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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Pediatric dosing for locally acting drugs in submissions to the U.S. Food and Drug Administration between 2002 and 2020

Abstract Deriving pediatric doses for locally acting drugs (LADs) presents a unique challenge because limited systemic exposure hinders commonly used approaches such as pharmacokinetic matching to adults. This study systematically evaluated drug development practices used for pediatric dose selection of LADs approved by the U.S. Food and Drug Administration from 2002 to 2020. The three study objectives were: (1) to determine the dose selection approach for the labeled pediatric dose, (2) to examine the studied pediatric dose(s), and (3) to evaluate the characteristics of the pediatric clinical programs used to support the labeled pediatric dose. A total of 187 pediatric submissions were characterized for the labeled and studied pediatric doses of LADs. The pediatric dose was predominantly labeled as a flat dose (91%) and at a single‐dose level (67%) similar to adults. The majority (68.4%) of the submissions had the same labeled dose for pediatrics and adults. Independent pharmacodynamic/efficacy studies in pediatric patients commonly (64.2%) provided supportive evidence for the labeled pediatric dose. Inhalation, nasal, and injectable submissions had the highest number of clinical trials, lowest usage of an extrapolation of efficacy approach, and utilized diverse approaches in selecting the studied pediatric doses. This article highlights approaches for LAD dosing in pediatric patients and can be used to inform drug development of these products in the pediatric population.

Research & Experimental Medicine↗
DOE OSTI2021

Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment

The coronavirus disease 2019 (COVID-19) global pandemic resulted in millions of people becoming infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and close to seven million deaths worldwide. It is essential to further explore and design effective COVID-19 treatment drugs that target the main protease of SARS-CoV-2, a major target for COVID-19 drugs. In this study, machine learning was applied for predicting the SARS-CoV-2 main protease binding of Food and Drug Administration (FDA)-approved drugs to assist in the identification of potential repurposing candidates for COVID-19 treatment. Ligands bound to the SARS-CoV-2 main protease in the Protein Data Bank and compounds experimentally tested in SARS-CoV-2 main protease binding assays in the literature were curated. These chemicals were divided into training (516 chemicals) and testing (360 chemicals) data sets. To identify SARS-CoV-2 main protease binders as potential candidates for repurposing to treat COVID-19, 1188 FDA-approved drugs from the Liver Toxicity Knowledge Base were obtained. A random forest algorithm was used for constructing predictive models based on molecular descriptors calculated using Mold2 software. Model performance was evaluated using 100 iterations of fivefold cross-validations which resulted in 78.8% balanced accuracy. The random forest model that was constructed from the whole training dataset was used to predict SARS-CoV-2 main protease binding on the testing set and the FDA-approved drugs. Model applicability domain and prediction confidence on drugs predicted as the main protease binders discovered 10 FDA-approved drugs as potential candidates for repurposing to treat COVID-19. Our results demonstrate that machine learning is an efficient method for drug repurposing and, thus, may accelerate drug development targeting SARS-CoV-2.

Research & Experimental Medicine↗
DOE OSTI2020

Discovery of a Natural Product That Binds to the Mycobacterium tuberculosis Protein Rv1466 Using Native Mass Spectrometry

Elucidation of the mechanism of action of compounds with cellular bioactivity is important for progressing compounds into future drug development. In recent years, phenotype-based drug discovery has become the dominant approach to drug discovery over target-based drug discovery, which relies on the knowledge of a specific drug target of a disease. Still, when targeting an infectious disease via a high throughput phenotypic assay it is highly advantageous to identifying the compound’s cellular activity. A fraction derived from the plant Polyalthia sp. showed activity against Mycobacterium tuberculosis at 62.5 μge/μL. A known compound, altholactone, was identified from this fraction that showed activity towards M. tuberculosis at an minimum inhibitory concentration (MIC) of 64 μM. Retrospective analysis of a target-based screen against a TB proteome panel using native mass spectrometry established that the active fraction was bound to the mycobacterial protein Rv1466 with an estimated pseudo-Kd of 42.0 ± 6.1 µM. Our findings established Rv1466 as the potential molecular target of altholactone, which is responsible for the observed in vivo toxicity towards M. tuberculosis .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Enhancing Docking Accuracy with PECAN2, a 3D Atomic Neural Network Trained without Co-Complex Crystal Structures

