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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
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 OSTI2020

Preparation of cyclohexene isotopologues and stereoisotopomers from benzene

The hydrogen isotopes deuterium (D) and tritium (T) have become essential tools in chemistry, biology and medicine1. Beyond their widespread use in spectroscopy, mass spectrometry and mechanistic and pharmacokinetic studies, there has been considerable interest in incorporating deuterium into drug molecules1. Deutetrabenazine, a deuterated drug that is promising for the treatment of Huntington’s disease2, was recently approved by the United States’ Food and Drug Administration. The deuterium kinetic isotope effect, which compares the rate of a chemical reaction for a compound with that for its deuterated counterpart, can be substantial1,3,4. The strategic replacement of hydrogen with deuterium can affect both the rate of metabolism and the distribution of metabolites for a compound5, improving the efficacy and safety of a drug. The pharmacokinetics of a deuterated compound depends on the location(s) of deuterium. Although methods are available for deuterium incorporation at both early and late stages of the synthesis of a drug6,7, these processes are often unselective and the stereoisotopic purity can be difficult to measure7,8. Here we describe the preparation of stereoselectively deuterated building blocks for pharmaceutical research. As a proof of concept, we demonstrate a four-step conversion of benzene to cyclohexene with varying degrees of deuterium incorporation, via binding to a tungsten complex. Using different combinations of deuterated and proteated acid and hydride reagents, the deuterated positions on the cyclohexene ring can be controlled precisely. In total, 52 unique stereoisotopomers of cyclohexene are available, in the form of ten different isotopologues. This concept can be extended to prepare discrete stereoisotopomers of functionalized cyclohexenes. Such systematic methods for the preparation of pharmacologically active compounds as discrete stereoisotopomers could improve the pharmacological and toxicological properties of drugs and provide mechanistic information related to their distribution and metabolism in the body.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host–microbiome metabolic interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A deep generative model enables automated structure elucidation of novel psychoactive substances

Over the past decade, the illicit drug market has been reshaped by the proliferation of clandestinely produced designer drugs. These agents, referred to as new psychoactive substances (NPSs), are designed to mimic the physiological actions of better-known drugs of abuse while skirting drug control laws. The public health burden of NPS abuse obliges toxicological, police and customs laboratories to screen for them in law enforcement seizures and biological samples. However, the identification of emerging NPSs is challenging due to the chemical diversity of these substances and the fleeting nature of their appearance on the illicit market. Here, in this study, we present DarkNPS, a deep learning-enabled approach to automatically elucidate the structures of unidentified designer drugs using only mass spectrometric data. Our method employs a deep generative model to learn a statistical probability distribution over unobserved structures, which we term the structural prior. We show that the structural prior allows DarkNPS to elucidate the exact chemical structure of an unidentified NPS with an accuracy of 51% and a top-10 accuracy of 86%. Our generative approach has the potential to enable de novo structure elucidation for other types of small molecules that are routinely analysed by mass spectrometry.

Cheminformatics↗
DOE OSTI2022

Zap1 is required for Candida glabrata response to fluconazole

ABSTRACT The increasing prevalence of fluconazole-resistant clinical isolates of Candida spp. strongly hinders the widespread use of the drug. To tackle this problem, great efforts have been made to fully understand the fungal response to fluconazole. In this work, we show that the role of Zap1 in Candida glabrata goes beyond regulating yeast adaptation to zinc deficiency. In line with our previous observation that deletion of ZAP1 makes yeast cells more sensitive to fluconazole, we found that the mutant CgΔzap1 accumulates higher levels of the drug, which correlates well with its lower levels of ergosterol. Surprisingly, Zap1 is a negative regulator of the drug efflux transporter gene CDR1 and of its regulator, PDR1. The apparent paradox of drug accumulation in cells where genes encoding transporters relevant for drug extrusion are being overexpressed led us to postulate that their activity could be impaired. In agreement, Zap1-depleted cells present, in addition to decreased ergosterol levels, an altered composition of membrane phospholipids, which together should impact membrane function and impair the detoxification of fluconazole. Overall, our study brings to light Zap1 as an important hub in Candida glabrata response to fluconazole.

