High-Throughput Self-Interaction Chromatography: Applications in Protein Formulation Prediction

Abstract

Purpose. Demonstrate the ability of an artificial neural network (ANN), trained on a formulation screen of measured second virial coefficients to predict protein self-interactions for untested formulation conditions. Materials and Methods. Protein self-interactions, quantified by the second virial coefficient, B 22 , were measured by self-interaction chromatography (SIC). The B 22 values of lysozyme were measured for an incomplete factorial distribution of 81 formulation conditions of the screen components. The influence of screen parameters (pH, salt and additives) on B 22 value was modeled by training an ANN using B 22 value measurements. After training, the ANN was asked to predict the B 22 value for the complete factorial of parameters screened (12,636 conditions). Twenty of these predicted values (distributed throughout the range of predictions) were experimentally measured for comparison. Results. The ANN was able to predict lysozyme B 22 values with a significance of p<0.0001 and RMSE of 2.6×10 −4 mol ml/g 2 . Conclusions. The results indicate that an ANN trained on measured B 22 values for a small set of formulation conditions can accurately predict B 22 values for untested formulation conditions. As a measure of protein-protein interactions correlated with solubility, B 22 value predictions based on a small screen may enable rapid determination of high solubility formulations.

The osmotic second virial coefficient for protein self-interaction: Use and misuse to describe thermodynamic nonideality

“…Despite the initial popularity of light scattering for determining second virial coefficients as a means of identifying conditions conducive to protein crystallization [8−10], the method has been largely supplanted by self-interaction chromatography [26][27][28][29][30][31][32][33][34]79] − a technique more compatible M A N U S C R I P T…”

High-throughput screening for developability during early-stage antibody discovery using self-interaction nanoparticle spectroscopy

“…This is problematic because weak antibody self-and cross-interactions are often responsible for aggregation and polyreactivity, respectively. 6,7,12,[14][15][16][17][18][19] Nevertheless, numerous assays such as self-interaction chromatography (SIC) [20][21][22][23][24][25] and cross-interaction chromatography (CIC) [26][27][28] have been designed to identify these possibly troublesome antibodies early in the discovery program to avoid downstream issues. In these chromatography assays, increased retention of mAbs passing through a column conjugated with identical mAbs or a pool of polyclonal serum antibodies is indicative of attractive self-or cross-interactions, respectively.…”

Protein solubilization: A novel approach

“…This is an established method to train and prevent over-fitting of an ANN and is described in Bishop’s book, Neural Networks for Pattern Recognition [27]. In a previous publication [28], our lab compared this method to that of a standard general linear model (GLM) and found the ANN to exhibit reduced prediction error compared to the GLM.…”