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Nonionic surfactant excipients (NISEs) are commonly added to biologics formulations to mitigate the effects of stress incurred by the active biotherapeutic during manufacturing, transport, and storage. During manufacturing, NISEs are added by dilution of a stock solution directly into a protein formulation, and their accurate addition is critical in maintaining the quality and integrity of the drug product and thus ensuring patient safety. This is especially true for the common NISEs, polysorbates 20 and 80 (PS20 and PS80, respectively) and poloxamer 188 (P188). With the increasing diversity of biologic modalities within modern pharmaceutical pipelines, there is thus a critical need to develop and deploy convenient and user-accessible analytical techniques that can rapidly and reliably quantify these NISEs under biopharmaceutically relevant conditions. We thus pursued 60 MHz benchtop quantitative NMR (qNMR) as a nondestructive and user-friendly analytical technique for the quantification of PS20, PS80, and P188 under such conditions. We demonstrated the ability of benchtop qNMR (1) to quantify simulated PS20, PS80, and P188 stock solutions representative of those used during the drug substance (DS) formulation step in biomanufacturing and (2) to quantify these NISEs at and below their target concentrations (≤0.025% w/v) directly in biologics formulations containing histidine, sucrose, and one of three biotherapeutic modalities (monoclonal antibody, antibody-drug conjugate, and Fc-fusion protein). Our results demonstrate that benchtop qNMR offers a fit-for-purpose, reliable, user-friendly, and green analytical route by which NISE of interest to the biopharmaceutical industry may be readily and reliably quantified. We conclude that benchtop qNMR has the potential to be applied to other excipient formulation components in the presence of various biological modalities as well as the potential for routine integration within analytical and QC laboratories across pharmaceutical development and manufacturing sites.

Quantitative nuclear magnetic resonance (qNMR) is a powerful analytical technology that is capable of quantifying the concentration of any analyte with exquisite accuracy and precision so long as it contains at least one nonlabile nuclear magnetic resonance (NMR)-active nucleus. Unlike with traditional analytical technologies, the concentrations of analytes do not directly influence the uncertainty in the quantification of NMR signals because an ideal NMR response depends only on the nature and amount of the nucleus being observed. Rather, in the absence of spectral artifacts and under favorable experimental conditions, the measurement uncertainty may be influenced by the following factors: (1) spectroscopic parameters such as the spectral width, number of time domain points, and acquisition time; (2) postacquisition data processing, such as apodization and zero-filling; (3) the signal-to-noise ratios (SNRs) and lineshapes of the two signals being used in a qNMR measurement; and (4) the method of signal quantification employed, such as numerical integration or lineshape fitting (LF). Here, a general Monte Carlo (MC) method that considers these factors is presented, with which the random and systematic contributions to qNMR measurement uncertainty may be calculated. Autocorrelation analysis of synthetic and experimental noise is used in a fingerprint-like approach to demonstrate the validity of the simulations. The MC method allows for a general quantitative assessment of measurement uncertainty without the need to acquire spectral replicates and without reference to the molecular structures and concentrations of analytes. Representative examples of qNMR measurement uncertainty simulations are provided in which the metrological performances of integration and LF are contrasted for signal pairs obtained using various acquisition and processing schemes in the low-SNR regime-an area where application of the proposed MC method may prove to be particularly salient.

One-dimensional heteronuclear relaxation dispersion NMR spectroscopy at 13 C natural abundance successfully characterized the dynamics of the hydrogen-deuterium exchange reaction occurring at the N ε position in l-arginine by monitoring C δ in varying amounts of D 2 O. A small equilibrium isotope effect was observed and quantified, corresponding to ΔG = -0.14 kcal mol -1 . A bimolecular rate constant of k D = 5.1 × 10 9 s -1 M -1 was determined from the pH*-dependence of k ex (where pH* is the direct electrode reading of pH in 10% D 2 O and k ex is the nuclear spin exchange rate constant), consistent with diffusion-controlled kinetics. The measurement of ΔG serves to bridge the millisecond time scale lifetimes of the detectable positively charged arginine species with the nanosecond time scale lifetime of the nonobservable low-populated neutral arginine intermediate species, thus allowing for characterization of the equilibrium lifetimes of the various arginine species in solution as a function of fractional solvent deuterium content. Despite the system being in fast exchange on the chemical shift time scale, the magnitude of the secondary isotope shift due to the exchange reaction at N ε was accurately measured to be 0.12 ppm directly from curve-fitting D 2 O-dependent dispersion data collected at a single static field strength. These results indicate that relaxation dispersion NMR spectroscopy is a robust and general method for studying base-catalyzed hydrogen-deuterium exchange chemistry at equilibrium.

The millisecond time scale motions in ribonuclease A (RNase A) were studied by solution NMR CPMG and off-resonance R1ρ relaxation dispersion experiments over a wide pH and temperature range. These experiments identify three separate protein regions termed Cluster 1, Cluster 2, and R33, whose motions are governed by distinct thermodynamic parameters. Moreover, each of these regions has motions with different pH dependencies. Cluster 1 shows an increase in activation enthalpy and activation entropy as the pH is lowered, whereas Cluster 2 exhibits the opposite behavior. In contrast, the activation enthalpy and entropy of R33 show no pH dependence. Compounding the differences, Δω values for Cluster 2 are characteristic of two-site conformational exchange, yet similar analysis for Cluster 1 indicates that this region of the enzyme exhibits conformational fluctuations between a major conformer and a pH-dependent average of protonated and deprotonated minor conformers.