Gaspar-Cordeiro, A. (ORCID:0000000202488965)↗
DOE OSTI2020

Supercomputing Pipelines Search for Therapeutics Against COVID-19

The urgent search for drugs to combat SARS-CoV-2 has included the use of supercomputers. The use of general-purpose graphical processing units (GPUs), massive parallelism, and new software for high-performance computing (HPC) has allowed researchers to search the vast chemical space of potential drugs faster than ever before. We developed a new drug discovery pipeline using the Summit supercomputer at Oak Ridge National Laboratory to help pioneer this effort, with new platforms that incorporate GPU-accelerated simulation and allow for the virtual screening of billions of potential drug compounds in days compared to weeks or months for their ability to inhibit SARS-COV-2 proteins. Here, this effort will accelerate the process of developing drugs to combat the current COVID-19 pandemic and other diseases.

60 APPLIED LIFE SCIENCES↗
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↗
DOE OSTI2021

Acute Stent-Induced Endothelial Denudation: Biomechanical Predictors of Vascular Injury

Recent concern for local drug delivery and withdrawal of the first Food and Drug Administration-approved bioresorbable scaffold emphasizes the need to optimize the relationships between stent design and drug release with imposed arterial injury and observed pharmacodynamics. In this study, we examine the hypothesis that vascular injury is predictable from stent design and that the expanding force of stent deployment results in increased circumferential stress in the arterial tissue, which may explain acute injury poststent deployment. Using both numerical simulations and ex vivo experiments on three different stent designs (slotted tube, corrugated ring, and delta wing), arterial injury due to device deployment was examined. Furthermore, using numerical simulations, the consequence of changing stent strut radial thickness on arterial wall shear stress and arterial circumferential stress distributions was examined. Regions with predicted arterial circumferential stress exceeding a threshold of 49.5 kPa compared favorably with observed ex vivo endothelial denudation for the three considered stent designs. In addition, increasing strut thickness was predicted to result in more areas of denudation and larger areas exposed to low wall shear stress. We conclude that the acute arterial injury, observed immediately following stent expansion, is caused by high circumferential hoop stresses in the interstrut region, and denuded area profiles are dependent on unit cell geometric features. Such findings when coupled with where drugs move might explain the drug–device interactions.

Conway, Claire↗
DOE OSTI2021

Molecular Dynamics Free Energy Simulations Reveal the Mechanism for the Antiviral Resistance of the M66I HIV-1 Capsid Mutation

While drug resistance mutations can often be attributed to the loss of direct or solvent-mediated protein−ligand interactions in the drug-mutant complex, in this study we show that a resistance mutation for the picomolar HIV-1 capsid (CA)-targeting antiviral (GS-6207) is mainly due to the free energy cost of the drug-induced protein side chain reorganization in the mutant protein. Among several mutations, M66I causes the most suppression of the GS-6207 antiviral activity (up to ~84,000-fold), and only 83- and 68-fold reductions for PF74 and ZW-1261, respectively. To understand the molecular basis of this drug resistance, we conducted molecular dynamics free energy simulations to study the structures, energetics, and conformational free energy landscapes involved in the inhibitors binding at the interface of two CA monomers. To minimize the protein−ligand steric clash, the I66 side chain in the M66I−GS-6207 complex switches to a higher free energy conformation from the one adopted in the apo M66I. In contrast, the binding of GS-6207 to the wild-type CA does not lead to any significant M66 conformational change. Based on an analysis that decomposes the absolute binding free energy into contributions from two receptor conformational states, it appears that it is the free energy cost of side chain reorganization rather than the reduced protein−ligand interaction that is largely responsible for the drug resistance against GS-6207.

Sun, Qinfang (ORCID:0000000173621543)↗
DOE OSTI2021

IMPECCABLE: Integrated Modeling Pipeline for COVID Cure by Assessing Better Leads

ABSTRACT The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silico methodologies need to be improved both to select better lead compounds, so as to improve the efficiency of later stages in the drug discovery protocol, and to identify those lead compounds more quickly. No known methodological approach can deliver this combination of higher quality and speed. Here, we describe an Integrated Modeling PipEline for COVID Cure by Assessing Better LEads (IMPECCABLE) that employs multiple methodological innovations to overcome this fundamental limitation. We also describe the computational framework that we have developed to support these innovations at scale, and characterize the performance of this framework in terms of throughput, peak performance, and scientific results. We show that individual workflow components deliver 100× to 1000× improvement over traditional methods, and that the integration of methods, supported by scalable infrastructure, speeds up drug discovery by orders of magnitudes. IMPECCABLE has screened ~10 11 ligands and has been used to discover a promising drug candidate. These capabilities have been used by the US DOE National Virtual Biotechnology Laboratory and the EU Centre of Excellence in Computational Biomedicine.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Targeting tissues via dynamic human systems modeling in generative design

