Pith. sign in

REVIEW 5 major objections 4 minor 66 references

Nuclear Magnetic Resonance Study of Monoclonal Antibodies Near an Oil-Water Interface

T0 review · 5 major / 4 minor · reviewed 2026-08-08 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Localized proton NMR shows that a model monoclonal antibody, in a voxel centered at an oil-water interface, keeps the same chemical resonances but shows line broadening, faster $T_2$ relaxation, and a roughly two-fold lower apparent…

desk verdict First localized NMR look at a mAb near an oil-water interface, with a solid new measurement but an overreach on the aggregate explanation. read the letter →

arxiv 2502.05563 v1 pith:OD7CJWKL submitted 2025-02-08 physics.bio-ph

Jamini Bhagu , Lissa C. Anderson , Samuel C. Grant , Hadi Mohammadigoushki This is my paper · ORCID
classification physics.bio-ph
keywords monoclonalantibodyNMRspectroscopyoil-waterinterfaceproteinadsorptionT2relaxationdiffusion-weightedself-similarityindexinterfacialaggregation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a monoclonal antibody's interaction with an oil-water interface can be seen in localized NMR measurements even though the antibody's chemical structure appears unchanged. Approaching the interface, the antibody's proton resonances stay at the same frequencies but broaden, relax faster, and diffuse roughly half as fast as in the bulk. The authors interpret this as antibody molecules associating near the interface, forming aggregates or multilayers in a thick interphase, and possibly exchanging with the bulk phase. This matters because antibody drugs encounter such interfaces during manufacturing, storage, and delivery, and aggregation is a known risk to safety and efficacy. The contribution is a demonstration that spatially resolved NMR can probe these interfacial effects in situ at formulation-relevant concentrations.

What carries the argument

The load-bearing tool is a spatially and spectrally selective proton NMR method, relaxation-enhanced point-resolved spectroscopy (REPRESS) with VAPOR water suppression, plus a diffusion-weighted variant (DW-REPRESS), which delivers spectra, $T_2$ values, and apparent diffusion coefficients from 375-um voxels placed at increasing distances from the oil-water interface. A custom pendant-drop tensiometer independently confirms that the antibody adsorbs to the interface by showing the interfacial tension falling over hundreds of seconds. The self-similarity index (SSI), a cross-correlation score between spectra, converts line-broadening trends into a single number, and the mono-exponential fit of signal attenuation versus diffusion weighting turns the DW-REPRESS data into an apparent diffusion coefficient.

What would settle it

A decisive check is to measure the water $T_2$ and apparent diffusion coefficient in the same interface voxel using an oil matched to the magnetic susceptibility of water, or a susceptibility-compensated sequence. If water shows the same interfacial drop as the antibody does, then susceptibility gradients, not antibody association, explain the observations; if water is unaffected while the antibody diffusion and relaxation change, the aggregation interpretation is supported. A second check would measure the mAb concentration within the voxel to rule out a local concentration increase masquerading as slower diffusion.

Watch

Extended reading notes

Core claim

At 900 MHz proton NMR on a model IgG2a-κ monoclonal antibody at 36.4 mg/mL against a proton-free fluorocarbon oil, voxels of 375 um show three mAb resonances near 3.9, 3.5, and 2.9 ppm. As the voxel moves from the bulk to the oil-water interface, these chemical shifts do not move and no new resonances appear, but the lines broaden and the self-similarity index falls; a water-only control shows a comparable SSI drop, indicating that magnetic susceptibility differences at the interface account for much of the apparent broadening. Diffusion-weighted measurements give an apparent diffusion coefficient of $6.8\times10^{-5}\ \mathrm{mm^2/s}$ in the bulk and $3.7\times10^{-5}\ \mathrm{mm^2/s}$ in the voxel centered at the interface, a drop of nearly a factor of two, with mono-exponential attenuation. The $T_2$ relaxation times differ across the three resonances and are shortened near the interface for the resonances at 2.9 and 3.5 ppm. The paper reads these observations as evidence that the antibody associates at the interface, possibly forming small aggregates and multilayers in a thick interphase that exchanges with bulk mAb nearby.