Decades of drug development research have explored a vast chemical space for highly active compounds. The exponential growth of virtual libraries enables easy access to billions of synthesizable molecules. Computational modeling, particularly molecular docking, utilizes physics-based calculations to prioritize molecules for synthesis and testing. Nevertheless, the molecular docking process often yields docking poses with favorable scores that prove to be inaccurate with experimental testing. To address these issues, several approaches using machine learning (ML) have been proposed to filter incorrect poses based on the crystal structures. However, most of the methods are limited by the availability of structure data. Here, we propose a new pose classification approach, PECAN2 (Pose Classification with 3D Atomic Network 2), without the need for crystal structures, based on a 3D atomic neural network with Point Cloud Network (PCN). The new approach uses the correlation between docking scores and experimental data to assign labels, instead of relying on the crystal structures. We validate the proposed classifier on multiple datasets including human mu, delta, and kappa opioid receptors and SARS-CoV-2 Mpro. Our results demonstrate that leveraging the correlation between docking scores and experimental data alone enhances molecular docking performance by filtering out false positives and false negatives.

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

AI-based language models powering drug discovery and development

The discovery and development of new medicines is expensive, time-consuming, and often inefficient, with many failures along the way. Powered by artificial intelligence (AI), language models (LMs) have changed the landscape of natural language processing (NLP), offering possibilities to transform treatment development more effectively. Here, we summarize advances in AI-powered LMs and their potential to aid drug discovery and development. We highlight opportunities for AI-powered LMs in target identification, clinical design, regulatory decision-making, and pharmacovigilance. We specifically emphasize the potential role of AI-powered LMs for developing new treatments for Coronavirus 2019 (COVID-19) strategies, including drug repurposing, which can be extrapolated to other infectious diseases that have the potential to cause pandemics. Finally, we set out the remaining challenges and propose possible solutions for improvement.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Outgrowth of erlotinib-resistant subpopulations recapitulated in patient-derived lung tumor spheroids and organoids

A model that recapitulates development of acquired therapeutic resistance is needed to improve oncology drug development and patient outcomes. To achieve this end, we established methods for the preparation and growth of spheroids from primary human lung adenocarcinomas, including methods to culture, passage, monitor growth, and evaluate changes in mutational profile over time. Primary lung tumor spheroids were cultured in Matrigel® with varying concentrations of erlotinib, a small molecule kinase inhibitor of epidermal growth factor receptor (EGFR) that is ineffective against KRAS mutant cells. Subtle changes in spheroid size and number were observed within the first two weeks of culture. Spheroids were cultured for up to 24 weeks, during which time interactions between different cell types, movement, and assembly into heterogeneous organoid structures were documented. Allele-specific competitive blocker PCR (ACB-PCR) was used to quantify low frequency BRAF V600E, KRAS G12D, KRAS G12V, and PIK3CA H1047R mutant subpopulations in tumor tissue residue (TR) samples and cultured spheroids. Mutant subpopulations, including multiple mutant subpopulations, were quite prevalent. Twelve examples of mutant enrichment were found in eight of the 14 tumors analyzed, based on the criteria that a statistically-significant increase in mutant fraction was observed relative to both the TR and the no-erlotinib control. Of the mutants quantified in erlotinib-treated cultures, PIK3CA H1047 mutant subpopulations increased most often (5/14 tumors), which is consistent with clinical observations. Thus, this ex vivo lung tumor spheroid model replicates the cellular and mutational tumor heterogeneity of human lung adenocarcinomas and can be used to assess the outgrowth of mutant subpopulations. Spheroid cultures with characterized mutant subpopulations could be used to investigate the efficacy of lung cancer combination therapies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Protein Factory on a Chip for Rapid Therapeutics

