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A method to determine confidence limits for the area, sedimentation coefficient, and molar mass of individual peaks from a SEDFIT c(s) distribution.

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Title: A method to determine confidence limits for the area, sedimentation coefficient, and molar mass of individual peaks from a SEDFIT c(s) distribution.
Authors: Philo, John S.1 (AUTHOR) jphilo@mailway.com
Source: European Biophysics Journal. Aug2025, Vol. 54 Issue 6, p321-329. 9p.
Subjects: Sedimentation analysis, Confidence intervals, Molar mass, Protein drugs, Statistical significance
Abstract: The c(s) sedimentation distribution method implemented in the program SEDFIT (Biophys J 78:1606–1619, 2000) is widely used for analyzing sedimentation velocity data, and is particularly useful for detecting low levels of aggregates or other minor components in protein pharmaceuticals. Unfortunately, this method does not provide confidence limits for the area or sedimentation coefficient of each resolved peak, which makes it difficult to assess whether differences from one sample to another are statistically significant. This paper describes a new method to obtain such confidence limits using the program SVEDBERG (Biophys J 72:435–444, 1997) by automatically translating a saved c(s) distribution into a discrete species model where the molar masses of all species are constrained to keep the f/f0 ratio constant for all species. This approach also then allows relaxing the constant f/f0 ratio constraint on one or more minor species to determine their true molar masses (independent of assumptions about hydrodynamic shape), and also determining the confidence limits on that molar mass. It is demonstrated that this approach will work for samples containing up to five minor components (six total species), and even when multiple minor species are present at levels of only a few tenths of 1%. [ABSTRACT FROM AUTHOR]
Copyright of European Biophysics Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A method to determine confidence limits for the area, sedimentation coefficient, and molar mass of individual peaks from a SEDFIT c(s) distribution.
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  Data: <searchLink fieldCode="AR" term="%22Philo%2C+John+S%2E%22">Philo, John S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jphilo@mailway.com</i>
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  Data: <searchLink fieldCode="JN" term="%22European+Biophysics+Journal%22">European Biophysics Journal</searchLink>. Aug2025, Vol. 54 Issue 6, p321-329. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Sedimentation+analysis%22">Sedimentation analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence+intervals%22">Confidence intervals</searchLink><br /><searchLink fieldCode="DE" term="%22Molar+mass%22">Molar mass</searchLink><br /><searchLink fieldCode="DE" term="%22Protein+drugs%22">Protein drugs</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+significance%22">Statistical significance</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: The c(s) sedimentation distribution method implemented in the program SEDFIT (Biophys J 78:1606–1619, 2000) is widely used for analyzing sedimentation velocity data, and is particularly useful for detecting low levels of aggregates or other minor components in protein pharmaceuticals. Unfortunately, this method does not provide confidence limits for the area or sedimentation coefficient of each resolved peak, which makes it difficult to assess whether differences from one sample to another are statistically significant. This paper describes a new method to obtain such confidence limits using the program SVEDBERG (Biophys J 72:435–444, 1997) by automatically translating a saved c(s) distribution into a discrete species model where the molar masses of all species are constrained to keep the f/f0 ratio constant for all species. This approach also then allows relaxing the constant f/f0 ratio constraint on one or more minor species to determine their true molar masses (independent of assumptions about hydrodynamic shape), and also determining the confidence limits on that molar mass. It is demonstrated that this approach will work for samples containing up to five minor components (six total species), and even when multiple minor species are present at levels of only a few tenths of 1%. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of European Biophysics Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00249-025-01741-3
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        Text: English
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      – SubjectFull: Sedimentation analysis
        Type: general
      – SubjectFull: Confidence intervals
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      – SubjectFull: Molar mass
        Type: general
      – SubjectFull: Protein drugs
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              Text: Aug2025
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