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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Radiation Exposure Alters Expression of Metabolic Enzyme Genes in Mice

Most administered pharmaceuticals are metabolized by the liver. The health of the liver, especially the rate of its metabolic enzymes, determines the concentration of circulating drugs as well as the duration of their efficacy. Most pharmaceuticals are metabolized by the liver, and clinically-used medication doses are given with normal liver function in mind. A drug overdose can result in the case of a liver that is damaged and removing pharmaceuticals from the circulation at a rate slower than normal. Alternatively, if liver function is elevated and removing drugs from the system more quickly than usual, it would be as if too little drug had been given for effective treatment. Because of the importance of the liver in drug metabolism, we want to understand the effects of spaceflight on the enzymes of the liver and exposure to cosmic radiation is one aspect of spaceflight that can be modeled in ground experiments. Additionally, it has been previous noted that pre-exposure to small radiation doses seems to confer protection against later and larger radiation doses. This protective power of pre-exposure has been called a priming effect or radioadaptation. This study is an effort to examine the drug metabolizing effects of radioadaptation mechanisms that may be triggered by early exposure to low radiation doses.

Wotring, V. E.↗
NASA NTRSDate not supplied

Decision Framework for Classifying the State of Knowledge for Pharmaceutical Stability in Spaceflight

The currently approved medication formulary for exploration-class spaceflight missions presently exceeds 200pharmaceuticals. However, these medications' physical and chemical stability during long-term exposure to the spaceflight environment remains uncharacterized. A multi-disciplinary team of pharmacy and spaceflight subject matter experts (SMEs) collaborated to develop a decision framework that will prioritize medications for future stability research. This framework prioritizes the physical stability, drug quality, and safety of the active pharmaceutical ingredient of finished drug products selected for specific design reference mission (DRM) drug formularies. The United States Pharmacopeia (USP) defines drug stability as "the extent to which a drug product retains, within specified limits, and throughout its period of storage and use, the same properties and characteristics that it possessed at the time of its manufacture.1"The candidate medication is assessed by applying a sequence of drug agnostic questions to evaluate the limits of stability information pertinent to spaceflight. The answers to these questions ultimately lead to one of three characterizations: green (not prioritized for further research for this DRM), yellow (requires further literature review/additional studies), or red (not suitable for this DRM based on current knowledge). The decision framework will rely on several information resources to inform the ultimate decision, including the ExMC Pharmacy Information Database, published literature, FDA/drug manufacturer monographs, and USP. The results of this framework will be used to classify the state of knowledge for pharmaceutical stability for any specified DRM and guide future research efforts accordingly. This presentation will provide an overview of how this decision framework was developed, detail the challenges and limitations of the pathways, and discuss how researchers can apply the lessons learned from this project to future work

S Kurian↗
DOE OSTI2024

Elucidating the Gas-Phase Behavior of Nitazene Analog Protomers Using Structures for Lossless Ion Manipulations Ion Mobility-Orbitrap Mass Spectrometry

2-benzylbenzimidazoles, or “nitazenes”, are a class of novel synthetic opioids (NSOs) that are increasingly being detected alongside fentanyl analogs and other opioids in drug overdose cases. Nitazenes can be 20x more potent than fentanyl but are not routinely tested for during postmortem or clinical toxicology drug screens; thus, their prevalence in drug overdose cases may be under-reported. Traditional analytical workflows utilizing liquid chromatography-tandem mass spectrometry (LC-MS/MS) often require additional confirmation with authentic reference standards to identify a novel nitazene. However, additional analytical measurements with ion mobility spectrometry (IMS) may provide a path towards reference-free identification, which would greatly accelerate NSO identification rates in toxicology labs. Presented here are the first IMS and collision cross section (CCS) measurements on a set of fourteen nitazene analogs using a Structures for Lossless Ion Manipulations (SLIM)-Orbitrap MS. All nitazenes exhibited two high intensity baseline-separated IMS distributions, which fentanyls and other drug and drug-like compounds also exhibit. Incorporating water into the electrospray ionization (ESI) solution caused the intensities of the higher mobility IMS distributions to increase the intensities of the lower mobility IMS distributions to decrease. Nitazenes lacking a nitro group at the R1 position exhibited the greatest shifts in signal intensities due to water. Furthermore, IMS-MS/MS experiments showed that the higher mobility IMS distributions of all nitazenes produced fragment ions with m/z 72, 100, and other low intensity fragments while the lower mobility IMS distributions only produced fragment ions with m/z 72 and 100. The IMS, solvent, and fragmentation studies provide experimental evidence that nitazenes potentially exhibit three gas-phase protomers. In conclusion, the cyclic IMS capability of SLIM was also employed to partially resolve four sets of structurally similar nitazene isomers (e.g., protonitazene/isotonitazene, butonitazene/isobutonitazene/secbutonitazene), showcasing the potential of using high-resolution IMS separations in MS-based workflows for reference-free identification of emerging nitazenes and other NSOs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Microbial vitamin biosynthesis links gut microbiota dynamics to chemotherapy toxicity