The potential for new, re-emerging, or engineered pandemic threats could challenge our nation's biosecurity, requiring rapid and flexible countermeasure development platforms. In healthcare, one of the main hindrances to the timely development of protein-based therapeutics for cancer, infectious disease, and other diseases has been a slow and expensive quality-controlled production stage. We present a protein-based technology that has the potential to address these crucial problems in biosecurity and healthcare by maximizing protein production and system portability, while minimizing production latency and cost. Specifically, we have developed a reusable, portable first-generation platform that leverages the advantages of cell-free protein synthesis (CFPS) and flow cells to create practical amounts of clinically relevant therapeutics. We have demonstrated that this technology is reusable and programmable, enabling a single platform that can synthesize multiple dosages of various therapeutics and vaccine in the field. Implementing this technology could mitigate pandemic threats by providing a rapidly-deployable (just-in-time), versatile (precision medicine) production of biologic therapeutics to the infected patient’s locale (point-of-care). In drug development, once promising drug variations are identified, the platform can be scaled to quickly produce practical amounts of protein for patient testing in the matter of days (vs. months) at a fraction of the cost of existing technology.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

Commercial researcher perspective

Protein crystallography--a research tool used to study the structure of the complex building blocks of living systems--has a lot to gain from space-based research. In order to know how a protein works in the human body, researchers must understand its molecular structure. Researchers have identified 150,000 different proteins in the body, but they now know the structure of less than a third of them. The only viable technique for analyzing the structure of these proteins is x-ray diffraction of the proteins in their crystal form. The better the quality of a protein crystal, the more useful it is to researchers who are trying to delineate its structure. The microgravity environment of space allows protein crystals to grow nearly undisturbed by convection and other gravity-driven forces that cause flaws to form in them on the ground. In space, lack of convection enables protein crystals to grow more slowly than they do on Earth, and the slower a protein crystal grows, the fewer flaws it will have. Protein crystal growth experiments have already flown on 14 Space Shuttle missions. This year's USML-1 Spacelab mission included protein crystal growth experiments conducted for commercial researchers. The results of protein crystal experiments flown thus far have been larger crystals with more uniform morphologies. The Center for Macromolecular Crystallography (A NASA-cosponsored CCDS) currently builds flight hardware to meet researchers' needs and handles sample loading and retrieval for flight experiments. Protein crystallography enables 'rational drug design': the development of drugs that bind only with the target protein and, hence, do not cause side effects. For example, pharmaceutical companies presently are interested in developing drugs that can inhibit purine nucleoside phosphorylase (PNP), a protein that plays a role in auto-immune diseases. To continue these kinds of investigations, researchers need a constant supply of protein crystals that are as free of flaws as possible. Space Station Freedom will provide the kind of research environment that will enable the production of such supplies. In addition, Freedom will provide the kind of long-duration facility required by protein crystal researchers: 40 percent of proteins require more than two weeks to crystallize.

Delucas, Larry↗
DOE OSTI2024

Verazine biosynthesis from simple sugars in engineered Saccharomyces cerevisiae

Steroidal alkaloids are FDA-approved drugs (e.g., Zytiga) and promising drug candidates/leads (e.g., cyclopamine); yet many of the ≥697 known steroidal alkaloid natural products remain underutilized as drugs because it can be challenging to scale their biosynthesis in their producing organisms. Cyclopamine is a steroidal alkaloid produced by corn lily (Veratrum spp.) plants, and it is an inhibitor of the Hedgehog (Hh) signaling pathway. Therefore, cyclopamine is an important drug candidate/lead to treat human diseases that are associated with dysregulated Hh signaling, such as basal cell carcinoma and acute myeloid leukemia. Cyclopamine and its semi-synthetic derivatives have been studied in (pre)clinical trials as Hh inhibitor-based drugs. However, challenges in scaling the production of cyclopamine have slowed efforts to improve its efficacy and safety profile through (bio)synthetic derivatization, often limiting drug development to synthetic analogs of cyclopamine such as the FDA-approved drugs Odomzo, Daurismo, and Erivedge. If a platform for the scalable and sustainable production of cyclopamine were established, then its (bio)synthetic derivatization, clinical development, and, ultimately, widespread distribution could be accelerated. Ongoing efforts to achieve this goal include the biosynthesis of cyclopamine in Veratrum plant cell culture and the semi-/total chemical synthesis of cyclopamine. Herein, this work advances efforts towards a promising future approach: the biosynthesis of cyclopamine in engineered microorganisms. We completed the heterologous microbial production of verazine (biosynthetic precursor to cyclopamine) from simple sugars (i.e., glucose and galactose) in engineered Saccharomyces cerevisiae (S. cerevisiae) through the inducible upregulation of the native yeast mevalonate and lanosterol biosynthetic pathways, diversion of biosynthetic flux from ergosterol (i.e., native sterol in S. cerevisiae) to cholesterol (i.e., biosynthetic precursor to verazine), and expression of a refactored five-step verazine biosynthetic pathway. The engineered S. cerevisiae strain that produced verazine contains eight heterologous enzymes sourced from seven different species. Importantly, S. cerevisiae-produced verazine was indistinguishable via liquid chromatography-mass spectrometry from both a commercial standard (Veratrum spp. plant-produced) and Nicotiana benthamiana-produced verazine. To the best of our knowledge, this is the first report describing the heterologous production of a steroidal alkaloid in an engineered yeast. Verazine production was ultimately increased through design-build-test-learn cycles to a final titer of 83 ± 3 μg/L (4.1 ± 0.1 μg/g DCW). Finally, this research lays the groundwork for future microbial biosynthesis of cyclopamine, (bio)synthetic derivatives of cyclopamine, and other steroidal alkaloid natural products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Selective Photoswitchable Allosteric Agonist of a G Protein-Coupled Receptor

