Toward the Development of Rapid, Automated Identification Tests for Neat Organic Liquids Using Benchtop NMR Instrumentation

Abstract

The advent of benchtop nuclear magnetic resonance (NMR) instrumentation has paved the way for the use of this technology away from traditional NMR facility settings. Still, a wider adoption of benchtop NMR systems for routine identification testing has been hampered by inherent instrumental limitations (including low sensitivity and reduced signal dispersion) and workflow automation challenges. The present study summarizes the results of a cross-company collaboration aiming at the development of rapid, automated identification tests for incoming materials in liquid form intended for pharmaceutical manufacturing. Potential scenarios that analysts may encounter during the development of identification tests using benchtop NMR instrumentation are described, and suitable strategies for data collection and analysis are discussed. Challenges and opportunities for benchtop NMR implementation are illustrated using common organic solvents and laboratory reagents in a neat form, for which reference NMR data are provided.

On the Use of Deuterated Organic Solvents without TMS to Report 1H/13C NMR Spectral Data of Organic Compounds: Current State of the Method, Its Pitfalls and Benefits, and Related Issues

“…However, this chemically induced shift (CIS) was much smaller for TMS for the vast majority of analytes tested. Other authors [6,7] have already noted work [4] and recognized it as a call to return to the roots of NMR [6], namely to the originally [8] proposed use of TMS as an internal reference standard confirmed in later papers [9,10].…”

“…In a recent paper by Napolitano et al [7], which deals with the chemical shift referencing strategy proposed by them for neat organic liquids using benchtop NMR systems, the following statement was made: "Unfortunately, relinquishing the use of deuterated solvents and other additives such as TMS can give rise to inconsistencies in chemical shift referencing." The authors wrote this sentence quoting two research papers [4,12] but no review articles.…”

Quantification of Biopharmaceutically Relevant Nonionic Surfactant Excipients Using Benchtop qNMR

“…For example, benchtop NMR has been used under flow conditions as a process analytical technology 28 (PAT), as a tool to quantify the PEO content in various poloxamers (including P188), 29 and for the automatic identification of neat organic liquids. 30 Extension of PS80 quantification in HPV VLP vaccine to low-field has recently been suggested by Rustandi. 18 Additionally, while this manuscript was undergoing peer review, a study by Le-McClain et al (independent of the work reported here) demonstrated the application of T 2 and diffusion filters with WET water suppression for the quantification of P188 in formulated bovine serum albumin (BSA) solutions.…”

“…Over the past decade, tremendous progress has been made in the development, deployment, and adoption of low-field NMR technology (also commonly referred to as benchtop NMR), providing a viable path toward the realization of the unique analytical capabilities of NMR for routine applications of biopharmaceutical interest. For example, benchtop NMR has been used under flow conditions as a process analytical technology (PAT), as a tool to quantify the PEO content in various poloxamers (including P188), and for the automatic identification of neat organic liquids . Extension of PS80 quantification in HPV VLP vaccine to low-field has recently been suggested by Rustandi .…”

Elucidating Structures of Complex Organic Compounds Using a Machine Learning Model Based on the 13C NMR Chemical Shifts

“…One basic assumption in the computation-assisted structure elucidation is that the correct structure will show a better agreement between the experimental data and the calculated NMR data (i.e., a higher R 2 , or a lower MAD/CMAD). Following this procedure, quantum chemical calculations, in particular, the calculated 13 C chemical shifts, have been successfully used to settle structural and stereochemical issues of various complex organic compounds such as natural products. − However, there are cases for which the standard statistical parameters (e.g., MAD) do not point to the correct structure, as both the experimental and the calculated chemical shifts are prone to errors, which make an imperfect agreement even for a correct match. ,− More sophisticated statistical methods are needed to better describe the correlation between the experimental and the computed data.…”