ENC – Experimental Nuclear Magnetic Resonance Conference

Exploring Alternative Approaches for Meaningful Results after Automatic NMR Data Analysis

Dimitris Argyropoulos, Sergey Golotvin, Rostislav Pol

Advanced Chemistry Development (ACD/Labs) Inc., Toronto, ON, Canada

The development of modern NMR spectrometers and methodologies has made it possible for scientists to acquire numerous 1D and 2D spectra in a very short time. Consequently, the subsequent processing and interpretation of this data has now become the bottleneck of many structure elucidation and verification workflows. This has left chemists and NMR spectroscopists searching for a reliable way to accelerate this analysis. Furthermore, with the escalating requirements of regulatory agencies and publishers, this becomes an increasingly daunting task. As a result, the adoption of Automated Structure Verification (ASV) systems has witnessed a recent surge in popularity.

In an ASV system, the conventional outcome is presented as the Match Factor (MF)^1,2; a numeric value ranging from 0 to 1 that indicates the level of agreement between the proposed structure and the recorded spectra. The determination of the MF involves the evaluation of various criteria encompassing multiple factors. These factors include the agreement of the observed chemical shifts, integral values, multiplicities, and 2D correlation peaks in relation to those predicted or expected for the structure.

This presentation delves into a comprehensive examination of these criteria, assessing their reliability, practical applicability in real-life spectra, and potential limitations, alongside the possibility of extracting more insightful information beyond a numerical value. We also take into account the presence or absence of multiple spectra and evaluate the impact of additional spectra on the final outcome. Ultimately, we propose supplementary metrics aimed at generating more meaningful outcomes from the ASV procedure with illustrative examples to substantiate our findings.

1. Golotvin S.S., Vodopianov E., Lefebvre B.A., Williams A.J., Spitzer T.D., Magn Reson Chem., 44, 524-38, 2006.
2. Golotvin, S.S., Vodopianov, E., Pol, R., Lefebvre, B.A., Williams, A.J., Rutkowske, R.D. and Spitzer, T.D., Magn. Reson. Chem., 45, 803-813, 2007.

Expanding the Scope of High-Throughput NMR: Handling Peculiar 2D Peaks in Automated Structure Verification

Karl Demmans, Michael McCarthy, Sergey Golotvin, Dimitris Argyropoulos, Uliana Bortnik
ACD/Labs, Toronto, ON, Canada

The synergy of modern NMR instruments, automated sample handling, and data interpretation through Automated Structure Verification (ASV) has ushered in an era of unparalleled high-throughput NMR capabilities. These automated systems are typically configured with standardized acquisition parameters¹ to cater to a diverse array of samples efficiently. Nevertheless, despite scientists’ best efforts to optimize these parameters, instances arise where these parameters yield spectra with peaks deviating from their anticipated positions or phase.

For instance, employing an HSQC experiment with an F1 width set to 0-160 ppm for samples containing aldehyde or SiCH3 groups may lead to peaks appearing at the periphery of the F1 window, if not entirely beyond it and aliased over.² Additionally, modern adiabatic HSQC-DEPT pulse sequences, designed to enhance experiment sensitivity,³ may yield inaccurate results in ASV systems for samples with cyclopropyl or acetylene protons. Peaks corresponding to these groups may manifest with the opposite phase in the resultant spectra.

Discrepancies between expected and observed peaks can introduce significant disruptions to high-throughput workflows, forcing chemists to manually review datasets and, in some cases, re-record spectra with parameters more suitable for that sample. However, by providing the ASV system prior knowledge of the proposed structure, and training it to recognize peaks with unexpected presentations, these disruptions can be avoided.
This poster examines such an automated solution that seamlessly addresses this challenge, demonstrating its effectiveness in high-throughput laboratories. A range of examples showcasing each case will be presented, underscoring the robustness and reliability of the proposed approach.

1. Golotvin, S.S., Vodopianov, E., Pol, R., Lefebvre, B.A., Williams, A.J., Rutkowske, R.D., Spitzer, T.D. (2007). Magn. Reson. Chem., 45, 803-813.
2. See for example Claridge, T., “High Resolution NMR Techniques in Organic Chemistry”, Elsevier, 3rd Edition, 2016.
3. Boyer, R. D., Johnson, R., Krishnamurthy, K. (2003). J. Mag. Res. 165, 253-259.

