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cin. Figure 9a–d illustrates ion electropherograms for these three, both without (left) and with (right) receptor. Figure 9e shows the reconstructed ion electropherogram, again both with and without receptor. It was also shown that the binding was sequence-specific, as Fmoc-DDAY, Fmoc-DDAF, and FmocDDAH did not bind to vancomycin in these experiments (87).

Chu et al. have also published more extensive results of using ACE-MS to successfully screen combinatorial libraries using larger numbers (500–1000 compounds) and a larger variety of amino acid residues (88). They felt that larger libraries would be difficult to analyze because the sensitivity of the MS instrument was not sufficient to handle many more compounds. They suggested that a more sensitive MS detection method, such as ion trap-MS, might increase the size of the library suitable for successful screening. Another way to increase the possible library size, they explained, was to remove the nonbinding peptides prior to introduction of the more interesting, strongly binding compounds, into ACE-MS. In order to remove many of the nonbinding ligands and thus possibly allow a larger initial library to be used, an affinity extraction step was developed in which the receptor was immobilized to a solid support. The peptides that did not bind were discarded, whereas the bound peptides were eluted and then analyzed with the ACE-MS method. This caused the binding peptides to be preselected and preconcentrated, which improved the capabilities of the ACE-MS method for screening combinatorial libraries (88).

Boutin et al. have also used HPCE [as well as NMR, MS, and tandem MS (MS/MS)] to analyze combinatorial libraries (89). Their goal was to provide a complete set of analytical data on a tetrapeptide library synthesized from 24 amino acids. The purpose of the work was to prove that all of the amino acids used in the synthesis were present in the resulting library, in the amounts theoretically calculated. Using HPCE, the library was split into different classes of compounds based on their net charges and the pH of the electrophoretic buffer. Integration of the areas under each curve, while taking into account the effect of the number of charges on the migration rate (amountarea/time), allowed an estimation of the number of compounds in each class. Correlation coefficients of 0.98 and higher (n 10) were found between the theoretically calculated number of compounds in each class and the values obtained by integration. HPCE was also used to show that the side chains of the amino acids were fully deprotected (89).

Dunayevskiy et al. showed the ability of HPCE-MS to determine the purity and composition of a library theoretically composed of 171 disubstituted xanthene derivatives, with the possibility of analyzing libraries of up to 1000 components (92). Previously, the ability of MS alone to analyze a library of up to 55 components was shown (93), but it was suggested that for more

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complex mixtures a second separation method needed to be added to better resolve the species. HPCE-MS was attempted. The investigators found that 124 of the possible 171 compounds overlapped in molecular weight, which would have made MS identification alone difficult if not impossible. When HPCE was used prior to MS, most of these 124 compounds were separated, with only eight MS/MS experiments needed to identify the 19 unresolved molecules with overlapping molecular weights. Figure 10 shows how the addition of an organic modifier, 40% methanol in this case, to the electrophoretic buffer improved the resolution. Figure 10a shows the comigrations without the organic modifier and 10b shows how the resolution was improved so that all six peaks could be distinguished when the modifier was added. This addition of an organic modifier caused only four MS/MS experiments to be needed for nine compounds (92).

Finally, Jung et al. published a communication showing that cyclohexapeptide libraries could be used as chiral selectors in HPCE (90). They explained that a lot of time and effort could be saved by using a library of possible chiral selector compounds, rather than attempting to test single compounds one by one. Furthermore, they suggested that some libraries might show cooperative effects between the components that could affect enantiose-

Figure 10 CE-MS electropherogram of different mixtures of xanthene derivatives in Tris-acetate buffer at pH 7.9. (a) Mixture 2, Ile/X/Ile(1), Ile/X/Pro (2), Ile/X/Ala (3), Pro/X/Pro (4), Pro/X/Ala (5), and Ala/X/Ala (6). (b) Mixture 2 dissolved in 20 mM Tris-acetate buffer at pH 7.9 containing 40% (vol/vol) MeOH. E 07 107 (92). (Reproduced with permission of the copyright holder, publisher and the Proceedings of the National Academy of Science USA).

