- Email: [email protected]
Tools for target identification and validation
Tools for target identification and validation Shenliang Wang, Tae Bo Sim, Yang-Suk Kim and Young-Tae Chang Reliable technologies for addressing target identification and validation are the foundation of successful drug development. Microarrays have been well utilized in genomics/proteomics approaches for gene/protein expression profiling and tissue/ cell-scale target validation. Besides being used as an essential step in analyzing high-throughput experiments such as those involving microarrays, bioinformatics can also contribute to the processes of target identification and validation by providing functional information about target candidates and positioning information to biological networks. Antisense technologies (including RNA interference technology, which is recently very ‘hot’) enable sequence-based gene knockdown at the RNA level. Zinc finger proteins are a DNA transcription-targeting version of knockdown. Chemical genomics and proteomics are emerging tools for generating phenotype changes, thus leading to target and hit identifications. NMR-based screening, as well as activity-based protein profiling, are trying to meet the requirement of high-throughput target identification. Addresses Department of Chemistry, New York University, New York, NY 10003, USA e-mail: [email protected]
Current Opinion in Chemical Biology 2004, 8:371–377 This review comes from a themed issue on Next-generation therapeutics Edited by Tudor Oprea and John Tallarico Available online 17th June 2004 1367-5931/$ – see front matter ß 2004 Elsevier Ltd. All rights reserved. DOI 10.1016/j.cbpa.2004.06.001
Abbreviations ABPP activity-based protein profiling DLBCL B-cell lymphoma dsRNA double-stranded RNA G6PD glucose-6-phosphate dehydrogenase HCC hepatocellular carcinoma NSCLC non-small cell lung cancer RISC RNA-induced silencing protein complex RNAi RNA interference SAR structure-activity relationships siRNA small interfering RNA SNP single nucleotide polymorphism ZFP zinc finger protein ZFP TFs ZFP transcription factors
Introduction Target identification and validation are the first key stages in the drug discovery pipeline. Therefore, www.sciencedirect.com
researchers are necessarily concerned with this initial aspect of the drug discovery process. In the past, researchers had a tendency to work on a handful of favored genes, often identified in the literature by academic groups, amenable to low-throughput analysis. Thus, a majority of successful drug discovery projects have targeted the relatively small numbers of protein classes that have proved amenable to pharmaceutical development. For example, around 40% of marketed small-molecule therapeutics target G-protein coupled receptors [1]. Other favored protein classes include ion channels, proteases, kinases and nuclear receptors [2–4]. With the publication of the human genome sequence [5,6], the newly revealed potential target pool shows promising prospect for drug development. Various tools and technologies have been used in different approaches to accelerate target identification and validation. In this review, we explore the recently developed, cutting-edge technologies and their potentials in this field (Figure 1).
Microarrays Target identification seeks to identify new targets, normally proteins (or DNA/RNA), whose modulation might inhibit or reverse disease progression. Current technologies enable researchers to attempt to correlate changes in gene (genomics) and protein (proteomics) expression with human disease, in the hope of finding new targets. Microarrays are a well-utilized tool in both academic and industrial research laboratories. They can be used to assess gene and protein expression (via nucleic acid or protein microarrays) to identify novel targets, and can also be used to validate the found targets at the tissue or cell scale (via tissue or cell microarrays) [7]. Nucleic acid microarrays
Today, nucleic-acid microarrays, which primarily use short oligonucleotides (15–25 nt), long oligonucleotides (50–120 nt) and PCR-amplified cDNAs (100–3000 base pairs) as array elements, are overwhelmingly dominant because of the relatively easy synthesis and the chemical robustness of DNA [8]. Data generated from genome sequencing projects in several organisms has provided the opportunity to build comprehensive maps of transcriptional regulation. Array-based gene expression analysis (immobilized DNA probes hybridizing to RNA or cDNA targets) has enabled parallel monitoring of cellular transcription at the level of the genome. Thus, nucleic-acid microarrays have had a significant impact on our understanding of normal and abnormal cell biochemistry and, thus, on the choice of targets for drug design. In oncology, Current Opinion in Chemical Biology 2004, 8:371–377
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Figure 1
