aciej Szaleniec a,∗, Agnieszka Dudzik a, Marzena Pawul a, Bartłomiej Kozik b
Instytut of Catalysis and Surface Chemistry Polish Academy of Science, Niezapominajek 8, 30-239 Kraków, PolandDepartment of Organic Chemistry, Jagiellonian University, Ingardena 3, 30-060 Kraków, Poland
r t i c l e i n f o
rticle history:eceived 27 January 2009eceived in revised form 28 June 2009ccepted 1 July 2009vailable online 7 July 2009
a b s t r a c t
The Quantitative Structure Retention Relationship (QSRR) modeling techniques are employed for predic-tion of retention behavior of chiral secondary alkylaromatic and alkylheterocyclic alcohols, derivatives of1-phenylethanol, separated on Chiracel OB-H column. Genetic algorithms and neural networks are used toobtain models predicting Retention Order Index (ROI) (R2 = 0.99), selectivity ROI log ˛ (R2 = 0.93) as well asretention factors (log k) for two types of mobile phases (90/10 and 85/15 n-hexane/isopropanol—R2 = 0.97
and 0.95). Additionally, a model that predicts log k for both mobile phase in function of i-PrOH concentra-tion is developed (R2 = 0.97). HOMO energy turns out to be the most important parameter in description oflog k while mixed steric-electrostatic interactions with chiral OH group and furan ring are responsible forthe chiral recognition. The models are used to assess the stereoselectivity of ethylbenzene dehydrogenase(EBDH), which catalyzes stereospecific syntheses of the investigated compounds. The high stereoselec-tivity of the enzyme is confirmed but reversion of EBDH enantioselectivity is predicted to take place in
′-biph
enetic algorithm the biosynthesis of 1-[1,1
. Introduction
The identification of chiral isomers of biologically active com-ounds is a crucial issue in modern pharmacology, toxicology andne chemical sciences. The standard approach to identification of
he optical isomer present in the sample is to perform separationsn chiral stationary phase (CSP) with the help of chiral standards.uch an analysis usually provides unequivocal answer to the ques-ion which isomer is present in the reaction mixture. However, inractice the analyst is very often confronted with the lack of appro-riate standard compounds. In such a case one can resort to theodeling of retention behavior. Having knowledge from the chro-atographic experiments with a group of standards it is possible
o predict retention of various new compounds. Well establisheduantitative Structure Retention Relationship (QSRR) model can beasily used for assessing the retention of in silico prepared analyteshich not only provides valuable information but can save a lot
f effort and resources otherwise spent on custom synthesis andethod development.
In case of non-chiral chromatographic separations one can resorto the QSRR approach [1–4]. Fortunately, chemical interactions
in most commonly used reversed-phase systems are fairly wellunderstood. Many retention prediction models were constructedutilizing various sets of chemical and topologic descriptors, suchas the classical Linear Solvatation Energy Relationship (LSER) the-ory developed by Taft and co-workers [5,6]. In these approachesone can usually use 2D descriptors that do not discriminate thespatial configuration of the molecule. Investigation of chiral sep-arations is much more complicated than for achiral systems as itrequires parameters that can address stereoisomerism of investi-gated compounds. Nevertheless, LSER theory was used to describethe separation factor (˛) and explain, based on combined achi-ral and chiral chromatographic results, which types of interactionsare involved in the chiral recognition mechanism [7,8]. Moreover,the chiral retention factor was also correlated with non-chiralchromatography-derived lipophilicity paramenters by Roussel etal. [9,10].
Usually, however, description of chiral separations on the basisof QSRR theory requires methodologies specially developed forsuch systems. This is due to the fact that between analyte andCSP both non-enantiospecific (named by Guiochon as non-selective
interactions with type-I sites) and chiral (with type-II sites) inter-actions occur [11,12]. Based on the studies of chiral separation ofalkylaromatic carboxylic acids, Wainer et al. suggested that the chi-ral recognition proceeds via two-step mechanism. The first stepinvolves the distribution of the solute from the mobile phase to
approach relies on 3D alignment of solutes carbon skeleton andas such is not suitable for analysis of compound with very differ-ent molecular structure (especially without aromatic ring whichdefines the superposition plane). Nevertheless, the statistically sig-nificant relations are pointed out. The collected experimental data
M. Szaleniec et al. / J. Chrom
SP and is responsible for the general retention process. Whenhe solute-CSP complex is formed, the chiral determination occurs.ue to the spatial specific interactions of the analyte, the two
somers vary slightly in the strength of interaction because ofifferent configuration in the chiral center [13]. The chiral recog-ition can be explained basing on the basis of Pirkle and House14] standard ‘three-point’ interaction model (also called geomet-ic phenomenon) or postulated by Wainer et al., conformationallyriven chiral recognition process (molecular phenomenon). The lat-er one assumes that both solute and CSP conformationally adjusto each other and the difference in the conformational changeetween isomers results in chiral recognition.
Consequently, in QSERR (quantitative structure enantioselectiv-ty retention relationships) studies one can address either variationn the non-enantiospecific strength of solute-CSP interactions dueo differences in the chemical structure and properties (such asolarizability, number of hydrogen atoms, etc.) which increaseshe overall retention of both isomers or the forces which lead tohiral discrimination. In this paper that issue was addressed by sep-rate modeling of a solely chiral separation (i.e. the retention orderogether with the selectivity) as well as a modeling of retentionactor (log k) which describes both chiral and non-chiral solute-CSPnteractions.
Various methodological approaches were developed to describeD interactions of solute with chiral selector [15]. One can resorto 3D quantitative structure activity relationship (QSAR) methodsuch as CoMFA (Comparative Molecular Field Analysis) [16]. Indeed,his methodology was successfully applied in the modeling of manyhiral systems [17–20] and is used in that paper. However, differentpproaches were also developed such as adaptation of 2D connec-ivity indexes or normal mode eigenvalues based EVA descriptorso correlate solutes chirality with either pharmacological activity21] or retention and selectivity parameters [20]. Another success-ul alternative was proposed by Aires-de Sousa and Gasteiger, i.e.onformation-independent and conformation-dependent chiralityodes (CICC and CDCC, respectively), which together with classifi-ation trees or Kohonen neural newtorks were successfully appliedo prediction of the retention order [22–26].
Another complementary to CoMFA approach is an enantiophoreoncept [27,28], based on pharmacophore idea used in drug devel-pment. The enantiophore supplies useful and simple method foratabase search (as potential solutes can be easily fitted to obtain 3-r 4-point enantiophore) and provide insight into chiral recognitionechanism in a similar manner as drug–receptor interactions are
ationalized. The concept of enantiophore and CICC was togetherecently applied by Del Rio and Gasteiger to predict retention orderf molecules separated on Whelk-O1 CSP [25].
Finally, the issue of solute-CSP interactions was also treated withirect ab initio, molecular mechanics and molecular dynamics cal-ulations [29,30]. These approaches described in more details inhe excellent review of Lipkowitz [30] provide valuable insight intohe mechanism of enantiodiscrimination or solvent effects and areble to address both selectivity and to some extent a retentionrder. For well-defined CSP-solute contacts (such as Whelk-O1)hey can provide very precise and quantitative information on thehiral recognition mechanism [29]. However, the calculations forolymer CSPs (like Chiralcel OB), due to the complexity of theodels, still lack the speed and robustness of QSRR techniques
15].Particularly interesting to this work are the studies of Del Rio
t al. [28] which describes enantiophore development for Chiracel
B stationary phase in 90:10 n-hexane/isopropanol. According to
he authors, Chiracel OB seems to be exceptional among modifiedellulose based CSPs as it exhibits well-defined enantioselectiveechanism, unlike other polymeric CSPs that have multiple bind-
ng sites. It is proposed that Chiracel OB enanthiophore is comprised
A 1216 (2009) 6224–6235 6225
of triangular arrangement of three points: H-bond donor, H-bondacceptor and lipophile or aromatic moiety.