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Figure 11 Enantiomeric resolution of (a) DNP-D, L-glutamic acid with c(DFXXXa) and (b) DNP-D, L-glutamic acid with c(RKXXXa). Specific conditions indicated elsewhere (90). (Reproduced with permission of the copyright holder, publisher and

Angew. Chem. Int. Ed. Engl.).

lectivity. They successfully separated 2,4-dinitrophenyl-D,L-glutamic acid, when they ran a cyclopeptide library composed of c(DFXXXa) in the running buffer at 20 kV (Fig. 11a). They improved the resolution and selectivity of this separation when they changed the library to c(RMXXXa) and used inverse polarity at 10 kV (Fig. 11b). Work is now in progress on the use of their method to screen for the most effective chiral selector and to then identify such component(s) (90).

From these examples, it can be seen that the use of HPCE to separate, identify, and analyze compounds in combinatorial libraries is on the increase. The questions are how to further improve HPCE approaches for combinatorial library searching, how to further improve the crude preseparation of uninteresting compounds in the library from more interesting and potentially binding ligands, and then how to improve the overall HPCE separations prior to use of MS or MS/MS routines to identify each individual binding library component.

V.FUTURE ROLE OF HPCE IN COMBINATORIAL LIBRARY ANALYSIS AND INTERPRETATION

CE appears to offer some very exciting and scientifically significant advantages insofar as being able to select (recognize) bioactive drug candidates and

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resolve these from less interesting subsets of a combinatorial library population. There are some lingering problems in using CE-based techniques, such as requiring a major instrumental commitment of CE and MS components. These ACE-MS techniques also require sophisticated operators, i.e., trained personnel familiar with both CE and MS operations and sample requirements. There is the need to develop good separation conditions for the library in the presence of the receptor molecule(s), so that just those compounds actually interacting with the receptor will be retarded or retained and separated from less interesting library components. There is of course also the need for purified receptor molecules, but that is true in any library search method, be that LC-, MS-, or CE-based. There are alternative techniques that might work, at times as well or even better than ACE-MS, such as affinity filtration or affinity membrane separation methods, prior to HPLC-MS or CE-MS, where the affinity step is performed apart from the separation instrument (precolumn). Because there are numerous separation conditions already available for many library mixtures, these can be readily utilized for ACE-MS library search methods/conditions, perhaps with little further method optimization. There is little question but that ACE-MS methods do work, though the maximum size of the library that can be successfully searched remains a question. There are also very few research groups that are routinely utilizing ACE-MS for combinatorial library searching, although this appears from the available literature to be a completely viable and successful technique, perhaps for a wide variety of compound (drug) types. There appears to be a growing role for ACE and ACE-MS in combinatorial library searching, and one would expect that its application will only continue to grow in the future.

VI. CONCLUSIONS

This chapter reviews some of the basic principles of CE operations, and how CE can be used to separate individual library members on the basis of their interaction and recognition by a receptor molecule. Also reviewed are some of the basic needs or requirements for a successful library searching approach in CE, such as having the receptor molecule in the sample before injection or in the CE buffer during separation. Both are totally viable approaches and have been described in the literature. The literature involving affinity approaches in CE, antigen–antibody recognition, antibody–drug interactions, resolution of ligand–receptor complexes from other components of the sample, and then the use of ACE and ACE-MS to resolve active, receptor-binding species from nonactive components of the original library mixture, has also been reviewed.

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In addition, this chapter describes how resolved library components can then be structurally identified by various MS approaches, so that active, lead compounds cannot only be shown active against a particular target receptor, but their actual structures can then be determined with almost 100% success using modern MS approaches and structure software. These techniques are clearly very new, but they draw on older, more established CE and MS methods, and therefore the hyphenated methods of ACE-MS also appear to be perfectly usable and reliable. These analyses, now possible by ACE-MS, are nothing more than separation/detection hyphenated methods, which are now combining an affinity recognition step in the ACE portion with structural determinations via the MS portion of the ACE-MS system. However, the affinity recognition step in the ACE part simplifies or isolates interesting, perhaps target, compounds from all other library components, making the job of the MS much simpler and more straightforward. The affinity recognition step and the less demanding MS analysis are perhaps the real attributes of using CE and ACEMS for combinatorial library searching to isolate active drug candidates against specific receptors (or ligands).