Load-bearing premise

The argument that slower diffusion and shorter $T_2$ near the interface mean antibody association assumes that magnetic-susceptibility gradients at the oil-water interface and local antibody concentration gradients are not responsible for the changes.

Editorial extensions

If this is right

  • Localized NMR can detect antibody association near an oil-water interface even when chemical shifts show no rearrangement, giving formulations a label-free in-situ probe.
  • The nearly two-fold lower apparent diffusion coefficient and shortened $T_2$ near the interface imply the interfacial influence extends well beyond a monolayer, consistent with a thick interphase of associated antibody.
  • Magnetic susceptibility effects must be quantified before line broadening near an interface is attributed to protein structural change, since the water control shows a comparable drop in self-similarity.
  • Measuring the rate and extent of antibody exchange between the oil-water interface and the bulk remains open and, the authors note, would likely require isotope labeling.
  • Extending the approach to air-water interfaces, other mAb types, and surfactant-containing formulations is a stated next step.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the interpretation is right, the ratio of interfacial to bulk apparent diffusion coefficient could serve as a label-free screening metric for interfacial aggregation propensity in antibody formulations.
  • A susceptibility-matched oil control or susceptibility-compensated diffusion weighting would settle whether the factor-of-two diffusion drop is molecular or field-related; the paper's own water SSI control shows such artifacts are not negligible.
  • Because the voxel volume mixes adsorbed layers with bulk solution, the measured ADC is a voxel average; varying the voxel size or using diffusion times longer than the interfacial exchange time could separate a slow interfacial population from a bulk population.
  • The same localized approach should transfer to air-water interfaces, but the larger susceptibility mismatch there makes the susceptibility control even more important.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 4 minor

Summary. The manuscript reports a localized 1H NMR study of a model IgG2a-κ monoclonal antibody near a fluorocarbon-oil/water interface. Using a 21.1-T magnet with REPRESS and diffusion-weighted REPRESS sequences, the authors acquire spectra, T2 relaxation data, and apparent diffusion coefficients (ADC) from a voxel centered at the interface and from bulk solution. They find that the number of chemical resonances and their chemical shifts do not change with proximity to the interface, while line broadening, a lower self-similarity index, differential T2 shortening, and a roughly factor-of-two lower ADC are observed in the interface-centered voxel. The paper interprets these changes as evidence that the mAb associates at the oil-water interface, possibly forming multilayers or small aggregates that exchange with bulk mAb near the interface.

Significance. If the central claim is substantiated, the paper would introduce a useful new in-situ approach for probing mAb behavior at pharmaceutically relevant oil-water interfaces, where existing methods such as neutron reflectivity have contrast limitations for thick interphases. The bulk ADC value is consistent with published mAb diffusion coefficients, the water-signal SSI control is a sensible partial check on susceptibility artifacts, and the dynamic surface-tension data independently confirm interfacial adsorption. The main weakness is quantitative: the reported voxel dimensions and the sub-micrometer interfacial layers invoked by the authors make the factor-of-two ADC change difficult to attribute to the proposed interfacial aggregate pool without additional evidence.

