Chemistry methods : new approaches to solving problems in chemistry最新文献

Nuclear magnetic resonance (NMR) is recognized as the gold standard method in fragment-based drug design for screening, hit validation, and affinity determination. However, its deployment at a large scale is limited by the cost and expertise needed to implement NMR as a routine drug discovery method. The increase in the adoption of fragment-based drug design created a need for biophysics to provide high-quality data for weak ligand-target interactions that can be implemented at scale. NMR must adapt its position in drug design operations to enter this new era. The recent development of commercially available benchtop NMR spectrometers in combination with hyperpolarization methods represents an opportunity for highly deployable and scalable systems.

Zeolite 13X is an excellent candidate for the capture of CO₂. However, this system needs to be investigated in more detail with regard to adsorption and diffusion. For this purpose, frequency response (FR) measurements were carried out with binderless zeolite 13X (NaMSX) and CO₂. The size of the particles and the sample amount were modified in order to investigate their effects on diffusion processes. Macropore diffusion was detected and the corresponding diffusion coefficients were determined. The mentioned system was additionally used to evaluate the performance of the in-house FR apparatus.

Solution state ¹H NMR spectroscopy provides valuable insights into molecular structure and conformation. However, when the spectrum exhibits severe signal overlap, it hampers the extraction of key structural information. Here, an ultraselective, ultrahigh resolution TOCSY method is introduced that greatly reduces spectral complexity, allowing the extraction of previously inaccessible spectral information. It combines the recently developed GEMSTONE excitation with homonuclear decoupling to provide highly simplified through-bond correlation 1D ¹H NMR spectra, showing all signals within the selected spin system as singlets. The new method can greatly facilitate the analysis of mixtures, as shown here for a mixture of Cinchona alkaloids (popular catalysts in asymmetric synthesis) and a mixture of glucocorticoids (used for treating conditions such as asthma).

In the past two to three decades, synthetic glycoconjugate vaccines have shown great potential in the prevention of severe infections and protection of high-risk populations. Conjugation of synthetic oligosaccharide haptens to carrier proteins is the key step for the vaccine preparation. In this review, the conjugation methods currently used in the synthesis of glycoconjugate vaccines from synthetic/homogeneous oligosaccharide haptens are summarized with the focus on the reaction conditions (pH and sugar/protein ratio) and performance. This information can help researchers choose the appropriate conjugation methods. Further research directions toward site-specific conjugations and fully homogeneous glycoconjugate vaccines are also discussed.

Tumors have a complex metabolism that differs from most metabolic processes in healthy tissues. It is highly dynamic and driven by the tumor cells themselves, as well as by the non-transformed stromal infiltrates and immune components. Each of these cell populations has a distinct metabolism that depends on both their cellular state and the availability of nutrients. Consequently, to fully understand the individual metabolic states of all tumor-forming cells, correlative mass spectrometric imaging (MSI) up to cellular resolution with minimal metabolite shift needs to be achieved. By using a secondary ion mass spectrometer (SIMS) equipped with an Orbitrap mass analyzer, we present a workflow to image primary murine tumor tissues up to cellular resolution and correlate these ion images with post acquisition immunofluorescence or histological staining. In a murine breast cancer model, we could identify metabolic profiles that clearly distinguish tumor tissue from stromal cells and immune infiltrates. We demonstrate the robustness of the classification by applying the same profiles to an independent murine model of lung cancer, which is accurately segmented by histological traits. Our pipeline allows metabolic segmentation with simultaneous cell identification, which in the future will enable the design of subpopulation-targeted metabolic interventions for therapeutic purposes.