The studied problem of prediction of retention factors, selec-tivity and elution order of particular optical isomer on cellulosetribenzoate CSP is addressed based on the example of 1-phenylethanol derivatives. This issue had been already investigatedby Wainer et al. over 20 years ago [13]. That study attributed chiralrecognition to the formation of diastereomeric solute-CSP com-plexes through hydrogen bond interactions between the solute’salcoholic hydrogen atom and a carbonyl oxygen atom of an estergroup on CSP and to stabilization of the aromatic ring in the chi-ral ravine of the cellulose. The paper also proved the importanceof phenyl substituents in Chiracel OB CSP without which the sta-tionary phase lost most of its chiral recognition capabilities. Thisindirectly suggests the importance of CSP-phenyl–aryl-solute inter-actions. Therefore, the results of Wainer et al. research suggeststhat only two out of three points of Del Rio et al. Chiracel OBenantiophore are crucial for chiral recognition of 1-phenylethanolderivatives —aromatic moiety and H-donor hydroxyl group. Suchdiscrepancy might be the result of different training sets as Del Rioet al. used structurally diverse 52-compound-set while in the paperof Wainer et al. and also in our present report only congeners of1-phenylethol were used (18 and 10 compounds, respectively).
In this paper, the problem is approached from the practicalpoint of view, where a number of chiral standards are availableand determination of elution order in analyzed racemic mixturesis the objective. Seventeen alcohol racemates were analyzed withHPLC method on Chiracel OB-H column but only for ten compoundsthe chiral standards were available (see Fig. 1). In order to iden-tify the elution order for the rest of the enantiomers (Fig. 2), thetheoretical QSERR models were constructed. The obtained resultswere applied in the examination of practical analytical problem, i.e.determination of chiral stereospecificity of ethylbenzene dehydro-genase [31], the enzyme that catalyzes stereospecific synthesis of1-phenylethanol derivatives.
Due to the relatively low structural diversity of examined com-pounds, the authors do not challenge the issue of the universaldetermination of interactions that are responsible for chiral recog-nition of secondary alcohols in Chiracel OB-H CSP. The applied
Fig. 1. Training compounds (1–10) for which both isomer standards were available.
6226 M. Szaleniec et al. / J. Chromatogr
Fig. 2. Validation compounds (11–15) for which only racemate standards were avail-a(b
sCcvipc
2
2
ratcfoTtb
by Ramachandran et al. [33] and (S)-5 was synthesized in
TTi
N
((((((((((((((((((((
ble and enzyme synthesis of one of the isomer was conducted and compounds16–17) for which only separation of racemic mixture was conducted or synthesisy EBDH was ambiguous.
eem to be sufficient to describe chromatographic interactions ofhiracel OB-H stationary phase with alkylaromatic and alkylhetero-yclic secondary alcohols. Therefore, obtained models are not onlyaluable as practical prediction tools but also provide an insightnto the nature of the solute-stationary phase interactions for thisarticular group of compounds (i.e. alkylaromatic and alkylhetero-yclic secondary alcohols).
. Experimental
.1. Data set up
The available set comprised of retention factors of seventeenacemic mixtures of alkylaromatic and alkylheterocyclic secondarylcohols. In this group for ten compounds (Fig. 1, Group I) the reten-ion order was determined with chiral standards (Table 1). In eachase, with the exception of 1-(2-furyl)ethanol (7), the first elutedraction was of S conformation. For the rest of compounds (Group II)
nly retention factors of both isomers were available (Fig. 2, Table 2).herefore, it was decided that theoretical QSRR model should berained for the Group I, while the compound from Group II shoulde treated as an evaluation set.
able 1he retention data for Group I compounds (training and validation groups) with identifi
sopropanol); ROI – Retention Order Index – −1 for first fraction, 1 for second fraction; RO
With the exception of 8 and 16, the EBDH was used as a cat-alyst in the synthesis of secondary alkylaromatic alcohols. For allcompounds of the Group I the S isomer was obtained as a pre-dominant product. For Group II, EBDH-synthesized alcohols wereeluted as a first fraction in all cases with the exception of 1-[1,1′-biphenyl]-4-ylethanol (17). This suggested that the first fractionsmight be of S conformation per analogy to the above-mentionedanalytical results for EBDH reaction mixtures of Group I. There-fore, these results were treated as additional premises for ad hocassignment of the earlier elution times to the S isomers (and theirfield point descriptors) of the Group II. This allowed calculationof correlation factors between experimental and predicted values.However, as for 16 and 17 premises from enzymatic test were con-fusing or unavailable, it was decided that these compounds shouldnot be used in validation of developed models. It means that dur-ing a model development the discrepancies between predicted andexperimental retention parameters for 16 and 17 were not treatedas a warning signal, due to the higher probability of inversion of theelution order.
2.2. Organic syntheses
The chiral standards of 1, 2, 7–10 were commercially avail-able (Aldrich, Fluka, Alfa Aesar, purity at least 95%—full list ofstandards is available in Supplementary Materials). The racemiccompounds 3–6, as well as their enantiopure stereoisomers, wereprepared by the reductions of the appropriate carbonyl com-pounds. 4-Hydroxy-substituted propiophenone and acetophenonewere reduced to corresponding racemic alcohols 3 and 4 withlithium aluminium hydride, according to the published proce-dure [32]. Hydrogenations of 3- and 2-hydroxyacetophenone werecarried out under milder conditions, with sodium borohydride(2.2 equiv) in anhydrous ethanol at 0 ◦C, to give racemates 5 and 6,respectively. Chiral standards (S)-5 and (R)-6, were obtained fromappropriate hydroxyacetophenones by enantioselective reductionswith (−)-ˇ-chlorodiisopinocampheylboran [(−)-DIP-ChlorideTM].In the case of (R)-6, we strictly followed the procedure described
accordance with method reported by Everhart and Craig [34].Unexpected difficulties during syntheses of the optically active1-(4-hydroxyphenyl)alkanols 3 and 4 encouraged us to modifyexisting methods. We worked out a three-step synthesis involving
cation of each isomer. Log k for both mobile phases (90:10 and 85:15 n-hexane toI log ˛ − log of selectivity ˛ multiplied by ROI.
a 1-[1,1′-Biphenyl]-4-ylethanol (17) in 85:15 mobile phase not resolved. The reten
imple, asymmetric, enzymatic reduction of the protected phenoliclkylaromatic ketones, that finally allowed us to synthesize missinghiral standards (S)-3 and (S)-4, with satisfactory optical purity. Thebove-mentioned procedure is still examined and will be publishedlsewhere.
Enantiomeric excesses of the compounds (S)-3 (80% ee), (S)-465% ee), (S)-5 (95% ee) and (R)-6 (89% ee) were determined byhiral HPLC analysis. The absolute configurations of the opticallyctive alcohols 3–5 with protected phenolic groups, were assignedrom 1H NMR spectra of the appropriate MTPA (�-methoxy-�-rifluoromethylphenylacetic acid) esters. The R configuration of theompound 6 was taken as previously evaluated [33].
