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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Metal–Organic Framework as a New Type of Magnetothermally‐Triggered On‐Demand Release Carrier

The development of external stimuli‐controlled payload systems has been sought after with increasing interest toward magnetothermally‐triggered drug release (MTDR) carriers due to their non‐invasive features. However, current MTDR carriers present several limitations, such as poor heating efficiency caused by the aggregation of iron oxide nanoparticles (IONPs) or the presence of antiferromagnetic phases which affect their efficiency. Herein, a novel MTDR carrier is developed using a controlled encapsulation method that fully fixes and confines IONPs of various sizes within the metal–organic frameworks (MOFs). This novel carrier preserves the MOF's morphology, porosity, and IONP segregation, while enhances heating efficiency through the oxidation of antiferromagnetic phases in IONPs during encapsulation. It also features a magnetothermally‐responsive nanobrush that is stimulated by an alternating magnetic field to enable on‐demand drug release. The novel carrier shows improved heating, which has potential applications as contrast agents and for combined chemo and magnetic hyperthermia therapy. It holds a great promise for magneto‐thermally modulated drug dosing at tumor sites, making it an exciting avenue for cancer treatment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump

Cholestatic liver injury is frequently associated with drug inhibition of bile salt transporters, such as the bile salt export pump (BSEP). Reliable in silico models to predict BSEP inhibition directly from chemical structures would significantly reduce costs during drug discovery and could help avoid injury to patients. We report our development of classification and regression models for BSEP inhibition with substantially improved performance over previously published models. We assessed the performance effects of different methods of chemical featurization, data set partitioning, and class labeling and identified the methods producing models that generalized best to novel chemical entities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Biomimetic Rebuilding of Multifunctional Red Blood Cells: Modular Design Using Functional Components

The design and synthesis of artificial materials that mimic the structures, mechanical properties, and ultimately functionalities of biological cells remains a current holy grail of materials science. Here based on a silica cell bio-replication approach, we report the design and construction of synthetic rebuilt red blood cells (RRBCs) that fully mimic the broad properties of native RBCs: size, biconcave shape, deformability, oxygen carrying capacity, long circulation time. Four successive nano-scale processing steps: RBC bioreplication, layer-by-layer polymer deposition, and precision silica etching, followed by RBC ghost membrane vesicles fusion are employed for RRBC construction. A panel of physicochemical analyses including zeta potential measurement, fluorescence microscopy, and antibody-mediated agglutination assay proved the recapitulation of RBC shape, size, and membrane structure. Flow-based deformation studies carried out in a microfluidic blood capillary model confirrned the ability of RRBCs to deform and pass through small slits and reconstitute themselves in a manner comparable to native RBCs. Circulation studies of RRBCs conducted ex-ovo in a chick embryo and in vivo in a mouse model demonstrated the requirement of both deformability and native cell membrane surface to achieve long-term circulation. To confer additional non-native functionalities to RRBCs, we developed modular procedures in which to load functional cargos such as hemoglobin, drugs, magnetic nanoparticles, and ATP biosensors within the RRBC interior to enable various functions, including oxygen delivery, therapeutic drug delivery, magnetic manipulation, and toxin biosensing and detection. Taken together, RRBCs represent a class of long circulating RBC-inspired artificial hybrid materials with a broad range of potential applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Discovery of structure–property relations for molecules via hypothesis-driven active learning over the chemical space

The discovery of the molecular candidates for application in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries necessitates the development of machine learning algorithms capable of rapid exploration of chemical spaces targeting the desired functionalities. Here, we introduce a novel approach for active learning over the chemical spaces based on hypothesis learning. We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data followed by introducing them as (probabilistic) mean functions for the Gaussian process. This approach combines the elements from the symbolic regression methods, such as SISSO and active learning, into a single framework. The primary focus of constructing this framework is to approximate physical laws in an active learning regime toward a more robust predictive performance, as traditional evaluation on hold-out sets in machine learning does not account for out-of-distribution effects which may lead to a complete failure on unseen chemical space. Here, we demonstrate it for the QM9 dataset, but it can be applied more broadly to datasets from both domains of molecular and solid-state materials sciences.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Machine learning approaches for integrating multi-omics data to expand microbiome annotation

