Polymorph Identification for Flexible Molecules: Linear Regression Analysis of Experimental and Calculated Solution- and Solid-State NMR Data Article Swipe

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Mohammed Rahman , Hugh R. W. Dannatt , Charles D. Blundell , Leslie P. Hughes , Helen Blade , Jake Carson , Ben P. Tatman , Steven T. Johnston , Steven P. Brown ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.1021/acs.jpca.3c07732

The Δδ regression approach of Blade et al. [ J. Phys. Chem. A 2020, 124(43), 8959-8977] for accurately discriminating between solid forms using a combination of experimental solution- and solid-state NMR data with density functional theory (DFT) calculation is here extended to molecules with multiple conformational degrees of freedom, using furosemide polymorphs as an exemplar. As before, the differences in measured ¹H and ¹³C chemical shifts between solution-state NMR and solid-state magic-angle spinning (MAS) NMR (Δδ_experimental) are compared to those determined by gauge-including projector augmented wave (GIPAW) calculations (Δδ_calculated) by regression analysis and a t-test, allowing the correct furosemide polymorph to be precisely identified. Monte Carlo random sampling is used to calculate solution-state NMR chemical shifts, reducing computation times by avoiding the need to systematically sample the multidimensional conformational landscape that furosemide occupies in solution. The solvent conditions should be chosen to match the molecule's charge state between the solution and solid states. The Δδ regression approach indicates whether or not correlations between Δδ_experimental and Δδ_calculated are statistically significant; the approach is differently sensitive to the popular root mean squared error (RMSE) method, being shown to exhibit a much greater dynamic range. An alternative method for estimating solution-state NMR chemical shifts by approximating the measured solution-state dynamic 3D behavior with an ensemble of 54 furosemide crystal structures (polymorphs and cocrystals) from the Cambridge Structural Database (CSD) was also successful in this case, suggesting new avenues for this method that may overcome its current dependency on the prior determination of solution dynamic 3D structures.

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Nuclear Magnetic Resonance

Statistics

Physics

Organic Chemistry

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Chemical shift Chemistry Solid-state nuclear magnetic resonance Linear regression Monte Carlo method Molecule Computational chemistry Statistical physics Physical chemistry Mathematics Nuclear magnetic resonance Statistics Physics Organic chemistry

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Type: article
Language: en
Landing Page: https://doi.org/10.1021/acs.jpca.3c07732
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Cited By: 7
References: 105
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DOI: https://doi.org/10.1021/acs.jpca.3c07732

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Title: Polymorph Identification for Flexible Molecules: Linear Regression Analysis of Experimental and Calculated Solution- and Solid-State NMR Data

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Language: en

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Publication year: 2024

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Authors: Mohammed Rahman, Hugh R. W. Dannatt, Charles D. Blundell, Leslie P. Hughes, Helen Blade, Jake Carson, Ben P. Tatman, Steven T. Johnston, Steven P. Brown

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Landing page: https://doi.org/10.1021/acs.jpca.3c07732

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Concepts: Chemical shift, Chemistry, Solid-state nuclear magnetic resonance, Linear regression, Monte Carlo method, Molecule, Computational chemistry, Statistical physics, Physical chemistry, Mathematics, Nuclear magnetic resonance, Statistics, Physics, Organic chemistry

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Cited by: 7

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