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Qi Tang, Sinan Wang, Xiongqi Zhai, Seon Beom Kim, Prabhakar Achanta, Gonzalo R. Malca-Garcia, Yuzo Nishizaki, Yi Wang, Yu Tang. Applications of quantitative 13C NMR in pharmaceutical analysis: From small molecule drugs to biopolymers[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2025.101346
Citation: Qi Tang, Sinan Wang, Xiongqi Zhai, Seon Beom Kim, Prabhakar Achanta, Gonzalo R. Malca-Garcia, Yuzo Nishizaki, Yi Wang, Yu Tang. Applications of quantitative 13C NMR in pharmaceutical analysis: From small molecule drugs to biopolymers[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2025.101346

Applications of quantitative 13C NMR in pharmaceutical analysis: From small molecule drugs to biopolymers

doi: 10.1016/j.jpha.2025.101346
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This work was supported by the Project of the National Key Research and Development Program of China (2022YFC3501802), the National Natural Science Foundation of China (82204614), the Natural Science Foundation of Zhejiang Province (LTGY23H290006 and LTGC24H280002), and the Pioneer" and "Leading Goose" R&D Program of Zhejiang (2023C03004).

  • Received Date: Jan. 16, 2025
  • Rev Recd Date: Mar. 25, 2025
  • Available Online: May 19, 2025
  • Chemical integrity is indispensable for advancing healthcare by ensuring the availability of high quality, safe, and effective pharmaceutical products. Ingredient quantification is particularly pivotal in this process. Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for both qualitative and quantitative analysis for complex systems. Compared with 1D quantitative 1H NMR (1H qNMR), quantitative 13C NMR (13C qNMR) holds some unique advantages. This technique offers a broader chemical shift range and the resulting much lesser signal overlap compare to 1H NMR spectroscopy. This review summarizes relevant studies on the use of 13C qNMR as a quantification technique, along with a focus on quantitative principles, influencing factors, and technical improvements of 13C NMR. The review also highlights its applicability in quantifying diverse molecular structures in pharmaceutical analysis. In addition, potential of low-field NMR, artificial intelligence (AI)-driven method development, and hyphenation of NMR with other techniques for 13C qNMR analysis is discussed and summarized as well. As a versatile method, 13C qNMR holds great potential, and ongoing research is expected to unlock its full capabilities and expand its range of applications.
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