Predictive quantitative structure retention relationship models for ion-exchange chromatography

Abstract

SummaryA database of probe molecules and their reported ion-exchange chromatographic data was collected from the literature, aflerwhich an extensive set of both traditional and novel molecular property descriptors were computed for each probe molecule. A genetic algorithm/partial least squares (GA/PLS) approach was then used on the data to create a predictive Quantitative Structure-Retention Relationship (QSRR) model of retention where a subset of the original data was used for training and the remainder of the data as a test set. The utility of this model was demonstrated by using it to predict the chromatographic behavior of compounds not included in the training set. The results presented in this paper demonstrate the utility of modern QSRR modeling to predict chromatographic behavior in ion-exchange systems.

Unique selectivity windows using selective displacers/eluents and mobile phase modifiers on hydroxyapatite

“…A Jupiter 5 m C4 300A column (4.6 mm × 50 mm) was purchased from Phenomenex (Torrance, CA). Ribonuclease A from bovine pancreas (RNaseA), ribonuclease B from bovine pancreas (RNaseB), ␣-chymotrypsinogen A from bovine pancreas (␣-ChyA), cytochrome C from equine heart (CytC), lysozyme from chicken egg white (Lys), conalbumin from chicken egg white (Conal), hemoglobin from bovine blood (Hemo), myoglobin from equine heart (Myo), avidin from chicken egg white, subtilisin A from Bacillus, elastase from porcine pancreas, papain from papaya latex, bromelain from pineapple stem, alcohol dehydrogenase from equine liver, trypsinogen from bovine pancreas, catalase from bovine liver, aprotinin from bovine lung, aconitase from porcine heart, albumin from bovine serum, neomycin sulfate (displacer 1), paromomycin sulfate (2), bekanamycin sulfate (3), amikacin sulfate (4), spermine (5), bis(hexamethylene)triamine (7), spermidine (8), 1,4-bis(3-aminopropyl)piperazine (9), diethylenetriamine (10), 4,7,10-trioxa-1,13-tridecanediamine (11), N,Ndiethyl-1,3-propanediamine (12), N,N-diethyldiethylenetriamine (13), 2-(2-aminoethylamino)ethanol (14), spectinomycin dihydrochloride pentahydrate (15), l-arginine methyl ester dihydrochloride (16), l-lysine methyl ester dihydrochloride (17), N-hexylethylenediamine (18), piperazine (19), cyclohexylamine (20), acetic acid (21), malonic acid (22), succinic acid (23), adipic acid (24), isocitric acid lactone (25), trans-aconitic acid (26), 1,2,4-butanetricarboxylic acid (27), 1,2,3,4-butanetetracarboxylic acid (28), glycine (29), 3-guanidinopropionic acid (30), 5-aminovaleric acid (31), pantothenic acid (32), aspartic acid (33), l-␤-homoglutamic acid hydrochloride (34), guanidinosuccinic acid (35), l-2,3-diaminopropionic acid hydrochloride (36), lysine (37), arginine (38), meso-2,3,-diaminosuccinic acid (39), ethylenediaminetetrapropionic acid (40), glycerol (41), threitol (42), adonitol (43), dulcitol (44), malic acid (45), tartaric acid (46), mucic acid …”

“…Mass action selective displacers typically have an affinity for the resin that lies between that of the two solutes being separated and can be readily predicted using the steric mass action formalism [23,24]. While selective displacement chromatography has been successfully employed in ion exchange systems [5,9,11,[14][15][16][17][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38], to date it has not been applied to CHA or any other multi-modal resin system.…”

Influence of structure properties on protein–protein interactions—QSAR modeling of changes in diffusion coefficients

“…QSARs still are mainly applied for small molecules during the development of bioactive compounds (Mazza et al, 2001). During the last two decades, QSAR models were successfully used to describe and to predict the experimental behavior of proteins and complex biopharmaceutical products during ionexchange (Mazza et al, 2001(Mazza et al, , 2002, mixed-mode (Chung et al, 2010;Yang et al, 2007), and hydrophobic interaction (Ladiwala et al, 2006) chromatography. Buyel et al (2013) used QSAR to predict the chromatographic separation of tobacco host cell proteins out of a complex feedstock.…”

To catch a thief: Opening a CTO and its effect on the contralateral donor artery FFR

“…Mass action selective displacers operate by having an affinity for the resin which lies between that of the two solutes being separated, and their efficacy can be readily predicted using the steric mass action formalism (Brooks and Cramer, 1992, 1996). While selective displacement chromatography has been successfully employed in ion exchange systems (Barnthouse et al, 1998; Gadam and Cramer, 1994; Gallant et al, 1996; Jayaraman et al, 1993, 1995; Kundu et al, 1995, 1997; Ladiwala et al, 2003; Liu et al, 2006; Mazza et al, 2002; Moore et al, 2005; Rege et al, 2004; Shukla et al, 1998; Tugcu et al, 2002, 2003), to date only a preliminary study has been carried out in hydroxyapatite resin systems (Morrison et al, 2010).…”