Preliminary: This final report corresponds to a grant (DE-SC0021216) that was awarded to the University of Montana. Mid-way through the grant period, I relocated from the University of Montana to the University of Arizona. The grant was ended at University of Montana in late 2022, with all efforts concluding on 08/26/22; the remaining funds supporting the project were relinquished by University of Montana, and were later awarded to University of Arizona under a new grant, with start date 04/01/23. This report focuses on results of research efforts at UMontana through 08/26/22. Results: We made progress in each of the three aims of the proposal. We released software that identifies and fills gaps in the annotation of metabolic proteins within bacterial genomes. We made substantial progress in developing software for alignment-based annotation of protein coding DNA, allowing for coding frameshifts caused by sequencing error. Finally, we made notable progress in developing AI methods (specifically: a neural embedding model) for identifying similarities between protein sequences based on amino-wise latent vectors. These efforts were supplemented by development of methods for protein modeling in support of predicting protein-drug binding activity, and by my leadership of a team in the NIH/DOE 2021 Petabyte-Scale Sequence Search hack-a-thon.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Studying the effect of drug-to-excipient ratio on drug release profile for drug coated balloons

Drug-coated balloons (DCB) have emerged as the alternative procedure for restenosis because of their ability to treat a variety of occlusion types with a uniform dose of anti-proliferative drugs. DCB are balloons coated with antiproliferative drugs encapsulated in a polymer matrix. There are several types of coating matrices used to produce DCB. In this study, the relationship between coating composition and drug release under physiologically relevant conditions was examined to understand how differences in coating composition impacts the drug transfer from the balloon surface to the simulated body fluids. To conduct the experiments, the balloons were coated with different paclitaxel (drug)-to-iopromide (excipient) ratios (3:1, 3:2 and 1:2) using an in-house developed micro-pipetting method. Scanning electron microscopy (SEM) images showed that the 3:1 PTX:IOP ratio produced a more uniform, crystalline microstructure with a thinner coating throughout the balloon surface compared to the other drug-to-excipient ratios. The 1:2 PTX:IOP ratio showed the least crystalline microstructure among the three ratios evaluated in this study. Three different drug elution conditions were tested. The amount of drug released to the medium was quantified by high performance liquid chromatography (HPLC). Our soaking study and submerge & deploy study showed that ~20% of the drug transferred to the target site under physiological conditions. A track and deploy method was performed using a “mock” artery, to simulate an in vitro environment. Coated balloons were passed through the mock artery to mimic tracking turns the balloon within the arteries during the angioplasty procedures. Seven elution samples were collected at different stages of the procedure. Finally, drug release results suggest that the higher excipient ratio helps to deliver the lipophilic drug to the target site under simulated conditions but causes higher drug loss during the balloon transfer process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Using diverse potentials and scoring functions for the development of improved machine-learned models for protein–ligand affinity and docking pose prediction

The advent of computational drug discovery holds the promise of significantly reducing the effort of experimentalists, along with monetary cost. More generally, predicting the binding of small organic molecules to biological macromolecules has far-reaching implications for a range of problems, including metabolomics. However, problems such as predicting the bound structure of a protein–ligand complex along with its affinity have proven to be an enormous challenge. In recent years, machine learning-based methods have proven to be more accurate than older methods, many based on simple linear regression. Nonetheless, there remains room for improvement, as these methods are often trained on a small set of features, with a single functional form for any given physical effect, and often with little mention of the rationale behind choosing one functional form over another. Moreover, it is not entirely clear why one machine learning method is favored over another. Here, we endeavor to undertake a comprehensive effort towards developing high-accuracy, machine-learned scoring functions, systematically investigating the effects of machine learning method and choice of features, and, when possible, providing insights into the relevant physics using methods that assess feature importance. Here, we show synergism among disparate features, yielding adjusted R 2 with experimental binding affinities of up to 0.871 on an independent test set and enrichment for native bound structures of up to 0.913. When purely physical terms that model enthalpic and entropic effects are used in the training, we use feature importance assessments to probe the relevant physics and hopefully guide future investigators working on this and other computational chemistry problems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Naegleria fowleri: Protein structures to facilitate drug discovery for the deadly, pathogenic free-living amoeba