GLOSSARY

Ab antibody Abs antibodies

ACE affinity capillary electrophoresis Ag antigen

Ab–Ag antibody-antigen complex Ab–En antibody–enzyme conjugate anti-BSA antibody to BSA

anti-Ab antibody of Ab

APCE affinity probe capillary electrophoresis BSA bovine serum albumin

CE HPCE capillary electrophoresis CEC capillary electrochromatography CGE capillary gel electrophoresis CIEF capillary isoelectric focusing CL chemiluminescence detection CZE capillary zone electrophoresis Cys A cyclosporin A

EC electrochemical detection

ELISA enzyme-linked immunosorbent assay

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EMMA enzyme-modulated microanalysis En enzyme

EOF electroosmotic flow

Fab fragment of intact antibody containing recognition region (epitope) Fab’ tow Fab fragments held together by at least one disulfide bridge

Fc crystalline fragment of intact antibody containing carbohydrate regions FL fluorescence detection

FITC fluorescein isothiocyanate

FMOC-Cl 9-fluorenyl methyl chloroformate FMOC 9-fluorenyl methyl formyl (grouping) FSCE free solution capillary electrophoresis FTIR Fourier transform infrared

hGH human growth hormone

HPAC high-performance affinity chromatography

HPIAC higher-performance immunoaffinity chromatography HPLC high-performance liquid chromatography

HRP horseradish peroxidase (enzyme)

IACE immunoaffinity capillary electrophoresis ICE immunoassay CE

ICA immunochromatographic analysis ID immuno-detection

IgG immunoglobulin

immuno-ACE immunoaffinity capillary electrophoresis LIF laser-induced fluorescence (detection)

MECC micellar electrokinetic capillary chromatography MEKC micellar electrokinetic chromatography

MS mass spectrometry or spectrometer MS/MS tandem mass spectrometry NMR nuclear magnetic resonance RIA radioimmunoassay detection

SEC size exclusion chromatography

TR tetramethylrhodamine FL tag (probe) UV ultraviolet detection

ACKNOWLEDGMENTS

Our knowledge and appreciation of affinity recognition in HPLC and HPCE has been attributable to several firms and individuals. For example, we must indicate our appreciation to numerous individuals within PerSeptive Biosys-

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tems, Inc., especially M. Meyes, R. Mhatre, T. Naylor, M. Vanderlaan, S. Martin, F. Regnier, and others, who over the years have provided us with guidance, suggestions, encouragement, and materials. Professor H. Zou is acknowledged for his early collaborations in the areas of HPIAC and ID. D. Fisher also collaborated on some of the early developments and optimization in utilizing ID in a postcolumn, HPLC format. Professor G. Li also collaborated on some of the early developments in utilizing HPIAC and ID, prior to interfacing with HPLC and then to our own development of affinity and immunoaffinity recognition studies in HPCE areas. Several graduate students and postdoctoral fellows or visiting scientists worked with us on the development of affinity CE applications, such as R.-L. Qian, R. Strong, B.-Y. Cho, H. Zou, and X. Liu. Certain early drafts of sections of this review were prepared by R.-L. Qian and X. Liu, for which we are grateful.

Finally, financial support and technical collaborations have been provided NU in antibody areas by Pharmacia and Upjohn Pharmaceutical Company, through the Animal Health and Drug Metabolism Division (J. Nappier and G. Fate). Additional antibody analysis collaborations have been possible through SmithKline Beecham Pharmaceuticals (D. Nesta and J. Baldoni). These contracts have allowed us to become involved in affinity and immunoaffinity CE areas. Isco Corporation, Thermo Separation Products (Thermo Quest), and Waters Corporation have all donated major instrumentation, materials, and supplies to our efforts in the areas of affinity CE. Colleagues at Supelco, Phase Separations, Ltd., J&W Scientific, and Unimicro Technologies have all donated coated or packed capillaries for studies in CE and CEC. We are very appreciative of all these collaborations and technical/financial assistance in developing CE, CIEF, and, most recently, ACE approaches for proteins and antibodies.

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