NEXT-GENERATION MIXTURE ANALYSIS TOOLS

Mixture analysis is a crucial step in pharmaceutical development and manufacturing. It ensures the accurate composition of each drug product, and analysis by NMR helps maintain consistent quality, efficacy, and safety for the end consumer.

By identifying any inconsistencies or impurities, manufacturers can prevent defective batches and ensure patients receive medications that meet strict quality standards. Come and learn about the latest industry techniques using NMR spectroscopy for mixture analysis by registering for PANIC’s Q3 Webinar.

Presentation abstracts:

Hyperpolarized 13C NMR metabolomics at natural abundance

• NMR spectroscopy is a central method for the analysis of complex mixtures, and this is particularly true in metabolomics. However, such analysis mostly relies on 1D 1H spectroscopy, which provides an acceptable sensitivity but suffers from ubiquitous overlap between complex analyte patterns. While NMR offers a wide range of multi-nuclear and multi-dimensional techniques for analyzing complex samples, these tools are underutilized in metabolomics. In the past few years, we showed how fast 2D NMR methods could be systematically incorporated into metabolomics workflows, providing improved sample classification and/or biomarker identification.1–3 These methods mainly homonuclear 2D experiments for sensitivity reasons provide a first stage of dispersion improvement. 13C NMR spectroscopy would be even more advantageous as it provides narrow singlets spread over a broad spectral range. In fact, 13C NMR would be ideal for metabolomics, were it not for the fact that its low sensitivity is not compatible with the detection of low-concentrated analytes at natural abundance.

• In this context, we recently showed that Dissolution Dynamic Nuclear Polarization (d-DNP) could provide a unique way to detect 13C NMR metabolomics spectral signatures with a sensitivity enhanced by several orders of magnitude.4 After a systematic optimization of a prototype d-DNP equipment, we showed that 13C NMR at natural abundance could be applied to plant extracts and incorporated in a full metabolomics workflow. More recently, we reported the first dDNP-enhanced 13C NMR analysis of a biofluid -urine- at natural abundance, offering unprecedented resolution and sensitivity for this challenging type of sample.5 We also showed that accurate quantitative information on multiple targeted metabolites could be retrieved through a standard addition procedure. Finally, we incorporated this approach into a first clinical metabolomics study.6 The analysis of urine samples from patients with different stages of chronic kidney disease (CKD) was performed using 13C d-DNP NMR and conventional 1H NMR metabolomics to explore the complementarity between the two methods. Results from 13C d-DNP NMR highlighted several biomarkers known to be biologically relevant, but also showed interesting complementarity with conventional 1H NMR, while raising exciting challenges associated with the analysis of spectral fingerprints stemming from this new approach.

Get More Gains: Increase Benchtop Utilization with Automation and Quantum Mechanics

• Advances in benchtop NMR technology have been helping increase access to the technique across industries. However, challenges remain to enable analytical groups to free up their constrained high-field resources by transitioning work to the bench, including more challenging data analysis due to overlapping signals and variable levels of NMR experience. USP and Mestrelab Research have collaborated to address these challenges with the new USP-ID software, delivered by Mestrelab Research, which incorporates advanced algorithms and libraries of field-independent references to help scientists get more information from benchtop NMR spectra, faster, even when signals are heavily overlapped. Join us to learn more about this novel approach to automated qNMR analysis and see a live demo of USP-ID.

Unraveling the chemical complexity of polysorbate surfactants

• Polysorbates are non-ionic surfactants used extensively in the development and manufacturing of pharmaceutical products. Despite their widespread use, evaluating the composition of polysorbates is difficult due to their heterogeneity. In this presentation, we will highlight several 1D and 2D NMR approaches to study the inherent chemical complexity of polysorbate surfactants at a molecular level. In addition, the use of NMR techniques to differentiate different classes of polysorbates and to investigate lot-to-lot variability will be discussed.

On a Schedule: Practical Non-Fourier Techniques for Deconvoluting Complex Formulation

• Quantitative mixture analysis is a universal objective across all scientific arenas. Most reactions, consumer products, and formulations are complex mixtures of a number of known and unknown components. Accessing concentration information in an efficient and robust framework is critical in discovery and commercialization pipelines. The advent of modern programming accessibility and computational power has given experimentalists unprecedented access to sophisticated algorithms and mathematical machinery which can drastically improve spectral resolution. NMR spectroscopy is particularly well-poised for broad adoption of these machine leaning tools considering the plethora of spectral filtration and decoupling techniques available to work in concert with digital deconvolution techniques. This talk will focus on an unconventional paradigm – how NMR experiments can be purposely tailored for machine learning. Non-Fourier techniques such as Hadamard spectroscopy and non-uniform sampling (NUS) can be judiciously employed to partially deconvolute spectra. Orchestration of these bespoke excitation profiles and modern signal processing algorithms can produce spectra with exquisite resolution ready for efficient downstream analysis.

