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Nature Medicine  9, 118 - 122 (2003)
doi:10.1038/nm0103-118

Advances in the use of synthetic combinatorial chemistry: Mixture-based libraries

Clemencia Pinilla1, 2, Jon R. Appel1, Eva Borràs1 & Richard A. Houghten1, 2

1 Torrey Pines Institute for Molecular Studies, San Diego, California, USA

2 Mixture Sciences, Inc., San Diego, California, USA

Correspondence should be addressed to Richard A. Houghten rhoughten@tpims.org
The conceptual and technical approaches that led to the explosive growth of combinatorial chemistry began approximately 20 years ago. In the past decade, combinatorial chemistry has continued to expand with new chemistries, technological improvements and, most importantly, a clear demonstration of its utility in the identification of active compounds for research and drug-discovery programs. This article describes the conceptual and practical breakthroughs that have been critical for the development of synthetic combinatorial methods and includes the most recent developments and applications of mixture-based combinatorial libraries.
Combinatorial techniques have their origins in Merrifield's seminal solid-phase synthesis of peptides in 1963 (refs. 1 and 2) and have been greatly accelerated by the parallel solid-phase synthesis methods developed during the mid-1980s. These parallel synthetic approaches were initially used for the synthesis of oligonucleotides, as first illustrated by the 'spot'3 approach, and for the synthesis of peptides using the 'pin'4 and 'tea-bag'5 approaches. Such approaches enabled hundreds of individual compounds to be prepared for use in bioassays in a fraction of the time and at a fraction of the cost of earlier linear 'one-at-a-time' solid-phase methods.

Combined with these conceptual advances, the recent increased understanding of heterocyclic chemistry on solid supports has expanded the strategies for the synthesis of such compounds, and has permitted a much larger number of these 'drug-like' molecules to be prepared. It is noteworthy that Leznoff6, 7 and Rapoport8 first carried out the synthesis of heterocycles on the solid phase in 1973 and 1976, respectively, but this powerful approach remained unappreciated and unexploited until Ellman's work in 1992 (ref. 9). The rapidly increasing number of organic reactions being adapted for use in solid-phase chemistry10, 11, 12 is further testament to the escalating importance of synthetic combinatorial libraries. These and other solid-phase synthetic approaches have led to the generation of large individual compound libraries of virtually all compound types, including low-molecular-weight acyclic and heterocyclic compounds11, 13, 14, peptidomimetics15, 16, 17 and oligosaccharides18.

Despite the 10- to 100-fold increase in the synthesis of individual compounds made possible by the pin, tea-bag and spot techniques, it soon became clear that the barrier in virtually all studies remained the number of compounds desired for studies in therapeutically relevant bioassays versus the number that could be synthesized. This led to the development of several different synthetic combinatorial methodologies that allowed the synthesis and activity profiling of literally millions of compounds (one-bead, one-compound and mixtures19, 20; see Supplementary Table 1 online). Even though these approaches were initially used successfully to identify peptide antigens recognized by monoclonal antibodies, it was clear that these same methods could be used in several different assays for a range of different chemical compounds, including heterocycles. In addition, when one-bead, one-compound libraries are tested in a manner in which a large number of beads are cleaved per well21, both approaches can be considered mixture-based, but they differ in their deconvolution approaches.