.3. Enzyme synthesis
The ethylbenzene dehydrogenase is the enzyme which nativelyatalyzes the oxidation of ethylbenzene to (S)-1-phenylethanolith 100% stereoselectivity [35]. It was recently shown that
t exhibits very high stereoselectivity with other alkylaromaticompounds [36]. The oxidation products of alkylaromatic andlkylheterocyclyc compounds were identified by means of GC-S and LC-MS as secondary alcohols and were shown to be
ither in predominantly S configuration (by comparison withvailable chiral standards) or to be the earlier eluting peak inomparison with elution order of the racemate. The enzymeccurred to be highly S-stereospecific producing in most casesolely S isomers with the exception of 1-(4-hydroxyphenyl)--propanol (3), 1-(4-hydroxyphenyl)ethanol (4) (Group I) and-(4-aminophenyl)ethanol (14) (Group II) where both isomersere present, but the earlier form (S for phenols) was always
he predominant one. Only in the case of the last compound,-[1,1′-biphenyl]-4-ylethanol (17), the enzyme produced mixturef both enantiomers with the latter fraction being predominant36]. The enzyme occurred to be inactive in synthesis of 1-4-bromophenyl)ethanol (8) and 1-(4-chlorophenyl)ethanol (16).herefore, these results can be used as weak premises determininghich of the two peaks in separated racemic mixture is of the S
onfiguration.
.4. HPLC separation
The chiral HPLC separations were performed on Agilent 1100ystem with DAD detector in normal-phase system on celluloseribenzoate polysaccharide chiral stationary phase (Daicel ChiracelB-H column, 250 mm × 4.6 mm). Two types of uniform isocratic
0.935a −1 −0.013 −0.009a
0.944a 1 0.013 0.009a
arameters were estimated from the shoulder-type peak distortion.
programs were used with n-hexane/isopropanol 90:10 and 85:15(v/v) mobile phase in 25 ◦C at 0.5 ml/min flow rate with 205 nm asthe selected detection wavelength. The temperature of the sepa-rations was controlled by thermostated column compartment andair-conditioning of the laboratory (ensuring solvent temperatureclose to 25 ◦C).
Each sample concentration was close to 1 mg/ml (depending onthe purity of the standard). Most of the samples were dissolved inpure n-hexane (1, 2, 7–13, 15, 16). Some (3–6, 17), were dissolved ina small quantity of i-PrOH initially and then diluted with n-hexaneto obtain final solvent composition of 90/10 n-hexane/isopropanol.Finally, the 1-(4-aminophenyl)ethanol (14) was dissolved in a purei-PrOH due to its small solubility in hydrophobic solvents. In orderto avoid changes in sample concentrations during the analysis dueto high volatility of n-hexane the autosampler was thermostated to4 ◦C.
Retention times were obtained as average of the two 1 �l sampleinjections. The hold-up time t0 value equalled 6.43 min and wasdetermined with 10 �l air injections for both mobile phases.
The reaction mixtures, where ethylbenzene dehydrogenaseacted as a catalyst, were analyzed from isopropanol solutionsaccording to the protocols optimized for each compound [36]adjusting the content of i-PrOH from 5% to 30% depending on thepolarity of the studied compound. However, it was proven that thevariation in isopropanol content does not influence the order ofelution and therefore, the conclusions drawn from these separa-tions could be applied to the analyses of the samples separated atstandard (90:10 and 85:15 n-hexane/isopropanol) conditions.
2.5. DFT modeling
Initial geometries of all studied compounds were built inCache Pro programme [37] and optimized with semi-empiricalPM3 method [38]. In order to obtain reliable lowest energy con-former of studied compound the Density Functional Theory (DFT)level quantum chemical modeling was performed with Gaussian03 package [39]. The initial alcohols geometric structures werefully optimized firstly in a vacuum on B3LYP [40] level of the-ory with 6-31G(d,p) basis sets and then reoptimized in PCM [41]model solvent medium. As n-hexane model solvent is not imple-
mented in the Gaussian 03 package the n-heptane was used as afair approximation of the mobile phase. At the end of each opti-mization the vibration analyses were conducted to ensure thatminimum geometry was obtained. The conformation analyses,scanning the dihedral angle between alkyl group and aromatic or
6228 M. Szaleniec et al. / J. Chromatogr
h(1aceft
2
enD
2
rcatse
CwssawtyIRvras
weeoapfn
existed (see Sections 2.1 and 2.3), were additionally treated as exter-nal validation group for models 2–4, i.e. the consistency of ROI
Fig. 3. The overlay of the structure of all investigated compounds.
eterocyclic ring, were performed for: 1-(4-hydroxyphenyl)ethanol4), 1-(4-metoxyphenyl)ethanol (13), 1-(2-naphthyl)ethanol (15),-[1,1′-biphenyl)]-4-ylethanol (17), and all 2- and 3-substitutedromatic (5, 6, 12) as well as for both heterocyclic (9, 10)ompounds. Moreover, the conformation of alcoholic group wasxamined in each compound. The separate optimizations were per-ormed for each of the localized deep minima and the structure ofhe lowest energy was used in further analysis.
.6. 2D QSRR descriptors
In prediction of log k following descriptors, were used: gen-rated by Cerius2 AlogP98, H-bond donor and H-bond acceptorumber as well as HOMO and LUMO energies calculated on theFT (PCM) level in Gaussian 03 package.
.7. CoMFA
All models were aligned with Material Studio 4.22 [42] usingigid-body, least-square, template fitting method (with heavy atomore of (S)-1-phenylethanol as a reference structure) (Fig. 3). Carbontoms of the ring and first two of the alkyl groups were used asethers. In case of 5-membered ring compounds all atoms wereuperimposed to the nearest five carbon atoms in such a way as tonsure the best overlay.
Comparative Molecular Field Analysis was performed withinerius2 molecular modeling package [43]. Partial atomic chargesere computed by the Gasteiger algorithm [44]. The energies of
teric and electrostatic interactions were calculated in the univer-al force field (UFF) [45] respectively with H+, CH3, CH3
+, CH3−,
nd OH− probe molecules, in a rectangular grid of 270 pointsith 2 Å step size. For modeling of global retention behavior
he logarithm of retention factor log k was used as dependent-variable. As a measure of retention order the Retention Orderndex (ROI) as well as ROI log ˛ (i.e. ROI log k2/k1) were applied.OI was defined as a classification descriptor which assumes −1alue for the first eluting fraction and +1 for the second one. As aesult it provides simple recognition of retention order and enablesnalysis of the selectivity (log ˛) along with the elution succes-ion.
Field points included in the subsequent analysis were selectedith genetic algorithms (500 equations population, 15,000 gen-
rations with non-fixed parameter number and 2–15 terms perquation limitation) usually based on the Group I compounds. The
nly exception was the modeling of ROI log ˛, where input vari-bles had to be selected for the whole data set. Usually, from theopulation of parameters used in 500 equations, descriptors with
requency occurrence up to 4% in the equation population or alter-atively variables from the top 10 models were selected and after
. A 1216 (2009) 6224–6235
elimination of internal co-linearity were used for development ofneural models.
2.8. Neural networks
For construction of the artificial neural network (ANN) mod-els, the commercially available software package Statistica NeuralNetworks 7.1 was applied [46]. The selection of input parameterswas conducted with genetic algorithm (GA) in the Material Studiopackage.
The regression ANNs were constructed for prediction of log k,ROI log ˛ and ROI based on 20 compounds from Group I thatwas further subdivided into training (16) and validation (4) sets.The training cases were used in optimization of neural weightsand selection of input vector. The validation cases were usedto stop training procedure when over-fitting occurred and toselect the best model from the population of investigated net-works. The models trained on such data were used to estimateretention parameters in the test set (Group II). The optimal net-work architecture was determined experimentally with IntelligentProblem Solver (ISP), which built and selected the best mod-els from linear (LIN), multilayer perceptron with linear outputneuron (MLP) as well as generalized regression neural networks(GRNN). The ISP optimized both input vector and number of neuronin the hidden layers (in case of MLP and GRNN). The architec-ture of the network is presented further on by the followingsyntax:
input : no input − no hiddenvariable neurons layer neurons
For example the ‘MLP 3:3-2-1:1’ means that multilayer percep-tion with three input variables and input neurons, two neuronsin the hidden layer and one output neuron encoding one outputvariable is used.