ABSTRACT Dose-limiting toxicities pose a major barrier to cancer treatment. While preclinical studies show that the gut microbiota influences and is influenced by anticancer drugs, data from patients paired with careful side effect monitoring remains limited. Here, we investigate capecitabine (CAP)-microbiome interactions through longitudinal metagenomic sequencing of stool from 56 advanced colorectal cancer patients. CAP significantly altered the gut microbiome, enriching for menaquinol (vitamin K2) biosynthesis genes. Transposon library screens, targeted gene deletions, and media supplementation revealed that menaquinol biosynthesis protectsEscherichia colifrom drug toxicity. Stool menaquinol gene and metabolite levels were associated with decreased peripheral sensory neuropathy. Machine learning models trained in this cohort predicted toxicities in an independent cohort. Taken together, these results suggest treatment-associated increases in microbial vitamin biosynthesis serve a chemoprotective role for bacterial and host cells. Further, our findings provide a foundation for in-depth mechanistic dissection, human intervention studies, and extension to other cancer treatments. IMPORTANCE Side effects are common during the treatment of cancer. The trillions of microbes found within the human gut are sensitive to anticancer drugs, but the effects of treatment-induced shifts in gut microbes for side effects remain poorly understood. We profiled gut microbes in colorectal cancer patients treated with capecitabine and carefully monitored side effects. We observed a marked expansion in genes for producing vitamin K2 (menaquinone). Vitamin K2 rescued gut bacterial growth and was associated with decreased side effects in patients. We then used information about gut microbes to develop a predictive model of drug toxicity that was validated in an independent cohort. These results suggest that treatment-associated increases in bacterial vitamin production protect both bacteria and host cells from drug toxicity, providing new opportunities for intervention and motivating the need to better understand how dietary intake and bacterial production of micronutrients like vitamin K2 influence cancer treatment outcomes.

Microbiology↗
DOE OSTI2024

A Deep Multimodal Representation Learning Framework for Accurate Molecular Properties Prediction

Drug discovery is a complex and challenging process, requiring the optimization of candidate compounds to identify those with the potential to become safe and effective drugs. Predicting molecular properties is an indispensable step in the drug discovery pipeline. Traditionally, this process is costly and time-intensive, involving multiple rounds of experiments and clinical trials, rendering it impractical for every candidate compound. Deep learning techniques have emerged as a promising approach to drug discovery to reduce the cost and time required to identify novel drugs. However, prevalent research in deep learning models focused on predicting molecular properties has primarily fixated on single-modal models, which utilize a single modality of data, neglecting the potential benefits of combining different data modalities. To overcome this limitation, we introduce MRL-Mol: a deep \textbf{M}ultimodal \textbf{R}epresentation \textbf{L}earning framework for accurate \textbf{Mol}ecular properties prediction. MRL-Mol harnesses three data modalities: sequence, graph, and image, augmenting the depth of comprehension. Leveraging a large-scale unlabeled dataset~($\sim$1M unique molecules), we pretrain MRL-Mol to extract inter- and intra-modal information. Our study demonstrates the superior performance of MRL-Mol in predicting molecular properties across six benchmark datasets, including both classification and regression tasks. Notably, MRL-Mol outperforms other state-of-the-art molecular properties prediction models. These findings suggest that by combining information from multiple data modalities, MRL-Mol can comprehend molecules better than single-modal deep learning models and identify molecular properties with better accuracy.