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data generated from high density oligonucleotide microarrays from Affymetrix containing 62 907 probe sets have been analyzed and compared, to identify 97 genes as physiological targets of the retinoblastoma protein pathway, deregulation of which is a hallmark of human cancer [9]. Further characterization of these genes should provide insights into how this pathway controls proliferation, thus providing potential therapeutic targets. Protein microarrays
Because most drug targets are proteins, protein and peptide microarrays are set to have an important impact on drug discovery. Protein arrays, an emerging yet very promising technology, are now being used to examine enzyme–substrate, DNA–protein and protein–protein interactions [10]. By profiling the differential expression of proteins using antibody arrays and correlating the changes to a disease phenotype, putative targets (and biomarkers) to a particular disease can be identified [11], Current Opinion in Chemical Biology 2004, 8:371–377
although to date, such microarrays have not been used to their full potential because of difficulties with the technology [12]. One example using protein microarrays made up of 83 different antibodies enables monitoring alterations of the protein levels in hepatocellular carcinomas (HCCs) and non-neoplastic liver tissue. Further analysis of altered proteins was performed using western blot analysis and tissue microarrays containing 210 HCC specimens and corresponding liver tissue. This approach revealed differential expression between HCC and normal liver of 32 of the 83 proteins examined: 21 of these were up-regulated and 11 down-regulated [13]. Another very interesting example is the use of fluorescent substrate compounds for protein microarrays [14]. Various enzymes immobilized on microarray slides are used to screen fluorescently labeled enzyme substrates, which are small molecules. More importantly, this strategy is based on the mechanism of the reaction between the ligands and proteins, thus demonstrating the approach as www.sciencedirect.com
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an activity-based and high-throughput method. Further application of this method may lie on targeting known drugs or biological active compounds. Tissue and cell microarrays
Almost all published tissue microarray studies have been related to the analysis of tumors [13,15]. An alternative to the use of whole-tissue specimens is the use of live cell microarrays, which can be used to identify potential drug targets by functionally characterizing large numbers of gene products in cell-based assays. In one example [13], a tissue microarray was constructed using 210 human HCCs and corresponding normal liver, and followed by immunohistochemical analysis to test the clinical value of the proteins that have been identified.
Bioinformatics Besides being used as an essential step in analyzing highthroughput experiments such as DNA microarrays, bioinformatics can contribute to the processes of target identification and validation by providing functional information of target candidates and positioning information on the biological networks. Numerous public biological databases are warehousing and providing a great amount of functional information for genes or proteins, and many useful bioinformatics tools are continuously developed. Enriched information and the bioinformatics tools have enabled in silico cloning of target candidates. SNAIL3, a potent target of pharmacogenomics in the field of oncology and regenerative medicine, is an example for in silico isolation by proper combination of available bioinformatics tools and information [16]. The gene was isolated by a similarity search of a known database and the characteristics of the sequence, such as chromosomal location, phylogeny and in silico expression analysis, were investigated by commonly available tools and databases. In-depth analysis of experimental data using bioinformatics can give new insight into the prediction of target validity in the clinical stage. The supervised machine learning algorithm was applied for the analysis of B-cell lymphoma (DLBCL) array data to select rational targets and to predict DLBCL treatment outcome. The classprediction strategy successfully classified two categories of patients with very different five year overall survival rates. The genes involved in B-cell receptor signaling, critical serine/threonine phosphorylation pathways and apoptosis, were selected as featured genes of DLBCL outcome [17]. The application of bioinformatics to SNPs (single nucleotide polymorphisms), one of the key factors for personalized medicine, and systems biology make it possible to investigate the relationship between sequence variation and physiological function in silico. The effects of SNPs in the two key enzymes, glucose-6-phosphate dehydrogenwww.sciencedirect.com
ase (G6PD) and pyruvate kinase, on human red blood metabolism were assessed through the in silico model. In the case of G6PD, the predicted result of overall cell behavior correlated with the severity of the clinical conditions. This kind of study should be useful for the development of personalized drugs and may reduce the time and cost at the clinical stage by pre-investigation of the individual’s genotype [18].
