How to Validate and Implement qNMR as a Platform Method for Oligonucleotides

The content of peptides and oligonucleotides in a sample, whether solid or liquid, is crucial for various applications. Traditional methods for analyzing peptides, such as Kjeldahl nitrogen determination, elemental analysis (EA), and amino acid analysis (AAA), necessitate the breaking down the protein under harsh conditions to ascertain the protein content.

For oligonucleotides, one of the most widely used quantification techniques is ultraviolet (UV) spectroscopy. However, this method requires time-consuming sample preparation, and uncertainties in the extinction coefficient can negatively affect its accuracy.

NMR spectroscopy is inherently quantitative, and quantitative NMR (qNMR) has been successfully applied in small molecule analysis for decades. However, for larger molecules such as peptides and oligonucleotides, the quantitative accuracy of 1H-NMR is compromised due to severe line broadening. This broadening is caused by higher-order structural elements, such as dimers or oligomers, under native conditions.

The analysis of peptides under non-native conditions (using a deuterated water mixture) and recording H-NMR data at elevated temperatures will be discussed in this webinar.

For oligonucleotides, 1H-NMR is not feasible; instead, data will be presented using 31P as the selected nuclei for qNMR. For both modalities—peptides and oligonucleotides—the methods were validated following the recommendations from both USP and ICHQ2(R2) regarding validation parameters such as linearity and platform applicability.

The content results obtained for both peptides and oligonucleotides using qNMR will be compared to the content results obtained through traditional methods. This comparison will highlight the effectiveness and accuracy of qNMR relative to established techniques.

Key learning points

  • Content determination of peptides and oligonucleotides by qNMR
  • Validation of the methods according to USP and ICHQ2(R2) guidelines
  • Considerations regarding platform applicability of the qNMR methods

About the speakers

Joan Malmstrøm is a Principal Scientist at Novo Nordisk A/S with a Ph.D. in organic chemistry from the University of Copenhagen and over 25 years of experience in structural elucidation of organic molecules using nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry.

At Novo Nordisk, she has worked on a diverse range of projects involving small molecules (APIs, raw materials, excipients), peptides, proteins, oligonucleotides, and large molecular weight polymers.

In recent years, her primary focus has been on developing NMR analytical methods suitable for analyzing various molecules involved in drug development. These methods are applied at all stages of the development pipeline and are GMP-validated as required.


Dr. Beaumont has studied and applied NMR techniques during his undergraduate studies at the University at Buffalo under the guidance of Dr. Thomas Szyperski and his Ph.D. program at Yale University under Dr. Patrick Loria.

He later joined Pfizer as an NMR specialist for biotherapeutic structure characterization and transitioned to project management of analytical laboratory automation. Passionate about enhancing quality of life and working efficiencies, Dr. Beaumont focuses on advancing scientific innovation through state-of-the-art NMR characterization of novel therapeutic modalities.

At Bruker, he collaborates with scientists to understand industry challenges and expand NMR applications and technology.

Other Webinars from Bruker BioSpin - NMR, EPR and Imaging

Life Science Webinars by Subject Matter

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.