Yang, Yuxin↗
NASA NTRS1982

Synthesis of adriamycin-coupled polyglutaraldehyde microspheres and evaluation of their cytostatic activity

Adriamycin was coupled to polyglutaraldehyde microspheres having an average diameter of 4500 A. The coupled microspheres remained stable during incubation with cells. Full cytostatic activity was observed when the coupled adriamycin was tested with murine or human leukemia and murine sarcoma cell lines. A 10-fold increase in sensitivity was obtained with drug-resistant human leukemia cell lines. Repeated use of the coupled microspheres in the cytostatic assays did not decrease their activity, indicating that these complexes can be recycled. The results suggest that coupled adriamycin sufficiently perturbs the plasma membrane to lead to cytostatic activity. It is proposed that this mode of drug delivery provides multiple and repetitious sites for drug-cell interactions. In addition, the drug-polymer complexes may overcome those forms of resistance that are the result of decreased drug binding at the cell surface.

Tokes, Z. A.↗
NASA NTRS1998

Microcapsules and Methods for Making

Methods of forming multi-lamellar microcapsules having alternating layers of hydrophilic and hydrophobic immiscible liquid phases have been developed using different polymer/solvent systems. The methods use liquid-liquid diffusion and simultaneous lateral phase separation, controlled by proper timed-sequence exposures of immiscible phases and low shear mixing, to form narrow size distributions of spherical, multilamellar microcapsules. The use of special formulations of solubilized drugs, surfactants, and polymeric co-surfactants in aqueous vehicles which are dispersed in hydrocarbon solvents containing small quantities of oil, low molecular weight co-surfactants and glycerides that are aqueous insoluble enables the formation of unique microcapsules which can carry large amounts of pharmaceuticals in both aqueous and non-aqueous solvent compartments. The liquid microcapsules are quickly formed in a single step and can include a polymeric outer 'skin' which protects the microcapsules during physical manipulation or exposure to high shear forces. Water-in-oil and oil-in-water microcapsules have been formed both in 1 x g and in microgravity, which contain several types of drugs co-encapsulated within different fluid compartments inside the same microcapsule. Large, spherical multi-lamellar microcapsules have been formed including a cytotoxic drug co-encapsulated with a radiocontrast medium which has advantages for chemoembolization of vascular tumors. In certain cases, crystals of the drug form inside the microcapsules providing zero-order and first order, sustained drug release kinetics.

Morrison, Dennis R.↗
NASA NTRS2000

In Situ Activation of Microcapsules

Disclosed are microcapsules comprising a polymer shell enclosing two or more immiscible liquid phases in which a drug, or a prodrug and a drug activator are partitioned into separate phases. or prevented from diffusing out of the microcapsule by a liquid phase in which the drug is poorly soluble. Also disclosed are methods of using the microcapsules for in situ activation of drugs where upon exposure to an appropriate energy source the internal phases mix and the drug is activated in situ.

Morrison, Dennis R.↗
NASA NTRS2003

Biomaterials and Magnetic fields for Cancer Therapy

The field of biomaterials has emerged as an important topic in the purview of NASA s new vision of research activities in the Microgravity Research Division. Although this area has an extensive track record in the medical field as borne out by the routine use of polymeric sutures, implant devices, and prosthetics, novel applications such as tissue engineering, artificial heart valves and controlled drug delivery are beginning to be developed. Besides the medical field, biomaterials and bio-inspired technologies are finding use in a host of emerging interdisciplinary fields such as self-healing and self-assembling structures, biosensors, fuel systems etc. The field of magnetic fluid technology has several potential applications in medicine. One of the emerging fields is the area of controlled drug delivery, which has seen its evolution from the basic oral delivery system to pulmonary to transdermal to direct inoculations. In cancer treatment by chemotherapy for example, targeted and controlled drug delivery has received vast scrutiny and substantial research and development effort, due to the high potency of the drugs involved and the resulting requirement to keep the exposure of the drugs to surrounding healthy tissue to a minimum. The use of magnetic particles in conjunction with a static magnetic field allows smart targeting and retention of the particles at a desired site within the body with the material transport provided by blood perfusion. Once so located, the therapeutical aspect (radiation, chemotherapy, hyperthermia, etc.) of the treatment, now highly localized, can be implemented.