Drug discovery is a complex, costly process with high failure rates. A successful drug should bind to a target, be deliverable to an intended site of activity, and promote a desired pharmacological effect without causing toxicity. Typically, these factors are evaluated in series over the course of a pipeline where the number of candidates is sequentially whittled down from a very large initial pool. One promise of AI-driven discovery is the opportunity to evaluate multiple facets of drug performance in parallel. However, despite ML-driven advancements, current models for pharmacological property prediction are exclusively trained to predict molecular properties, ignoring important, dynamic biodistribution and bioactivity effects. Here, we present our progress towards incorporating quantitative systems physiology models into an AI-enabled drug design and molecular generation pipeline. Within a genetic algorithm, we include human-relevant physiologically based pharmacokinetic (PBPK) models. These PBPK models leverage properties that are predicted by a fine-tuned molecular language model. Together, these models will aid in capturing the mapping between molecules and therapeutic outcomes that is necessary to accelerate the drug discovery process.

Fox, Zach↗
DOE OSTI2023

Allosteric Tuning of Caspase–7: Establishing the Nexus of Structure and Catalytic Power

Caspase-7 (C7), a cysteine protease involved in apoptosis, is a valuable drug target for its role in human diseases (e. g., Parkinson's, Alzheimer's, sepsis). The C7 allosteric site has great potential for small-molecule targeting, but numerous drug discovery efforts have identified precious few allosteric inhibitors. Here we present the first selective, drug-like inhibitor of C7 along with several other improved inhibitors based on our previous fragment hit. We also provide a rational basis for the impact of allosteric binding on the C7 catalytic cycle by using an integrated approach including X-ray crystallography, stopped-flow kinetics, and molecular dynamics simulations. Our findings suggest allosteric binding disrupts C7 pre-acylation by neutralization of the catalytic dyad, displacement of substrate from the oxyanion hole, and altered dynamics of substrate binding loops. This work advances drug targeting efforts and bolsters our understanding of allosteric structure–activity relationships (ASARs).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Screening for anticancer properties of thiazolidinedione compounds in a galactose media metastatic breast cancer cell model

The metastatic tumors of breast cancer lead to the mortality of patients in part due to the failure of chemotherapeutics to reach the brain and emergence of drug resistance. With the increase in drug resistance, we focused on targeting mitochondrial metabolism, which was recently found to be a viable and novel drug target in breast cancer. Here we screened several ligands for anticancer activity in a phenotypic screen using the MTT dye as a marker for cell proliferation. MCF-7 breast cancer cells were treated with compounds in the presence of galactose to ensure mitochondrial interaction by the compounds. The lead compound of our group, NL-1, a thiazolidinedione (TZD), was found to inhibit cell proliferation with an IC{sub 50} of 7.1 μM, whereas its unsaturated derivative CI-987 was more potent with a IC{sub 50} of 3.3 μM. The TZDs were affected by substitution on both the aromatic ring and the TZD warhead. Taken together, these compounds can serve as lead structures for mitochondrial targeted drug discovery programs in breast cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Management of Pulmonary Arterial Hypertension in the Pediatric Patient

Purpose of Review: Pediatric pulmonary arterial hypertension (PAH) is associated with significant morbidity and mortality. Herein we review the diagnosis and classification for pediatric PAH and detail the current therapeutic options available for use in the pediatric PAH population. Recent Findings: Classification and treatment of pediatric PAH is guided by adult criteria and treatment algorithms, yet the distribution of factors contributing to PAH in children differs significantly from that seen in adults. It is necessary to understand these differences in order to appropriately tailor therapy to the needs of the child or adolescent. An expanding array of targeted PAH drugs are now approved for use in adults, and many of these drugs are used “off-label” to treat children and adolescents with PAH. Use of these novel therapies has coincided with marked improvement in outcomes, suggesting significant benefit. However, because most of these drugs have not been studied in rigorous randomized, controlled trials in children, it is critical that physicians understand their mechanisms of action, potential benefits, and safety profiles. Summary: Pediatric PAH outcomes have improved substantially in the modern era, coinciding with the “off-label” use of targeted PAH drugs in children and adolescents. Ideally, care should be provided at centers with specialized expertise in the diagnosis and treatment of pediatric PAH by providers who understand the appropriate diagnostic algorithms, classification schemes, and treatment approaches.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Development and validation of a quantitative proton NMR method for the analysis of pregnenolone