Antisense technology A key strategy in target validation is to determine what happens, with respect to phenotype and/or the expression of other genes in cells or model organisms, if a gene of interest is either deleted or its activity is inhibited. Gene knockout mimics the activity of a drug that completely inhibits the normal function of the gene’s product. A temporary knockout, a so called knockdown, is another popular alternative for real-time analysis of the gene function. Several important strategies for gene knockdown involve the use of specific RNAs and/or RNA or DNA analogues [19]. Antisense oligonucleotides
Complementary to a portion of a target mRNA molecule, oligonucleotides are the original type of molecule used for blocking protein synthesis of the target mRNA, and thus achieving the knockdown of the target gene [20]. One example is the identification of COX17 as a therapeutic target for non-small cell lung cancer (NSCLC) [21]. Following the observation of overexpression of the COX17 transcript on a cDNA microarray, increased expression of COX17 in all of eight primary NSCLCs and in 11 of 15 NSCLC cell lines examined was documented using semiquantitative reverse transcriptionPCR, by comparison with normal lung tissue. Treatment of NSCLC cells with antisense S-oligonucleotides or vector-based small interfering RNAs of COX17 suppressed expression of COX17 and also suppressed growth of the cancer cells. Combined with other data, this suggests that selective suppression of COX17 expression could form a promising new strategy for treating lung cancers. RNA interference
Named by Science as the ‘Breakthrough of the year’ for 2002 [22], RNA interference (RNAi), another type of technology involving sequence-specific RNA for use in gene knockdown, has attracted even greater interest in the scientific community. The mechanism of RNAi has been reviewed in several very good articles [23–25]. The long double-stranded RNAs (dsRNAs) that are introduced are recognized and processed to small pieces of 21–25 nt dsRNA (i.e. small interfering RNA; siRNA). The siRNAs contain two perfectly complementary RNA strands, which can guide the RNA-induced silencing protein complex (RISC) to the target mRNAs and induce their destruction through cleaving the mRNA in Current Opinion in Chemical Biology 2004, 8:371–377
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the middle of the target region via an as-yet unknown nuclease. This guidance of RISC to target mRNA is highly sequence specific and, thus, target specific. RNAi coupled with large-scale cell-based screens holds great promise for target identification. In one example, libraries were prepared containing dsRNAs corresponding to all kinases and phosphatases, which equates to approximately 43% of the genes predicted from the completed Drosophila genome sequence. Coupled with a quantitative cultured cell assay, two gene products reported to function in Wingless signaling were identified as Hedgehog pathway components. This approach avoided such limits as selectivity of mutational targeting, complexities of anatomically based phenotypic analysis, or difficulties in subsequent gene identification, and should make possible the systematic identification of components within each pathway, thus leading to potential therapeutic targets [26]. Another example concerns in vivo RNAi target validation. Synthetic siRNA duplexes were delivered into mouse hepatocytes in vivo by hydrodynamic tail vein injection of Cy5-labedled Fas siRNA. Fas mRNA and protein expression in hepatocytes was measured by RNase protection assay and immunoblotting. The results show that Fas siRNA reduced Fas mRNA expression eight- to ten-fold. Moreover, specificity was confirmed since Fas siRNA treatment did not affect the expression of other Fas-related genes [27]. Furthermore, the antisense oligonucleotides or siRNAs can also be used as therapeutics. Mivirsen of ISIS recently received the FDA’s approval, and several others are under clinical trial [25]. In this way, the target and hit compound can be discovered and validated at the same time. As a further role, in this case mRNA serves as the target rather than the protein, which shows significant advantages.
