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. Author manuscript; available in PMC: 2025 Aug 27.
Published in final edited form as: Magn Reson Chem. 2025 May 26;63(8):585–592. doi: 10.1002/mrc.5536

Quantitative Solid-State NMR Spectroscopy (qSSNMR) in Pharmaceutical Analysis

Zhaoxi Zheng 1, Kang Chen 2, Yang Liu 3, Eric J Munson 4, Yongchao Su 5
PMCID: PMC12379110  NIHMSID: NIHMS2105842  PMID: 40417854

Abstract

Pharmaceutical analysis is essential to drug development and quality assurance, ensuring that products meet stringent safety and efficacy standards. Quantitative solid-state NMR (qSSNMR) has become a key technique, enabling precise quantification and characterization of solid drug formulations. This mini-review highlights the evolution of qSSNMR, focusing on improvements in detection limits, resolution, and high-throughput capabilities. This review explores technical advancements and applications for analyzing complex pharmaceutical mixtures. While challenges remain for widespread adoption, efforts in automation, user-friendly software, and collaboration aim to address these.

Keywords: complex drug formulations, pharmaceutical analysis, qSSNMR, quantitative solid-state NMR, solid-state characterization

1 |. General Overview

Drug analysis is a fundamental aspect of the pharmaceutical industry, as these measurements are key assessments toward pharmaceutical products safety, efficacy, and quality. The rigorous evaluation of active pharmaceutical ingredients (APIs) and excipients is essential for regulatory compliance and to meet the expectations of healthcare providers and patients [1, 2]. Accurate drug analysis helps in identifying the chemical composition, purity, stability, and bioavailability of pharmaceutical formulations, which are critical factors influencing therapeutic outcomes [3–5].

To achieve these objectives, a variety of analytical tools are employed in pharmaceutical analysis [6]. Conventional techniques such as high-performance liquid chromatography (HPLC) [7], mass spectrometry (MS) [8], and solution-state nuclear magnetic resonance (NMR) [9] are widely adopted due to their ability to provide detailed information about the chemical and physical properties of drug substances. These methods are favored for their sensitivity, specificity, and reliability, allowing for the detection of impurities and the quantification of active ingredients even at trace levels [6]. However, the solvation process can induce significant changes in the drug’s physical and chemical properties, resulting in the loss of unique spectroscopic signatures that are essential for accurate identification and quantification of the drug [10, 11].

How can we revolutionize the way we approach drug analysis, especially for solid formulations? Solid-state nuclear magnetic resonance (SSNMR) stands at the forefront of pharmaceutical analysis, offering unparalleled insights into the structural and compositional intricacies of solid drug formulations [12–15]. Recently, SSNMR has gained prominence for its unique capability to analyze solid formulations directly, preserving the integrity of the information necessary for effective drug analysis [16]. SSNMR is particularly valuable for characterizing polymorphism, monitoring crystalline-amorphous transitions, and detecting low-level impurities in their native states [16]. The adoption of SSNMR is further driven by the need for more comprehensive and accurate assessments of complex pharmaceutical products.

2 |. Complex Drug Analysis and qSSNMR

As more complex drugs are being developed, the pharmaceutical industry faces increasing challenges in the characterization and quantification of APIs and excipients within complex solid and semi-solid formulations. As drug delivery systems, formulation compositions, and manufacturing processes grow more intricate, the demand for advanced analytical techniques capable of providing detailed insights into pharmaceutical materials has never been greater. Accurate and precise quantification is essential, as the precise measurement of APIs and excipients in solid formulations directly impacts the safety, efficacy, and quality of medications [17]. However, quantifying pharmaceutical materials in their native solid or semi-solid state remains a significant challenge, primarily due to the limited available techniques of solid-state analysis with the required accuracy and sensitivity for meaningful stability and quality assessments [18]. Furthermore, the complexity of multicomponent solid formulations, which include APIs, polymerics, excipients, and impurities, necessitates analytical techniques with sufficient chemical resolution and sensitivity to deconvolute these components effectively [19, 20].

