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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Production of High Specific Activity 155 Tb, 161 Tb and 203 Pb for Research and Clinical Applications: Effective Target Design, Target Material Recycling and Radioisotope Separation (Final Technical Report)

The overall objectives of this project were (1) to develop methods for the production and separation of a diagnostic and therapeutic or “theranostic” pair of radioisotopes, terbium-155 ( 155 Tb) and terbium-161 ( 161 Tb) and (2) to train graduate students and postdoctoral fellows in technologies and methods used in radionuclide production. Radionuclides can be incorporated into drugs called radiopharmaceuticals that target a specific disease (e.g., cancer). The need for theranostic radionuclides is escalating with the clinical translation of radiopharmaceuticals due to their implementation in personalized medicine, which has demonstrated enhanced patient treatments. High purity and high specific activity radionuclides are critical for theranostic agent development, for example to maintain diagnostic image quality, to minimize radiation dose to the patient, and to increase uptake in the targeted tissue (e.g., tumor), especially in the case of receptor- and antigen-targeted agents. The 155 Tb (diagnostic) and 161 Tb (therapeutic) radioisotopes that were generated through this project are a theranostic pair with demonstrated potential for the development and translation into individualized, targeted, and dosimetry-driven radiotherapies. However, the development of such radiotherapies has been hindered by the lack of a routine and reliable supply of these isotopes in the United States. Methods for the production, separation, and supply of 155 Tb and 161 Tb were investigated and developed in this project. Further, the strong emphasis throughout the project on the training of graduate students and postdoctoral fellows has helped to ensure and enhance the nuclear science workforce through the training of the next generation of highly qualified scientists in nuclear and radiochemistry. This grant also continued a collaboration between scientists at the University of Washington (UW), the University of Missouri (MU) and Brookhaven National Laboratory (BNL). All three institutions were involved in the project, but to different degrees on the various tasks through which the overall objectives were met.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Radiosensitizing the Vasculature of Primary Brainstem Gliomas Fails to Improve Tumor Response to Radiation Therapy

Diffuse intrinsic pontine gliomas (DIPGs) arise in the pons and are the leading cause of death from brain tumors in children. DIPGs are routinely treated with radiation therapy, which temporarily improves neurological symptoms but generally fails to achieve local control. Because numerous clinical trials have not improved survival from DIPG over standard radiation therapy alone, there is a pressing need to evaluate new therapeutic strategies for this devastating disease. Vascular damage caused by radiation therapy can increase the permeability of tumor blood vessels and promote tumor cell death.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Preclinical Risk Evaluation of Normal Tissue Injury With Novel Radiosensitizers

Genotoxic damage induced by radiation triggers a highly coordinated DNA damage response, and molecular inhibitors of key nodes within this complex response network can profoundly enhance the antitumor efficacy of radiation. This is especially true for drugs targeting the catalytic subunit of DNA-dependent protein kinase, which is a core component of the nonhomologous end-joining DNA repair pathway, and ataxia telangiectasia mutated, which coordinates cell cycle arrest, apoptosis, and DNA repair functionalities after radiation exposure. Unlike the more modest in vitro radiosensitizing effects seen with classic sensitizing agents such as cisplatin, 5-fluorouracil, or taxanes, DNA-dependent protein kinase or ataxia telangiectasia mutated inhibitors provide much more robust sensitizing effects in vitro, as might be anticipated from targeting these key DNA repair modulators. However, patients with homozygous inactivating mutations of ataxia telangiectasia mutated or mice with homozygous defects in DNA-dependent protein kinase (severe combined immunodeficiency) have profoundly enhanced acute normal tissue radiation reactions. Therefore, there is significant potential that the combination of small molecule inhibitors of these kinases with radiation could cause similar dose-limiting acute normal tissue toxicities. Similarly, although less understood, inhibition of these DNA repair response pathways could markedly increase the risk of late radiation toxicities. Because these potent radiosensitizers could be highly useful to improve local control of otherwise radiation-resistant tumors, understanding the potential for elevated risks of radiation injury is essential for optimizing therapeutic ratio and developing safe and informative clinical trials. In this review, we will discuss 2 straightforward models to assess the potential for enhanced mucosal toxicity in the oral cavity and small intestine established in our laboratories. We also will discuss similar strategies for evaluating potential drug–radiation interactions with regard to increased risks of debilitating late effects.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Drug-induced kidney injury: challenges and opportunities

