Solid-Phase Synthesis and Combinatorial Technologies

424 APPLICATIONS OF SYNTHETIC LIBRARIES

Target identification is the next, crucial phase. Currently marketed drugs interact with about 400 known targets (genes or gene products), which are mostly receptors, enzymes, or ion channels. The completion of the Human Genome Project, which is expected in the next five years, will unravel all of the approximately 100,000 human genes and will provide their detailed genetic and physical maps. Of these, the estimated number of important genes to cure diseases ranges between 3000 and 10,000. All these potential targets will be known to everybody, and the competitive advantage will be to identify, faster than the competition, the pharmaceutically relevant targets and to associate the significant ones to a given disease. The conventional sources for target identification (literature, in-house research, competitor information) are already and will be more heavily overwhelmed in the future by susceptibility genetics (2, 3), which establishes an association between a gene and a disease by studying a patient population. The link may be determined by differential gene expression (DGE) analysis (4, 5), which analyzes the alterations of gene transcription comparing healthy versus diseased tissues, by proteomics (6, 7), which profile the proteins from a cellular or tissue source, again comparing normal versus diseased samples, or by bioinformatic data mining (8, 9), which browses the genetic databases and identifies potential new targets by comparison of their sequence with known genes of interest. The identification of these targets may take advantage, at some level, of biological libraries (see Chapter 10) as tools, but synthetic libraries are not significantly involved in this phase.

9.1.3 From Function to Target

The identified target is first validated, discriminating between relevant and nonrelevant targets for a given disease. Even if a target is related to a disease, it may not be essential, and to interfere with it may not lead to novel, useful drugs. Functional genomics, or functional gene analysis (FGA) (10, 11), is fundamental in linking genomics research with the discovery of disease-relevant targets, addressing the question of their function or dysfunction in disease states. Current technologies in FGA rely on gene under- (12) or overexpression (13), on knock-out and interaction studies in cell cultures or in transgenic animals (14, 15), and on tissue distribution of new genes or on in vivo pharmacological studies in animals. These technologies use a wide variety of techniques, including combinatorial technology–related tools such as ribozymes, antisense oligonucleotides, aptamers, and antibodies. Synthetic libraries, though, are not involved in this phase of the drug discovery process; the only exception is a novel approach, termed chemical genetics and described in the next subsection (see also Sections 7.5.1 and 7.5.2), which will provide significant assistance to elucidate biological pathways relevant to diseases and to validate novel targets via their interaction with small molecules.

The consequence of validation is the selection of a target that is promising; that is, affecting it will likely result in curing the disease of interest. An important criterion is target tractability, related to the ease of screening chemical diversity for activity on the target. In fact, there is no therapeutic use for a target where one or preferably more screening assays cannot be designed and realized (see also next section).

9.1 PHARMACEUTICAL APPLICATIONS 425

9.1.4 Chemical Genetics: From Chemical Entities to Valuable Targets

The use of chemical tools to elucidate complex biological pathways is not a recent discovery; many past examples determined the identity and the function of a target through a chemical entity interacting with the macromolecule. This pharmacological approach has been largely obscured by the advent of genetics and genomics (see Sections 9.1.2 and 9.1.3), but has still the power to unravel, or to provide information about complex pathways for which genetic manipulation protocols are not yet assessed.

A higher throughput version of this approach takes advantage of combinatorial technologies and has been recently developed and exploited with the name of chemical genetics (16–18). More specifically, two complementary approaches have been defined and will be exemplified:

•forward chemical genetics (FCG), where a large collection of drug-like compounds is tested for its modulation of a complex biological pathway. If one, or more, modulators are found, their molecular target (either novel, or not known to interact with the pathway) is identified and its role in the pathway is elucidated. This provides both useful chemical entities to be progressed and unvaluable proprietary information regarding novel targets for relevant diseases;

•reverse chemical genetics (RCG), where a small molecule which targets a specific pathway or which causes a specific phenomenon is incubated with a large genetic collection, to spot if the expression of any of the gene products is affected. Knowing that the whole human genome will be soon available for such experiments in microarrays, and that most of the sequenced genes will not be immediately linked to a function, this approach will be important for the ambitious goal “to identify a small molecule partner for every gene product” (16).

Mayer et al. (19) reported the use of FCG to identify compounds that affect the cellular mitotic process through a screening cascade reported in Fig. 9.2. A commercially available, diverse collection of 16,320 compounds was screened on a whole cell primary cytoblot assay (20) measuring the phosphorylation level of a nucleolar protein called nucleolin (step a). This protein is phosphorylated when cells enter mitosis, and inhibitors of the mitotic process are expected to increase the level of phosphonucleolin. 139 positive compounds were able both to penetrate the cell and to increase the level of phosphorylated nucleolin.

