A cherished goal of chemists is to design and synthesize compounds with a specific set of properties. This goal is particularly urgent in biological and medicinal chemistry as a part of the drug discovery process. Two powerful new tools in this effort are structure-based design (I. D. Kuntz, Science 257, 1078-1082 (1992).; I. D. Kuntz, et al., Accts. Chem. Res. 27, 117-123 (1994)) and combinatorial chemistry (L. A. Thompson, et al., Chem Rev. 96, 555-600 (1996); E. M. Gordon, et al., J. Med. Chem. 37, 1385-1401 (1994)). Structure-based design uses information gleaned from crystallographic and magnetic resonance experiments on a target macromolecule, frequently an enzyme, to guide the selection or design of inhibitors. Computation plays a major role in this endeavor (I. D. Kuntz, et al., Accts. Chem. Res. 27, 117-123 (1994); N. C. Cohen, et al., J. Med. Chem. 33, 883-894 (1990)). Combinatorial chemistry is based on general chemical transformations that allow different building blocks to be combined in high yield. These transformations can be performed in parallel to synthesize libraries of related compounds rapidly and efficiently (L. A. Thompson, et al., Chem Rev. 96, 555-600 (1996); E. M. Gordon, et al., J. Med. Chem. 37, 1385-1401 (1994)). Nonetheless, the discovery of a new lead compound or the improvement of the properties of an existing lead are still demanding tasks.
Combinatorial approaches to ligand identification initially focused on biopolymer libraries prepared by either chemical or biological methods (M. A. Gallop, et al., J. Med. Chem. 37, 1233-1251 (1994)). For these libraries, all possible combinations of the building blocks are typically used since there are only four natural nucleotide building blocks for aptamer libraries and 20 proteinogenic amino acid building blocks for peptide libraries. Both the structures of the compounds and the theoretical number of compounds in the library are determined by setting the length of the biopolymer chain. Recently, considerable efforts have been directed toward the preparation of libraries of compounds that encompass a wider spectrum of chemical transformations, leading to a broader range of properties than found in peptides or oligonucleotides (L. A. Thompson, et al., Chem Rev. 96, 555-600 (1996); E. M. Gordon, et al., J. Med. Chem. 37, 1385-1401 (1994)). These new approaches introduce significant challenges into library design.
A crucial element of any library design is the procedure for selecting which compounds to synthesize. This includes the choice of the scaffold, the basic reactions and the nature of the building blocks. If the building blocks are readily available components such as amines, aldehydes or carboxylic acids, the number of potential compounds to be considered can be quite large. For example, combining three building blocks with thousands of components at each position leads to over 1 billion compounds. While different strategies have distinct practical limits, typically a researcher is prepared to synthesize only thousands of spatially separate compounds and tens of millions of compounds in mixtures. Furthermore, evaluation and deconvolution of a very large library become rate-limiting activities (N. K. Terrett, et al., Bioorg. Med. Chem. Lett. 5, 917-922 (1995)). Thus, there would be significant advantages to a method of reducing the synthetic effort to a small subset of compounds biased towards the desired properties.
How can the potential choices be efficiently reduced? The standard strategies are diversity selection and directed selection. Diversity approaches attempt to maximize the sampling of chemical and biological properties given a fixed number of compounds (R. J. Simon, et al., Proc. Natl. Acad. Sci. U.S.A. 89, 9367-9371 (1992)). In directed libraries the size and often the diversity of the library is reduced by selecting those building blocks that are predicted to have favorable interactions with the target, or by eliminating candidates that are a priori believed to have unfavorable interactions. A directed library can be based on substrate preferences, information about known inhibitors or, on an assessment of the potential interaction of specific functional groups with the target. Both diverse and directed strategies permit a multistage attack with second libraries generated from active compounds found in the first round.
The development of general and efficient approaches to identify small, non-peptidic inhibitors of aspartic proteases continues to be of interest because of their important roles in therapeutically relevant processes (K. Takahashi, Ed., Aspartic Proteinases Structure, Function, Biology, and Biomedical Implications (Plenum Press, New York, 1995); J. Adams, et al., Ann. Rep. Med. Chem. 31, 279-288 (1996); J. J. Edmunds, et al., Ann. Rep. Med. Chem. 31, 51-60 (1996); D. K. Miller, Ann. Rep. Med. Chem. 31, 249-268 (1996)). Aspartic acid proteases are a widely distributed family of enzymes that play important roles in fungi, plants, vertebrates and retroviruses. The aspartic acid proteases (characterized by having two aspartic acid residues in the active site) catalyze the hydrolysis of amide bonds with specificity for peptide bonds located between large hydrophobic residues. A number of aspartic acid proteases are important pharmaceutical targets, including renin, cathepsin D, the human immunodeficiency virus (HIV) proteases, human t-cell leukemia virus type 1 (HTLV-1) protease and candida albicans aspartic acid protease.
Potent inhibitors of these enzymes can be readily accessed by the incorporation of an isostere that mimics the geometry of the tetrahedral intermediate in place of the scissile bond of the peptide substrate. Unfortunately, these inhibitors have limited therapeutic utility due to the poor oral availability and/or short circulating half-lives that result from their peptidic nature. For this reason, it would be advantageous if structure-based design and combinatorial chemistry techniques could be used to develop non-peptide inhibitors of aspartic acid proteases.