Abstract Drug-induced kidney injury (DIKI) is a frequently reported adverse event, associated with acute kidney injury, chronic kidney disease, and end-stage renal failure. Prospective cohort studies on acute injuries suggest a frequency of around 14%–26% in adult populations and a significant concern in pediatrics with a frequency of 16% being attributed to a drug. In drug discovery and development, renal injury accounts for 8 and 9% of preclinical and clinical failures, respectively, impacting multiple therapeutic areas. Currently, the standard biomarkers for identifying DIKI are serum creatinine and blood urea nitrogen. However, both markers lack the sensitivity and specificity to detect nephrotoxicity prior to a significant loss of renal function. Consequently, there is a pressing need for the development of alternative methods to reliably predict drug-induced kidney injury (DIKI) in early drug discovery. In this article, we discuss various aspects of DIKI and how it is assessed in preclinical models and in the clinical setting, including the challenges posed by translating animal data to humans. We then examine the urinary biomarkers accepted by both the US Food and Drug Administration (FDA) and the European Medicines Agency for monitoring DIKI in preclinical studies and on a case-by-case basis in clinical trials. We also review new approach methodologies (NAMs) and how they may assist in developing novel biomarkers for DIKI that can be used earlier in drug discovery and development.

Connor, Skylar (ORCID:0000000233479180)↗
DOE OSTI2021

Benchmarking blockchain-based gene-drug interaction data sharing methods: A case study from the iDASH 2019 secure genome analysis competition blockchain track

Blockchain distributed ledger technology is just starting to be adopted in genomics and healthcare applications. Despite its increased prevalence in biomedical research applications, skepticism regarding the practicality of blockchain technology for real-world problems is still strong and there are few implementations beyond proof-of-concept. We focus on benchmarking blockchain strategies applied to distributed methods for sharing records of gene-drug interactions. We expect this type of sharing will expedite personalized medicine. We generated gene-drug interaction test datasets using the Clinical Pharmacogenetics Implementation Consortium (CPIC) resource. We developed three blockchain-based methods to share patient records on gene-drug interactions: Query Index, Index Everything, and Dual-Scenario Indexing. We achieved a runtime of about 60 s for importing 4,000 gene-drug interaction records from four sites, and about 0.5 s for a data retrieval query. Our results demonstrated that it is feasible to leverage blockchain as a new platform to share data among institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Preclinical tumor organoid models in personalized cancer therapy: Not everyone fits the mold

Highlights: • In personalized cancer medicine, each patient receives a specific treatment. • The tumor microenvironment and interpersonal differences can affect treatment. • Organoid culture systems fully recapitulate the tumor microenvironments. In contrast to conventional cancer treatment, in personalized cancer medicine each patient receives a specific treatment. The response to therapy, clinical outcomes, and tumor behavior such as metastases, tumor progression, carcinogenesis can be significantly affected by the heterogeneous tumor microenvironment (TME) and interpersonal differences. Therefore, using native tumor microenvironment mimicking models is necessary to improving personalized cancer therapy. Both in vitro 2D cell culture and in vivo animal models poorly recapitulate the heterogeneous tumor (immune) microenvironments of native tumors. The development of 3D culture models, native tumor microenvironment mimicking models, made it possible to evaluate the chemoresistance of tumor tissue and the functionality of drugs in the presence of cell-extracellular matrix and cell-cell interactions in a 3D construction. Various personalized tumor models have been designed to preserving the native tumor microenvironment, including patient-derived tumor xenografts and organoid culture strategies. In this review, we will discuss the patient-derived organoids as a native tumor microenvironment mimicking model in personalized cancer therapy. In addition, we will also review the potential and the limitations of organoid culture systems for predicting patient outcomes and preclinical drug screening. Finally, we will discuss immunotherapy drug screening in tumor organoids by using microfluidic technology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

PRIMED for the Future: Purposing Raw Intake for Machine Learning-Enabled Detection (Final Report)