The equations obtained for linear networks are presented asstandardized multiple linear regression equation with ˇ coeffi-cients in place of linear constants. ˇ coefficients provide relativeimportance of the variable in the model.
2.9. Cross-validation
Leave-one-out (LOO) and 5-fold cross-validation of models wereperformed in Material Studio package. For linear models (models2–5) LOO and 5-fold algorithm were used to obtain cross-validatedR2 and PRESS parameters. The cross-validations were performedfor MLR models (corresponding to the linear networks devel-oped in Statistica package) over training set (16 compounds) thusproviding direct comparison of robustness between the cross-validated MLR models and the linear neural networks. For model1 (MLP neural network) the LOO and 5-fold cross-validationwas preformed over randomly partitioned (16:4 ratio) train-ing and validation cases. The validation cases were involved inthe MLP cross-validation as they influence the end of the net-work training and therefore, have some influence on the modelparameters.
After successful prediction of the retention order with model1 those evaluation cases (Group II), for which additional premises
prediction was checked. However, as for some compounds of GroupII the additional information were unavailable compound 16 and 17were excluded from this set. Models, which exhibited high discrep-ancies in ROI prediction with estimation of model 1 were discardedfrom further analysis.
atogr.
3
3
Tgwopioowuorpdil
3
GnCp1
TVp
N
((((((((((((((((((((
1111111111
1111
M. Szaleniec et al. / J. Chrom
. Results and discussion
.1. Chiral chromatography
The retention data of all analyzed compounds are presented inable 1 (for training and validation cases) and Table 2 (evaluationroup—test cases). For most of cases from Group I the S isomersere eluted as the first ones. The only reversion of the elution
rder occurred in case of 1-(2-furyl)ethanol (9). In order to sim-lify prediction of the retention order the Retention Order Index
s introduced. ROI assumes −1 value for the first eluting fractionf the racemic alcohol and +1 for stereoisomer eluting as a secondne. In order to address the selectivity of the separations togetherith the issue of retention order the combined index ROI log ˛ is
sed. This parameter not only yields information on a magnitudef stereoisomers separation but also provides information on theetention order. As such it can be associated with a particular com-ound (while pure ˛ describes pair of isomers and cannot be used toevelop QSERR with each stereoisomer). The log scale for selectiv-
ty is used in order to retain uniform scale with models predictingog k.
.2. Prediction of elution order
The simplest model that performs flawless classification of
roup I compound into R and S isomers is the MLP 2:2-1-1:1 neuraletwork (model 1). It utilizes two input parameters, CH3/80 andH3
+/170 field points, requires 1 neuron in the hidden layer androvides predicted ROI values at its output. It was trained with00 epochs of back propagation, 20 epochs of conjunct gradient
able 3alues of residuals obtained for all developed models. Set column informs if the case warticular model.
umber Set Model 1 Model 2–10%
S)-1 Validation 3E−04 0.044R)-1 Training −1E−05 −0.054S)-2 Training 3E−04 −0.025R)-2 Validation −1E−05 −0.032S)-3 Training 3E−04 0.016R)-3 Training −1E−05 0.013S)-4 Validation 3E−04 0.033R)-4 Training −1E−05 −0.011S)-5 Training 3E−04 0.012R)-5 Training −1E−05 0.002S)-6 Training 3E−04 0.029R)-6 Training −1E−05 0.009S)-7 Training 3E−04 −0.010R)-7 Validation −1E−05 0.013S)-8 Training 3E−04 −0.017R)-8 Training −1E−05 0.032S)-9 Training −6E−03 0.004R)-9 Training −8E−05 −0.012S)-10 Training 3E−04 0.000R)-10 Training −1E−05 0.015
1 Test 3E−04 −0.0581 Test −1E−05 −0.0112 Test 3E−04 0.1322 Test −1E−05 0.0503 Test 3E−04 −0.0133 Test −1E−05 −0.0554 Test 3E−04 −0.0304 Test −1E−05 0.0395 Test 3E−04 0.0585 Test −1E−05 −0.011
6 Test 3E−04 −0.0196 Test −1E−05 −0.0017 Test 3E−04 −0.0457 Test −1E−05 0.069
A 1216 (2009) 6224–6235 6229
and 33 epochs of conjunct gradient algorithm with momentum.The training, validation and test errors are 7.6 × 10−4, 1.2 × 10−4
and 1.2 × 10−3, respectively. The model 1 for all S isomer in GroupII predicts ROI value of −1 while correctly predicting inversionof retention order for 1-(2-furyl)ethanol (9) (see Table 3). There-fore, the overall R2 between predicted and experimental valuesis 0.99 (training R2 = 0.99, validation R2 = 1.00). The 5-fold cross-validation yields slightly lower R2 values (R2 training = 0.70, R2
validation = 0.99) but all classifications are consistent with themodel 1.
The sensitivity analysis shows that the most important parame-ter is CH3/80. This field point lies in the vicinity of alcoholic group inS isomers and attains high energy due to steric interaction with thehydroxyl. It also assumes different energies for 9 (positive in caseof R isomer, while rest are negative and close to 0, positive in caseof S isomer but smaller than the rest of compounds which exhibitmaximum interaction energy). The other point is responsible fordiscrimination between 1-(2-furyl)ethanol (9) stereoisomers. TheR isomer has smaller interaction energy with CH3
+/170 than therest of compounds which allows encoding of inversion of elutionorder phenomenon. The spatial point distribution is presented onthe Fig. S1 in the Supplementary Materials.
The model predicts usual retention order for compounds 16 and17 indicating, that their retention orders are classical (which wasexpected in case of 16 due to similar behavior of 7 and 8). Therefore,
this result also suggests reversed stereoselectivity in the enzymaticsynthesis of 17.
This simple model shows that Chiracel OB-H discriminatesbetween secondary alkylaromatic alcohols based on position ofthe chiral OH group with the exception of 9, where oxygen from
as used for model development (training), validation or evaluated (test) with the
Fig. 4. Correlation plot between the experimental and predicted ROI log ˛ for (a)90/10 mobile phase model 2–10% (LIN 6:6–1:1 neural network (R2 = 0.93, R = 0.97)and (b)) 85/15 mobile phase model 2–15% (LIN 6:6–1:1 neural network (R2 = 0.93,R = 0.96). S-2-CH3 stands for (S)-1-(2-methylphenyl)ethanol (12) while (S)-4-Phand (R)-4-Ph stand for (S)- and (R)-1-[1,1′-biphenyl]-4-ylethanol (17). Dashed
230 M. Szaleniec et al. / J. Chrom
eterocyclic ring is involved in the chiral recognition. For all otherompounds the unfavorable interaction between S–OH and station-ry phase leads to the decreased retention of S isomers.
