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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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DOE OSTI2026

Multinuclear Solid-State NMR and NMR Crystallography of Solid Forms of Creatine and Creatinine

Creatine is a performance-enhancing supplement with two widely available commercial solid forms, namely, creatine monohydrate (creatine·H 2 O) and creatine HCl, the latter of which does not have a reported crystal structure. Moreover, commercial formulations of creatine may contain creatinine, an undesired impurity phase resulting from the self-cyclization of creatine during manufacturing. Therefore, reliable methods for characterizing the different solid forms of creatine and detecting the presence of creatinine are essential. Herein, we address these challenges using 13 C, 15 N, and 35 Cl solid-state NMR (SSNMR) spectroscopy to obtain distinct spectral fingerprints for creatine·H 2 O and creatine HCl, along with creatinine and creatinine HCl. The acquisition of these SSNMR spectra offers a robust approach for both the rapid characterization of each solid form and the detection of the impurity phases. Additionally, quadrupolar NMR crystallography-guided crystal structure prediction (QNMRX-CSP) was applied for the de novo crystal structure determination of creatine HCl, which was validated by the subsequently determined single-crystal X-ray diffraction (SCXRD) structure. Finally, to investigate the relationship between NMR parameters and structural features, 13 C and 15 N chemical shifts and 35 Cl electric field gradient (EFG) tensors were computed from geometry-optimized structures of the four solid forms by using dispersion-corrected DFT-D2* methods. Finally, this integrative approach offers a powerful framework for advancing the structural understanding and quality control of creatine-based supplements and next-generation formulations, as well as a wide range of other solid pharmaceuticals and nutraceuticals.

NMR↗

DOE OSTI2024

Cu(II) Stability and UV-Induced Electron Transfer in a Metal–Organic Hybrid: An EPR, DFT, and Crystallographic Characterization of Copper-Doped Zinc Creatininium Sulfate

Single-crystal X-ray diffraction and electron paramagnetic resonance (EPR) spectroscopic experiments, complemented by quantum chemical DFT calculations, were carried out on the copper-doped metal–organic hybrid and Tutton salt analogue zinc creatininium sulfate to determine its crystal structure, to characterize the electronic structure of the doped Cu(II) binding site, and to propose a pathway for an excited-state, proton-coupled electron transfer (PCET) process in UV-exposed crystals. The crystal structure is isomorphous to that of cadmium creatininium sulfate, which has the transition ion, not in direct coordination with the creatinine, but forming a hexahydrate complex, which is bridged to a creatininium through an intervening sulfate ion. The EPR g (2.446, 2.112, 2.082) and copper hyperfine (A Cu : -327, -59.6, 10.8 MHz) tensor parameters are consistent with doped copper replacing host zinc in the metal–hexahydrate complex. These parameters are similar to those observed for copper hexahydrate in doped Tutton salt systems at low temperature, where the unpaired electron occupies mainly the copper 3d x 2 –y 2 orbital. At room temperature in the Tutton systems, vibration couplings stemming from a dynamic Jahn–Teller effect cause tensor averaging which results in a reduction in their maximum g-tensor and hyperfine tensor values. However, like for the doped isomorphous Cd creatinine crystal, the Cu(II) EPR exhibits little, or no room temperature averaging compared to its low temperature pattern. Samples exposed to 254 nm UV light generate a carbon-centered free radical species, characterized by an isotropic g-tensor (g = 2.0029) and an alpha-proton hyperfine coupling (-24 -14 +4 G). These parameters identify it as a creatinine radical cation formed by the oxidative release of one of its C2 methylene hydrogens. DFT calculations confirm the unpaired electronic structures of both the Cu(II) site and free radical. The growth in radical concentration with an increase in the UV exposure time coincides with a decrease in the copper EPR signal, indicating a coupled light-induced oxidation reduction process. A comparison of the crystal structure with the EPR parameters and DFT results provides evidence for a UV-induced PCET.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

The impact of kidney function on Alzheimer’s disease blood biomarkers: implications for predicting amyloid-β positivity