The COVID-19 pandemic demonstrated how a novel, elusive, and diffuse biological threat can engender uncertainty and misinformation, and it underscored the need for flexible analytical modalities agnostic to the identity of biological material. Yet even before the pandemic, recognition of the limitations of the current, list-based approach, which focuses on known pathogens and biotoxins, and of the importance of agent-agnostic biodetection was growing within the biosecurity community. In a 2018 report on “Biodefense in the Age of Synthetic Biology,” for example, the National Academy of Sciences stated that “an overreliance on the Select Agent List is a systematic weakness affecting many aspects of the United States’ current biodefense mitigation capability”. More recently, a group of biodefense researchers proposed the identification and adoption of “bioagent-agnostic signatures (BASs)” as a way of detecting and characterizing not only existing agents but also novel ones, an approach they believe will “enable a more flexible and resilient biodefense posture”. Indeed, the future of biodetection requires us to begin developing novel analytics that can identify anomalies and/or characteristics that indicate a potential threat, whether known or unknown, without looking for a specific signature that has been identified previously. To assess potential threats more rapidly, it is critical to develop agnostic artificial intelligence (AI)/machine learning (ML) systems that can be employed for real-time assessment of the nature and source of a perturbation. Such systems should be multi scale and multi-dimensional, integrating sensor data from a range of biological, chemical, and physical application spaces. Emerging deep learning (DL) models demonstrate exceptional promise for identification of discriminatory features within multi-dimensional datasets. DL models have the capacity to recognize and encode highly complex patterns in a wide range of input data modalities, including images, text, and biological/chemical/physical spectra. As such, they can execute a wide range of assessments and determinations that have traditionally required a human operator. The promise of advances in DL is apparent in the realm of human health and medicine. DL models have been validated for evaluating a variety of clinical threats to human health in a range of contexts, including infection and cancer, and they demonstrated improved performance in predicting stroke relative to human neurologists in some categories of data. Continuously evolving advances in AI/ML are expected to support more efficient evaluation of raw sequence, spectroscopy, and spectrometry data. For instance, recent advances and deployment of large language models (LLM) such as Generative Pre training Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) have already motivated application of these models for biological function prediction. As frameworks such as LLMs become larger and more complex in their representations, their capacity to serve as pre-trained models that can be fine-tuned for biological/biodetection purposes will similarly be amplified. While existing and emerging AI/ML have found broad applicability and use cases in the clinical sciences, development for environmental evaluation and biodetection has been limited. Functionalizing such capabilities for this purpose requires an understanding of the existing technical landscape and how the respective tools and algorithms are currently being employed. This landscape awareness then allows an assessment of the current practical capabilities of existing models and the anticipated requirements and development efforts that will be needed to adapt available algorithms for biodetection applications relevant to DHS. Leveraging expertise in biodetection, ML, and operational biodetection, the effort described in this report is comprised of a systematic landscape assessment (Subtask 2.1), comparative evaluation (Subtask 2.2), and formulation of a value proposition (Subtask 2.3) for the prospect of ML-enabled, agnostic biodetection from raw, or minimally-processed, datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Evaluation Challenges for the Application of Extended Reality Devices in Medicine

Augmented and virtual reality devices are being actively investigated and implemented for a wide range of medical uses. However, significant gaps in the evaluation of these medical devices and applications hinder their regulatory evaluation. Addressing these gaps is critical to demonstrating the devices’ safety and effectiveness. We outline the key technical and clinical evaluation challenges discussed during the US Food and Drug Administration’s public workshop, “Medical Extended Reality: Toward Best Evaluation Practices for Virtual and Augmented Reality in Medicine” and future directions for evaluation method development. Evaluation challenges were categorized into several key technical and clinical areas. Finally, we highlight current efforts in the standards communities and illustrate connections between the evaluation challenges and the intended uses of the medical extended reality (MXR) devices. Participants concluded that additional research is needed to assess the safety and effectiveness of MXR devices across the use cases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Cancer genomics predicts disease relapse and therapeutic response to neoadjuvant chemotherapy of hormone sensitive breast cancers