As tubulin is a major target for antimitotics, the positives were screened for their in vitro effects on tubulin polymerization (step b). 52 compounds inhibited tubulin polymerization, and one stimulated it; these structures (see a few representatives, 9.1–9.5, in Fig. 9.3), which could represent a starting point for an optimization program, were archived. The remaining 86 positives not interacting with tubulin were progressed and tested in mammalian epithelial kidney cells (BS-C-1) stained with various fluorescent reagents to visualize microtubules, actin and chromatin, i.e. the essential structural/mechanochemical mitiotic spindle (step c). 27 compounds, although increasing the number of normal mitotic cells and thus confirming the result of primary screening, did not have any immediate effect and were deemed to act on

426 APPLICATIONS OF SYNTHETIC LIBRARIES

screening set: 16,320 compounds

139 positives

secondary screening: b tubulin polymerization

5 compounds specifically acting on mitotic spindle

Figure 9.2 Forward Chemical Genetics (FCG): screening aimed to compounds interacting with the mitotic spindle components during mitosis.

other components of the mitotic process. These compounds were archived. 42 compounds affected the mitotic spindle (chromatin and microtubules, no effect on actin cytoskeleton) but affected microtubules also in interphase cells not yet entered into the mitotic phase. These compounds were also archived. 12 compounds showed multiple, aspecific effects and were thus discarded (Fig. 9.2).

The five remaining compounds were specifically acting on the mitotic spindle, and did not alter its components in interphase cells. One of them in particular prevented the formation of the spindle in most mitotic cells, replacing it with a monoastral microtubule formation surrounded by chromosomes; the compound (9.6, Fig. 9.3) was thus named monastrol. The authors compared monastrol effects with several published effects (21–23) related to inhibition of Eg5, a member of the BimC kinesin family, and showed monastrol to be the α selective Eg5 inhibitor (Eg5-driven microtubule motility inhibition = 14 µM). This compound is both the first permeable and selective inhibitor of a specific kinesin, and may have many possible applications as a tool or as the starting point for a chemical optimization program.

The same compound collection was screened by Stockwell et al. (24) on a primary FCG assay directed towards activators of a regulatory reporter gene (p3TPLux)

H monastrol

Figure 9.3 Structures of inhibitors of tubulin polymerization (9.1–9.5) and of monastrol 9.6, an inhibitor of mitotic spindle assembly, found through FCG.

sensitive to TGF-β (25) (step a, Fig. 9.4). The determination of the mechanism of action for the activators of p3TPLux should have provided insights into the TGF-β signalling pathway. Four positive compounds (9.7–9.10, Fig. 9.4) were identified and submitted to a secondary cytoblot screen for 5-bromodeoxyuridine incorporation (20) to determine their inhibition of DNA synthesis (step b, Fig. 9.4). All of them inhibited DNA synthesis, as happens for TGF-β signalling.

Compounds 9.8 and 9.10 were submitted to an RCG screen using the vast majority of Saccaromyces cerevisiae genome (around 5800 genes) on a microarray format (step a, Fig. 9.5). The high similarity between the human and fungal genome was expected to ensure useful indications for the molecular targets of the two molecules. The gene expression of the whole microarray, checked with the use of fluorescent dyes, did not change in the presence of 9.10. The expression of five genes (Fig. 9.5) was significantly increased by 9.8. Two out of the five genes are involved in metal transport, respectively

428 APPLICATIONS OF SYNTHETIC LIBRARIES

screening set: 16,320 compounds

4 positives 9.7-9.10: p3TPLux acivators DNA synthesis inhibitors

9.10

9.7R = H

9.8R = Me

9.9R = n-Bu

Figure 9.4 Forward Chemical Genetics (FCG): screening aimed to compounds activating the reporter gene p3TPLux and interfering with TGF-β signalling, and structures of activators

9.7–9.10.

for Zn (ZRT1) (26) and Cu-depended Fe transport (FET3) (27); HSP26 is a heat shock protein activated by osmotic stress (28), and the two uncharacterized genes are likely to be involved in similar, metal-related processes.

Compounds 9.8 and 9.10 were further characterized for their metal binding capacity; 9.8 confirmed a high and quite selective affinity for Zn2+, Fe3+ and Cu2+, and its activation of TGF-β signaling was reversed (Fe3+) or largely reduced (Zn2+, Cu2+) by addition of metal chloride salts. RCG thus provided insight on TGF-β signalling (several potential genes involved) and on the relevance of metal ions for this process.