Peaks of NMR spectra directly inform chemical identity and quantity. The practice of using solution NMR for quantitative drug analysis, that is, quantitative NMR (qNMR), started as early as the 1960s, for example, quantifying of aspirin, phenacetin, and caffeine in tablets [21] and later in the 1980s’ quantifying of dicyclomine in tablets [22, 23]. In recent years solution-state qNMR has been further developed for complex drug mixtures analysis like heparin [24] and pentosan [25], novel oligonucleotide therapeutics [26, 27], as well as complex drug products like paclitaxel in albumin nanoparticles [28] and difluprednate in nanoemulsion [29]. However, more complications arise for solid and semisolid dosage forms due to different microstructural forms, for example, crystalline versus amorphous [30, 31]. These formulation properties are termed Q3 quality attributes [32], representing the arrangement of matter or microstructural properties within a formulation [33]. Routine analytical tests for qualitative (Q1) and quantitative (Q2) analysis can be performed on extracts of drug products, which inevitably eliminates Q3 features. Therefore, non-invasive analytical methods would be ideal for Q3 characterization. Among all analytical tools, high-resolution SSNMR spectroscopy can be performed directly on drug dosage forms including (but not an exhaustive list) powders, tablets, crystals, gels, in their native crystalline or amorphous forms, leading to unambiguous interpretation of structural states of drug, excipients, the interactions between them and sometimes, distribution of drugs in each state [12–15]. This new Q3 SSNMR application differs from classic NMR usage toward chemical structure elucidation but on the challenging issue of microstructure state and their distribution.

3 |. History and Applications of qSSNMR

The evolution of quantitative SSNMR (qSSNMR) has been marked by significant milestones that have transformed its application in pharmaceutical science. Since its introduction to pharmaceutical science in the 1980s [37, 38], SSNMR has increasingly established itself as a robust, reliable, and quantitative tool for analyzing pharmaceutical materials from chemical to biological modalities [16, 23, 39]. Figure 1 illustrates the timeline of qSSNMR applications in pharmaceutical science, showing its evolution over each decade. The first qSSNMR study, conducted in 1990 by Suryanarayanan and coworkers, utilized 13C NMR to analyze carbamazepine anhydrates [40], marking the beginning of its use as a transformative analytical tool. Nearly two decades later, in 2007 19F NMR was employed for quantification [41, 42], offering distinct advantages owing to the four-fold higher gyro-magnetic ratio and 100-fold greater natural abundance of 19F compared with 13C, enabling better selectivity at natural abundance. The field saw significant advancements in the near 2020s as more nuclei of 1H, 31P, and 35Cl were explored for qSSNMR applications [43–46]. For example, 1H quantification in the solid state became more accessible with recent advancements in UF-MAS, which dramatically improved sensitivity and resolution by averaging out dipolar interactions [34, 46–48].

FIGURE 1 |.

FIGURE 1 |

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Timeline of quantitative solid-state NMR (qSSNMR) applications in pharmaceutical science, with a forward outlook toward achieving lower limits of detection (LOD), higher resolution, and faster high-throughput (HT) capabilities. These advancements are driven by innovations in new and evolving applications within pharmaceutical analysis [16], ultrafast magic angle spinning (UF-MAS) [34], dynamic nuclear polarization (DNP) [35], and automated sample handling and experimental optimization techniques [36].

Among solid-state characterization techniques, SSNMR is often regarded as the gold standard, frequently used to calibrate other orthogonal methods [49, 50]. Compared with conventional analytical methods, qSSNMR offers distinct advantages and excels in several key areas of pharmaceutical analysis. SSNMR is particularly effective in characterizing polymorphism, crystalline-amorphous transitions, and detecting low-level components in both drug substances and drug products, without interference from excipients [16].

Building on its historical development, qSSNMR has found diverse applications in pharmaceutical analysis. Some representative qSSNMR applications are illustrated in Figure 2. Polymorphism, the attributes of a substance to exist in multiple crystalline or amorphous forms, can significantly influence a drug product’s dissolution, solubility, bioavailability, and stability. qSSNMR is a powerful tool to distinguish between polymorphic forms by leveraging their unique spectroscopic fingerprints and distinct relaxation behaviors, allowing researchers to identify and quantify various polymorphs and phases within a formulation (see Figure 2A), which are part of the Q3 quality attributes used for complex generic drug development [32]. The ability of qSSNMR to monitor crystalline-amorphous interconversions is crucial for understanding the stability of drug products during processing and storage. These phase transitions can significantly alter the physical and chemical properties of APIs, thereby affecting their performance and safety. By using qSSNMR, researchers can track these transitions in real time, providing valuable data for formulation development and quality control. Additionally, qSSNMR excels at detecting low-level components, such as impurities or degradation species within intact drug products, where traditional analytical techniques may require extensive drug product extraction. The sensitivity of qSSNMR allows for the identification and quantification of even trace components within complex matrices. For example, a recent study by Su and coworkers [53] shows that 19F qSSNMR enables the detection of a very low drug loading of 0.04% w/w (see Figure 2C). Moreover, 19F and 13C qSSNMR techniques have been developed to quantify amorphous APIs and polymer crystallization in extruded implants [54, 55]. This capability is necessary for ensuring the quality of pharmaceutical products, as even small amounts of impurities can have significant implications for patient health.