Presenter: Prof. Patrick Giraudeau (Nantes University)

Prof. Patrick Giraudeau studied physics and chemistry at the University of Nantes, where he received his Ph.D. degree in 2008. Then he worked as a postdoctoral researcher in the Department of Chemical Physics at the Weizmann Institute of Science (Israel). In 2009, he became an associate professor at the University of Nantes, where he became a full professor in 2017, and where he now leads the analytical chemistry research group and the NMR facility. In 2014, he became a fellow of the Institut Universitaire de France, and he received a consolidator grant from the European Research Council in 2019. His research activities at the CEISAM research institute focus on the development of quantitative NMR methods for the analysis of complex mixtures, including applications to metabolomics and reaction monitoring. Research highlights include the development of fast multi-dimensional quantitative experiments at high field and also on benchtop spectrometers, as well as recent investigations in dissolution dynamic nuclear polarization. Prof. Patrick Giraudeau is the vice-president of the Ampere Society, a member of the Euromar Board of Trustees and of the executive board of MetaboHub. He is deputy editor of Magnetic Resonance in Chemistry (Wiley) and associate editor of Magnetic Resonance (Copernicus). He is the author of more than 120 peer-reviewed publications.

Presenter: Kristie Adams (Mestrelab Research)

Kristie boasts over twenty years of hands-on experience in the field of practical applied NMR spectroscopy, including expertise in a variety of nuclei and homo- and heteronuclear NMR protocols. She is highly skilled in NMR spectrometer operation, maintenance and troubleshooting, software and IT infrastructure support.

Additionally, she has wide-ranging expertise in NMR spectral data collection and data interpretation for a wide variety of materials and sample types, including small molecule drugs and excipients, polymers, biologics (peptides, proteins, glycopeptides, polysaccharides, DNA/RNA), foods and natural products (oleoresins, powdered spices, skim milk powder) and dietary supplements using structure elucidation techniques, one- and two-dimensional fingerprinting and natural variation studies.

Presenter: Ben Shapiro (US Pharmacopoeia)

Ben joined USP in 2020 and is responsible for digital product development aimed at transforming the way scientists receive and consume chemical reference information. Ben developed Quantitative Nuclear Magnetic Resonance (qNMR) software for NMR analysis applications in pharmaceutical R&D, quality control, process chemistry, and analytical chemistry and is working to develop new categories of digital standards to help scientists ensure the quality of medicines they test. Ben also leads private sector business development for the Medicine Supply Map, a supply chain intelligence product that provides visibility of the generic pharmaceutical supply chain and drug shortage risks.

Ben holds a master of science degree in materials science and a bachelor of science degree in chemical engineering both from the University of Maryland, as well as an MBA from The Johns Hopkins University Carey Business School.

Presenter: José G. Napolitano (Genentech Inc.)

José G. Napolitano obtained his Ph.D. in chemistry from Universidad de La Laguna (Spain) in 2010, followed by a postdoctoral sojourn at the University of Illinois at Chicago in Guido Pauli’s group. His career in pharmaceutical industry began in 2013, when he joined AbbVie’s discovery organization in Lake County, IL. In late 2019, he joined Genentech’s synthetic molecule pharmaceutical sciences (SMPS) department in South San Francisco, CA, where he currently leads the structure elucidation group (SEG). His research interests include the application of qualitative and quantitative NMR approaches to solve complex structure elucidation problems, as well as the implementation of NMR to advance our understanding of chemical transformations.

Presenter: Ben Reiner (Dow Chemical)

Ben is currently an Associate Research Scientist in Core R&D at Dow Chemical. He occupies a hybrid data science role emphasizing practical digital deployment. His research focuses on leveraging NMR spectroscopy and modern informatics approaches to understand fundamental materials properties from a molecular perspective. Ben is trained as a synthetic organometallic chemist; his expertise in spectroscopy has been afforded solely through brute force practice. Ben’s PhD training was split between Brandeis University and the Ohio State University. During graduate school he was a freelance spectroscopist for small, local pharmaceutical companies. He completed a postdoctoral fellowship at the University of Minnesota before joining Dow in early 2020. Outside of work Ben has also been a trained EMT and firefighter.