9.1.5 From Target to Hit

This is the drug discovery phase where the accumulated biological knowledge is converted into relevant, novel chemical entities that will be progressed along the line to hopefully become new drugs on the market. The interdependent progression of implied biological and chemical activities is thoroughly described below, with the assistance of several examples.

This phase requires the initial development of an assay able to measure significant biological interactions of a molecule with the target. This is the foundation of pharmaceutical research, and traditionally there was no real need for close interaction

9.1 PHARMACEUTICAL APPLICATIONS 429

screening set: around 5800 S. cerevisiae genes

N N N

9.8 metal binder

with high affinity for Zn2+, Fe3+ and Cu2+

Figure 9.5 Reverse Chemical Genetics (RCG): screening aimed to elucidate the mode of action and the main features of compounds 9.8 and 9.10.

at the assay development stage between chemists and biologists. Medicinal chemists, in fact, prepared large amounts of molecules, which were then tested by the biologists on a variety of assays and were fully characterized. Eventually, the acquired structure– activity information was used to orient further chemical efforts. While this classical approach was accurate and successful on many occasions, the low throughput of both synthesis and screening was not appropriate for a quick and economical drug discovery process. This is the engine that gave rise first to high-throughput screening (HTS) and then to its chemical counterpart, high-throughput chemical synthesis or, as it is better known, combinatorial chemistry (see Section 4.1.2 for the historical background).

The significant change is represented by a quick and inexpensive biological filter to rapidly discard nonactive molecules and to focus further efforts on a few confirmed and attractive positives. The assay, thus, is automated to test even hundreds of thousands of compounds in a relatively short time, and this introduces some limitations. The following are among the most relevant:

•Only simple assays can be automated, while complex protocols will cause problems and will not produce reliable results.

•Automation must not affect the robustness of the assay, because automated protocols cannot afford last-minute adjustments or modifications.

430APPLICATIONS OF SYNTHETIC LIBRARIES

•Automated assays must be miniaturized, partly because they will typically receive small quantities of compound from collections or chemical libraries, but also to reduce the costs of biological reagents.

•Automated assays must be developed using several formats, on an assay-specific base, and it is essential to possess the necessary expertise to select and automate the best choice for a given target.

•Automated instrumentation and data management software are necessary to analyze and archive the large number of data produced by a screening campaign.

In principle, any type of biological assay can be automated, and several excellent reviews have covered this topic (29–38). Assay development and automation are not covered further here, but their influence on chemistry at the early stages of drug discovery will be examined.

At the very start of the so-called hit identification phase, the project is driven by the available information. If the biological target is already well-characterized, either a pharmacophoric/structural model or even structures of known ligands/inhibitors are available. This structural information will determine the class(es) of compounds to be tested and the size of the screening set. This knowledge-based, or focused, approach calls for similarity with a model/structure, rather than for diversity, and the screening sets are typically smaller (see Sections 5.4.2 and 5.4.3). More focused approaches are dealt with in Section 9.1.7. A more frequent situation, due to the input of novel genomic-derived targets, deals with unknown or, at best, poorly characterized targets, which are less tractable but also highly rewarding if novel, active structures are identified. The goal here is to test significant chemical diversity on a single, automated primary assay to fish out positives. These are then profiled through a screening cascade of secondary assays, and one or more structures are eventually selected as starting points for an exploratory chemical project. Ideally, large sets (hundreds of thousands) of compounds from collections and from synthetic libraries are screened, providing that a reliable and robust primary screening assay has been set up. Pooling strategies to reduce the number of wells per screening can also be adopted (39).

The screening of a large, diversity-based set of compounds/libraries implies the selection of candidates. Selected libraries should be built around a relevant (possibly proprietary) common core scaffold, maximize their chemical diversity, and provide novel, druglike individuals according to well-known computational filters (see Section 5.3). Each pharmaceutically biased-targeted primary library is tested on many biological targets; thus a large number of library equivalents are prepared and stored to be used when needed. High-quality SP pool libraries are usually the preferred format, but also medium–large discrete libraries are becoming very popular as hit-seeking libraries. Each screening set contains library individuals or compounds from collections in very small amounts to fully exploit the synthetic efforts and maximize the compound availability for multiple screening.

The positive compounds from the primary screening are further profiled to check their usefulness. This characterization includes preliminary physicochemical property determination, toxicity data, and specificity/selectivity data when possible. The secondary screening cascade will restrict the screening outcome to a small number of