FIGURE 2 |.

FIGURE 2 |

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Representative applications of qSSNMR. (A) Quantification of crystalline neotame Forms A and G in mixtures with amorphous neotame. Below the spectra, the linear correlation between SSNMR results and the weight percent of neotame calculated by mass is illustrated [51]. (B) Full 13C spectral deconvolution and fitting of polymeric API patiromer with SSNMR quantification results of model monomer units shown above [52]. (C) Left: 1H-19F cross polarization (CP) spectra of a 0.04% w/w crystalline Compound I ASD sample with and without spin–lattice relaxation at rotating frame (T1ρ) filtration, along with a calibration plot quantifying 0.04%–10% w/w crystalline Compound I [53]. Right: Posaconazole drug and its chemical structure. (D) Left: Pioglitazone drug and its chemical structure. Right: one- and two-dimensional 1H-1H spectra of a multicomponent formulation with 2% w/w pioglitazone free base (PIO-FB) and 18% w/w PIO-HCl, along with the corresponding correlation curve of PIO-FB weight fraction under 60 kHz MAS [46]. (E) 35Cl static SSNMR spectrum (red and blue) of pure PIO-HCl and the corresponding calculated spectrum (green), with a plot of PIO-HCl weight fractions versus sample mass [45]. (F) 31P SSNMR of MK-A crystalline and amorphous reference standards. Below shows the linear correlation between results obtained by Raman and SSNMR [44]. Figures adapted with permission from American Chemical Society and Elsevier.

4 |. Recent Advancements in qSSNMR

More NMR technological advancements have greatly expanded the capabilities of qSSNMR, as illustrated in Figure 3. First, the development of advanced pulse programming techniques, such as spectral editing and relaxation filters [57, 60, 61], has enhanced the ability to distinguish between closely related species in complex mixtures. Additionally, the introduction of UF-MAS [34], which spins samples at 60 kHz or higher, has revolutionized both the sensitivity and resolution of SSNMR experiments. UF-MAS enables the rapid acquisition of high-resolution 1H NMR data, making it invaluable for high-throughput analysis in pharmaceutical applications. While UF-MAS enhances resolution and sensitivity, challenges such as rotor packing consistency, particularly for small-diameter rotors, and the maintenance of stable high-frequency spinning continue to pose barriers to fully automated workflows. Furthermore, the implementation of dynamic nuclear polarization (DNP) [58], which transfers polarization from electrons to target nuclei, has significantly broadened qSSNMR applications by enhancing sensitivity, even in pharmaceutical materials at natural abundance [62–64]. Another key advancement is the development and use of cryogenically cooled MAS SSNMR probes, or CryoProbes [59], which drastically reduce electronic noise and significantly improve the signal-to-noise ratio (S/N) of NMR spectra.

FIGURE 3 |.

FIGURE 3 |

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Technological advancements in qSSNMR. (A) Three-spin coherence CH2 selection pulse sequence [56] (top) and its application (bottom) in a cellulose-derived pharmaceutical excipient, hydroxyl propyl methylcellulose acetate succinate. The CH2 selection highlights the OCH2 C6 while suppressing OCH [57]. (B) UF-MAS rotor loaded on a stage under optical microscopy (left) and its application (right) in pharmaceuticals. A comparison of 1D and 2D 1H NMR spectra for posaconazole was performed, highlighting the differences between spectra acquired under 40–110 kHz and those obtained under conventional 12 kHz MAS [31]. (C) Schematic illustration of 1H DNP (top left), DNP MAS unit (bottom left), and its application (right). DNP-enhanced 13C (top right) and 15N CP MAS spectra (bottom right) of natural abundance posaconazole deuterated vinyl acetate amorphous solid dispersions [58]. (D) Schematic illustration of CP MAS CryoProbe (left) and its applicati.n (right) in the quantitative analysis of posaconazole. Comparison of 1D 13C (top) and 15N (bottom) CP spectra of crystalline POSA Form I obtained using the CryoProbe (red) and a standard 4 mm probe (blue) [59]. Figures adapted with permission from American Chemical Society, Elsevier, and Wiley.