Impaired kidney function has a potential confounding effect on blood biomarker levels, including biomarkers for Alzheimer’s disease (AD). Given the imminent use of certain blood biomarkers in the routine diagnostic work-up of patients with suspected AD, knowledge on the potential impact of comorbidities on the utility of blood biomarkers is important. We aimed to evaluate the association between kidney function, assessed through estimated glomerular filtration rate (eGFR) calculated from plasma creatinine and AD blood biomarkers, as well as their influence over predicting Aβ-positivity. We included 242 participants from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort, comprising cognitively unimpaired individuals (CU; n = 124), mild cognitive impairment (MCI; n = 58), AD dementia (n = 34), and non-AD dementia (n = 26) patients all characterized by [ 18 F] AZD-4694. Plasma samples were analyzed for Aβ42, Aβ40, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), tau phosphorylated at threonine 181 (p-tau181), 217 (p-tau217), 231 (p-tau231) and N-terminal containing tau fragments (NTA-tau) using Simoa technology. Kidney function was assessed by eGFR in mL/min/1.73 m 2 , based on plasma creatinine levels, age, and sex. Participants were also stratified according to their eGFR-indexed stages of chronic kidney disease (CKD). We evaluated the association between eGFR and blood biomarker levels with linear models and assessed whether eGFR provided added predictive value to determine Aβ-positivity with logistic regression models. Biomarker concentrations were highest in individuals with CKD stage 3, followed by stages 2 and 1, but differences were only significant for NfL, Aβ42, and Aβ40 (not Aβ42/Aβ40). All investigated biomarkers showed significant associations with eGFR except plasma NTA-tau, with stronger relationships observed for Aβ40 and NfL. However, after adjusting for either age, sex or Aβ-PET SUVr, the association with eGFR was no longer significant for all biomarkers except Aβ40, Aβ42, NfL, and GFAP. When evaluating whether accounting for kidney function could lead to improved prediction of Aβ-positivity, we observed no improvements in model fit (Akaike Information Criterion, AIC) or in discriminative performance (AUC) by adding eGFR to a base model including each plasma biomarker, age, and sex. While covariates like age and sex improved model fit, eGFR contributed minimally, and there were no significant differences in clinical discrimination based on AUC values. We found that kidney function seems to be associated with AD blood biomarker concentrations. However, these associations did not remain significant after adjusting for age and sex, except for Aβ40, Aβ42, NfL, and GFAP. While covariates such as age and sex improved prediction of Aβ-positivity, including eGFR in the models did not lead to improved prediction for any biomarker. Our findings indicate that renal function, within the normal to mild impairment range, does not seem to have a clinically relevant impact when using highly accurate blood biomarkers, such as p-tau217, in a biomarker-supported diagnosis.

60 APPLIED LIFE SCIENCES↗

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 OSTI2024

Personalized, disease-stage specific, rapid identification of immunosuppression in sepsis

Introduction Data overlapping of different biological conditions prevents personalized medical decision-making. For example, when the neutrophil percentages of surviving septic patients overlap with those of non-survivors, no individualized assessment is possible. To ameliorate this problem, an immunological method was explored in the context of sepsis. Methods Blood leukocyte counts and relative percentages as well as the serum concentration of several proteins were investigated with 4072 longitudinal samples collected from 331 hospitalized patients classified as septic (n=286), non-septic (n=43), or not assigned (n=2). Two methodological approaches were evaluated: (i) a reductionist alternative, which analyzed variables in isolation; and (ii) a non-reductionist version, which examined interactions among six (leukocyte-, bacterial-, temporal-, personalized-, population-, and outcome-related) dimensions. Results The reductionist approach did not distinguish outcomes: the leukocyte and serum protein data of survivors and non-survivors overlapped. In contrast, the non-reductionist alternative differentiated several data groups, of which at least one was only composed of survivors (a finding observable since hospitalization day 1). Hence, the non-reductionist approach promoted personalized medical practices: every patient classified within a subset associated with 100% survival subset was likely to survive. The non-reductionist method also revealed five inflammatory or disease-related stages (provisionally named ‘early inflammation, early immunocompetence, intermediary immuno-suppression, late immuno-suppression, or other’). Mortality data validated these labels: both ‘suppression’ subsets revealed 100% mortality, the ‘immunocompetence’ group exhibited 100% survival, while the remaining sets reported two-digit mortality percentages. While the ‘intermediary’ suppression expressed an impaired monocyte-related function, the ‘late’ suppression displayed renal-related dysfunctions, as indicated by high concentrations of urea and creatinine. Discussion The data-driven differentiation of five data groups may foster early and non-overlapping biomedical decision-making, both upon admission and throughout their hospitalization. This approach could evaluate therapies, at personalized level, earlier. To ascertain repeatability and investigate the dynamics of the ‘other’ group, additional studies are recommended.

Immunology↗