1. Selorm Modzabi, Marianna A. Busch and Kenneth W. Busch
Selection of appropriate chiral selectors for chiral analysis by regression modeling of spectral data
Selorm Modzabi, Marianna A. Busch and Kenneth W. Busch
Baylor University
One Bear Place #97348
Waco, TX 76798

2. Need for chiral analysis*
Pharmaceutical industry
Drug development
Process control
Agro-chemical industry
Food and beverage industry
Fragrance industry
Basic research
*Chiral Analysis, K. W.Busch & M. A. Busch Eds., Elsevier, 2006

3. CHIRAL ANALYSIS BY REGRESSION MODELING OF SPECTRAL DATA
(CARMSD)
Modern chemical instrumentation allows us to combine—
Multivariate (multi-wavelength) data collection
with
Multivariate modeling
to give a powerful combination that can extract latent information from the multivariate data that would not be possible with univariate measurements

4. Basic Strategy of CARMSD 1. Calibration Phase
Prepare a set of calibration samples
Same total concentration of chiral analyte
Different known enantiomeric compositions
Fixed concentration of chiral auxiliary

5. Basic Strategy of CARMSD 1. Calibration Phase
Collect spectral data on the calibration set
Perform PLS-1 regression modeling

6. Basic Strategy of CARMSD 2. Validation Phase
Prepare a new set of validation samples
Collect spectral data
Enter the spectral data into the regression model and predict the enantiomeric compositions
Compare the predicted enantiomeric compositions with the known values

7. CARMSD
Clearly the chiral auxiliary is at the heart of the CARMSD method.
Regression modeling depends on changes in the spectral signature with enantiomeric composition of the sample.
The larger these spectral changes are, the easier it is to develop robust regression models.

8. CARMSD Chiral Selectors used to date Cyclodextrins
Modified cyclodextrins
Surfactants & mixed cyclodextrins
Chiral Surfactants
Chiral Ionic Liquids

9. Enantiomer-CD transient inclusion complexes
Enantiomeric discrimination by transient noncovalent complex formation with cyclodextrins— An Example
Enantiomeric pair CD CD
Diastereomeric pair
(hypothetical)
Enantiomer-CD transient inclusion complexes 9

10. Chiral selectors used to date
Chiral analytes: amino acids, pharmaceuticals, other organics
Chiral selector
Analyte concentration
Prediction error (RMSEP) range
Cyclodextrins (CDs)
3.75 – 7.50 mM
0.02 – 0.07
Modified cyclodextrins (MCDs)
7.5 mM
0.05 – 0.6
Chiral surfactants (CSs) 1.5 – 6 % w/v
1.0 x x 10-6 M
0.02 – 0.05
Chiral ionic liquids (CILs) 30 and 150 mM
5 mM
0.05 – 0.09
Analyte to selector (CDs, MCDs) mole ratio: = 1 : 2
RMSEP = [S (xip – xi)2/n]1/2 : xi = known mole fraction, xpi = predicted mole fraction & n = total samples
predicted

11. Problems with CDs Limited Solubility
Extent of interaction depends on formation constant of inclusion complex
Possibility of more than one complex in solution (R—CD, CD—R—CD, etc.)
Inclusion complex formation depends on size of analyte in relation to cyclodextrin

12. What about other possible chiral auxiliaries?

13. Use of Chiral Amines
Ion-pair formation as a means of enantiomeric discrimination
Formation of quaternary ammonium salts of carboxylic acids

14. CARMSD with a Homochiral Amine
Chiral selector: (S)-1-phenylethylamine (S-PEA)
Determination of enantiomeric composition of Tyrosine with
(S)-phenylethylamine using UV spectroscopy
Zwitterion
Diastereomeric ion pairs 14

15. Use of (S)-1-phenylethylamine
Determination of enantiomeric composition of Tyrosine
(Tyr to S-PEA ratio = 1 : 1)
Original spectra
Isosbestic point
(343 nm) 15

16. Effect of pH on spectrum
Effect of varying PEA/Tyr
mol ratios at neutral pH
Effect of varying PEA/Tyr mol ratios in acid solution

17. Job’s Plot
Job’s plot in neutral solution indicating 1:1 ion pair formation
Job’s plot in acid solution indicating lack of ion pair formation

18. Determination of enantiomeric composition of Tyrosine with (S)-phenylethylamine using UV spectroscopy— PLS1 Calibration Model Plots
Randomly Selected Calibration Samples: , , 0.300,
0.400, 0.500, 0.650, 0.850, 0.950