Zinc finger proteins Zinc finger proteins (ZFPs) have remarkable versatility for recognizing different sequences of DNA, and variations in the amino acid sequence of the C2H2 domains allow them to be targeted to different locations in the genome. Each zinc finger is a short stretch of 30 amino acids, containing two conserved cysteines and two conserved histidines. These proteins have been used as the DNA-binding domains of novel transcription factors (ZFP TFs). ZFP TFs can be applied to potential new drug target validation in two ways. One direct way involves designing ZFPs to validate the phenotypic effects of activating or repressing a gene. Alternatively, libraries of ZFP TFs might be used to screen cells for desired phenotypic changes [28]. An example shows that designed ZFP TFs can knock down the mRNA expression of a predetermined target gene, while providing single gene specificity when 16 000 human genes were analyzed [29]. A ZFP TF repressor that binds an 18-bp Current Opinion in Chemical Biology 2004, 8:371–377
recognition sequence within the promoter of the endogenous CHK2 gene gives a >10-fold reduction in CHK2 mRNA and protein. Moreover, the extent of repression achieved by this highly specific engineered TF was sufficient to abolish CHK2 function in two different assays and cell types, while recent data using RNAi or siRNA targeted to CHK2 in human cells reduced CHK2 protein by only 60–75% [30]. Thus, ZFP TFs were demonstrated as precise tools for target validation, and even for clinical therapeutics.
Haplotype analysis Recently, haplotype analysis has attracted more and more interest. The phenomenon of haploinsufficiency, in which loss of function of just one gene copy leads to an abnormal phenotype, has led to the assumption that, regardless of their frequency, haploinsufficient loci define a set of genes whose dosage and function are critical to the organism. Building on this idea, lowering the dosage of a single gene may result in a heterozygote that is sensitized to any drug that acts on the product of this gene. This haploinsufficient phenotype thereby identifies the gene product of the heterozygous locus as the likely drug target [31]. One example of its use in target identification is the work of Lum et al. [32] in which a genome-wide pool of tagged heterozygotes is used to assess the cellular effects of 78 compounds in Saccharomyces cerevisiae. A mixture of isogenic yeast mutants was generated by pooling 3503 heterozygous deletion strains engineered with strain-specific molecular barcode tags. Competitive growth of the mutant pool was carried out for 20 generations in the presence of selected compounds. Genomic DNA was isolated from cultures before and after outgrowth. Barcode tags were amplified and labeled by PCR and later hybridized onto DNA microarrays. Of the 78 compounds analyzed, 18 resulted in no drug-specific fitness changes, 56 resulted in a small number of significant outliers, and four resulted in widespread fitness changes, thus proving this process to be a powerful tool for understanding drug activities and leading to target identification.
Chemical-driven random mutagenesis Much of the above discussion concerned experiments in silico and in vitro, rather than whole-organism physiology. These studies still cannot predict the integrated response of a potential drug as accurately as work in living systems, thus in vivo testing is a somewhat more reliable method for target identification and validation [33]. Random mutagenesis of the mouse, driven by N-ethyl-N-nitrosourea, is important for large-scale drug target identification and validation. The technique is used in both reverse and forward genetics to characterize genes and dissect the molecular basis of generated phenotypes, respectively [34]. Several large-scale programs are still underway, and their results are crucial for unraveling the real character of a target in complex biological systems. www.sciencedirect.com
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Chemical genomics and proteomics Rather than finding drugs for targets in the conventional pharmaceutical approach, forward chemical genomics, in a sense, finds targets for known drugs. Its goal is to discover the specific molecular targets and pathways that are modulated by particular chemical molecules (i.e. study the biochemistry underling the phenotype changes induced by chemicals). Tagged library approach
Various kind of chemicals have been used to generate novel chemotypes and once an effect is found, the next step is to identify the biological target using an affinity matrix made of the immobilized hit compound. However, the synthesis of an efficient affinity matrix without the hit compound’s activity loss has been shown to be challenging, or sometimes totally impossible, due to the difficulties of adequate linker attachment (Figure 2). Some efforts have been put to this point, and promising results are debuting using the tagged library approach [35]. In this approach, a tagged library was synthesized through combinatorial methods and screened in the presence of the tag in phenotypic assays. Thus, selected active library compounds were directly connected to the resin beads, or to an affinity moiety, without need for a further structure– activity relationship study utilizing the already-existing tag. This approach dramatically accelerated the connection of the functional screening to the affinity matrix step by reducing the time needed from months to days. Another way to overcome this problem is by displacement affinity chromatography screening. One example used g-phosphate-linked ATP-sepharose as the affinity matrix,
to isolate the entire purine-binding proteome from an animal or cell lysate. The purine-binding proteomes were then screened by diluting the matrix with quinoline drugs, resulting in the identification of two human proteins, aldehyde dehydrogenase 1 and quinone reductase 2 [36]. High-throughput NMR-based screening is also utilized for fast identification of drug–target interaction [37]. Integrated object-oriented pharmacoengineering (IOPE) is a three-step technology to build focused combinatorial libraries of potential inhibitors for enzymes, using cogent NMR data derived from representatives of these protein families. Within days, the NMR structurally oriented library valency engineering data used to design these libraries are gathered. In this approach, one can screen many compounds in a given subfamily of proteins, such as kinases or dehydrogenases. Although no specific example is provided, it is implied that this method can be used for most of the members of the subfamily at the same time and can also provide a specific protein–ligand pair, which can be further optimized to targets and hits.