major comments (5)
  1. [§III.B, Fig. 6(c)] The factor-of-two reduction in the mAb ADC at the interface is presented as evidence of mAb association and aggregate formation, but the volume fraction of the proposed interfacial layer cannot support this effect. The DW-REPRESS voxel is stated as 1.5x1.5x0.75 mm, so the water-side half-voxel is roughly 375 µm thick. The interphase thickness cited from Kalonia et al. is 200-300 nm, which is about 0.1% of that half-voxel; even a tenfold local concentration increase would contribute less than one percent of the detected mAb signal. A two-pool model that reduces the apparent ADC to roughly half the bulk value requires about half of the detected mAb signal to reside in a slow pool, yet no aggregate mass fraction, local concentration profile, or slow-pool fraction is reported. Given that the manuscript itself states that particle formation cannot be assessed by MRI and that exchange-rate quantification would require isotope labeling, the attribution of the ADC drop to aggregates or a thick interphase is not quantitatively supported.
  2. [II.C, III.B, and Conclusion] The voxel size is reported inconsistently: the abstract and Section II.C state 375 µm, the Methods section states 1.5x1.5x0.75 mm, and the text near Fig. 6 and the Conclusion refers to a 187 µm voxel and a 'voxel less than 200 µm'. These numbers differ by up to a factor of four in each dimension, and the discrepancy directly affects the volume-fraction argument and the spatial-resolution claims. The authors should specify the exact voxel dimensions, identify which dimension corresponds to the slice-selection axis, and reconcile the abstract and text.
  3. [II.D, Figs. 4(c), 6(c), and 8(c)] No uncertainties or error bars are reported for the diffusion coefficients or T2 values. The factor-of-two ADC difference in Fig. 6(c) and the differential T2 changes in Fig. 8(c) are the central quantitative results, so the paper needs confidence intervals, the number of replicate measurements, and the fitting residuals or goodness-of-fit metrics. Without these, the reader cannot judge whether the apparent differences are statistically significant or whether the mono-exponential fits are actually distinguishable from bi-exponential alternatives.
  4. [III.B, SSI control and Discussion] The susceptibility control is incomplete for the D and T2 interpretations. The water SSI analysis shows that susceptibility gradients broaden the water resonance, but the water ADC similarity is not a sufficient control for mAb T2 and ADC because water T2 was not measured and because susceptibility-induced dephasing can attenuate signal and bias T2 and diffusion measurements differently for short-T2 species. To support the claim that the observed T2 shortening and ADC reduction are due to mAb association rather than susceptibility artifacts, the authors should either measure water T2 in the same voxels, use susceptibility-compensated diffusion-weighting schemes, or compare with a susceptibility-matched control interface.
  5. [III.B, Eq. (1)] The mono-exponential character of the diffusion attenuation is used to argue that restriction or exchange are unlikely contributors to the slower interfacial ADC. This inference is not valid: a two-site fast-exchange or partial-volume model with a small slow pool can produce a single exponential decay with a reduced apparent diffusion coefficient over the accessible B range. Thus the mono-exponential fit does not exclude the exchange/aggregation interpretation, nor does it rule out a mixture of free and hindered mAb populations.
minor comments (4)
  1. [Abstract and Section II.C] The symbol 'um' should be typeset as 'µm' throughout; additionally, the sentence 'The voxel sizes used in these experiments was...' contains a subject-verb agreement error.
  2. [Fig. 6 caption] The caption reports the diffusion coefficients as D = 6.8 × 10−5 and D = 3.7 × 10−5 mm2/s, while the text compares these to literature values in m2/s; please state both units consistently and define the conversion explicitly.
  3. [III.B, chemical shift assignments] The assignments of the three resonances to specific amino-acid side chains are tentative; consider labeling them as tentative or providing a reference spectrum or additional evidence to support the assignments.
  4. [III.B, line broadening] The statement that 'the spatial resolution of MRI is on the order 10 µm' appears without a citation and seems inconsistent with the claimed voxel dimensions; please clarify the intended meaning.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: measured ADC/T2 values are fit to standard equations and checked against external bulk values; self-citations are methodological only.

full rationale

The paper's central quantities are directly measured NMR observables. The apparent diffusion coefficient is obtained by fitting Eq. (1) to DW-REPRESS signal attenuation, and T2 is obtained by fitting Eq. (2) to echo-time series; neither fit parameter is subsequently relabeled as a prediction. The bulk mAb ADC (6.8e-5 mm2/s) is compared with published values for similar mAb solutions ("the current bulk mAb data is consistent with such diffusion coefficients"), providing an external benchmark. The water SSI control is an internal control, not an input constructed from the mAb result. The only self-citations (refs. 47, 49, 50) concern the pendant-drop apparatus and NMR diffusometry methods and are not load-bearing for the interfacial interaction claim. The inference from slower interface ADC and shorter T2 to mAb association/aggregation/exchange is an interpretation; its validity may be weakened by susceptibility gradients, partial-volume mixing, and lack of a slow-pool fraction, but that is a scientific-correctness concern, not circularity. No equation in the paper reduces to its own input, and no author-derived uniqueness or ansatz is imported to force a conclusion.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No arbitrary constants are fitted to force the conclusion. The paper relies on standard NMR signal equations and on two domain assumptions: the interface-centered voxel represents interfacial effects, and susceptibility mismatch does not dominate the relaxation and diffusion signals. The susceptibility assumption is the most fragile because the water SSI control demonstrates a measurable susceptibility effect.

