Analysis of nonideality: insights from high concentration simulations of sedimentation velocity data

Abstract

The Aviv fluorescence detection system (Aviv-FDS) has allowed the performance of sedimentation velocity experiments on therapeutic antibodies in highly concentrated environments like formulation buffers and serum. Methods were implemented in the software package SEDANAL for the analysis of nonideal, weakly associating AUC data acquired on therapeutic antibodies and proteins (Wright et al. Eur Biophys J 47:709–722, 2018, Anal Biochem 550:72–83, 2018). This involved fitting both hydrodynamic, ks, and thermodynamic, BM1, nonideality where concentration dependence is expressed as s = so/(1 + ksc) and D = Do(1 + 2BM1c)/(1 + ksc) and so and Do are values extrapolated to c = 0 (mg/ml). To gain insight into the consequences of these phenomenological parameters, we performed simulations with SEDANAL of a monoclonal antibody as a function of ks (0–100 ml/g) and BM1 (0–100 ml/g). This provides a visual understanding of the separate and joint impact of ks and BM1 on the shape of high-concentration sedimentation velocity boundaries and the challenge of their unique determination by finite element methods. In addition, mAbs undergo weak self- and hetero-association (Yang et al. Prot Sci 27:1334–1348, 2018) and thus we have simulated examples of nonideal weak association over a wide range of concentrations (1–120 mg/ml). Here we demonstrate these data are best analyzed by direct boundary global fitting to models that account for ks, BM1 and weak association. Because a typical clinical dose of mAb is 50–200 mg/ml, these results have relevance for biophysical understanding of concentrated therapeutic proteins.

Determination of specific and non-specific protein–protein interactions for beta-lactoglobulin by analytical ultracentrifugation and membrane osmometry experiments

“…Furthermore, a recent analysis of self-interaction and crossterm interactions for therapeutic antibodies in a highly concentrated environment via SV-AUC experiments by Correia et al has shown that the absolute values of the retrieved individual cross-terms do not show significant variations in direct comparison (variation by 5-10%). 10,50 Such small differences cannot be resolved from our SV-AUC data. In this context, it was further shown that an extensive systematic study via SV-AUC over a broad range of protein loading concentrations with different detection systems, such as the Aviv AU-FDS optical system, is required to study the wide range of protein interactions, which eventually provides insight into self and crossterm interactions.…”

“…dimeric and trimeric oligomers of a protein) in solution. 45,50 Correia et al showed the analysis of SV-AUC experiments with respect to concentration-dependent sedimentation and diffusion coefficient and expressed the interactions constants as matrices, with elements k s, ij and B 22, ij . 50 The individual terms represent self-interactions (diagonal matrix elements) and cross-correlations (non-diagonal matrix elements) between the individual defined species.…”

“…In the case of two species in solution ( i.e. monomers and dimers), the concentration-dependent sedimentation coefficient s 1 ( c ) of the first species is written as: 50 The sedimentation coefficient at infinite dilution of the first species is s 1 0 . The mass concentration of the two individual species are denoted c 1 and c 2 , respectively.…”

“…The mass concentration of the two individual species are denoted c 1 and c 2 , respectively. Furthermore, the respective concentration-dependent diffusion coefficient D 1 ( c ) is expressed as: 50 The diffusion coefficient at infinite dilution of the first species is D 0 1 . The molar masses of the two individual species are denoted as M P,1 and M P,2 , respectively.…”

Analytical ultracentrifugation: still the gold standard that offers multiple solutions

“…Since β-lactoglobulin is one of the most abundant components of cow and goat milk, information on their solution properties and interactions are of high significance. Another example of AUC-FDS is in the study of monoclonal antibodies (mAbs) under high non-ideal conditions reported by Correia et al (Correia et al 2020). Their analysis of simulated data provides insights into the improvements in the SEDNAL data analysis package.…”