Ramachandran, Narayanan↗
NASA NTRS2008

Pharmacokinetics of Acetaminophen in Hind Limbs Unloaded Mice: A Model System Simulating the Effects of Low Gravity on Astronauts in Space

The pharmacokinetics (PK) of medications administered to astronauts could be altered by the conditions in Space. Low gravity and free floating (and associated hemodynamic changes) could affect the absorption, distribution, metabolism and excretion of the drugs. Knowledge of these alterations is essential for adjusting the dosage and the regimen of drug administration in astronauts. Acquiring of such knowledge has inherent difficulties due to limited opportunities for experimenting in Space. One of the approaches is to use model systems that simulate some of the Space conditions on Earth. In this study we used hind limbs unloaded mice (HLU) to investigate the possible changes in PK of acetaminophen, a widely used analgesic with high probability of use by astronauts. The HLU is recognized as an appropriate model for simulating the effects of low gravity on hemodynamic parameters. Mice were tail suspended (n = 24) for 24-96 hours prior to introduction of acetaminophen (150 - 300 mg/kg). The drug (in aqueous solution containing 10% ethyl alcohol by volume) was given orally by a gavage procedure and after the administration of acetaminophen mice were additionally suspended for 30 min, 1 and 2 hours. Control mice (n = 24) received the same dose of acetaminophen and were kept freely all the time. Blood specimens were obtained either from retroorbital venous sinuses or from heart. Acetaminophen concentration was measured in plasma by the fluorescent polarization immunoassay and the AxSYM analyzer (Abbott Laboratories). In control mice peak acetaminophen concentration was achieved at 30 min. By 1 hour the concentration decreased to less than 50% of the peak level and at 2 hours the drug was almost undetectable in the serum. HLU for 24 hours significantly altered the acetaminophen pharmacokinetic: at 30 min the acetaminophen concentrations were significantly (both statistically and medically significant) lower than in control mice. The concentrations also reduced less significantly after 1 and 2 hours. At 2 hours approximately 20% of the drug still remained in the circulation. After 96 hrs of HLU the changes in acetaminophen PK were less prominent. These data indicate that short term HLU causes significant changes in acetaminophen PK most likely associated with HUL-related hemodynamic changes. However, after 96 hour these changes diminished. This suggests hemodynamic adaptation to the HUL conditions that possibly occurs also in real space conditions.

Peterson, Amanda↗
NASA NTRS2011

Risk of Therapeutic Failure due to Ineffectiveness of Medication

Given that terrestrial medical practices must be used as the basis for drug choice and use on missions, there is a possibility that medications used will be ineffective or inappropriate for the actual circumstances encountered on missions. Because the human body undergoes a variety of physiological changes during spaceflight, there is a risk that terrestrial medications may not perform as expected when used during spaceflight. Alterations in physiology due to spaceflight could result in unexpected drug action on the body (pharmacodynamics) or in unusual drug absorption, distribution, metabolism or excretion (pharmacokinetics). The spaceflight environment may also have direct effects on stored drugs themselves, leading to premature inactivation or degradation of stored drugs.

Woring, Virginia E.↗
NASA NTRS2012

Changes in Liver Metabolic Gene Expression after Radiation Exposure

The health of the liver, especially the rate of its metabolic enzymes, determines the concentration of circulating drugs as well as the duration of their efficacy. Most pharmaceuticals are metabolized by the liver, and clinically-used medication doses are given with normal liver function in mind. A drug overdose can result in the case of a liver that is damaged and removing pharmaceuticals from the circulation at a rate slower than normal. Alternatively, if liver function is elevated and removing drugs from the system more quickly than usual, it would be as if too little drug had been given for effective treatment. Because of the importance of the liver in drug metabolism, we want to understand any effects of spaceflight on the enzymes of the liver. Exposure to cosmic radiation is one aspect of spaceflight that can be modeled in ground experiments.

Peters, C. P.↗
DOE OSTI2024

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A Review of Polymeric Nanoparticle Design, Synthesis, and Biomedical Applications

Polymeric nanoparticles (PNPs) are solid particles that contain macromolecular polymers ranging in size from 1-1000 nm. They have a wide variety of structures that are determined by their identity as either natural biopolymers, synthetic polymers or a combination of both. In the design of PNPs, the polymeric system, size, internal structure, additional molecules, and synthetic pathway can be engineered, allowing for a multitude of properties including differing responses to physiological environments and the incorporation of a drug. The easily accessible fine tuning of PNPs allows for a multitude of applications in medicine. PNPs allow for increased pharmacokinetics, efficacy, safety, and targeting strength when utilized as carriers for drugs. Currently there are a wide range of PNPs approved by the Food and Drug Administration for use in clinical settings and many more being investigated in clinical studies. There is also research that is being conducted for the usage of PNPs in controlled drug delivery systems and as carriers through the blood brain barrier to aid in treating neurological diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Multiomic Network Analysis Identifies Dysregulated Neurobiological Pathways in Opioid Addiction