Enhancing Efficiency of Structure Revision by Combination of CASE and DFT Methods

Alexei V. Buevich, Mikhail Elyashberg.² Sriram Tyagarajan,’ Mihir Mandal’

‘Merck & Co., Inc., Rahway, NJ, USA

²ACD/Labs, Toronto, ON, M5C 185 Canada

Structure revisions can be costly, necessitating extensive use of spectroscopic data, computational chemistry, and total synthesis. This becomes especially true when researchers must resynthesize a biologically active compound, only to find it inactive due to an incorrect and unknown structure. To mitigate these costs, we proposed utilizing Computer-Assisted Structure Elucidation (CASE) with Density Functional Theory (DFT) methods for structure revisions [1-3]. Traditionally employed for addressing constitutional isomerism, the CASE method, when combined with DFT, offers unparalleled accuracy and robustness, and the ability to tackle stereochemical problems as well. We will showcase real cases of structure revision challenges to illustrate the advantages of this combined approach. These instances involve situations where time- and resource-intensive total syntheses were employed for structure revisions. Our demonstration will highlight that incorporating CASE with DFT could potentially have avoided the need for syntheses of initially proposed incorrect and supposedly correct structures. Notably, we have shown that incorrect structures can be identified within seconds, and the correct structures can be established within minutes to hours, utilizing the originally published NMR and MS data. This approach remains effective even in cases where the spectral data are incomplete or contain typographical errors [4].

References:

1. A.V. Buevich and M. E. Elyashberg, J. Nat. Prod. 2016, 79, 3105.
2. A.V. Buevich and M. E. Elyashberg, Magn. Reson. Chem. 2018, 58, 493.
3. A.V. Buevich and M. E. Elyashberg, Magn. Reson. Chem. 2020, 58, 594.
4. M. Elyashberg, S. Tyagarajan, M. Mandal and A. V. Buevich, Molecules 2023, 28, 3796.

Enhancing Analytical Workflows: A Digital Twin for Automated Structure Verification and Quantification

Lauren Lytwak¹, Sarah Srokosz², Albert Farré Pérez¹, Dimitris Argyropoulos², Shahriar Jahanbakht², Hans De Bie², Markus Obkircher¹, Coralie Leonard¹

¹ MilliporeSigma, Round Rock
² ACD/Labs, Toronto, Canada

In the last few decades, highly pure physical reference materials have played a crucial role in analytical and pharmaceutical chemistry. These highly valuable materials have been used for the structure verification and quantification of active compounds and excipients. These physical reference materials are handled similarly to chemicals for synthesis. This means they are purchased from a supplier and are thus limited by availability as well as processing and shipping times. Once the physical material is received, the user must prepare the standard for analysis, perform the measurement, analyze the resulting data, and finally dispose of the material in a suitable way.
However, unlike in synthetic workflows, the user does not technically need access to the physical material itself. What they are using in their analysis is actually the analytical data corresponding to their sample and the reference. Therefore, if users could access high-quality pre-processed data suitable for comparative analysis with their own sample, the costly, error-prone steps associated with procuring and handling the physical reference material can be eliminated from their workflows. Given how frequently these workflows are performed in many industries, the potential time and resource savings offered by such a solution are significant.

Here, we present the first steps towards this future of analytical testing with MilliporeSigma (Merck KGaA, Darmstadt, Germany)’s ChemisTwin, an online platform to facilitate analysis workflows englobing multiple analytical techniques. The ChemisTwin portal contains an extensive database of digital reference materials (dRMs), serving as digital twins of the physical reference materials. These dRMs are based on a digital package of datasets that define a physical material and are produced from high-quality physical materials, ensuring full traceability for the end user.

To build up the library of dRMs, NMR data was prioritized due to its reproducibility and simplicity when compared to other well-established techniques such as chromatography and mass spectrometry. In addition, NMR is one of the most used analytical techniques in organic chemistry and industry. These dRMs pair the experimental 1H NMR spectra acquired by MilliporeSigma with ACD/Labs’ NMR prediction algorithms to account for differences in experimental settings—such as solvent and magnetic field strength—between the user’s data and the dRM.

For scientists and analysts, ChemisTwin leverages the automation and spectral comparison technology of ACD/Labs’ NMR Workbook Suite to automatically compare their sample with the dRM and provides a detailed report of the results. This first-of-its-kind tool allows users to verify, identify, and/or quantify their analytes of interest directly from the corresponding raw analytical data of their sample.

ChemisTwin provides scientists with an efficient, sustainable, and more error-proof alternative to manual comparative analysis using physical reference materials. In this poster, we present a case study to illustrate the relative benefits of using ChemisTwin to verify the identity of a target compound versus other closely related structures using NMR data.