Mixture-based libraries, when arranged in a positional scanning format22, provide extensive structure−activity information in any given assay. This is inherent with this approach as positional scanning libraries are composed of systematically arranged mixtures having defined and mixture positions. Thus, information regarding the activity of each functionality is obtained for each position of the library. In contrast, only the chemical information of the identified active compounds is obtained from one-bead, one-compound libraries. A conceptual illustration of a positional scanning synthetic combinatorial library (PS-SCL), as well as the representation of a hexapeptide and a bicyclic guanidine PS-SCL, is presented (Fig. 1). The illustration shows a simple example of a tripeptide PS-SCL composed of 27 tripeptides (3 amino acids at each position, 33 = 27). This tripeptide PS-SCL would be composed of three sub-libraries (OXX, XOX, XXO) and nine separate mixtures in which the defined positions (O) contain one of the three amino acids and the mixture positions (X) contain a mixture of all three amino acids. It is important to note that each of the three sub-libraries varies only in the location of the defined amino acid, but contains the same peptides that are arranged differently. If RAT is the only active peptide, then RAT would be responsible for the activity found for the mixtures RXX, XAX and XXT from each of the sub-libraries. Thus, the active compound, in this case RAT, is identified by synthesizing the combination of the defined amino acids in the most active mixtures at each position. The positional scanning concept can be used to prepare libraries of any length or compound class. While in the tripeptide illustration 27 tripeptides would be arranged in 9 mixtures, a hexapeptide PS-SCL using 20 amino acids at each position results in a library composed of approximately 64 million hexapeptides arranged in only 120 mixtures22, and a bicyclic guanidine PS-SCL having three diversity positions results in a library of over 100,000 compounds arranged in only 141 mixtures14, 23.

Figure 1. Conceptual illustration of a tripeptide PS-SCL (top) and representation of a peptide (middle) and heterocyclic (bottom) PS-SCL.
Figure 1 thumbnail

O, defined functionality; X, mixture of functionality.



Full FigureFull Figure and legend (72K)
Active compounds identified from mixture-based libraries
Mixture-based libraries have been used in a wide range of bioassays by several groups in pharmaceutical companies and academic institutions23. Mixture-based library approaches will continue to find favor with researchers who wish to decrease the time and costs of screening, have limited resources, have limited knowledge of their biological target, and/or have assays that are not amenable to classic high-throughput approaches. The most commonly used libraries and deconvolution methods include iterative20, 24, positional scanning22 and the sequencing of resin-bound peptides or tags from one-bead, one-compound libraries19, 25, 26. Mixtures also enable large numbers of compounds to be tested in classically 'low-throughput' assays (for example, tissue and/or in vivo systems) and in those systems in which target reagents are limited by availability or cost.

The first in vivo demonstration of the use of mixtures involved the measurement of blood pressure and heart rate following the injection of mixtures of tens of thousands of peptides27. Also, the simultaneous pharmacokinetic in vivo screening of mixtures has proven to be beneficial to drug-discovery programs by increasing the throughput of lead identification and optimization28, 29. It should be noted that the direct in vivo study of mixtures requires a specific and readily quantifiable measure, and factors that affect metabolism and distribution, such as aggregation and protein binding, must be considered.

In the past five years several reports have been presented on the use of PS-SCLs as substrates for the analysis of protease specificity as an alternative to traditional methods, such as the synthesis and testing of large numbers of individual compounds, that are generally tedious and often produce an incomplete analysis. PS-SCLs allow the unbiased analysis of thousands of substrates in a short period of time. The first report of PS-SCLs as substrates presented the substrate specificity for nine human caspases and granzyme B (refs. 30 and 31). A later study reported on the modification of the linker and the fluorochrome that allowed the incorporation of different defined amino acids at the P1 position, and this library was used to analyze the specificity of plasmin and thrombin32, 33. More recently, PS-SCLs have been used for the characterization of homologous tryptases34, isoforms of cercarial elastases35, parasite and human asparaginyl endopeptidases36, clinically derived HIV drug-resistant protease variants37, and the proteasome in the presence and absence of activators38, 39. These studies have provided specificity information that corresponded with data derived from traditional methods. Furthermore, this information has allowed the identification of sensitive and selective substrates and potent inhibitors, as well as the definition of potential physiological substrates.

Examples of small-molecule and heterocyclic combinatorial libraries for the identification of therapeutic leads directly from library screening and deconvolution are summarized (Table 1). These libraries vary in design and complexity, and have been used for a wide range of targets in biochemical and cell-based assays. This recent work demonstrates that the initial combinatorial concepts presented and used for peptides and model systems can be successfully extended to different chemical diversities and medically relevant targets.