assumptions (3)
  • standard math Standard NMR signal equations S(B)=S(0) exp(-B D) and S(TE)=S(0) exp(-TE/T2) describe the measured decays.
    Used in Section II.D to extract the apparent diffusion coefficient and T2 from signal attenuation data.
  • domain assumption The voxel centered at the interface samples mAb in the water phase near the interface in a way that is representative of interfacial effects on mAb.
    The authors acknowledge that the voxel is much larger than the nanometer-scale adsorbed layer, so the signal mixes interfacial and bulk mAb; the interpretation still attributes observed changes to interfacial interactions.
  • domain assumption Magnetic susceptibility mismatch between FC-43 oil and water is small enough that observed line broadening, T2, and diffusion changes are dominated by mAb dynamics rather than susceptibility gradients.
    Stated in Section III.B with refs 57-59. The authors' own water SSI control shows a susceptibility-related SSI decline, which makes this assumption load-bearing for the T2 and diffusion interpretation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Nuclear Magnetic Resonance Study of Monoclonal Antibodies Near an Oil-Water Interface." pith.science (2026). https://pith.science/paper/OD7CJWKL

@misc{pith2026250205563,
  author       = {Pith},
  title        = {Pith review of: Nuclear Magnetic Resonance Study of Monoclonal Antibodies Near an Oil-Water Interface},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OD7CJWKL}},
  note         = {Machine review of arXiv:2502.05563}
}
read the original abstract

Monoclonal antibodies (mAb) represent an important class of biologic therapeutics that can treat a variety of diseases including cancer, autoimmune disorders or respiratory conditions (e.g. COVID-19). However, throughout their development, mAb are exposed to air-water or oil-water interfaces that may trigger mAb partial unfolding that can lead to the formation of proteinaceous aggregates. Using a combination of dynamic surface tensiometry and spatially resolved 1D 1H NMR spectroscopy, this study investigates if adsorption of a model IgG2a-\k{appa} mAb to the oil-water interface affects its structure. Localized NMR spectroscopy was performed using voxels of 375 um, incrementally approaching the oil-water interface. Dynamic interfacial tension progressively decreases at the oil-water interface over time, confirming mAb adsorption to the interface. Localized NMR spectroscopy results indicate that, while the number of mAb-related chemical resonances and chemical shift frequencies remain unaffected, spectral line broadening is observed as voxels incrementally approach the oil-water interface. Moreover, the spin-spin (T2) relaxation of the mAb molecule was measured for a voxel centered at the interface and shown to be affected differentially across the mAb resonances, indicating a rotational restriction for mAb molecules due to presence of the interface. Finally, the apparent diffusion coefficient of the mAb for the voxel centered at the interface is lower than the bulk mAb. These results suggest that this specific mAb interacts with and may be in exchange with bulk mAb phase in the vicinity of the interface. As such, these localized NMR techniques offer the potential to probe and quantify alterations of mAb properties near interfacial layers.

Figures

Figures reproduced from arXiv: 2502.05563 by the authors.

Figure 1
Figure 1. FIG. 1. A schematic of the pendant drop method used for measuring the dynamic surface tensions [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 3
Figure 3. FIG. 3. A series of image sequences captured from pendant drop experiments illustrating the [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. (c), the signal attenuation is depicted as a function of diffusion weighting for water molecules in both the bulk and in the voxel centered at the oil-water interface. First, the resulting water diffusivity appears to be similar and does not seem to be influenced significantly by the presence of the interface. This result is not surprising as water molecules are not expected to behave differently near the oil-water … view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: FIG. 5 [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: FIG. 6. 1D [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: FIG. 7. 1D [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]
Figure 8
Figure 8. Figure 8: FIG. 8. The signal attenuation associated with different echo times for various chemical resonances [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

66 extracted references · 66 canonical work pages

  1. [1]

    M., Surana, R

    Weiner, L. M., Surana, R. & Wang, S. Monoclonal antibodies: versatile platforms for cancer immunotherapy. Nature Reviews Immunology 10, 317–327 (2010)

    work page 2010

  2. [2]