G protein-coupled receptors (GPCRs) are the most common targets of drug discovery. However, the similarity between related GPCRs combined with the complex spatiotemporal dynamics of receptor activation in vivo has hindered drug development. Photopharmacology offers the possibility of using light to control the location and timing of drug action by incorporating a photoisomerizable azobenzene into a GPCR ligand, enabling rapid and reversible switching between an inactive and active configuration. Recent advances in this area include (i) photoagonists and photoantagonists that directly control receptor activity but are nonselective because they bind conserved sites, and (ii) photoallosteric modulators that bind selectively to nonconserved sites but indirectly control receptor activity by modulating the response to endogenous ligand. In this study, we designed a photoswitchable allosteric agonist that targets a nonconserved allosteric site for selectivity and activates the receptor on its own to provide direct control. This work culminated in the development of aBINA, a photoswitchable allosteric agonist that selectively activates the G i/o -coupled metabotropic glutamate receptor 2 (mGluR2). aBINA is the first example of a new class of precision drugs for GPCRs and other clinically important signaling proteins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Calibration and Validation of a Mechanistic COVID-19 Model for Translational Quantitative Systems Pharmacology – A Proof-of-Concept Model Development for Remdesivir

With the ongoing global pandemic of coronavirus disease 2019 (COVID-19), there is an urgent need to accelerate the traditional drug development process. Many studies identified potential COVID-19 therapies based on promising nonclinical data. However, the poor translatability from nonclinical to clinical settings has led to failures of many of these drug candidates in the clinical phase. In this study, we propose a mechanism-based, quantitative framework to translate nonclinical findings to clinical outcome. Adopting a modularized approach, this framework includes an in silico disease model for COVID-19 (virus infection and human immune responses) and a pharmacological component for COVID-19 therapies. Here, the disease model was able to reproduce important longitudinal clinical data for patients with mild and severe COVID-19, including viral titer, key immunological cytokines, antibody responses, and time courses of lymphopenia. Using remdesivir as a proof-of-concept example of model development for the pharmacological component, we developed a pharmacological model that describes the conversion of intravenously administered remdesivir as a prodrug to its active metabolite nucleoside triphosphate through intracellular metabolism and connected it to the COVID-19 disease model. After being calibrated with the placebo arm data, our model was independently and quantitatively able to predict the primary endpoint (time to recovery) of the remdesivir clinical study, Adaptive Covid-19 Clinical Trial (ACTT). Our work demonstrates the possibility of quantitatively predicting clinical outcome based on nonclinical data and mechanistic understanding of the disease and provides a modularized framework to aid in candidate drug selection and clinical trial design for COVID-19 therapeutics.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2001

Versatile Vials

Advanced Fuel Research, Inc. (AFR) developed a new sol-gel material coating for sample vials to be used in surface-enhanced Raman (SER) spectroscopy. In the fields of biomedical and forensic science, the coated vials are used to identify physiological chemicals present in blood and urine. The vials are used in the pharmaceutical industry in the area of drug development and discovery. In the drug enforcement field, using the vials helps with detection of trace amounts of drugs on surfaces. The SER vials will also help to identify contaminants in water sources, such as various inorganics and pesticides. The new vials have advantages over other SER materials. Former products required that samples be pre-mixed or dissolved into specific solvents or reagents. The SER vials do not have these limitations, and measurements can be performed using any solvent. Furthermore, the signal appears in less than one second. The sol-gel material will stay active for at least one year and the vials are compatible with any Raman spectrometer. Most importantly, the SER activity can be duplicated from vial-to-vial, allowing for reproducible results.

