High-resolution multidimensional parametric estimation for nuclear magnetic resonance spectroscopy

The Food and Drug Administration recently approved a fixed dose combination of aspirin and omeprazole that has been introduced for the treatment of gastrointestinal disorders, as it reduces the risk of upper gastrointestinal and cardiovascular adverse events in aspirin-treated patients. Therefore, an optimized eco-friendly, simple, fast, and precise quantitative nuclear magnetic resonance spectroscopy technique was presented for the concurrent estimation of that mixture in their single and combined dosage forms. The quantitative nuclear magnetic resonance spectroscopy concurrent estimation of both drugs was achieved using phloroglucinol as the internal standard and dimethyl sulfoxide as a deuterated solvent. An ideal set of acquisition parameters was determined to be 128 scans, 10 s relaxation latency, and 90° pulse angle. The selected quantitative signal of aspirin was the doublet of doublet signal appeared at 7.945 ppm, while that of omeprazole was the singlet signal at 8.18 ppm. The singlet signal at 5.69 ppm was selected for the internal standard. The spectra were subjected to integration, baseline correction, and auto phase correction. The developed quantitative proton nuclear magnetic resonance spectroscopy method was found to be rectilinear over the range of 0.05–4.0 mg mL−1 for both drugs. The detecting and quantifying limits for both drugs were approximately 0.01 and 0.03 mg mL−1, respectively. Neither labelling nor pretreatment steps were needed to assay the studied drugs using our developed quantitative nuclear magnetic resonance spectroscopy method. The proposed nuclear magnetic resonance spectroscopy approach was effectively evaluated in terms of linearity (r = 0.9999), accuracy, and precision (%RSD < 1.08). Furthermore, the suggested technique was investigated to analyze the studied drugs in their single and combined dosages. This work enables clinicians to simultaneously monitor aspirin and omeprazole levels in both single and fixed-dose combination tablets, ensuring precise dosing and effective treatment management. For patients, it supports safer therapy by reducing the risks associated with improper dosing or drug interactions in combination therapies. After evaluating the method's greenness, whiteness and blueness, it was determined that the suggested approach was environmentally friendly. The suggested approach was compared with the previously reported methods from both an analytical and eco-friendly perspective.

Current approaches for integrating solution NMR spectroscopy and small-angle scattering to study the structure and dynamics of biomolecular complexes

The analytical potential of nuclear magnetic resonance (NMR) spectroscopy has been recognized since the early days of NMR. It is well known that the NMR signal is linear (proportional to the spin concentration), specific (spin groups have characteristic chemical shifts), and nondestructive (signal recording does not affect the sample). The last aspect is particularly important for biomedical studies, which culminated in NMR imaging (MRI) becoming an indispensable diagnostic tool. The broad application of the quantitative aspect of NMR only started after the development of NMR technology (magnets, electronics, and computers), which, in addition to selectivity, enabled the reproducibility and repeatability of data collection, even in biological systems. In this presentation, quantitative NMR microscopy and NMR spectroscopy will be illustrated by examples from studies of biological systems (live mice, fixed tissue, and body fluids). For instance, it will be shown how NMR spectroscopy can be used to quantify total proteins in biological fluids, irrespective of the protein composition, without the need for method calibration.

Nuclear magnetic resonance spectroscopy (NMR) is one of the most important instrumental techniques used to elucidate the molecular structure of organic compounds. However, there are not many good simple applications of using NMR for the quantitative assay of impure or mixtures of organic substances. The current experiment was developed as part of an ongoing effort to increase exposure to quantitative proton NMR methodology in the undergraduate chemistry laboratory curriculum. The objective of the experiment is to determine the percent composition of a two component mixture of short chain fatty acid methyl esters using chemical shift and integration values obtained from running solvent-less samples both with and without an internal reference standard. We report on the experimental methods and results obtained from examining the quantitative NMR properties of a series of mixtures of methyl acetate (M.A.) and methyl propionate (M.P.) solutions ranging from 100% MA to 100% MP. Also similar data was obtained for the MA - methyl butyrate (MB) pair. The results exhibit a linear relationship of the percent composition of the methyl acetate binary mixtures found by proton NMR with the theoretical (i.e. samples prepared based on measured weights) percent compositions. The experiment confirms the quantitative usage of 1H NMR and serves as an educational tool for the undergraduate chemical laboratory.