    A., Henderson, S

    Buss, N. A., Henderson, S. J., McFarlane, M., Shenton, J. M. & de Haan, L. Monoclonal antibody therapeutics: history and future. Current Opinion in Pharmacology 12, 615–622 (2012)

    work page 2012

  3. [3]

    I., Neefjes, J

    van Kasteren, S. I., Neefjes, J. & Ovaa, H. Creating molecules that modulate immune responses. Nat Rev Chem 2, 184–193 (2018)

    work page 2018

  4. [4]

    M., Jones, S

    Ecker, D. M., Jones, S. D. & Levine, H. L. The therapeutic monoclonal antibody market. mAbs 7, 9–14 (2015)

    work page 2015

  5. [5]

    Grilo, A. L. & Mantalaris, A. The increasingly human and profitable monoclonal antibody market. Trends Biotechnol. 37, 9–16 (2019)

    work page 2019

  6. [6]

    & Sautou, V

    Le Basle, Y., Chennell, P., Tokhadze, N., Astier, A. & Sautou, V. Physicochemical Stability of Monoclonal Antibodies: A Review. Journal of Pharmaceutical Sciences 109, 169–190 (2020)

    work page 2020

  7. [7]

    Rosenberg, A. S. Effects of Protein Aggregates: An Immunologic Perspective. in Proceedings of the 2005 AAPS Biotec Open Forum on Aggregation of Protein Therapeutics (2006)

    work page 2006

  8. [8]

    S., Verthelyi, D

    Rosenberg, A. S., Verthelyi, D. & Cherney, B. W. Managing Uncertainty: A Perspective on Risk Pertaining to Product Quality Attributes as They Bear on Immunogenicity of Therapeutic Proteins. J. Pharm. Sci. 101, 3560–3567 (2012)

    work page 2012

Show all 66 references
  1. [9]

    & Lang, D

    Va´zquez‐Rey, M. & Lang, D. A. Aggregates in Monoclonal Antibody Manufacturing Processes. Biotechnology and Bioengineering 108, 1494–1508 (2011)

    2011

  2. [10]

    Ricci, M. S. et al. In‐use physicochemical and microbiological stability of biological parenteral products. Am J Health Syst Pharm. 72, 396–407 (2015)

    2015

  3. [11]

    L., Shieh, I

    Leiske, D. L., Shieh, I. C. & Tse, M. L. A Method To Measure Protein Unfolding at an Air−Liquid Interface. Langmuir 32, 9930−9937 (2016)

    2016

  4. [12]

    B., Lewus, R., Bee, J

    Mehta, S. B., Lewus, R., Bee, J. S., Randolph, T. W. & Carpenter, J. F. Gelation of a monoclonal antibody at the silicone oilewater interface and subsequent rupture of the interfacial gel results in aggregation and particle formation. J Pharm Sci. 104, 1282–1290 (2015)

    2015

  5. [13]

    Li, Z. et al. Neutron Reflection Study of Surface Adsorption of Fc, Fab, and the Whole mAb. ACS Appl. Mater. Interfaces 9, 23202−23211 (2017)

    2017

  6. [14]

    Pan, F. et al. Interfacial Adsorption of Monoclonal Antibody COE 3 at the Solid/Water Interface. ACS Appl. Mater. Interfaces 10, 1306–1316 (2018)

    2018

  7. [15]

    Smith, C. et al. Antibody adsorption on the surface of water studied by neutron reflection. MABS 9, 466–475 (2017)

    2017

  8. [16]

    & Mueller, R

    Mahler, H.‐C., Senner, F., Maeder, K. & Mueller, R. Surface Activity of a Monoclonal Antibody. Journal of Pharmaceutical Sciences 98, 4525–4533 (2009)

    2009

  9. [17]

    S., Zhang, Z

    Tein, Y. S., Zhang, Z. & Wagner, N. J. Competitive Surface Activity of Monoclonal Antibodies and Nonionic Surfactants at the Air–Water Interface Determined by Interfacial Rheology and Neutron Reflectometry. Langmuir 36, 7814–7823 (2019)

    2019

  10. [18]

    Bee, J. S. et al. Production of particles of therapeutic proteins at the air–water interface during compression/dilation cycles. Soft Matter 8, (2012). 18