Several studies provide insight into the landscape of breast cancer genomics with the genomic characterization of tumors offering exceptional opportunities in defining therapies tailored to the patient’s specific need. However, translating genomic data into personalized treatment regimens has been hampered partly due to uncertainties in deviating from guideline based clinical protocols. Here we report a genomic approach to predict favorable outcome to treatment responses thus enabling personalized medicine in the selection of specific treatment regimens. The genomic data were divided into a training set of N = 835 cases and a validation set consisting of 1315 hormone sensitive, 634 triple negative breast cancer (TNBC) and 1365 breast cancer patients with information on neoadjuvant chemotherapy responses. Patients were selected by the following criteria: estrogen receptor (ER) status, lymph node invasion, recurrence free survival. The k-means classification algorithm delineated clusters with low- and high- expression of genes related to recurrence of disease; a multivariate Cox’s proportional hazard model defined recurrence risk for disease. Classifier genes were validated by Immunohistochemistry (IHC) using tissue microarray sections containing both normal and cancerous tissues and by evaluating findings deposited in the human protein atlas repository. Based on the leave-on-out cross validation procedure of 4 independent data sets we identified 51-genes associated with disease relapse and selected 10, i.e. TOP2A, AURKA, CKS2, CCNB2, CDK1 SLC19A1, E2F8, E2F1, PRC1, KIF11 for in depth validation. Expression of the mechanistically linked disease regulated genes significantly correlated with recurrence free survival among ER-positive and triple negative breast cancer patients and was independent of age, tumor size, histological grade and node status. Importantly, the classifier genes predicted pathological complete responses to neoadjuvant chemotherapy (P < 0.001) with high expression of these genes being associated with an improved therapeutic response toward two different anthracycline-taxane regimens; thus, highlighting the prospective for precision medicine. Our study demonstrates the potential of classifier genes to predict risk for disease relapse and treatment response to chemotherapies. The classifier genes enable rational selection of patients who benefit best from a given chemotherapy thus providing the best possible care. The findings encourage independent clinical validation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

The First Virtual Human Global Summit: Prepublication Meeting Report

This is the prepublication report for the First Virtual Human Global Summit held in October 2023. Organized collaboratively by Frederick National Laboratory for Cancer Research, Brookhaven National Laboratory, University College London, and Eviden, the 2023 Virtual Human Global Summit convened global thought leaders to bring together multiple perspectives across domains and organizations. The intent of the Summit was to foster collaboration among key leaders internationally whose combined efforts are required to advance patient-focused precision medicine through medical digital twins. Participants represented cancer and biomedical research, industry, infrastructure, clinical research, community health, non-profit organizations, government, and general public interests. The Summit included over 80 attendees across three continents including native American tribes. The Summit was held to share insights about the state of the art for medical digital twins, provide motivating opportunities and identify key challenges along the path to improved health and wellness through virtual human models and personalized digital twins. The Summit included sessions emphasizing the primary areas of research, infrastructure, clinical application, government support, and adoption/sustainability. Several examples of digital twins were referenced or mentioned through the course of the Summit including digital twin approaches in cancer, radiation oncology, molecular scale digital twins, diabetes, and sepsis. The summit report includes perspectives on the current state, challenges and guidance across research, infrastructure, clinical translation and community adoption, as well as multiple key insights from industry perspectives.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

A human antibody epitope map of the malaria vaccine antigen Pfs25

Pfs25 is a leading antigen for a malaria transmission-blocking vaccine and shows moderate transmission-blocking activity and induction of rapidly decreasing antibody titers in clinical trials. A comprehensive definition of all transmission-reducing epitopes of Pfs25 will inform structure-guided design to enhance Pfs25-based vaccines, leading to potent transmission-blocking activity. Here, we compiled a detailed human antibody epitope map comprising epitope binning data and structures of multiple human monoclonal antibodies, including three new crystal structures of Pfs25 in complex with transmission-reducing antibodies from Malian volunteers immunized with Pfs25 conjugated to EPA and adjuvanted with AS01. These structures revealed additional epitopes in Pfs25 capable of reducing transmission and expanded this characterization to malaria-exposed humans. This work informs immunogen design to focus the antibody response to transmission-reducing epitopes of Pfs25, enabling development of more potent transmission-blocking vaccines for malaria.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Phenopacket-tools: Building and validating GA4GH Phenopackets

The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The effect of protein mutations on drug binding suggests ensuing personalised drug selection

The advent of personalised medicine promises a deeper understanding of mechanisms and therefore therapies. However, the connection between genomic sequences and clinical treatments is often unclear. We studied 50 breast cancer patients belonging to a population-cohort in the state of Qatar. From Sanger sequencing, we identified several new deleterious mutations in the estrogen receptor 1 gene (ESR1). The effect of these mutations on drug treatment in the protein target encoded by ESR1, namely the estrogen receptor, was achieved via rapid and accurate protein–ligand binding affinity interaction studies which were performed for the selected drugs and the natural ligand estrogen. Four nonsynonymous mutations in the ligand-binding domain were subjected to molecular dynamics simulation using absolute and relative binding free energy methods, leading to the ranking of the efficacy of six selected drugs for patients with the mutations. Our study shows that a personalised clinical decision system can be created by integrating an individual patient’s genomic data at the molecular level within a computational pipeline which ranks the efficacy of binding of particular drugs to variant proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Structure-based development of a subtype-selective orexin 1 receptor antagonist