Source record↗
DOE OSTI2022

Quantitative methods and modeling to assess COVID–19–interrupted in vivo pharmacokinetic bioequivalence studies with two reference batches

The coronavirus disease 2019 (COVID-19) has presented unprecedented challenges to the generic drug development, including interruptions in bioequivalence (BE) studies. Per guidance published by the US Food and Drug Administration (FDA) during the COVID-19 public health emergency, any protocol changes or alternative statistical analysis plan for COVID-19-interrupted BE study should be accompanied with adequate justifications and not lead to biased equivalence determination. In this study, we used a modeling and simulation approach to assess the potential impact of study outcomes when two different batches of a Reference Standard (RS) were to be used in an in vivo pharmacokinetic BE study due to the RS expiration during the COVID-19 pandemic. Simulations were performed with hypothetical drugs under two scenarios: (1) uninterrupted study using a single batch of an RS, and (2) interrupted study using two batches of an RS. The acceptability of BE outcomes was evaluated by comparing the results obtained from interrupted studies with those from uninterrupted studies. The simulation results demonstrated that using a conventional statistical approach to evaluate BE for COVID-19-interrupted studies may be acceptable based on the pooled data from two batches. An alternative statistical method which includes a “batch” effect to the mixed effects model may be used when a significant “batch” effect was found in interrupted four-way crossover studies. However, such alternative method is not applicable for interrupted two-way crossover studies. Overall, the simulated scenarios are only for demonstration purpose, the acceptability of BE outcomes for the COVID19-interrupted studies could be case-specific.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Reexamining Fat: Exploring Diversity, Plasticity, Development, Functional Implication, and Therapeutic Options

Obesity has become so prevalent in many developed countries that it is increasingly perceived as a new norm, despite decades of interventions and drug development. Although research continues to explore novel strategies, no single approach to date has demonstrated sustained success in reducing its population-level dominance. This underscores the need to better evaluate and integrate the growing body of knowledge surrounding obesity’s multifaceted nature. Stamped under one ‘fat’ name, adipose tissue varies by color, location, morphology, composition, and function. This variability suggests a level of complexity that demands deeper investigation. Although the relevance and roles of different adipose types have been extensively discussed throughout the literature, their interdependence, synergy, and collective impact on the body remain to be fully expounded. This review aims to further consolidate and elucidate the available information on the different adipose tissue types and their association with obesity and metabolic health. We also discuss existing and emerging therapeutic strategies, highlighting their respective strengths and limitations.

BAT↗
DOE OSTI2023

Exploring novel HIV ‐1 reverse transcriptase inhibitors with drug‐resistant mutants: A double mutant surprise

Abstract HIV‐1 reverse transcriptase (RT) remains a key target for HIV drug development. As successful management of the disease requires lifelong treatment, the emergence of resistance mutations is inevitable, making development of new RT inhibitors, which remain effective against resistant variants crucial. To this end, previous computationally guided drug design efforts have resulted in catechol diether compounds, which inhibit wildtype RT with picomolar affinities and appear to be promising preclinical candidates. To confirm that these compounds remain potent against Y181C, a widespread mutation conferring resistance to first generation inhibitors, they were screened against the HIV‐1 N119 clinical isolate, reported as a Y181C single mutant. In comparison to a molecular clone with the same mutation, N119 appears less susceptible to inhibition by our preclinical candidate compounds. A more detailed sequencing effort determined that N119 was misidentified and carries V106A in combination with Y181C. While both indolizine and naphthalene substituted catechol diethers are potent against the classical Y181C single mutant, the addition of V106A confers more resistance against the indolizine derivatives than the naphthalene derivatives. Crystal structures presented in this study highlight key features of the naphthyl group, which allow these compounds to remain potent in the double mutant, including stronger interactions with F227 and less reliance on V106 for stabilization of the ethoxy‐uracil ring, which makes critical hydrogen bonds with other residues in the binding pocket.

Biochemistry & Molecular Biology↗