Activity-based protein profiling The activity of proteins is regulated by widespread posttranslational regulation in vivo, thus protein abundance may not directly correlate with protein activity. Accordingly, methods for activity-based protein profiling (ABPP) may serve as a valuable complement to conventional abundance variations based approaches [38]. Since it was hypothesized that chemical probes capable of directly reporting on the integrity of enzyme active sites in complex proteomes might serve as effective activity-based
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profiling tools, ABPP probes were designed to contain at least a reactive group for binding and covalently modifying the active sites of enzymes and one or more reporter groups (tags), such as biotin and/or a fluorophore, for the rapid detection and isolation of probe-labeled enzymes. Recently groups such as azides have been used to substitute the bulky tag groups, followed by so-called click chemistry to label the proteins. A cluster of proteases, lipases, and esterases that distinguished cancer lines across a panel of human breast and melanoma cancer cells have been identified by this method [39].
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Howbrook DN, van der Valk AM, O’Shaughnessy MC, Sarker DK, Baker SC, Lloyd AW: Developments in microarray technologies. Drug Discov Today 2003, 8:642-651.
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Novel therapeutic target identification and validation is a highly complex and resource-intensive process, which requires an integral use of various tools, approaches and information. It will be largely affected by the genomic and proteomic tools in the post-genome era, which may speed up the whole biological/chemical/medical community, and lead to the high-throughput, low cost, and the means to save time and energy. Currently, microarray technology continues to rapidly identify novel transcriptional cascades, biological processes and disease markers. This technology represents one of the first functional genomics platforms that exploit genome sequence data to analyze a biological process (gene transcription) on a gene-by-gene basis. However, this tool is also facing some problems such as consistency of experimental results [40], standardization in protocols [41], and also data comparison obtained from different platforms [42]. siRNAs can be synthesized in large quantities and thus can be used robotically to analyze a large number of sequences emerging from genome projects in a costeffective manner. However, the major obstacle to the use of siRNAs and also antisense oligonucleotides as therapeutics is the difficulty involved in effective in vivo delivery, although some efforts have been made to address these problems [43]. It is also essential for target validation to construct a proper animal model, since there have already been several misled cases [44,45]. As the experiments proceed, numerous databases are warehousing data, yet further mining of these databases could also be a bottleneck in the whole process of drug development, which requires the further development of bioinformatics. Although we are facing some growing pains in the whole development process, the retrospective analysis of our time 20 years from now will remark on the challenges today as a leading force that attracts our interests and drives us forward.
References and recommended reading Papers of particular interest, published within the annual period of review, have been highlighted as: of special interest of outstanding interest 1.