CoMFA models are a complimentary way to probeigand–receptor interactions when compared to Chiracel OBnantiophore proposed by Del Rio et al. [28], i.e. both enantiophorend CoMFA models should describe corresponding molecularnteractions. As all analyzed solutes possess a ring which was useds a superimpose template we can assume that the presence ofn aromatic moiety is required by chiral site for all investigated-phenylethanol derivatives separated on Chiracel OB-H. Besideshis, two points are involved in enatiodiscrimination: CH3/80hich is involved in strong destabilization of CSP-S complexes of
ll but (S)-9 (while (R)-9 is slightly stabilized) and CH3+/170 which
s involved in destabilization of (R)-9. This difference betweenand the rest of solutes also comes, beside sheer molecular
issimilarity, from the conformational arrangement imposed byhe furan ring and hydroxyl group intramolecular interactions.his results in a different OH position in comparison to the restf compounds (see Fig. 3) and consequently different interac-ion with the field points. Nevertheless, one can see that S–OHroup in diastereomeric complex encounters steric hindrance.owever, CH3/80 highly (R > 0.9) correlates with acceptor/donor
OH−) interactions localized around both R and S hydroxyl groups.herefore, the complex-destabilizing steric effect of interactionith (S)–OH can be replaced by complimentary stabilizing H-bond
cceptor interaction with (R)–OH. The similar classifying modelan be achieved by replacing CH3/80 with HO−/224. However,uch a model is not able to describe correctly interactions of (S)-9eading to incorrect description of reversion of elution order (i.e.oth isomers receive negative ‘S-like’ code). Summing up, it can beaid that enantiophore of the secondary alkylaromatic alcohols isndeed composed of three points but only two are involved in thenteractions with particular stereoisomer (as observed by Wainert al. [13]). In each case the aromatic ring is involved (see below)long with either stabilizing H-bond acceptor interaction withR)–OH groups or destabilizing (steric) interaction with (S)–OHroups. One can also speculate that the complex of CSP withR)-9 is somehow destabilized due to unfavorable interactionsf H-bond acceptor (usually electronegative atom) with oxygenrom furan ring which is reflected by CH3
+/170. Of course thehird Del Rio et al. enantiophore point, H-bond donor, could note observed with these studies, as analyzed solutes beside OHroup do not have another H-bonding acceptor in the vicinity ofhe chiral center. Therefore, for 1-phenylethanol derivatives havingdditional H-bond acceptor substituent in the alkyl group, thehree-point binding mode proposed by Del Rio et al. may indeed beeen. Apparently, selection of the training compounds in the QSRRodeling can, to some extent, influence the result of the research.
.3. Prediction of selectivity (ROI log ˛)
The modeling of ˛ selectivity was a natural next step inhe modeling of chiral separation mechanism of 1-phenylethanolerivatives. As was already mentioned above, in order to modelelectivity a combined descriptor is applied, i.e. ROI multipliedy log ˛. As a result log ˛ for the first eluting stereoisomerssumes negative value while for the later eluting fractions is posi-ive.Unexpectedly, to select good input parameters, GA analysis hado be conducted over all cases (34 isomers) for 15,000 generations,ith 500 equations (5–10 terms per equation) population. Result-
ng equations exhibited very high correlation and cross-validation2 (in the range of 0.98). As a next step 8 input parameters of theest equation were transferred to ISP and used to select best archi-ecture of neural model. Compounds were divided into training16), validation (4) and test (14) groups. The best model obtained
line provides 95% confidence interval of the predicted dependent variable (seeSupplementary Materials for more information).
is a linear network LIN 6:6–1:1 (model 2–10%), which has train-ing, validation and test error equal to 0.0465, 0.0727 and 0.1185respectively. Model 2–10% performs correct prediction of retentionorder in Group I. Moreover, predictions for compounds from GroupII are consistent with the retention order calculated by model 1.The R2 between experimental and predicted values (Fig. 4a) is 0.93(training R2 = 0.98, validation R2 = 0.95, LOO c–v R2 = 0.95, 5-fold c–vR2 = 0.93).
The ROI log ˛ obtained for both mobile phases (90/10 and 85/15)correlate with each other at R2 level of 0.99. Therefore, based on thesame descriptors one can easily construct model of selectivity formobile phase containing 15% of i-PrOH (model 2–15%). For such amodel training, validation and test error are 0.04727, 0.0799 and0.1196, respectively, while R2 between experimental and predictedvalues (Fig. 4b) is 0.93 (training R2 = 0.98, validation R2 = 0.99, LOOc–v R2 = 0.93, 5-fold c–v R2 = 0.94).
The only significant deviation from the experimental values isfound in case of 12, as both models predict smaller selectivity for
1-(2-methylphenyl)ethanol (12). Interestingly, the log ˛ predictedfor 17 are 5–8 times bigger than that found in experiment, butthe retention order is predicted to proceed without any inversionphenomenon.
M. Szaleniec et al. / J. Chromatogr.
Fcrm
p
CRfiaHoyspmnioeh6dCm
and test errors are: 0.039, 0.073 and 0.097, respectively. TheR2 between predicted and experimental data (Fig. 6a) is 0.97(training R2 = 0.98, validation R2 = 0.99, LOO c–v = 0.94, 5-fold c–vR2 = 0.95).
ig. 5. 3D localization of field points involved in model 2–10%. The grey and blackrosses indicate localization of the field points that increase or decrease ROI log ˛,espectively. Crosses were scaled according to relative descriptor importance in the
odel.
Two MLR equations corresponding to LIN 6:6–1:1 networks areresented below:
model 2–10%
ROI log ˛10% = 0.42CH3−/34 + 0.4HO−/58 − 0.80HO−/99
− 0.16HO−/128 − 1.13CH3+/80 + 0.17CH3
+/159
+ 0.25
n = 16, R = 0.99, R2 = 0.98, corr. R2 = 0.97, F(6, 9) = 72.32,
LOO c–v R2 = 0.95, PRESS = 0.019
model 2–15%
ROI log ˛15% = 0.45CH3−/34 + 0.40HO−/58 − 0.79HO−/99
− 0.16HO−/128 − 1.14CH3+/80 + 0.16CH3
+/159
+ 0.26
n = 16, R = 0.99, R2 = 0.98, corr. R2 = 0.96, F(6, 9) = 66.80,
LOO c–v R2 = 0.93, PRESS = 0.025
The networks use CH3+/80 as a parameter corresponding to
H3/80, which was used by model 1 to distinguish between S andisomers. However, for correct determination of selectivity more
eld points have to be used, namely CH3−/34, CH3
+/159 for stericnd mixed electrostatic–steric interactions and HO−/58, HO−/99,O−/128 for acceptor–donor interactions (see Fig. 5). The analysisf ˇ coefficients in model 2 equations and network sensitivity anal-ses suggest, that electrostatic–steric interaction at CH3
+/80 has thetrongest influence on the ROI log ˛. As in case of model 1 this fieldoint is responsible for discrimination between S and R isomers. Forost S isomers but 9 high positive energy values of CH3
+/80 yieldegative value of ROI log ˛. The rest of points seems to be involved
n modulation of selectivity (Fig. 5) and is localized far from stere-genic center near the para position and below all molecules. Thexception is HO−/128, which is in close contact with both S and Rydroxyl groups and assumes small values only for (R)-1, (R)-6, (S)-
, (R)-10 and (R)-12. The next in the importance is HO−/99 whichetects polar substituents in para position followed by HO−/58 andH3
−/34. The HO−/128 descriptor is the least important in bothodels.
A 1216 (2009) 6224–6235 6231
3.4. Prediction of retention
From among all studied 2D descriptors only number of H-bonddonor and acceptor atoms, HOMO energy and energy differencebetween HOMO and LUMO orbitals exhibited statistically sig-nificant correlation with log k. The strongest correlation existedbetween retention factors and energy of HOMO. The variation ofHOMO orbital energy described 82% and 78% of variation in log k(10% and 15% of isopropanol, respectively). However, this parameterassumes the same (or very close) values for either S or R isomers and,in consequence, cannot be used as a chiral discrimination descrip-tor. Therefore, in order to obtain a model which correctly predictsboth chiral separation and overall retention one must also use the3D descriptors.