BACKGROUND: Opioid addiction is a worldwide public health crisis. In the United States, for example, opioids cause more drug overdose deaths than any other substance. However, opioid addiction treatments have limited efficacy, meaning that additional treatments are needed. METHODS: To help address this problem, we used network-based machine learning techniques to integrate results from genome-wide association studies of opioid use disorder and problematic prescription opioid misuse with transcriptomic, proteomic, and epigenetic data from the dorsolateral prefrontal cortex of people who died of opioid overdose and control individuals. RESULTS: Here we identified 211 highly interrelated genes identified by genome-wide association studies or dysregulation in the dorsolateral prefrontal cortex of people who died of opioid overdose that implicated the Akt, BDNF (brain-derived neurotrophic factor), and ERK (extracellular signal-regulated kinase) pathways, identifying 414 drugs targeting 48 of these opioid addiction–associated genes. Some of the identified drugs are approved to treat other substance use disorders or depression. CONCLUSIONS: Our synthesis of multiomics using a systems biology approach revealed key gene targets that could contribute to drug repurposing, genetics-informed addiction treatment, and future discovery.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Membrane protein reconstitution : New possibilities for structural biology, biophysical methods, and antibody/drug discovery

Nearly one-third of all proteins in eukaryotes are membrane proteins. Moreover, roughly 60% of Food and Drug Adminstration (FDA)-approved small-molecule drugs act on membrane proteins, which includes G protein–coupled receptors (GPCRs), ion channels, and transporters. Here, the vast majority of these membrane proteins are cell-surface accessible and thus amenable to drug discovery. At the same time, they are considerably more challenging to reconstitute and prepare for structure initiatives, antibody discovery, and drug screening. This series of reviews introduces the reader to current reconstitution systems, biophysical characterization of the membrane proteins and associated lipids, and common applications involving nuclear magnetic resonance (NMR), mass spectrometry (MS), and cryo-electron microscopy (cryo-EM).

36 MATERIALS SCIENCE↗
DOE OSTI2024

Chemical manipulation of an activation/inhibition switch in the nuclear receptor PXR

Nuclear receptors are ligand-activated transcription factors that can often be useful drug targets. Unfortunately, ligand promiscuity leads to two-thirds of receptors remaining clinically untargeted. PXR is a nuclear receptor that can be activated by diverse compounds to elevate metabolism, negatively impacting drug efficacy and safety. This presents a barrier to drug development because compounds designed to target other proteins must avoid PXR activation while retaining potency for the desired target. This problem could be avoided by using PXR antagonists, but these compounds are rare, and their molecular mechanisms remain unknown. Here, we report structurally related PXR-selective agonists and antagonists and their corresponding co-crystal structures to describe mechanisms of antagonism and selectivity. Structural and computational approaches show that antagonists induce PXR conformational changes incompatible with transcriptional coactivator recruitment. These results guide the design of compounds with predictable agonist/antagonist activities and bolster efforts to generate antagonists to prevent PXR activation interfering with other drugs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

SmileyLlama: modifying large language models for directed chemical space exploration

Here we show that large language models (LLMs) can be transformed via supervised fine-tuning of engineered prompts into SmileyLlama for exploring the chemical space of drug molecules. We benchmark SmileyLlama against pretrained LLMs and chemical language models trained from scratch for generating valid and novel drug-like molecules, and use direct preference optimization to both improve SmileyLlama’s adherence to a prompt and as part of the iMiner reinforcement learning framework to predict molecules with optimized three-dimensional conformations and high binding affinity to drug targets. By training an LLM to speak directly as a chemical language model, while retaining most of its natural language capabilities, we show that SmileyLlama can reliably generate molecules with user-specified properties rather than acting only as a chatbot with knowledge of chemistry or as a virtual assistant. While SmileyLlama is geared toward drug discovery, the supervised fine-tuning/direct preference optimization/LLM framework can be extended to other chemical, biological and materials applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