Prediction retention

Jinno, K. and Yokoyama, Y., Retention prediction for polymer additives in reversed-phase liquid chromatography, J. Chromatogr., 550, 325, 1991. 

Baba, Y., Fukuda, M., and Yoza, N., Computer-assisted retention prediction system for oligonucleotides in gradient anion-exchange chromatography, /. Chromatogr., 458, 385, 1988. 

The qualitative analysis of retention behaviour in liquid chromatography has now become possible. Quantitative retention-prediction is, however, still difficult the prediction of retention time and the optimization of separation conditions based on physicochemical properties have not yet been completely successful. One reason is the lack of an ideal stationary phase material. The stationary phase material has to be stable as part of an instrument, and this is very difficult to achieve in normal-phase liquid chromatography because the moisture in organic solvents ages the silica gel. 

Dnring the infancy of IPC, retention prediction commonly faced trial-and-error procednres that attempted to make the problem univariate, holding all experimental conditions constant except one. This one-at-a-time changing of parameters, without regard to parameter interactions, is still practiced and may, in a time consuming way, improve performance. The description of the dependence of retention on the mobile phase composition parameters is the focus of interest of model makers becanse an a priori retention prediction is highly desirable. Optimization is finding the nnique combination of values of adjustable parameters that yields the best performance possible for a set of requirements. 

Pore size was also found to be the main factor affecting separation selectivity of C18 columns from different manufacturers, compared to evaluate the applicability of sequence-specific retention calculator peptide retention prediction algorithms. Differences in end capping chemistry did not play a major role while the introduction of embedded polar groups to the C18 functionality enhanced the retention of peptides containing hydrophobic amino acid residues with polar groups [6]. 

The above-given Martin equation form the basis for the Kovats retention index system in gas chromatography as well as for several HPLC retention prediction schemes. It must be noted here that the relationships between retention parameters and carbon numbers are usually linear at some limited range of the aliphatic chain length up to 6-8 carbon atoms in reversed-phase HPLC [491. 

Substituent electronic constants used to derive simple QSRR for real retention prediction potency have seldom succeeded. A wider application in that respect found the Hansch substituent hydrophobic constants, n 8], and Dross et al. [64] or Hansch and Leo [65] fragmental hydrophobic constants, /. The sums of these constants (plus corrections due to intramolecular interactions) account for the retention in reversed-phase liquid chromatographic systems [7,12). 

In Eq. (11.10) kp is the retention parameter of a parent compound, is the corresponding value for the derivative carrying n substituents and r, are retention increments due to individual substituents i. Having appropriate values for functional groups of interest one needs only to determine the retention of the parent structure and can next calculate the retention of a derivative. To get reliable predictions, the correction factors are introduced in Eq. (11.10) accounting for mutual interactions between substituents (electronic, steric, hydrogen bonding) [41,70], In cases of polyfunctional analytes the interactions between substituents make retention predictions of rather limited value. 

Multivariate methods of data analysis were first applied in chromatography for retention prediction purposes [7. More recently, principal component analysis (PCA), correspondence factor analysis (CFA) and spectral mapping analysis (SMA) have been employed to objectively cla.ssify. stationary phase materials according to the retention... 

QSRR are employed by analytical chemists to help to identify unknown members of individual classes of analytes of pharmacological, toxicolr ical. environmental or chemical interest. At the same time. QSRR of g(K)d retention prediction potency helps to identify. structural descriptors of analytes, which also provide an efficient prediction of properties other than the chromatographic ones. This way the chromatographic. systems are identified which allows for a fast and convenient evaluation of analyte hydrophobicity. 

From the viewpoint of application, QSRR equations in TLC are mainly used for retention prediction. The explanation of the separation mechanism awaits further investigation. With the application of various statistical methods, it is possible to select the primary retention-effect factors from many solute related factors which will offer explanations of separation mechanisms. 

Hancock,T, Put, R., Coomans, D., Vander Heyden, Y. and Everingham, Y. (2005) A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies. Chemom. InteU. Lab. Syst., 76, 185—196. 

Valko, K. (1995) Retention prediction of pharmaceutical compounds. J Chromatogr Libr. 57,4792. 

Hamoir, T. Massart, D.L. Retention prediction for p-adrenergic blocking drugs in normal-phase liquid chromatography. J.ChromatogrA, 1994, 673, 1-10 [column temp 30 cyanopropyl column also ace-butolol, alprenolol, bunitrolol, bupranolol, carazolol, mepindolol, metipranolol, metoprolol, nadolol, oxprenolol, penbutolol, pindolol, practolol, prenalterol, propranolol, tertatolol]... 

Further utility of relations between structure and retention will be dependent on (a) acquisition of a significant number of reference compounds for comparative purposes and (b) advancing retention prediction capabilities. A recent successful use of modern computational techniques [140] to predict retention of aromatic compounds from certain molecular parameters appears indicative of this trend. 

C. Isocratic Retention Predicted from Gradient Runs... 

Isocratic retention, prediction from gradient runs, 204-205 errors in, 209-211 ion-exchange HPLC, 206-208 reversed-phase HPLC 205-206 Isocmtic retention data, relationship to... 

Microcomputer-Assisted Retention Prediction in Reversed-Phase Liquid Chromatography... 

In practice, it is difficult to achieve this because standard materials may not be commercially available or are highly toxic and theM-fore can create new pollution problems. To overcome this disadvantage, an alternate approach has been proposed by ilie authors (6-10). That attempt is retention prediction. If retention of solutes can be predicted at appropriate experimental condition, optimization procedure can be more easily attained in a short time. 

In this contribution, we will describe the basic approach to construct the retention prediction system in reversed-phase LC for alkyl-benzenes, polycyclic airomatic hydrocau bons (PAHs) aind polau group substituted benzenes, baised on the use of sudi established relationships between retention and physicodiemlcal parameters of these compounds. The system has been constructed on a 16—bit microcomputer, and the application for optimization of sepairation conditions will be demonstrated. 

JlNNO AND KAWASAKI Microcomputer-Assisted Retention Prediction... 

Multiple regression analyses were performed by the use of aJlEECOM 800 computer (Mitsubishi Electric, Co., Ltd., Osaka, Japan). The computer system for retention prediction was a 16-bit mioixjcomputer NEC 9801 (Nippon Electric, Co., Ltd., Tokyo, Japan), and the programs were written in BASIC language. 

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