    2012

  11. [19]

    & Mahler, H

    Kiese, S., Papppenberger, A., Friess, W. & Mahler, H. C. Shaken, not stirred: Mechanical stress testing of an IgG1 antibody. J. Pharm. Sci. 97, (2008)

    2008

  12. [20]

    Lin, G. L. et al. Interfacial dilatational deformation accelerates particle formation in monoclonal antibody solutions. Soft Matter 12, (2016)

    2016

  13. [21]

    Kannan, A., Shieh, I. C. & Fuller, G. G. Linking aggregation and interfacial properties in monoclonal antibody‐surfactant formulations. Journal of Colloid and Interface Science 550, 128–138 (2019)

    2019

  14. [22]

    Li, Z. et al. Coadsorption of a Monoclonal Antibody and Nonionic Surfactant at the SiO2/Water Interface. ACS Appl. Mater. Interfaces 10, 44257−44266 (2018)

    2018

  15. [23]

    Kanthe, A. et al. No ordinary proteins: Adsorption and molecular orientation of monoclonal antibodies. Science Advances 7, 2873 (2021)

    2021

  16. [24]

    G., Skoda, M

    Couston, R. G., Skoda, M. W., Uddin, S. & van der Walle, C. F. Adsorption behavior of a human monoclonal antibody at hydrophilic and hydrophobic surfaces. mAbs 5, (2013)

    2013

  17. [25]

    Kalonia, C. K. et al. Protein adsorption and layer formation at the stainless steel–solution interface mediates shear‐induced particle formation for an IgG1 monoclonal antibody. Molecular pharmaceutics 15, 1319–1331 (2018)

    2018

  18. [26]

    Basu, P., krishnan, S., Thirumangalathu, R., Randolph, T. W. & Carpenter, J. F. IgG1 Aggregation and Particle Formation Induced by Silicone–water Interfaces on Siliconized Borosilicate Glass Beads: A Model for Siliconized Primary Containers. Journal of Pharmaceutical Sciences ...

    2013

  19. [27]

    Gerhardt, A. et al. Protein Aggregation and Particle Formation in Prefilled Glass Syringes. Journal of Pharmaceutical Sciences 103, 1601–1612 (2014)

    2014

  20. [28]

    Ruane, S. et al. Interfacial Adsorption of a Monoclonal Antibody and Its Fab and Fc Fragments at the Oil/Water Interface. Langmuir 35, 13543–13552 (2019)

    2019

  21. [29]

    C., Negulescu, P

    Kannan, A., Shieh, I. C., Negulescu, P. G., Chandran Suja, V. & Fuller, G. G. Adsorption and Aggregation of Monoclonal Antibodies at Silicone Oil–Water Interfaces. Molecular Pharmaceutics 18, 1656–1665 (2021)

    2021

  22. [30]

    Poppe, L. et al. Profiling Formulated Monoclonal Antibodies by 1H NMR Spectroscopy. Anal. Chem. 85, 9623−9629 (2013)

    2013

  23. [31]

    W., Delaglio, F., Schiel, J

    Arbogast, L. W., Delaglio, F., Schiel, J. E. & Marino, J. P. Multivariate Analysis of Two‐ Dimensional 1H, 13C Methyl NMR Spectra of Monoclonal Antibody Therapeutics To Facilitate Assessment of Higher Order Structure. Analytical Chemistry 89, 11839–11845 (2017)

    2017

  24. [32]

    J., Uddin, S., Walle, C

    Kheddo, P., Cliff, M. J., Uddin, S., Walle, C. F. van der & Golovanov, A. P. Characterizing monoclonal antibody formulations in arginine glutamate solutions using 1H NMR spectroscopy. mAbs 8, 1245–1258 (2016)

    2016

  25. [33]

    Brinson, R. G. et al. Enabling adoption of 2D‐NMR for the higher order structure assessment of monoclonal antibody therapeutics. mAbs 11, 94–105 (2019)

    2019

  26. [34]

    Brinson, R. G. & Marino, J. P. 2D J‐correlated proton NMR experiments for structural fingerprinting of biotherapeutics. Journal of Magnetic Resonance 307, 106581 (2019)