When analyzing the Free Induction Decay (FID) signal produced by nuclear magnetic resonance (NMR) spectroscopy, Fourier transforms (FT) are used to decompose time-domain signals arising from nuclear interactions. This transformation enables the extraction of frequency-domain information, allowing for the recognition of patterns within the generated NMR spectra. Most modern NMR processing software applies FT to generate the final spectra. Researchers process FID using various techniques, such as phase correction, windowing, and FT, to enhance the interpretation of the obtained spectra. This processing step requires careful consideration of the characteristics of the original data and can also be influenced by the researchers' experience, often making it time-consuming to produce reliable results. However, recent advancements in artificial intelligence, particularly deep learning, have demonstrated superior pattern recognition capabilities compared to humans in complex scenarios. These developments have been successfully applied to various aspects of NMR spectroscopy. In this study, we demonstrate that neural networks can replace FT in NMR spectroscopy, enabling robust and rapid prediction of spectra and peak lists from FID signals. Our results confirm that deep learning can efficiently process NMR data to generate final spectra. As a proof of concept, we present the resulting spectra, along with peak lists predicted by supplying only FID input to the deep learning algorithm. The generated peak lists can be considered as spectra with infinite resolution.

Nuclear magnetic resonance spectrometry (NMR) finds numerous applications in pharmacy, cosmetic, and food control as well as in developing tools for "big data" analysis. However, there remains a need for automated tools to assess instrument system suitability in real time for each particular routine sample. An automated procedure has been introduced to monitor a number of characteristics (resolution, symmetry, and half width) in real time after the measurement of two samples distributed by the vendor (0.3% CHCl3 in acetone-d6 with tetramethylsilane and 2mM sucrose in H2 O-D2 O). The results over 11months were discussed in terms of average values, standard deviations, and spectrometer variability. Moreover, multivariate statistical procedure was implemented to evaluate metrics generated from three NMR spectrometers. Performance of three NMR spectrometers (500MHz with BBO Prodigy Cryoprobe, 500MHz with BBFOPLUS SmartProbe, and 600MHz with BBO Cryoprobe) differed significantly. The developed routine was also applied to calculate the performance characteristics during routine quantitative NMR experiments. The procedure was evaluated for NMR spectra of 659 active pharmaceutical ingredients dissolved in CDCl3 , DMSO, and CH3 OD. This test is more preferable than the routine procedure using standard solutions because the performance is estimated separately for each matrix at the specific time point of measurements. Our automated routine is the ideal tool for any NMR laboratory. In full automation, the NMR data are validated directly for each sample, making unnecessary daily measurements of standard solutions and manual evaluation to their NMR spectra.

Background Creatine is a substance that the body uses to produce energy. The creatine concentration in urine is widely used as a tool to diagnose creatinuria. Urine creatinine is used to identify patients at risk of recurrent nephrolithiasis or kidney stones, and commonly serves as a means to normalize the concentrations of other metabolites in urine. Here we describe the development of a low cost, high-throughput nuclear magnetic resonance spectroscopy (NMR)-based assay to simultaneously measure urine creatine and creatinine. Methods The NMR samples are tested on the Vantera® Clinical Analyzer, a 400MHz NMR instrument designed specifically for the clinical laboratory. The NMR data are automatically transferred and processed. Urine creatinine was quantified as reported previously while creatine concentrations were determined using a newly developed non-negative deconvolution algorithm that utilized both of its NMR signal peaks. Results Deming regression analysis comparing creatine and creatinine concentrations in urine between NMR and chemistry methods were conducted. The linearity was observed over a wide range of concentrations for creatine (up to 100 mg/dL) and creatinine (up to 1,308 mg/dL). In a population of apparently healthy adults, the estimated reference interval for creatine was &amp;lt; 10 mg/dL and the reported reference interval for creatinine was &amp;lt; 174.5 mg/dL. The linearity of our assay was established well beyond these intervals. In addition, the new algorithm, by using the combination of two NMR peaks of creatine, provided a more stable model to tolerate underlying signals from potential interfering substances. Conclusion The newly developed NMR-based high-throughput assay exhibited excellent performance characteristics to quantify the creatine and creatinine in urine for clinical diagnostic purposes. It showed results comparable to the currently utilized chemistry tests and provides a useful tool to simultaneously measure the creatine and creatinine in urine for clinical and research use.