Naegleria fowleri is a pathogenic, thermophilic, free-living amoeba which causes primary amebic meningoencephalitis (PAM). Penetrating the olfactory mucosa, the brain-eating amoeba travels along the olfactory nerves, burrowing through the cribriform plate to its destination: the brain’s frontal lobes. The amoeba thrives in warm, freshwater environments, with peak infection rates in the summer months and has a mortality rate of approximately 97%. A major contributor to the pathogen’s high mortality is the lack of sensitivity of N . fowleri to current drug therapies, even in the face of combination-drug therapy. To enable rational drug discovery and design efforts we have pursued protein production and crystallography-based structure determination efforts for likely drug targets from N . fowleri . The genes were selected if they had homology to drug targets listed in Drug Bank or were nominated by primary investigators engaged in N . fowleri research. In 2017, 178 N . fowleri protein targets were queued to the Seattle Structural Genomics Center of Infectious Disease (SSGCID) pipeline, and to date 89 soluble recombinant proteins and 19 unique target structures have been produced. Many of the new protein structures are potential drug targets and contain structural differences compared to their human homologs, which could allow for the development of pathogen-specific inhibitors. Five of the structures were analyzed in more detail, and four of five show promise that selective inhibitors of the active site could be found. The 19 solved crystal structures build a foundation for future work in combating this devastating disease by encouraging further investigation to stimulate drug discovery for this neglected pathogen.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Optimization of Spirulina for biomanufacturing and the delivery of protein therapeutics

Lumen Bioscience has developed a novel cyanobacterial platform that enormously decreases the cost of pharmaceuticals used to prevent and treat illnesses. Arthrospira platensis (spirulina), is a photosynthetic microorganism that has been consumed as a dietary supplement for centuries in part because of its high protein content. Commercial cultivation operations have matured over the last 50 years to allow large-scale cultivation in outdoor ponds. Lumen recently discovered methods that transform this commercially important cyanobacterium into a genetically tractable platform for bioengineering. The high protein accumulation in spirulina makes it an ideal chassis for heterologous protein expression, and centuries of safe human consumption suggest its utility as an administration vehicle for biologic drugs. Lumen can express a broad range of recombinant proteins in spirulina and has developed manufacturing processes and strains to mucosally deliver bioactive proteins to treat and/or prevent disease. For example, Lumen has produced an oral cocktail of dried whole-cell spirulina biomass, containing 3 toxin-neutralizing antibodies and 1 endolysin, that is efficacious in a preclinical in vivo models of C. difficile infection. Lumen has also demonstrated that intranasal administration of a spirulina-manufactured neutralizing antibody can prevent disease by a respiratory pathogen, SARS-CoV-2, in a hamster model. Lumen’s current pipeline includes therapeutics to treat or prevent C. difficile infection, COVID-19, inflammatory bowel disease, cardiometabolic disease, and traveler’s diarrhea. Lumen deploys strategies to maximize the expression of these therapeutics for optimal dose sizing, a key limiting factor with past food crop-based expression systems. Spirulina can express exogenous proteins at unusually high levels (>15% of dry weight) but reaching this maximum value requires optimization of multiple interacting factors. We describe our current statistics design of experiments approach that optimizes these factors with minimal replicates. This maximizes exogenous protein expression allowing Lumen to generate spirulina-based therapeutics for a rapidly expanding range of medical uses.

Heinnickel, Mark↗
DOE OSTI2022

Structures of drug-specific monoclonal antibodies bound to opioids and nicotine reveal a common mode of binding

Opioid-related fatal overdoses have reached epidemic proportions. Because existing treatments for opioid use disorders offer limited long-term protection, accelerating the development of newer approaches is critical. Monoclonal antibodies (mAbs) are an emerging treatment strategy that targets and sequesters selected opioids in the bloodstream, reducing drug distribution across the blood-brain barrier, thus preventing or reversing opioid toxicity. We previously identified a series of murine mAbs with high affinity and selectivity for oxycodone, morphine, fentanyl, and nicotine. To determine their binding mechanism, we used X-ray crystallography to solve the structures of mAbs bound to their respective targets, to 2.2 Å resolution or higher. Structural analysis showed a critical convergent hydrogen bonding mode that is dependent on a glutamic acid residue in the mAbs’ heavy chain and a tertiary amine of the ligand. Further, characterizing drug-mAb complexes represents a significant step toward rational antibody engineering and future manufacturing activities to support clinical evaluation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Chemical–Switching Strategy for Synthesis and Controlled Release of Norcantharimides from a Biomass–Derived Chemical

Catalytic strategies were developed to synthesize and release chemicals for applications in fine chemicals, such as drugs and polymers, from a biomass-derived chemical, 5-hydroxymethyl furfural (HMF). The combination of the diene and aldehyde functionalities in HMF enabled catalytic production of acetalized HMF derivatives with diol or epoxy reactants to allow reversible synthesis of norcantharimide derivatives upon Diels-Alder reaction with maleimides. Reverse-conversion of the acetal group to an aldehyde yielded mismatches of the molecular orbitals in norcantharimides to trigger retro Diels-Alder reaction at ambient temperatures and released reactants from the coupled molecules under acidic conditions. In conclusion, these strategies provide for the facile synthesis and controlled release of high-value chemicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The Road Ahead for Metal–Organic Frameworks: Current Landscape, Challenges and Future Prospects