As in previous cases genetic algorithms were used to selectinitial population of input parameters. Top seven variables wereused by ISP that scanned 2000 neural networks for the bestmodel. The best results are obtained for linear model LIN 5:5–1:1that utilized beside HOMO energy four field point parameters:H+/117, CH3/73, CH3/227 and CH3
−/62. The training, validation
Fig. 6. Correlation plot between the experimental log k and log k for: (a) model3–10% LIN 5:5–1:1 neural network (R = 0.99, R2 = 0.97) and (b) model 4–15% LIN4:4–1:1 neural network (R2 = 0.95, R = 0.97). Dashed line provides 95% confidenceinterval of the predicted dependent variable.
6 atogr
tmbs5ebiv4L
ppda4
bi
3olfif(
FToi
232 M. Szaleniec et al. / J. Chrom
As log k10% correlates with log k15% at R2 level equal to 0.98,he same neural model can be retrained to predict retention for
obile phase containing 15% of i-PrOH with R2 = 0.95. However,ased on the same population of descriptors one can obtain evenimpler model (4 parameters) which performance is not worse fromparameter model. In LIN 4:4–1:1 (training error 0.057, validation
rror 0.081, test error 0.158) neural network CH3/227 is replacedy highly correlated with it CH3/274 (R = 0.85) and CH3
−/62 is notntroduced into the model due to its low statistical significance andalue of ratio below 1.0 in sensitivity analysis. As a result, model–15%, exhibits R2 of 0.95 (training R2 = 0.96, validation R2 = 0.99,OO R2 = 0.93, 5-fold c–v R2 = 0.92, see Fig. 6b).
Both models, beside accurate prediction of retention, correctlyredict the elution order in Group II and are 100% consistent withredictions of model 1 (see Fig. 6 and Table 3). Moreover, they pre-ict fairly well the selectivity, with R2 value between experimentalnd predicted ROI log ˛ of 0.75 and 0.85 for model 3–10% and model–15%, respectively.
Model 3 and model 4 correspond to MLR equations providedelow with normalized ˇ coefficients describing relative variable
n = 16tr., R = 0.98tr., R2 = 0.96, corr. R2 = 0.92,
LOO c–v = 0.93, PRESS = 0.039 F = 70.49
The analysis of ˇ coefficients and sensitivity analysis of model–10% suggests that HOMO descriptor has the strongest influence
+
n the value of log k from the whole model while H /117 has theowest. Both ˇ coefficients and sensitivity analysis points at CH3/73eld point as a second in the importance. The field point is localized
ar below all molecules and its energy oscillates around −0.3 kcalsee Fig. 7a). It does not correlate precisely with log k but it assumes
ig. 7. 3D localization of field points involved in (a) model 3–10% (b) model 4–15%.he grey and black crosses indicate localization of the field points that increaser decrease log k, respectively. Crosses were scaled according to relative descriptor
mportance in the model.
. A 1216 (2009) 6224–6235
different values almost for each compound which allows modula-tion of overall retention described by HOMO parameter. CH3/227is involved in encoding of inversion of elution order of 9 as itattains less negative value for (S)-9 isomer (higher retention). More-over, energies of the rest of compounds are narrowly distributedand separated from energies of 1-(2-furyl)ethanol (9). Third in theimportance is CH3
−/62 descriptor, which correlates with log k atR = 0.6 level and assumes positive energy values for more polar com-pounds. Moreover, it discriminates 6 and 9 attaining low energyvalues most probably due direct interactions with negatively chargeoxygen atoms from the substituted ring. If that point is removedinversion of elution order for 9 is no longer predicted correctly.
In case of model 4–15% the analysis of ˇ coefficients marksHOMO energy as the most important parameter, followed by H+/117and finally, almost equally important, CH3/274 and CH3/73. Thesensitivity analysis points at H+/117 field point as the most cru-cial, followed by HOMO orbitals. The other two points are equallyimportant. In model 4 the CH3/274 descriptor plays the same role asCH3/227 in model 3, i.e. it is involved in the recognition of (S)-9 and,in consequence, in a simulation of its different chromatographicbehavior (Fig. 7b).
As to the role of HOMO it seems that the higher is its energyfor a particular compound, the longer is the retention time. In alky-laromatic alcohols the HOMO orbital describes delocalized electrondensity of aromatic ring, the free electron pairs of the oxygen atomand some electron density of the alkyl group. As in tribenzoatecellulose CSP the retention will originate from both �–� inter-actions, disperse interaction of alkyl chain as well as hydrogenbonding between hydroxyl groups of alcohol and carbohydrate,the electrons described by HOMO naturally would play a vitalrole. The occurrence of frontier orbitals in QSRR equations is fre-quently explained as solute–stationary phase donor (HOMO) andacceptor (LUMO) interactions (e.g. Fabian and co-workers [47] andWainer and co-workers [48]). This may suggest that upon forma-tion of solute—CSP complex that is involved in overall retention(but not directly in chiral recognition), some form of charge trans-fer from analyte toward tribenzoate cellulose moiety takes place.The higher HOMO orbital is localized on the energy scale the lessenergy such process would require. However, the occurrence ofthe statistically significant correlation does not automatically implythe cause–consequence relationship. Although, the introduction ofheteroatoms into the studied alcohols increases the energy of theHOMO orbital it allows binding with the stationary phase with addi-tional dipole–dipole or hydrogen bond interactions. Indeed, log kcorrelates with H-donor atom number on 0.78 (10% IPA) and 0.71(15% IPA) level. Therefore, one cannot determine type of interac-tions involved based only on the statistical analysis. Nevertheless,it seems apparent that the issue of overall polarity of the com-pound (which is also described by the energy difference betweenfrontier orbitals) is responsible for retention on polar CSP. This isadditionally confirmed by model 1S developed for all cases, whichutilizes AlogP98. The model indicates that for high log P values (highhydrophobicity) short retention is observed. This result is in accor-dance with the correlation of log k with Hansch fragment constant[49] found by Wainer et al. [13]. Moreover, log k—solutes hydropho-bicity relation expressed in terms of lipophylicity was previouslyobserved in case of the polysaccharide CSPs [9,10].
4. Quality of models
Figs. 4 and 6, Figs. S2 and S4 present marked with dashed linesthe 95% confidence interval of the estimated values (either log kor ROI log ˛). These intervals describe the confidence range of theestimation of a particular dependent variable (see SupplementaryMaterials). Table 3 provides the list of residuals for all investigated
M. Szaleniec et al. / J. Chromatogr. A 1216 (2009) 6224–6235 6233
Table 4Statistical parameters of prediction models. Training R2—determined for 16 training cases; validation R2—determined for 4 validation cases, c–v R2—leave-one-out R2 fortraining cases, 5-fold c–v R2—5-fold cross-validation R2 for training and validation cases in case of model 1 and training cases for model 2–5, overall R2—for correlation withall data, presuming classical order of elution as predicted by model 1; PRESS—predictive sum of squares.
Model Training R2 Validation R2 LOO c–v R2 5-Fold c–v R2 Overall R2 PRESS
odels while Table 4 sums up their statistical parameters. Botheave-one-out and 5-fold cross-validations yielded c–v R2 valuesf the same range as that obtained for models. Only for traininget of model 1 5-fold cross-validation showed lower model perfor-ance which, however, did not influence the result of classification.oreover, both absolute and normalized residuals are graphically
resented on Fig. S5 (of the Supplementary Materials). As can bexpected, the smallest deviations and best R2 parameters are foundor the simplest model 1, predicting ROI. Fig. S5a shows that residu-ls are clearly bigger in the evaluation group than in the training andalidation groups. This is natural, as the predictions of the modelsre done for the compounds with different substituents than thoseresent in compounds of the training group.