Chapter 1 of this thesis provides an overview of the structure and function of proteins as well as their investigation via NMR spectroscopy. Proteins are one of the most important building blocks of life. They are part of every organism and fulfill a variety of functions including, for example, the catalysis of many chemical reactions, which is the reason various processes in an organism become possible in the first place. Proteins are made up of amino acids, which are linked together via peptide bonds during the protein biosynthesis. The 22 proteinogenic amino acids result in an almost unlimited number of sequential combinations that give proteins their versatility. The sequence of amino acids in a protein is also called the primary structure. From this, locally limited structural motifs such as α-helices and β-sheets are formed, representing the secondary structure. Further intramolecular interactions create the tertiary structure of a protein, which can be seen as the overall three-dimensional structure of the protein. Furthermore, several individual proteins can assemble into a larger complex to form a quaternary structure. There are various methods for investigating the structure of proteins, including nuclear magnetic resonance (NMR) spectroscopy. In NMR spectroscopy, atomic nuclei with a non-zero nuclear spin are polarized by a constant magnetic field and then excited using radio frequency pulses. The emitted resonance frequencies are detected in the form of a free induction decay (FID) and converted into an NMR spectrum by a Fourier transformation. Many NMR experiments are available for researching proteins. By default, a two-dimensional 1H15N-HSQC spectrum is measured, which provides signals for each N-H bond in a protein meaning that one signal is obtained for each N-H in the backbone of the amino acids. Using 3D NMR experiments, these signals can then be assigned to the respective amino acids in the protein sequence. This makes it possible to investigate the dynamics of proteins as well as the formation of complexes with a ligand or another biomacromolecule if they interact. Further 3D NMR experiments also make it possible to assign the side-chains of the protein. With the help of the determined resonance assignments and spatial information from various NOESY spectra, a protein structure can then be calculated using a suitable computer program. Recently, the prediction of protein structures using AI (artificial intelligence)-supported programs has also become more prevalent. In particular, the artificial neural network AlphaFold has been extremely successful in predicting correct structures without any experimental data provided by the user. Chapter 2 of this thesis introduces the research field of small proteins. These are proteins with a length of usually less than 100 amino acids, although definitions in literature vary and upper limits of, for example, 50 or 200 amino acids exist as well. Various other names are also used to describe small proteins, such as μ-proteins or sORF-encoded peptides. The field of small proteins is still relatively new, as they were overlooked in research for many years. One reason for this was the lower limit of 100 codons that was set for the identification of open reading frames (ORFs) in genome projects. This served to exclude numerous biologically irrelevant ORFs from genome annotation. However, this meant that functional ORFs coding for small proteins were also ignored. Another reason was that experimental methods were not designed for the analysis of small proteins, meaning they could often not be detected at all. However, new analytical methods such as ribosome profiling and a generally increased focus on the detection of small proteins have led to their detection in all domains of life. In the research group of Prof. Dr. Schwalbe, small proteins of various prokaryotes were structurally characterized as part of a special priority program by the DFG. NMR spectroscopy was primarily used for this purpose, as it is the method of choice for the investigation of small proteins. The results of this research were published in several articles, two of which are part of this dissertation.

Sensitive voltammetric determination of hydroxyzine and its main metabolite cetirizine and identification of oxidation products by nuclear magnetic resonance spectroscopy

Different types of immobilized free radicals as well as immobilized paramagnetic gadolinium(III) complexes were synthesized and used as relaxation agents to shorten the spin−lattice relaxation times T1 of analytes in quantitative continuous-flow 13C nuclear magnetic resonance spectroscopy (NMR). The immobilized paramagnetic relaxation agents were characterized with electron paramagnetic resonance spectroscopy (EPR) and extended X-ray absorption fine structure spectroscopy (EXAFS). With these types of paramagnetic relaxation agents, it was possible to obtain a 4-fold increase in signal-to-noise ratio per unit time, and an almost quantitative NMR spectra in continuous-flow 13C NMR spectroscopy could be acquired with this method.