19. Determination of enantiomeric composition of Tyrosine with (S)-phenylethylamine using UV spectroscopy— Results of CARMSD
Cross validation of calibration samples: , , 0.300, 0.400, 0.500, 0.650, 0.850, 0.950
Actual D-Tyr mole fraction
Predicted D-Tyr mole fraction
Predicted L-Tyr mole fraction
0.100
0.101
0.899 (0.900)
0.250
0.750 (0.750)
0.300
0.303
0.697 (0.700)
0.400
0.401
0.599 (0.600)
0.450
0.462
0.538 (0.550)
0.650
0.659
0.341 (0.350)
0.750
0.758
0.242 (0.250)
0.800
0.802
0.198 (0.200)
0.900
0.901
0.099 (0.100)
RMSEP: D-Tyr & L-Tyr = 0.006
Values in bracket = Actual mole fraction of L-Tyr
Cross validation plot 19

20. Determination of enantiomeric composition of Phenylalanine with (S)-phenylethylamine using UV spectroscopy- Results of CARMSD
Cross validation of calibration samples: , 0.100, 0.200, 0.392, 0.500, 0.527, 0.700, 0.950
Actual D-Phe mole fraction
Predicted D-Phe mole fraction
Predicted L-Phe mole fraction
0.150
0.185
0.823 (0.850)
0.267
0.264
0.738 (0.733)
0.352
0.348
0.649 (0.648)
0.468
0.464
0.529 (0.532)
0.486
0.483
0.515 (0.514)
0.527
0.524
0.465 (0.473)
0.600
0.597
0.397 (0.400)
0.650
0.640
0.346 (0.350)
0.819
0.814
0.161 (0.181)
RMSEP: D-Phe = & L-Phe = 0.011
Values in bracket = Actual mole fraction of L-Phe 20

21. Determination of enantiomeric composition of Alanine with(S)-phenylethylamine using UV spectroscopy- Results of CARMSD
Cross validation of calibration samples: , 0.100, 0.250, 0.350, 0.500, 0.650, 0.750, 0.850
Actual L-Ala mole fraction
Predicted L-Ala mole fraction
Predicted D-Ala mole fraction
0.200
0.211
0.789 (0.800)
0.300
0.279
0.721 (0.700)
0.400
0.404
0.596 (0.600)
0.600
0.631
0.369 (0.400)
0.700
0.692
0.308 (0.300)
RMSEP: D-Ala & L-Ala = 0.018
Values in bracket = Actual mole fraction of D-Ala 21

22. Use of Chiral Alcohols Fischer Esterification
Esterification results in the formation of true covalent diastereomers

23. CARMSD with Homochiral (S)-(+)-1,2-propanediol
Determination of enantiomeric composition of Phenylalanine with
(S)-1,2-propanediol using UV spectroscopy
Possible products 23

24. Determination of enantiomeric composition of Phenylalanine with 1,2-propanediol using UV spectroscopy
Original UV spectra (15 samples)
Mean centered UV spectra (15 samples) 24

25. Determination of enantiomeric composition of Phenylalanine with 1,2-propanediol using UV spectroscopy- PLS1 Calibration Model Plots
Randomly selected calibration samples: 0.050, 0.150, 0.250, 0.300, 0.500, 0.750, and 0.950

26. Determination of enantiomeric composition of Phenylalanine with 1,2-propanediol using UV spectroscopy- Results of CARMSD
Randomly selected calibration samples: 0.050, 0.150, 0.250, 0.300, 0.500, 0.750, and 0.950
Actual D-Phe mole fraction
Predicted D-Phe mole fraction
Predicted L-Phe mole fraction
0.103
0.0848
0.915 (0.897)
0.400
0.407
0.593 (0.600)
0.451
0.425
0.575 (0.549)
0.597
0.596
0.404 (0.403)
missing
0.773
0.227 (missing)
0.801
0.208 (0.199)
0.851
0.859
0.141 (0.149)
0.877
0.123 (missing)
RMSEP: D-Phe & L-Phe = 0.014
Values in bracket = Actual mole fraction of L-Phe 26

27. CARMSD with noncovalent diastereomers vs
CARMSD with noncovalent diastereomers vs. CARMSD with covalent diastereomers
RMSEP figure of merit analysis of chiral discrimination strategies
0.1
0.2
0.3
0.4
0.5
0.6
Cyclodextrins
Modified
Chiral Surfactants
Chiral Ionic Liquids
Covalent/ionic
Diatereomers
LEL
UEL
UEL - LEL
LEL = lower RMSEP limit and UEL = upper RMSEP limit