Table 1. Representative examples of lead compounds identified from small-molecule heterocyclic libraries
Table 1 thumbnail

Full TableFull Table
Among the most important factors that must be considered for the successful use of combinatorial libraries, especially mixture-based libraries, is the careful development and reproducibility of the chemistry, an understanding and optimization of the sensitivity of the biological assay, and the correct data analysis of the screening results that allow deconvolution to active individual compounds. Several new solid-phase synthetic approaches40, 41 have been used to prepare heterocyclic positional scanning libraries and these libraries are currently undergoing screening.

One of the most widespread applications of combinatorial chemistry, and in particular PS-SCLs, has been in the field of immunology. PS-SCLs made up of all the possible peptides for a given length were first applied to the study of antibody specificity and the identification of cross-reactive B-cell epitopes22, 42, and more recently for the study of T-cell specificity43, 44, 45. For example, recent T-cell studies have focused on the identification of immunodominant epitopes in infectious diseases46, 47, 48, autoimmune disorders44, 49, 50, 51 and the determination of tumor antigens52, 53, 54, 55. Nonapeptide and decapeptide libraries containing trillions of different peptides have been used to study the specificities of both CD4+ and CD8+ T-cell clones. The information derived from the screening of PS-SCLs is used to optimize T-cell epitopes of known specificity or to identify novel T-cell ligands for clones of unknown specificity56.

When a native T-cell epitope is known, approximately 25% of the identified epitope mimics are found to be superagonists. These can be up to three orders of magnitude more effective than the native ligand48, 49. When the specificity of a clone is unknown, use of PS-SCLs offers a non-biased strategy to rapidly identify new ligands through the testing of literally trillions of potential candidates. Several of these agonists are effective immunogens capable of inducing potent T-cell−mediated immune responses to the native peptide ligand in vitro48, 50, 52 and in humans54. It is important to point out that some of these identified 'mimic epitopes' have multiple non-conservative changes of the native sequence that would have been extremely difficult to predict by traditional methods. Hence, this method permits the identification of sequences that correspond to proteins that undergo mutations or post-translational modifications that could not have been found using genetic-based databases. Furthermore, the identification of novel ligands not related to the native T-cell ligand has led to new principles regarding T-cell receptor (TCR) degeneracy. When studying a T-cell clone known to recognize an autoantigen, cross-reactive sequences from viral and bacterial proteins can be identified. Thus, molecular mimicry, which has been postulated to have a role in the development of autoimmune diseases, can now be systematically studied. PS-SCLs, therefore, represent a powerful emerging tool for the identification of important epitopes that could be used in the treatment and diagnosis of infectious diseases, as well as cancer and autoimmune diseases.

PS-SCL-based biometrical analysis
The studies using PS-SCLs with T cells of known specificity, when compared with the results from single substitution analogs, clearly demonstrate that each amino acid within a peptide contributes to recognition almost independently and in an additive fashion49. This observation led to the development of a new strategy: a PS-SCL−based biometrical analysis that integrates data acquisition from the screening of PS-SCLs and protein sequence databases to identify peptide ligands47. This biometrical analysis compares the information derived from libraries composed of trillions of peptides with the millions of decapeptide segments of proteins contained in a protein database to rank and predict the most stimulatory peptide ligands. Within the highest-ranking sequences, active natural peptides are identified with unprecedented efficiency.

This strategy is straightforward. The experimental data from the screening of a PS-SCL provide the necessary information to generate a numerical matrix. The matrix entry for a particular amino acid in a specific position is based on the activity value for the mixture from the PS-SCL corresponding to that amino acid defined in that position. The biometrical analysis then uses this matrix to score every peptide of the same length for the tested PS-SCL for every protein in the Genpept database by moving a scoring window across the known protein sequences in one-amino-acid increments. Using a decapeptide as an example, every protein within the database is broken down into ten-amino-acid peptides that overlap the entire protein sequence. Each peptide is given a predictive score generated from the matrix whose value is based on the activity of the PS-SCL. The database analysis results in a list of ranked peptide sequences and identifies the corresponding natural protein having the highest scored peptides. At this step, selected peptides are synthesized and their activities determined.