In situ quantitative 1H nuclear magnetic resonance spectroscopy discriminates between raw and steam cooked potato strips based on their metabolites

Q11 peptide nanofibers are used as a biomaterial for applications such as antigen presentation and tissue engineering, yet detailed knowledge of molecular-level structure has not been reported. The Q11 peptide sequence was designed using heuristics-based patterning of hydrophobic and polar amino acids with oppositely charged amino acids placed at opposite ends of the sequence to promote antiparallel β-sheet formation. In this work, we employed solid-state nuclear magnetic resonance spectroscopy (NMR) to evaluate whether the molecular organization within Q11 self-assembled peptide nanofibers is consistent with the expectations of the peptide designers. We discovered that Q11 forms a distribution of molecular structures. NMR data from two-dimensional (2D) 13C-13C dipolar-assisted rotational resonance indicate that the K3 and E9 residues between Q11 β-strands are spatially proximate (within ∼0.6 nm). Frequency-selective rotational echo double resonance (fsREDOR) on K3 Nζ and E9 Cδ-labeled sites showed that approximately 9% of the sites are close enough for salt bridge formation to occur. Surprisingly, dipolar recoupling measurements revealed that Q11 peptides do not assemble into antiparallel β-sheets as expected, and structural analysis using Fourier-transform infrared spectroscopy and 2D NMR alone can be misleading. 13C PITHIRDS-CT dipolar recoupling measurements showed that the most abundant structure consists of parallel β-sheets, in contrast to the expected antiparallel β-sheet structure. Structural heterogeneity was detected from 15N{13C} REDOR measurements, with approximately 22% of β-strands having antiparallel nearest neighbors. We cannot propose a complete structural model of Q11 nanofibers because of the complexity involved when examining structurally heterogeneous samples using NMR. Altogether, our results show that while heuristics-based patterning is effective in promoting β-sheet formation, designing a peptide sequence to form a targeted β-strand arrangement remains challenging.

An experimental approach is described for late-stage lead diversification of frontrunner drug candidates using nanomole-scale amounts of lead compounds for structure-activity relationship development. The process utilizes C-H bond activation methods to explore chemical space by transforming candidates into newly functionalized leads. A key to success is the utilization of microcryoprobe nuclear magnetic resonance (NMR) spectroscopy, which permits the use of low amounts of lead compounds (1-5 μmol). The approach delivers multiple analogues from a single lead at nanomole-scale amounts as DMSO-d6 stock solutions with a known structure and concentration for in vitro pharmacology and absorption, distribution, metabolism, and excretion testing. To demonstrate the feasibility of this approach, we have used the antihistamine agent loratadine (1). Twenty-six analogues of loratadine were isolated and fully characterized by NMR. Informative SAR analogues were identified, which display potent affinity for the human histamine H1 receptor and improved metabolic stability.

Nuclear magnetic resonance spectroscopy has been recently used for quantitative estimation of drug substance which has numerous merits in pharmaceutical applications when compared to traditional methods of chromatography. Present study focuses on method development and validation of quantitative proton nuclear magnetic spectroscopy method for estimation of alectinib hydrochloride drug substance. This method involves use of internal standard in presence of alectinib hydrochloride. For quantitation, 1H NMR signals at 8.4 ppm and 6.7 ppm corresponding to analyte proton of alectinib and ethyl 4-(dimethyl amino) benzoate internal standard respectively were used. Moreover, this method was validated as per ICH guidelines for accuracy, precision, linearity, limit of detection, limit of quantitation, range and robustness. Assay estimation by Q-NMR is facile and time-efficient which does not require reference standard of analyte and yet accurate and precise alternative as compared to estimation by other chromatographic techniques.

A DECREASE IN ¹ H NUCLEAR MAGNETIC RESONANCE SPECTROSCOPICALLY DETERMINED CITRATE IN HUMAN SEMINAL FLUID ACCOMPANIES THE DEVELOPMENT OF PROSTATE ADENOCARCINOMA