    2019

  27. [35]

    B., Rogers, G

    Poppe, L., Jordan, J. B., Rogers, G. & Schnier, P. D. On the Analytical Superiority of 1D NMR for Fingerprinting the Higher Order Structure of Protein Therapeutics Compared to Multidimensional NMR Methods. Anal. Chem. 87, 5539−5545 (2015)

    2015

  28. [36]

    B., DePaz, R

    Taraban, M. B., DePaz, R. A., Lobo, B. & Yu, Y. B. Water proton NMR: a tool for protein aggregation characterization. Analytical chemistry 89, 5494–5502 (2017)

    2017

  29. [37]

    Chen, H.‐Y., Wilson, C. B. & Tycko, R. Enhanced spatial resolution in magnetic resonance 19 imaging by dynamic nuclear polarization at 5 K. Proceedings of the National Academy of Sciences 119, e2201644119 (2022)

    2022

  30. [38]

    Time‐independent point‐spread function for NMR microscopy

    McFarland, E. Time‐independent point‐spread function for NMR microscopy. Magnetic resonance imaging 10, 269–278 (1992)

    1992

  31. [39]

    Perevozchikova, T., Nanda, H., Nesta, D. P. & Roberts, C. J. Protein adsorption, desorption, and aggregation mediated by solid‐liquid interfaces. Journal of pharmaceutical sciences 104, 1946– 1959 (2015)

    2015

  32. [40]

    Strickley, R. G. & Lambert, W. J. A review of Formulations of Commercially Available Antibodies. Journal of Pharmaceutical Sciences 110, 2590‐2608.e56 (2021)

    2021

  33. [41]

    Jiskoot, W., Hawe, A., Menzen, T., Volkin, D. B. & Crommelin, D. J. A. Ongoing Challenges to Develop High Concentration Monoclonal Antibody‐based Formulations for Subcutaneous Administration: Quo Vadis? Journal of Pharmaceutical Sciences 111, 861–867 (2022)

    2022

  34. [42]

    Wang, S. et al. Stabilizing two IgG1 monoclonal antibodies by surfactants: Balance between aggregation prevention and structure perturbation. European Journal of Pharmaceutics and Biopharmaceutics 114, (2017)

    2017

  35. [43]

    Zhang, Z. et al. Adsorption of non‐ionic surfactant and monoclonal antibody on siliconized surface studied by neutron reflectometry. Journal of Colloid and Interface Science 584, 429–438 (2021)

    2021

  36. [44]

    Katz, J. S. et al. Emerging Challenges and Innovations in Surfactant‐mediated Stabilization of Biologic Formulations. Journal of Pharmaceutical Sciences 111, 919–932 (2022)

    2022

  37. [45]

    Duvaud, S. et al. Expasy, the Swiss Bioinformatics Resource Portal, as designed by its users. Nucleic Acids Res 49, W216–W227 (2021)

    2021

  38. [46]

    Bjellqvist, B. et al. The focusing positions of polypeptides in immobilized pH gradients can be predicted from their amino acid sequences. Electrophoresis 14, 1023–1031 (1993)

    1993

  39. [47]

    & Shoele, K

    Mohammadigoushki, H. & Shoele, K. Cavitation Rheology of Model Yield Stress Fluids Based on Carbopol. Langmuir 39, 7672–7683 (2023)

    2023

  40. [48]

    & Gruetter, R

    Tkáč, I., Starčuk, Z., Choi, I.‐Y. & Gruetter, R. In vivo 1H NMR spectroscopy of rat brain at 1 ms echo time. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 41, 649–656 (1999)

    1999

  41. [49]

    Holder, S., Grant, S. C. & Mohammadigoushki, H. Nuclear Magnetic Resonance Diffusometry of Linear and Branched Wormlike Micelles. Langmuir 37, 3585−3596 (2021)

    2021

  42. [50]

    Scigliani, A., Grant, S. C. & Mohammadigoushki, H. Probing self‐assembled micellar topologies via micro‐scale diffusive dynamics of surfactants. Journal of Colloid and Interface Science 642, 565–573 (2023)

    2023

  43. [51]