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10. Zhu H, Snyder M: Protein chip technology. Curr Opin Chem Biol 2003, 7:55-63. 11. Mitchell P: A perspective on protein microarrays. Nat Biotechnol 2002, 20:225-229. 12. Paul Cutler: Protein arrays: The current state-of-the-art. Proteomics 2003, 3:3-18. 13. Tannapfel A, Anhalt K, Hausermann P, Sommerer F, Benicke M, Uhlmann D, Witzigmann H, Hauss J, Wittekind C: Identification of novel proteins associated with hepatocellular carcinomas using protein microarrays. J Pathol 2003, 201:238-249. This is the first study to use human HCC tumor tissue for protein analysis, complementing the gene expression studies of HCC. Also, the described immunoassay on microarrays is an attractive method, as equipment and antibodies are readily available and assays are simple, scalable and reproducible. 14. Chen GYJ, Uttamchandani M, Zhu Q, Wang G, Yao SQ: Developing a strategy for activity-based detection of enzymes in a protein microarray. ChemBioChem 2003, 4:336-339. This is the first microarray strategy that allows high-throughput, activitybased detection of enzymes immobilized on a glass slide. To link a fluorescent tag onto the compounds to be screened enables automated high-throughput screening. This strategy is based on the mechanism of the reaction between the ligands and proteins, thus the activity between enzymes and ligands can be detected by the fluorescent strength. 15. Zaharieva BM, Simon R, Diener PA, Ackermann D, Maurer R, Alund G, Hartmut Knonagel H, Rist M, Wilber K, Franz Hering, et al.: High-throughput tissue microarray analysis of 11q13 gene amplification (CCND1, FGF3, FGF4, EMS1) in urinary bladder cancer. J Pathol 2003, 201: 603-608. 16. Katoh M, Katoh M: Identification and characterization of human SNAIL3 (SNAI3) gene in silico. Int J Mol Med 2003, 11:383-388. 17. Shipp MA, Ross KN, Tamayo P, Weng AP, Kutok JL, Aguiar RCT, Gaasenbeek M, Angelo M, Reich M, Pinkus GS et al.: Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 2002, 8:68-74. 18. Jamshidi N, Wiback SJ, Palsson BØ: In silico model-driven assessment of the effects of single nucleotide polymorphisms (SNPs) on human red blood cell metabolism. Genome Res 2002, 12:1687-1692. 19. Scherer LJ, Rossi JJ: Approaches for the sequence specific knockdown of mRNA. Nat Biotechnol 2003, 21:1457-1465. A retrospect of the history of sequence-specific knockdown technology, including antisense oligonucleotides, ribozymes, RNAi and the comparative study of them. www.sciencedirect.com
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20. Kurreck J: Antisense technologies: Improvement through novel chemical modifications. Eur J Biochem 2003, 270:1628-1644. A clear review of the chemical modification of antisense compounds to address problems such as protection against nucleolytic attack, their cellular uptake and correct intracellular localization. Thought-provoking for further work to modify the chemical structure of the antisense compounds. 21. Suzuki C, Daigo Y, Kikuchi T, Katagiri T, Nakamura Y: Identification of COX17 as a therapeutic target for non-small cell lung cancer. Cancer Res 2003, 63:7038-7041. 22. Couzin J: Small RNAs make big splash. Science 2002, 298:2296-2297. 23. Hannon GJ: RNA interference. Nature 2002, 418:244-251. 24. Agami R: RNAi and related mechanisms and their potential use for therapy. Curr Opin Chem Biol 2002, 6:829-834. 25. Milhavet O, Gary DS, Mattson MP: RNA interference in biology and medicine. Pharmacol Rev 2003, 55:629-648. 26. Lum L, Yao S, Mozer B, Rovescalli A, Kessler DV, Nirenberg M, Beachy PA: Identification of Hedgehog pathway components by RNAi in Drosophila cultured cells. Science 2003, 299:2039-2045. A good case study for genome-wide RNAi screening. An RNAi-based approach to functional genomics in a Drosophila cultured cell assay has provided a rapid screen that is sufficiently sensitive to detect known maternal and zygotic functions, showing the potential of systematic analysis of other biological processes. 27. Song E, Lee SK, Wang J, Ince N, Ouyang N, Min J, Chen J, Shankar P, Lieberman J: RNA interference targeting Fas protects mice from fulminant hepatitis. Nat Med 2003, 9:347-351. 28. Jamieson AC, Miller JC, Pabo CO: Drug discovery with engineered zinc-finger proteins. Nat Rev Drug Discov 2003, 2:361-368. 