Optimization of Ion-Exchange Protein Separations Using a Vector Quantizing Neural Network

Abstract

In this work, a previously proposed methodology for the optimization of analytical scale protein separations using ion-exchange chromatography is subjected to two challenging case studies. The optimization methodology uses a Doehlert shell design for design of experiments and a novel criteria function to rank chromatograms in order of desirability. This chromatographic optimization function (COF) accounts for the separation between neighboring peaks, the total number of peaks eluted, and total analysis time. The COF is penalized when undesirable peak geometries (i.e., skewed and/or shouldered peaks) are present as determined by a vector quantizing neural network. Results of the COF analysis are fit to a quadratic response model, which is optimized with respect to the optimization variables using an advanced Nelder and Mead simplex algorithm. The optimization methodology is tested on two case study sample mixtures, the first of which is composed of equal parts of lysozyme, conalbumin, bovine serum albumin, and transferrin, and the second of which contains equal parts of conalbumin, bovine serum albumin, tranferrin, beta-lactoglobulin, insulin, and alpha -chymotrypsinogen A. Mobile-phase pH and gradient length are optimized to achieve baseline resolution of all solutes for both case studies in acceptably short analysis times, thus demonstrating the usefulness of the empirical optimization methodology.

Doehlert uniform shell designs and chromatography

“…The main optimized parameters considered in the development of sample protocols have been the reaction yield [35], the recovery [36][37][38] and the analytical signal intensity [39,40]. The majority of the studies on the improvement of the instrumental conditions focused generally on the optimization of the mobile phase [41][42][43][44][45][46][47]. Other instrumental parameters considered in a lesser extent are the flow rate [41,46,48], the analysis time [48], and column temperature [46].…”

Optimization of Extraction Parameters for Enhanced Production of Ovotransferrin from Egg White for Antimicrobial Applications

“…NMDS is a single-objective optimization approach for searching the space of n-dimensional real vectors [ 20 ]. Earlier reports have shown that NMDS has been successfully applied for the modelling and optimization of a variety of chemical and biological processes [ 21 , 22 ]. Since it only uses the values of the objective functions without any derivative information (explicit or implicit), it falls into the general class of direct search methods [ 23 , 24 ].…”

Application of Uniform Design Genetic Algorithm in Separation Condition Optimization of 14 Drugs

“…The complexities of real samples have showed the necessity of optimization of chromatographic separation conditions [2].…”