The efficacy of the PS-SCL−based biometrical analysis has been demonstrated with T-cell clones of known specificity47, 52, 53, 55 (Fig. 2a). In all cases, the known T-cell ligand ranked within the first 200 peptides. It is important to note that these are the first 200 peptides out of more than 20 million scored in the analysis of the viral and human databases. Moreover, the specificity of a cerebrospinal-fluid−derived T-cell clone from a patient suffering from Lyme disease was elucidated, and several peptides representing both Borrelia burgdorferi and human protein sequences were identified46. A more powerful strategy would result from the identification of T-cell ligands of unknown specificity using PS-SCL−based biometrical analysis combined with the study of overexpression of genes in a given target organ determined by cDNA microarray chips. This is of special interest for tumor-relevant targets in individual patients, as well as for proteins involved in autoimmune diseases.

Figure 2. Elucidation of T-cell specificity and the identification of ligands for the mu-opioid receptor using biometrical analysis.
Figure 2 thumbnail

a, Summary of the results of the use of PS-SCL−based biometrical analysis for CD4+ and CD8+ clones of known specificity. For each T-cell clone, the known antigen, database used and the ranking of the known antigen are shown. b, Scoring distribution of the hexapeptides analyzed from the human database using the screening data of a hexapeptide PS-SCL against the mu-opioid receptor. c, The rank, the sequence and the protein source of the known enkephalin ligands are shown.



Full FigureFull Figure and legend (42K)
This PS-SCL−based biometrical approach is not, however, limited to the study of T-cell specificity. On the contrary, it can be used to identify ligands within proteins in databases for any molecular interaction that has been, or can be, studied with PS-SCLs composed of L-amino acids. In the following example, the predictive power of this powerful strategy is demonstrated for the G-protein-coupled mu-opioid receptor, for which several natural ligands, including methionine-enkephalin and leucine-enkephalin, are known. The activity of the mixtures of the hexapeptide PS-SCL (made up of 52 million hexapeptides) showed that in most of the positions the amino acids of the known ligands were within the defined amino acids in the most active mixtures. The scoring matrix derived from the screening data57 was used to score and rank all the hexapeptides within the human database, which is composed of 49,765 proteins that in turn represent approximately 13 million hexapeptides. The scoring distribution of all the hexapeptides in the human database shows that the number of peptides with high scores is low compared with the total number of peptides scored (Fig. 2b). The five known enkephalin ligands ranked within the top 50 peptides out of 13 million, demonstrating the predictive value of the PS-SCL−based biometrical analysis for G-protein coupled receptors (Fig. 2c).

A similar approach for the biometrical analysis of data generated from heterocyclic libraries in a positional scanning format is currently being explored. The screening data from these libraries are being integrated with physicochemical and pharmacokinetic properties of the central pharmacophore and its functionalities, and this approach will compliment the deconvolution of these libraries. This approach will be more efficient in the identification of therapeutic leads from libraries made up of hundreds of thousands to millions of heterocyclic compounds.

Summary
The methods encompassed by combinatorial chemistry are now more than 15 years old. As with virtually all innovations, combinatorial methods have been slowly understood and accepted, and this has been especially true for mixture-based combinatorial libraries. The resistance to mixture-based methods may be due to the conceptual distance between these approaches and the traditional 'one-at-a-time' methods the pharmaceutical industry has successfully used for decades. Complex mixtures composed of thousands to even billions of different compounds are now being used by an increasing number of groups in a variety of biological assays for the successful identification of highly active, novel ligands. It is expected, however, that combinatorial methods will continue to be critical tools for research and drug discovery programs, including functional proteomics.

Note: Supplementary information is available on the Nature Medicine website.

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Acknowledgments
We thank R. Martin and R. Simon for their contributions to the development of the biometrical analysis first used in T-cell studies; C. Dooley for the opioid receptor research; D. Wilson and S. Blondelle for their involvement in the use of combinatorial libraries in many different biological assays; and J. Ostresh, A. Nefzi and the chemistry group at Torrey Pines Institute for Molecular Studies for the continuing development of synthetic chemistry for the preparation of mixture-based combinatorial libraries. Supported by NCI grant PO1 CA78040, NIDA grant RO1 DA09410 and MSNRI funding.

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