In case of models predicting selectivity the biggest absoluteeviations are encountered for 1-(2-methylphenyl)ethanol (12)ith systematic shift of both isomers towards more positive values.owever, the analysis of normalized plot (Fig. S5b) suggests that theiggest errors are introduced into prediction of ROI log ˛ of the firstraction of 1-(2-naphthyl)ethanol (15) and both fractions of 1-[1,1′-iphenyl]-4-ylethanol (17). The biggest systematic deviation for theodel 3–10% is found for 1-(4-methylphenyl)ethanol (11) and 15,here both isomers are shifted towards higher retention while the
ighest normalized deviation are encountered for 12. Model 4–15%xhibits the biggest systematic residual shifts for 14 (negative) asell as for 11 (positive). However, the normalized plot shows that
he error is not higher than 20% of the predicted log k value.The residual distribution seems to be random and does not
xhibit trends that mark bad fitting of the model. It may seem that inase of models predicting retention, the model quality would bene-t from the extension of the training group especially with methyl-ubstituted congeners. However, even if the network is obtainedor the whole data set as a training group the residuals for 12 aretill high. This indicates apparent inability of the network to per-orm precise prediction of 12 retention. It might be associated withhe lack of crucial molecular interaction. This in turn might havets basis in the GA that selected independent variables based onhe training group. This can be avoided if the variables are selectedor the whole data set with both GA and stepwise MLR (to excludeuto-correlations). In case of prediction of log k10% this leads to 6-erm-equation (model S1) utilizing some different field points andlogP98 (R2 = 0.98 see Supplementary Material—Figs. S2 and S3).owever, such an approach exploits the knowledge that is restricted
o the evaluation group, which is generally not a good practice inhe model development. Such a model can be used however, tonvestigate molecular interactions crucial for retention behavior ofhe whole data set, provided more rigorous models were previouslyeveloped for prediction of the retention order.
. The effect of the mobile phase
As expected, the increase of concentration of the polar organicolvent (isopropanol) in the mobile phase leads to decrease in
0.92 0.95 0.0390.95 0.96 0.096
the retention time of all investigated compounds. This effect isa result of competitive interactions of the polar organic solventand solute molecules with stationary phase [50] and is from thepractical point of view an equivalent to the increased organic sol-vent content in RPLC. The retention of investigated compounds forboth mobile phases turn out to be linearly correlated (R2 = 0.98)which enables application of prediction models developed for onephase to the other. Moreover, one can easily recalculate the log kfrom 10% of i-PrOH into 15% i-PrOH conditions by the followingformulalog k15% = −0.0784 + 0.8984 × logk10%
However, as was shown by Wainer et al. [51], the competitionfor the CSP biding sites is in fact a saturable process and the max-imum effect can be reached for a certain concentration of polarmodifier. As a result the observed linear correlation is true onlylocally for similar i-PrOH concentrations. The relation in a widerconcentration range usually turns out to be of non-linear type.This was exemplified by Armstrong and Berthod in non-linear log k(ethanol concentration) relation found for separation of 5-methyl-5-phenylhydantoin in the NP mode [8] and by Weiner et al. forseparation of 1-phenylethanol derivatives on Chiracel OB [13].
Establishing this relation for 1-phenyletanol derivatives wouldrequire broader investigation over wider range of i-PrOH concentra-tion. However, such a study for the selected data set is difficult dueto increasingly high retention for 14 (app. 2 h for 10% of isopropanol)from one side and decrease in resolution with higher isopropanolcontent for 17 (solute virtually not resolved in 15% of isopropanol).
Nevertheless, the observed linear relation enables formulationof a simple model describing retention obtained in both mobilephases (LIN 6:6–1:1, training, validation and test errors: 0.04659,0.0704 and 0.0839). Model 5 exhibits very good statistical param-eters (overall R2 = 0.97, training R2 = 0.97, validation R2 = 0.95, LOOc–v R2 = 0.94, 5-fold c–v R2 = 0.95, see Fig. S4) and corresponds tothe following MLR equation:
model 5
log k = −0.43C% + 0.76HOMO + 0.48CH3/73 + 0.39CH3−/62
+ 0.44CH3/227 − 0.29H+/117 + 9.64
n = 32, R = 0.98, R2 = 0.97, corr. R2 = 0.96, F = 117.53,
LOO c–v R2 = 0.95, PRESS = 0.096
As can be seen from the equation, model 5 utilizes same vari-ables as model 3. The only new parameter is the concentration ofi-PrOH–C%. Negative ˇ at C% means that the increase of isopropanol
concentration leads to the decrease of retention factor.
Such a model could not be built for selectivity, as the influ-ence of i-PrOH on log ˛ is not uniform. Even normally one expectsa decrease of ˛ upon an increase of a polar solvent concentra-tion, quite the reverse effect was observed for several investigated
6 atogr
ssaspa
6
isaa4tieMdm
7
tcptTtehobwtfir
dtpuClcis
[ogsrbpifo
scso
[
[[[[[[[[
[
[[[[[
[[
[
[[[[
[[[[[
[[[
234 M. Szaleniec et al. / J. Chrom
olutes (1, 6, 11–13). However, these changes were in fact relativelymall and in our opinion do not provide enough data for soundnalysis. Similar inconsistent behavior of ˛, i.e. no change or noystematic change, for 1-phenylethanol, 1-phenylpropanol and 2-henylpropan-1-ol in a much wider isopropanol range (1–25%) waslso observed by Weiner et al. [13].
. Stereospecificity of ethylbenzene dehydrogenase
The modeling of retention order in Group II proved that EBDHs indeed highly S-stereospecific enzyme. Almost for all its sub-trates (12 compounds), EBDH catalyzes oxidation to S secondarylcohols. The exceptions are found for compounds substituted inromatic ring in para position with polar substituents (3 ee% = 90%,ee% = 60% and 14 ee% = 51%). Moreover, the synthesis of 17 seems
o proceed predominant towards R isomer although the S isomers also present among the reaction products. However, it is tooarly to speculate on the possible causes of the observed effects.ore detailed investigation of the enzyme stereospecificity is con-
ucted with the help of both LC-MS methods and quantum chemicalodeling of the reaction mechanism.
. Conclusions
The problem of prediction of elution order and chiral separa-ion is fully analyzed based on experimental results, premises fromhromatographic analysis of enzymatic reaction mixtures and com-uter modeling. Various approaches are used in order to determinehe chromatographic behavior of the training and evaluation cases.he simplest model 1 describes elution order by means of Reten-ion Order Index, predicting classical behavior for all solutes in thevaluation group. Interaction with alcoholic hydroxyl group andeterocyclic oxygen atom of furan ring is identified as a crucialne for determination of retention order. Model 2 is developed foroth mobile phases and predicts both ROI and selectivity (log ˛)ith high quality (R2 = 0.93). Both models correctly predict reten-
ion order and are able to model log ˛ with high accuracy based oneld point descriptors. Crucial interactions with chiral center anding substituents are indentified.
Three models describing log k are developed, that are able to pre-ict retention order and log k with excellent accuracy (R2 from 0.94o 97). Selectivity log ˛ is also described alas only with moderaterecision (R2 in the range of 0.8). Apparently, different molec-lar interactions are responsible for retention and selectivity ofSP. HOMO energy turns out to be the most crucial parameter in
og k determination. It can be associated either with solute-CSPharge transfer interaction or correlation of HOMO level shifts withncreased solutes polarity due to introduction of heteroatomic sub-tituents.