This perspective highlights the transformative potential of Metal–Organic Frameworks (MOFs) in environmental and healthcare sectors. It discusses work that has advanced beyond technology readiness levels of >4 including applications in capture, storage, and conversion of gases to value added products. This work showcases efforts in the most salient applications of MOFs which have been performed at a great cadence, enabled by the federal government, large companies, and startups to commercialize these technologies despite facing significant challenges. This article also forecasts the role of nanoscale MOFs in healthcare, including strides toward personalized medicine, advocating for their use in custom-tailored drug delivery systems. Lastly, we underscore the potential acceleration in MOF research and development through the integration of machine learning and AI, positioning MOFs as versatile tools poised to address global sustainability and health challenges.

atmospheric chemistry↗
DOE OSTI2021

Nanoconfinement and mass transport in metal–organic frameworks

The ubiquity of metal–organic frameworks in recent scientific literature underscores their highly versatile nature. MOFs have been developed for use in a wide array of applications, including: sensors, catalysis, separations, drug delivery, and electrochemical processes. Often overlooked in the discussion of MOF-based materials is the mass transport of guest molecules within the pores and channels. Given the wide distribution of pore sizes, linker functionalization, and crystal sizes, molecular diffusion within MOFs can be highly dependent on the MOF-guest system. In this paper, we discuss the major factors that govern the mass transport of molecules through MOFs at both the intracrystalline and intercrystalline scale; provide an overview of the experimental and computational methods used to measure guest diffusivity within MOFs; and highlight the relevance of mass transfer in the applications of MOFs in electrochemical systems, separations, and heterogeneous catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Neutron diffraction reveals protonation states in pyridoxal‐5′‐phosphate‐free and glycine external aldimine‐bound serine hydroxymethyltransferase

Serine hydroxymethyltransferase (SHMT) is a critical enzyme in the one-carbon (1C) metabolism pathway catalyzing the reversible conversion of L-Ser into Gly and concurrent transfer of 1C unit to tetrahydrofolate (THF) to give 5,10-methylene-THF (5,10-MTHF), which is used in the downstream syntheses of biomolecules critical for cell proliferation. The cellular 1C metabolism is hijacked by many cancer types to support cancer cell proliferation, making SHMT a promising target for the design and development of novel small-molecule antimetabolite chemotherapies. To advance structure-assisted drug design, knowledge of SHMT catalysis is crucial, but can only be fully realized when the atomic details of each reaction step governed by the acid–base catalysis are elucidated by visualizing active site hydrogen atoms. Here, we used room-temperature neutron crystallography to directly determine protonation states in Thermus thermophilus SHMT (TthSHMT), capturing protomer A in the apo form lacking the coenzyme pyridoxal 5′-phosphate (PLP), and protomer B as a ternary complex with PLP–Gly-external aldimine and (6S)-5-methyltetrahydrofolate (5MTHF). We observed protonation of the Schiff base nitrogen in PLP–Gly and neutrality of the catalytic Lys226 side chain in the ternary complex, whereas Lys226 is protonated and positively charged in the apo-active site. Furthermore, we obtained an X-ray structure of TthSHMT in complex with the substrate THF, which binds identically as 5MTHF at the peripheral binding site. In conclusion, the unique structural and functional information provided by neutron crystallography, in combination with X-ray structures, can be employed in the rational design of SHMT inhibitors.

X-ray crystallography↗
DOE OSTI2023

Broadly neutralizing humanized SARS-CoV-2 antibody binds to a conserved epitope on Spike and provides antiviral protection through inhalation-based delivery in non-human primates

The COVID-19 pandemic represents a global challenge that has impacted and is expected to continue to impact the lives and health of people across the world for the foreseeable future. The rollout of vaccines has provided highly anticipated relief, but effective therapeutics are required to further reduce the risk and severity of infections. Monoclonal antibodies have been shown to be effective as therapeutics for SARS-CoV-2, but as new variants of concern (VoC) continue to emerge, their utility and use have waned due to limited or no efficacy against these variants. Furthermore, cumbersome systemic administration limits easy and broad access to such drugs. As well, concentrations of systemically administered antibodies in the mucosal epithelium, a primary site of initial infection, are dependent on neonatal Fc receptor mediated transport and require high drug concentrations. To reduce the viral load more effectively in the lung, we developed an inhalable formulation of a SARS-CoV-2 neutralizing antibody binding to a conserved epitope on the Spike protein, ensuring pan-neutralizing properties. Administration of this antibody via a vibrating mesh nebulization device retained antibody integrity and resulted in effective distribution of the antibody in the upper and lower respiratory tract of non-human primates (NHP). In comparison with intravenous administration, significantly higher antibody concentrations can be obtained in the lung, resulting in highly effective reduction in viral load post SARS-CoV-2 challenge. This approach may reduce the barriers of access and uptake of antibody therapeutics in real-world clinical settings and provide a more effective blueprint for targeting existing and potentially emerging respiratory tract viruses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

