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Pharmaceutical Research, Vol. 26, No. 2, February 2009 (# 2008) DOI: 10.1007/s11095-008-9737-6
Research PaperHigh-Throughput Self-Interaction Chromatography: Applications in Protein
Formulation Prediction
David H. Johnson,1,4 Arun Parupudi,2 W. William Wilson,2 and Lawrence J. DeLucas3
Received July 8, 2008; accepted September 24, 2008; published online October 16, 2008
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, B22, were
measured by self-interaction chromatography (SIC). The B22 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 B22 value was modeled by training an ANN using B22 value measurements. After training, the ANN was asked to predict the B22 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 B22 values with a significance of p<0.0001 and RMSE of2.6104 mol ml/g2.
Conclusions. The results indicate that an ANN trained on measured B22 values for a small set of formulation conditions can accurately predict B22 values for untested formulation conditions. As a measure of proteinprotein interactions correlated with solubility, B22 value predictions based on a small screen may enable rapid determination of high solubility formulations.
KEY WORDS: artificial neural network; formulation development; physical protein stability; self-interaction chromatography; systematic screening.
INTRODUCTION
A proteins interaction with itself and with other proteins affects important characteristics such as its solubility (1), aggregation (2) and ability to crystallize (3). Measurement of second virial coefficients, B22 (4), provides one method to quantify protein interactions at the molecular level. B22 is a
measure of the entirety of two body protein self-interactions that includes contributions from excluded volume, electro-static factors (attractive and repulsive) and hydrophobic interactions. In terms of McMillanMeyer solution theory (5), B22 is related to a potential of mean force which describes all of the interaction forces between protein molecules in a dilute solution. Positive B22 values correspond to net repulsive forces of the protein and are correlated with increased protein solubility in...