Our study confirms the conclusion drawn by Wainer et al.13], who attributed chiral recognition of alkylaromatic alcoholsn tribenzoate cellulose CSP to H-bond interaction with hydroxylroup. Stabilization of the aromatic ring seems to be rather respon-ible for the selectivity (i.e. the extent of resolution) than the chiralecognition. The aromatic ring is apparently involved in the sta-ilization of the CSP-solute complex, and may be responsible forositioning of the solute in the CSP chiral cavity, thus indirectly
nfluencing the efficiency of the chiral recognition. Additionally, dif-erent CSP-ring interactions seem to be involved in determinationf retention and selectivity.
Finally, the obtained models are applied to estimation of EBDHtereoselectivity. High stereoselectivity of the catalyzed reaction isonfirmed for most of investigated cases. The reversion of usual Stereoselectivity in the case of 1-[1,1′-biphenyl]-4-ylethanol (17) isbserved.
[[[
[
. A 1216 (2009) 6224–6235
Acknowledgements
The authors acknowledge the computational grant KBN/SGI2800/PAN/037/2003 and financial support of the scientific net-work EKO-KAT. The authors gratefully thank anonymous refereesfor the thoughtful discussion and constructive remarks.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, inthe on-line version, at doi:10.1016/j.chroma.2009.07.002.
References
[1] R. Kaliszan, Chem. Rev. 107 (2007) 3212.[2] S. Schefzick, Ch. Kibbey, M.P. Bradley, J. Comb. Chem. 6 (2004) 916.[3] R. Puta, Y. Vander Heyden, Anal. Chim. Acta 602 (2007) 164.[4] K. Heberger, J. Chromatogr. A 1158 (2007) 273.[5] P.C. Sadek, P.W. Carr, R.M. Doherty, M.J. Kamlet, R.W. Taft, M.H. Abraham, Anal.
Chem. 57 (1985) 2971.[6] P.W. Carr, R.M. Doherty, M.J. Kamlet, R.W. Taft, W. Melander, C. Horvath, Anal.
Chem. 58 (1986) 2674.[7] A. Berthod, C.R. Mitchell, D.W. Armstrong, J. Chromatogr. A 1166 (2007) 61.[8] C.R. Mitchell, D.W. Armstrong, A. Berthod, J. Chromatogr. A 1166 (2007) 70.[9] C. Roussel, C. Popescu, Chirality 6 (1994) 251.10] C. Roussel, B. Bonnet, A. Piederriere, C. Suteu, Chirality 13 (2001) 56 (ref.
therein).11] T.D. Booth, I.W. Wainer, J. Chromatogr. A 737 (1996) 157.12] G. Götmar, T. Fornstedt, G. Guiochon, Chirality 12 (2000) 558.13] I.W. Wainer, R.M. Stiffin, T. Shibata, J. Chromatogr. 411 (1987) 139.14] W.H. Pirkle, D.W. House, J. Org. Chem. 44 (1979) 1957.15] A. Del Rio, J. Sep. Sci. 32 (2009) 1566.16] R.D. Cramer III, D.E. Patterson, J.D. Bunce, J. Am. Chem. Soc. 110 (1988) 5959.17] A. Del Rio, P. Piras, Ch. Roussel, Chirality 18 (2006) 498.18] S. Schefzick, M. Lämmerhofer, W. Lindner, K.B. Lipkowitz, M. Jalaie, Chirality 12
(2000) 742.19] C. Altomare, A. Carotti, S. Cellamare, F. Fanelli, F. Gasparrini, C. Villani, P.-A.
Carrupt, B. Testa, Chirality 5 (1993) 527.20] W.M.F. Fabian, W. Stampfer, M. Mazur, G. Uray, Chirality 15 (2003) 271.21] A. Golbraikh, D. Bonchev, A. Tropsha, J. Chem. Inf. Comput. Sci. 41 (2001) 147.22] J. Aires-de-Sousa, J. Gasteiger, J. Chem. Inf. Comput. Sci. 41 (2001) 369.23] J. Aires-de-Sousa, J. Gasteiger, J. Mol. Graph. Model 20 (2002) 373.24] J. Aires-de-Sousa, J. Gasteiger, I. Gutman, D. Vidovi, J. Chem. Inf. Comput. Sci. 44
(2004) 831.25] A. Del Rio, J. Gasteiger, QSAR Comb. Sci. 27 (2008) 1326.26] S. Caetano, J. Aires-de-Sous, M. Daszykowski, Y. Vander Heyden, Anal. Chim.
Acta 544 (2005) 315.27] C. Roussel, A. Del Rio, J. Pierrot-Sanders, P. Piras, N. Vanthuyne, J. Chromatogr.
A 1037 (2004) 311.28] A. Del Rio, P. Piras, C. Roussel, Chirality 17 (2005) S74.29] C. Zhao, N.M. Cann, J Chromatogr. A 1149 (2007) 197.30] Lipkowitz, J. Chromatogr. A 906 (2001) 417.31] M. Szaleniec, C. Hagel, M. Menke, P. Nowak, M. Witko, J. Heider, Biochemistry
46 (2007) 7637.32] T. Saito, Y. Nishimoto, M. Yasuda, A. Baba, J. Org. Chem. 71 (2006) 8516.33] P.V. Ramachandran, B. Gong, H.C. Brown, Tetrahedron Lett. 35 (1994) 2141.34] E.T. Everhart, J.C. Craig, J. Chem. Soc. Perkin Trans. 1 (1991) 1701.35] O. Kniemeyer, J. Heider, J. Biol. Chem. 276 (2001) 21381.36] A. Dudzik, M. Pawul, M. Szaleniec, B. Kozik, M. Witko, in: B. Sulikowski (Ed.),
Proceedings of Catalysis for Society Conference, Kraków, 2008, p. 236.37] CAChe WS Pro v 7.5′′ , Fujitsu Limited, Tokyo, Japan.38] J.J.P. Stewart, J. Comp. Chem. 10 (1989) 221.39] Gaussian 03, Revision D.01, M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.E. Scuse-
ria, M.A. Robb, J.R. Cheeseman, J.A. Montgomery, Jr., T. Vreven, K.N. Kudin, J.C.Burant, J.M. Millam, S.S. Iyengar, J. Tomasi, V. Barone, B. Mennucci, M. Cossi, G.Scalmani, N. Rega, G. A. Petersson, H. Nakatsuji, M. Hada, M. Ehara, K. Toyota,R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, M.Klene, X. Li, J.E. Knox, H.P. Hratchian, J.B. Cross, V. Bakken, C. Adamo, J. Jaramillo,R. Gomperts, R.E. Stratmann, O. Yazyev, A.J. Austin, R. Cammi, C. Pomelli, J.W.Ochterski, P.Y. Ayala, K. Morokuma, G.A. Voth, P. Salvador, J.J. Dannenberg, V.G.Zakrzewski, S. Dapprich, A.D. Daniels, M.C. Strain, O. Farkas, D.K. Malick, A.D.Rabuck, K. Raghavachari, J.B. Foresman, J.V. Ortiz, Q. Cui, A.G. Baboul, S. Clifford,J. Cioslowski, B.B. Stefanov, G. Liu, A. Liashenko, P. Piskorz, I. Komaromi, R.L. Mar-tin, D.J. Fox, T. Keith, M.A. Al-Laham, C.Y. Peng, A. Nanayakkara, M. Challacombe,P.M. W. Gill, B. Johnson, W. Chen, M. W. Wong, C. Gonzalez, J.A. Pople, Gaussian,Inc., Wallingford CT (2004).
40] A.D.J. Becke, Chem. Phys. 98 (1993) 5648.41] M. Cossi, G. Scalmani, N. Rega, V. Barone, J. Chem. Phys. 117 (2002) 43.42] Accelrys, Inc., Accelrys Material Studio v4.2.2, San Diego: Accelrys Software Inc.