One fold, many functions—M23 family of peptidoglycan hydrolases

Bacterial cell walls are the guards of cell integrity. They are composed of peptidoglycan that provides rigidity to sustain internal turgor and ensures isolation from the external environment. In addition, they harbor the enzymatic machinery to secure cell wall modulations needed throughout the bacterial lifespan. The main players in this process are peptidoglycan hydrolases, a large group of enzymes with diverse specificities and different mechanisms of action. They are commonly, but not exclusively, found in prokaryotes. Although in most cases, these enzymes share the same molecular function, namely peptidoglycan hydrolysis, they are leveraged to perform a variety of physiological roles. A well-investigated family of peptidoglycan hydrolases is M23 peptidases, which display a very conserved fold, but their spectrum of lytic action is broad and includes both Gram- positive and Gram- negative bacteria. In this review, we summarize the structural, biochemical, and functional studies concerning the M23 family of peptidases based on literature and complement this knowledge by performing large-scale analyses of available protein sequences. This review has led us to gain new insight into the role of surface charge in the activity of this group of enzymes. We present relevant conclusions drawn from the analysis of available structures and indicate the main structural features that play a crucial role in specificity determination and mechanisms of latency. Our work systematizes the knowledge of the M23 family enzymes in the context of their unique antimicrobial potential against drug-resistant pathogens and presents possibilities to modulate and engineer their features to develop perfect antibacterial weapons.

Razew, Alicja↗
DOE OSTI2023

Ex vivo to in vivo model of malignant peripheral nerve sheath tumors for precision oncology

Malignant peripheral nerve sheath tumors (MPNST) are aggressive soft tissue sarcomas that often develop in patients with neurofibromatosis type 1 (NF1). To address the critical need for novel therapeutics in MPNST, we aimed to establish an ex vivo 3D platform that accurately captured the genomic diversity of MPNST and could be utilized in a medium-throughput manner for drug screening studies to be validated in vivo using patient-derived xenografts (PDX). Genomic analysis was performed on all PDX-tumor pairs. Selected PDX were harvested for assembly into 3D microtissues. Based on prior work in our labs, we evaluated drugs (trabectedin, olaparib, and mirdametinib) ex vivo and in vivo. For 3D microtissue studies, cell viability was the endpoint as assessed by Zeiss Axio Observer. For PDX drug studies, tumor volume was measured twice weekly. Bulk RNA sequencing was performed to identify pathways enriched in cells. We developed 13 NF1-associated MPNST-PDX and identified mutations or structural abnormalities in NF1 (100%), SUZ12 (85%), EED (15%), TP53 (15%), CDKN2A (85%), and chromosome 8 gain (77%). We successfully assembled PDX into 3D microtissues, categorized as robust (>90% viability at 48 h), good (>50%), or unusable (<50%). We evaluated drug response to “robust” or “good” microtissues, namely MN-2, JH-2-002, JH-2-079-c, and WU-225. Drug response ex vivo predicted drug response in vivo, and enhanced drug effects were observed in select models. These data support the successful establishment of a novel 3D platform for drug discovery and MPNST biology exploration in a system representative of the human condition.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

UNNT: A novel Utility for comparing Neural Net and Tree-based models

The use of deep learning (DL) is steadily gaining traction in scientific challenges such as cancer research. Advances in enhanced data generation, machine learning algorithms, and compute infrastructure have led to an acceleration in the use of deep learning in various domains of cancer research such as drug response problems. In our study, we explored tree-based models to improve the accuracy of a single drug response model and demonstrate that tree-based models such as XGBoost (eXtreme Gradient Boosting) have advantages over deep learning models, such as a convolutional neural network (CNN), for single drug response problems. However, comparing models is not a trivial task. To make training and comparing CNNs and XGBoost more accessible to users, we developed an open-source library called UNNT (A novel Utility for comparing Neural Net and Tree-based models). The case studies, in this manuscript, focus on cancer drug response datasets however the application can be used on datasets from other domains, such as chemistry.

59 BASIC BIOLOGICAL SCIENCES↗