufacture,” Harmon said.
SNL’s research on nanobodies for emerging viruses (funded by LDRD) received national recognition in October as a recipient of a 2021 R&D 100 Award, which honors the 100 most technologically significant products and advancements in the past year. This research also received acclamation at the 2021 National Lab Accelerator Pitch Event, where scientists present seasoned investors with business model ideas based on innovations at DOE laboratories. A video of Sandia’s presentation at the event can be viewed here. Sandia is currently exploring multiple opportunities for licensing this research and partnering with others in the bio and chemical defense, diagnostics and medical research fields. (SAND2021-14775E)
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