Orexin receptors belong to the superfamily of G-protein–coupled receptors (GPCRs) which represent the largest class of drug targets in humans. Despite the recent progress in structural biology, the development of subtype-selective orexin receptor and GPCR ligands in general remains challenging, due to the high sequence similarity among individual receptor subtypes. However, subtype-selective molecules are key to discerning the individual contributions of receptor subtypes to (patho-)physiology. Starting from the clinically used, non–subtype-selective orexin receptor antagonist suvorexant, we demonstrate how docking, crystallography, medicinal chemistry, and in vitro pharmacology can be combined to exploit single amino acid sequence differences for the discovery of subtype-selective probes. Such compounds will help to better understand orexin receptor pharmacology and develop promising drug candidates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Tumor Control Probability of Radiosurgery and Fractionated Stereotactic Radiosurgery for Brain Metastases

As part of the American Association of Physicists in Medicine Working Group on Stereotactic Body Radiotherapy, tumor control probability (TCP) after stereotactic radiosurgery (SRS) and fractionated stereotactic radiosurgery (fSRS) for brain metastases was modeled based on pooled dosimetric and clinical data from published English-language literature.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

NASMDR: a framework for miRNA-drug resistance prediction using efficient neural architecture search and graph isomorphism networks

Abstract As a frontier field of individualized therapy, microRNA (miRNA) pharmacogenomics facilitates the understanding of different individual responses to certain drugs and provides a reasonable reference for clinical treatment. However, the known drug resistance-associated miRNAs are not yet sufficient to support precision medicine. Although existing methods are effective, they all focus on modelling miRNA-drug resistance interaction graphs, making their performance bounded by the interaction density. In this study, we propose a framework for miRNA-drug resistance prediction through efficient neural architecture search and graph isomorphism networks (NASMDR). NASMDR uses attribute information instead of the commonly used interactive graph information. In the cross-validation experiment, the proposed framework can achieve an AUC of 0.9468 on the ncDR dataset, which is 2.29% higher than the state-of-the-art method. In addition, we propose a novel sequence characterization approach, k-mer Sparse Nonnegative Matrix Factorization (KSNMF). The results show that NASMDR provides novel insights for integrating efficient neural architecture search and graph isomorphic networks into a unified framework to predict drug resistance-related miRNAs. The codes for NASMDR are available at https://github.com/kaizheng-academic/NASMDR.

Zheng, Kai↗
DOE OSTI2026

Identification of potent inhibitors of JUN N-terminal kinases for treatment of endometriosis and associated pain

Endometriosis, defined as the ectopic growth of endometrial tissue outside of the uterine cavity, is an inflammatory and hormone-dependent disease that causes excruciating pelvic pain, infertility, and significantly decreases quality of life in affected patients. The JUN N-terminal kinases (JNKs) are a leading class of nonhormonal therapeutic targets that have been validated in preclinical models of endometriosis and in a Phase 1/2 clinical trial. Despite their therapeutic potential, JNK inhibitors with increased potency and specificity are needed to address the inflammatory pathology of endometriosis and to prevent disease progression. Leveraging a DNA-encoded chemical library collection of ~4 billion compounds, we identified lead inhibitor CDD-2428 and optimized derivatives, CDD-2728 and CDD-3013, with excellent binding affinity to JNK1-3 (K d = 0.12 to 3.7 nM), enhanced selectivity, metabolic stability, and cellular permeability. Crystallographic and biochemical studies confirmed that CDD-3013 exhibited superior kinase selectivity with improved efficacy compared to existing JNK inhibitors. In primary endometriosis cell models, CDD-2728 and CDD-3013 suppressed JNK-dependent inflammatory signaling, dampening pathways linked to pain, invasion, angiogenesis, and macrophage recruitment. In an endometriosis mouse model, both CDD-2728 and CDD-3013 reduced endometriotic lesion size, macrophage infiltration, and cellular proliferation, showing in vivo efficacy. When tested in a lipopolysaccharide-induced hyperalgesia model, CDD-2728 and CDD-3013 decreased markers of induced pain, as measured by changes in a dynamic weight bearing test and Grimace scores. These findings nominate CDD-2728 and CDD-3013 as potent, nonhormonal therapeutic candidates for endometriosis with broad anti-inflammatory and analgesic activity, addressing a critical unmet clinical need.

Madasu, Chandrashekhar [Department of Pathology an↗