29. Tan S, Guschin D, Davalos A, Lee YL, Snowden AW, Jouvenot Y, Zhang HS, Howes K, McNamara AR, Lai A et al.: Zinc-finger protein-targeted gene regulation: genomewide single-gene specificity. Proc Natl Acad Sci USA 2003, 100:11997-12002. Designed ZFP TF can repress target gene expression with single-gene specificity within the human genome, with even higher specificity than RNAi. 30. Ahn J, Urist M, Prives C: Questioning the role of checkpoint kinase 2 in the p53 DNA damage response. J Biol Chem 2003, 278:20480-20489. 31. Giaever G, Shoemaker DD, Jones TW, Liang H, Winzeler EA, Astromoff A, Davis RW: Genomic profiling of drug sensitivities via induced haploinsufficiency. Nat Genet 1999, 21:278-283. 32. Lum PY, Armour CD, Stepaniants SB, Cavet G, Wolf MK, Butler JS, Hinshaw JC, Garnier P, Presetwich GD, Leonardson A et al.: Discovering modes of action for therapeutic compounds using a genome–wide screen of yeast heterozygotes. Cell 2004, 116:121-137. This study focuses on a genome-wide pool of tagged heterozygotes to assess the cellular effects of 78 compounds in Saccharomyces cerevisiae. Specifically, lanosterol synthase in the sterol biosynthetic pathway was identified as a target of molsidomine and the rRNA processing exosome was identified as a potential target of 5-fluorouracil.
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33. Frantz S: In vivo we trust. Nat Rev Drug Discov 2003, 2:501. 34. Russ A, Stumm G, Augustin M, Sedlmeier R, Wattler S, Nehls M: Random mutagenesis in the mouse as a tool in drug discovery. Drug Discov Today 2002, 7:1175-1183. 35. Khersonsky SM, Jung DW, Kang TW, Walsh DP, Moon HS, Jo H, Jacobson EM, Shetty V, Neubert TA, Chang YT: Facilitated forward chemical genomics using tagged triazine library and zebrafish embryo screening. J Am Chem Soc 2003, 125:11804-11805. Presents an exciting, practical and model application of the tagged library approach for forward chemical genomics. A thorough and complete study from the discovery of a novel small-molecule binder through in vivo embryonic screening to a full characterization of the ligand’s protein complement. 36. Graves PR, Kwiek JJ, Fadden P, Ray R, Hardeman K, Coley AM, Foley M, Haystead TJ: Discovery of novel targets of quinoline drugs in the human purine binding proteome. Mol Pharmacol 2002, 2:1364-1372. Due to the unbiased nature of proteome-mining methodology, all proteins that interact with the drug of choice can be identified, including proteins that may contribute to unwanted side effects, thus leading to a better understanding of the precise mechanism of action. Reiteration of drug structure combined with proteome mining can result in the identification of compounds that display increased specificity toward given proteins. 37. Sem DS, Yu L, Coutts SM, Jack R: Object-oriented approach to drug design enabled by NMR SOLVE: first real-time structural tool for characterizing protein-ligand interactions. J Cell Biochem Suppl 2001, 37:99-105. 38. Speers AE, Adam GC, Cravatt BF: Activity-based protein profiling in vivo using a copper(I)-catalyzed azide-alkyne [3 R 2] cycloaddition. J Am Chem Soc 2003, 125:4686-4687. This methodology offers a replacement of the bulky fluorescent tag with a sterically inconspicuous azide group; proteins can be labeled by click chemistry as a following step. 39. Jessani N, Liu Y, Humphrey M, Cravatt BF: Enzyme activity profiles of the secreted and membrane proteome that depict cancer cell invasiveness. Proc Natl Acad Sci USA 2002, 99:10335-10340. 40. Stears RL, Martinsky T, Schena M: Trends in microarray analysis. Nat Med 2003, 9:140-145. 41. Benes V, Muckenthaler M: Standardization of protocols in cDNA microarray analysis. Trends Biochem Sci 2003, 28:244-249. 42. Ferl GZ, Timmerman JM, Witte ON: Extending the utility of gene profiling data by bridging microarray platforms. Proc Natl Acad Sci USA 2003, 100:10585-10587. 43. Sioud M, Sørensen DR: Cationic liposome-mediated delivery of siRNAs in adult mice. Biochem Biophys Res Commun 2003, 312:1220-1225. 44. Hirst WD, Abrahamsen B, Blaney FE, Calver AR, Aloj L, Price GW, Medhurst AD: Differences in the central nervous system distribution and pharmacology of the mouse 5hydroxytryptamine-6 receptor compared with rat and human receptors investigated by radioligand binding, site-directed mutagenesis, and molecular modeling. Mol Pharmacol 2003, 64:1295-1308. 45. Hill R: NK1 (substance P) receptor antagonists – why are they not analgesic in humans? Trends Pharmacol Sci 2000, 21:244-246.