These technological innovations have transformed the landscape of pharmaceutical analysis by enabling researchers to obtain high-quality spectra. They further pave the way for high-throughput and automated qSSNMR (see Figure 4). The general workflow includes several key steps: (i) automated sample handling, which manages sample transfer, insertion, and ejection; (ii) data acquisition, where advanced techniques and pulse sequences improve resolution and sensitivity; and (iii) streamlined data analysis, providing quantitative results through reliable spectral interpretation, including spectral deconvolution, modeling, and fitting. Conceptually, high-throughput qSSNMR automation significantly reduces the time and labor traditionally associated with SSNMR, enhancing efficiency while improving data reliability and reproducibility. This makes qSSNMR more accessible for routine use in pharmaceutical laboratories.

FIGURE 4 |.

FIGURE 4 |

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Conceptual and aspirational workflow of high-throughput qSSNMR automation for pharmaceutical analysis.

5 |. Challenges in qSSNMR

Despite its many advantages, qSSNMR has faced challenges in gaining widespread adoption in the pharmaceutical industry [16, 65]. Some of the primary barriers, aside from sensitivity, include (i) Sample preparation challenges: Preparing solid samples for analysis can be complex, for example, grinding may introduce artifacts or alter the compound’s solid forms. (ii) Data interpretation complexity: qSSNMR spectra are often difficult to interpret due to line broadening and peak overlaps, especially with complex molecules or mixtures, requiring specialized expertise and advanced data analysis techniques. (iii) High cost: The equipment and infrastructure needed for qSSNMR are expensive, limiting accessibility for small-scale pharmaceutical companies and research groups. (iv) Competition from other techniques: More widely adopted techniques, considered easier to use, often compete with qSSNMR in the pharmaceutical industry.

Nevertheless, there have been collective and growing efforts to overcome these barriers, especially for complex drug products where traditional methods could be less chemistry-specific or informative. For example, advancements in automation and user-friendly software for data collection and analysis are continually evolving [66]. Additionally, the increasing availability of training resources and collaborative platforms is helping to demystify qSSNMR and promote its broader adoption among pharmaceutical scientists. Furthermore, the integration of qSSNMR with other orthogonal analytical techniques, such as X-ray diffraction (XRD) [67, 68], Raman spectroscopy [69], and differential scanning calorimetry (DSC) [53], by leveraging the resolution, quantification level, and accessibility of each tool, offers exciting opportunities for the comprehensive characterization of pharmaceutical materials. By combining the strengths of these methods, researchers can gain a more holistic and accurate understanding of complex formulations, ultimately enhancing drug development processes.

qSSNMR remains in its early stages, taking advantage of external calibration methods and lacking standardized protocols, in comparison with qNMR. Looking ahead, qSSNMR in pharmaceutical analysis is promising in informing microstructural Q3 quality attributes of complex drug dosage forms. qSSNMR’s ability to provide detailed compositional and structural data positions it as a critical tool in this evolving analytical landscape. It is anticipated that qSSNMR analysis will extend beyond solid dosages of small molecule drugs to include biologics formulated in solid state [70], suspensions [71–73] or frozen solution [74–76]. For example, ssNMR has recently been used to quantify the mobile water [74] and various species of phosphate buffer ions [75] during the freezing process of biologics. Additionally, standardizing qSSNMR procedures will be essential for establishing it as a routine analytical technique in pharmaceutical laboratories. Collaborative efforts between academic, industrial, and regulatory institutes will be key to developing best practices and guidelines for qSSNMR applications [77, 78].

6 |. Conclusion

In conclusion, qSSNMR is emerging as a transformative analytical technique in the pharmaceutical industry, providing unique capabilities for characterizing and quantifying APIs and excipients in solid formulations. SSNMR technological advancements, along with its applications in key areas such as polymorphism, phase transitions, and low-level component detection, highlight the technique’s increasing importance in pharmaceutical analysis. qSSNMR not only enhances the accuracy of pharmaceutical analysis but may also play a pivotal role in ensuring the quality of medications. As the industry adopts automation and aims to improve the reliability and efficiency of analytical methods, qSSNMR is positioned to have a role in drug development, quality control, and quality assurance. By promoting collaboration and standardization across various sectors, we are excited about the potential to fully unlock the benefits of qSSNMR, ultimately contributing to the quality of drug products and patient outcomes.

Footnotes

Conflicts of Interest

The authors declare no conflicts of interest.

Disclaimer

This article reflects the views of the authors and should not be construed to represent US FDA’s views or policies. The authors declare the following competing financial interests: E.J.M. is a partial owner of Kansas Analytical Services, which provides solid-state NMR services to the pharmaceutical industry. E.J.M. is a partial owner of Spectral NMR Technologies, which provides specialized NMR equipment for non-destructive testing of pharmaceutical products. No data from either Kansas Analytical Services or Spectral NMR Technologies are presented.

Data Availability Statement

Scientific data included in this review article are available in the cited references.

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