Pregnenolone (PREG) is an endogenous steroid frequently sold as an over-the-counter dietary supplement touted to promote neurological and immunological health. While the PREG dietary supplement is added to the diet for health benefits, there are no FDA approved PREG drugs. However, compounded PREG drug products are available to U.S. patients. The FDA works with state regulatory authorities on the oversight of compounding activities, including developing 503A and 503B lists of bulk substances that compounders are permitted to use. PREG is one of the substances publicly nominated to be included on the 503B list. Compounded hormone therapies such as those using PREG are of interest given the lack of standardization in compounded drug products which may increase the possibility of underdosing, overdosing, or contamination. However, no USP monograph currently exists to evaluate the quality of PREG drug substance or product. To address knowledge gaps and assist in quality control, a simple and rapid quantitative proton nuclear magnetic resonance spectroscopy (qNMR) method for the identification and assay of PREG in different types of PREG products was developed and validated. PREG samples were characterized using 1D 1 H and 2D 1 H– 13 C HSQC NMR spectra. The qNMR assay method (taking approximately 10 min per NMR spectrum) was validated for precision, accuracy, specificity, robustness and linearity per ICH Q2(R1) guidance. The method was validated in a range from 0.032 to 3.2 mg/mL. As a proof of concept, seven PREG bulk substance samples, three tablet and two capsule PREG dietary supplements were assessed by the qNMR analytical procedure. NMR data from all tested samples met the expected criteria for identification and assay. The results demonstrate the potential of qNMR for the quality assessment of different types of PREG samples.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Describing the Influence of Ball-milling on the Amorphization of Flubendazole Using the PDF and RMC Methods with X-ray Powder Diffraction Data

Flubendazole (FBZ) is a poorly water-soluble drug, and different methodologies have been proposed to improve its oral bioavailability. Obtaining the amorphous drug phase is an alternative to improve its water solubility. Several techniques for drug amorphization, such as spray drying, lyophilization, melt quenching, solvent-evaporation, and ball milling, can yield various types of structural disorder and possibly render variations in physicochemical properties. Herein, we focus on evaluating the influence of the ball-milling process on the amorphization of FBZ. The characterization of the average global and local structures before, during, and after the milling process is described by sequential Rietveld refinements, pair distribution function analysis, and the Reverse Monte Carlo method. In conclusion, we show that preserving the local structure (nearest molecules) can be responsible for avoiding the fast structure recrystallization commonly observed when using the solvent-evaporation process for the studied drug.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

STK35L1 regulates host cell cycle-related genes and is essential for Plasmodium infection during the liver stage of malaria

Protein kinases of both the parasite and the host are crucial in parasite invasion and survival and might act as drug targets against drug-resistant malaria. STK35L1 was among the top five hits in kinome-wide screening, suggesting its role in malaria's liver stage. However, the role of host STK35L1 in malaria remains elusive. In this study, we found that STK35L1 was highly upregulated during the infection of Plasmodium berghei (P. berghei) in HepG2 cells and mice liver, and knockdown of STK35L1 remarkably suppressed the sporozoites' infection in HepG2 cells. We showed that STAT3 is upregulated and phosphorylated during P. berghei sporozoites' infection, and STAT3 activation is required for both the upregulation of STK35L1 and STAT3. Furthermore, we found that ten cell cycle genes were upregulated in the sporozoite-infected hepatocytes. Knockdown of STK35L1 inhibited the basal expression of these genes except CDKN3 and GTSE1 in HepG2 cells. Thus, we identified STK35L1 as a host kinase that plays an obligatory role in malaria's liver stage and propose that it may serve as a potential drug target against drug-resistant malaria.

60 APPLIED LIFE SCIENCES↗