    Bushberg, J. T. & Boone, J. M. The Essential Physics of Medical Imaging. (Lippincott Williams & Wilkins, 2011)

    2011

  44. [52]

    & Byrd, R

    Chao, F.‐A. & Byrd, R. A. Protein dynamics revealed by NMR relaxation methods. Emerging topics in life sciences 2, 93–105 (2018)

    2018

  45. [53]

    J., Dratz, E

    Deese, A. J., Dratz, E. A., Hymel, L. & Fleischer, S. Proton NMR T1, T2, and T1 rho relaxation studies of native and reconstituted sarcoplasmic reticulum and phospholipid vesicles. Biophysical journal 37, 207–216 (1982)

    1982

  46. [54]

    NMR relaxation and protein mobility

    Wagner, G. NMR relaxation and protein mobility. Current opinion in structural biology 3, 748–754 (1993)

    1993

  47. [55]

    J., Price, W

    Easteal, A. J., Price, W. E. & Woolf, L. A. Diaphragm cell for high‐temperature diffusion measurements. Tracer diffusion coefficients for water to 363 K. Journal of the Chemical Society, Faraday Transactions 1: Physical Chemistry in Condensed Phases 85, 1091–1097 (1989). 20

    1989

  48. [56]

    Nguyen, D., Chen, C., Pettitt, B. M. & Iwahara, J. NMR Methods for Characterizing the Basic Side Chains of Proteins: Electrostatic Interactions, Hydrogen Bonds, and Conformational Dynamics. Methods Enzymol 615, 285–332 (2019)

    2019

  49. [57]

    Fabry, M. E. & San George, R. C. Effect of magnetic susceptibility on nuclear magnetic resonance signals arising from red cells: a warning. Biochemistry 22, 4119–4125 (1983)

    1983

  50. [58]

    L., Peck, T

    Olson, D. L., Peck, T. L., Webb, A. G., Magin, R. L. & Sweedler, J. V. High‐resolution microcoil 1H‐NMR for mass‐limited, nanoliter‐volume samples. Science 270, 1967–1970 (1995)

    1995

  51. [59]

    & Ruiz‐Suarez, J

    Gutiérrez‐Mejía, F. & Ruiz‐Suarez, J. AC magnetic susceptibility at medium frequencies suggests a paramagnetic behavior of pure water. Journal of magnetism and magnetic materials 324, 1129–1132 (2012)

    2012

  52. [60]

    A., Shire, S

    Pindrus, M. A., Shire, S. J., Yadav, S. & Kalonia, D. S. The effect of low ionic strength on diffusion and viscosity of monoclonal antibodies. Molecular Pharmaceutics 15, 3133–3142 (2018)

    2018

  53. [61]

    & Ardekani, A

    Kumar, G. & Ardekani, A. M. Concentration‐Dependent Diffusion of Monoclonal Antibodies: Underlying Mechanisms of Anomalous Diffusion. Molecular Pharmaceutics 21, 2212– 2222 (2024)

    2024

  54. [62]

    X., Williamson, N

    Cai, T. X., Williamson, N. H., Ravin, R. & Basser, P. J. Disentangling the effects of restriction and exchange with diffusion exchange spectroscopy. Frontiers in physics 10, 805793 (2022)

    2022

  55. [63]

    Yearley, E. J. et al. Small‐angle neutron scattering characterization of monoclonal antibody conformations and interactions at high concentrations. Biophysical journal 105, 720–731 (2013)

    2013

  56. [64]

    Wood, C. V. et al. Kinetics and competing mechanisms of antibody aggregation via bulk‐ and surface‐mediated pathways. Journal of Pharmaceutical Sciences 109, 1449–1459 (2020)

    2020

  57. [65]

    Ball, V. et al. Kinetics of exchange processes in the adsorption of proteins on solid surfaces. Proceedings of the National Academy of Sciences 91, 7330–7334 (1994)

    1994

  58. [66]

    & Schaaf, P

    Ball, V., Bentaleb, A., Hemmerle, J., Voegel, J.‐C. & Schaaf, P. Dynamic aspects of protein adsorption onto titanium surfaces: mechanism of desorption into buffer and release in the presence of proteins in the bulk. Langmuir 12, 1614–1621 (1996)

    1996

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.