Current Opinion in Chemical Biology 2004, 8:371–377
Current Opinion in Chemical Biology 2004, 8:371–377 This review comes from a themed issue on Next-generation therapeutics Edited by Tudor Oprea and John Tallarico Available online 17th June 2004 1367-5931/$ – see front matter ß 2004 Elsevier Ltd. All rights reserved. DOI 10.1016/j.cbpa.2004.06.001
Abbreviations ABPP activity-based protein profiling DLBCL B-cell lymphoma dsRNA double-stranded RNA G6PD glucose-6-phosphate dehydrogenase HCC hepatocellular carcinoma NSCLC non-small cell lung cancer RISC RNA-induced silencing protein complex RNAi RNA interference SAR structure-activity relationships siRNA small interfering RNA SNP single nucleotide polymorphism ZFP zinc finger protein ZFP TFs ZFP transcription factors
Introduction Target identification and validation are the first key stages in the drug discovery pipeline. Therefore, www.sciencedirect.com
researchers are necessarily concerned with this initial aspect of the drug discovery process. In the past, researchers had a tendency to work on a handful of favored genes, often identified in the literature by academic groups, amenable to low-throughput analysis. Thus, a majority of successful drug discovery projects have targeted the relatively small numbers of protein classes that have proved amenable to pharmaceutical development. For example, around 40% of marketed small-molecule therapeutics target G-protein coupled receptors [1]. Other favored protein classes include ion channels, proteases, kinases and nuclear receptors [2–4]. With the publication of the human genome sequence [5,6], the newly revealed potential target pool shows promising prospect for drug development. Various tools and technologies have been used in different approaches to accelerate target identification and validation. In this review, we explore the recently developed, cutting-edge technologies and their potentials in this field (Figure 1).
Microarrays Target identification seeks to identify new targets, normally proteins (or DNA/RNA), whose modulation might inhibit or reverse disease progression. Current technologies enable researchers to attempt to correlate changes in gene (genomics) and protein (proteomics) expression with human disease, in the hope of finding new targets. Microarrays are a well-utilized tool in both academic and industrial research laboratories. They can be used to assess gene and protein expression (via nucleic acid or protein microarrays) to identify novel targets, and can also be used to validate the found targets at the tissue or cell scale (via tissue or cell microarrays) [7]. Nucleic acid microarrays
Today, nucleic-acid microarrays, which primarily use short oligonucleotides (15–25 nt), long oligonucleotides (50–120 nt) and PCR-amplified cDNAs (100–3000 base pairs) as array elements, are overwhelmingly dominant because of the relatively easy synthesis and the chemical robustness of DNA [8]. Data generated from genome sequencing projects in several organisms has provided the opportunity to build comprehensive maps of transcriptional regulation. Array-based gene expression analysis (immobilized DNA probes hybridizing to RNA or cDNA targets) has enabled parallel monitoring of cellular transcription at the level of the genome. Thus, nucleic-acid microarrays have had a significant impact on our understanding of normal and abnormal cell biochemistry and, thus, on the choice of targets for drug design. In oncology, Current Opinion in Chemical Biology 2004, 8:371–377
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data generated from high density oligonucleotide microarrays from Affymetrix containing 62 907 probe sets have been analyzed and compared, to identify 97 genes as physiological targets of the retinoblastoma protein pathway, deregulation of which is a hallmark of human cancer [9]. Further characterization of these genes should provide insights into how this pathway controls proliferation, thus providing potential therapeutic targets. Protein microarrays
Because most drug targets are proteins, protein and peptide microarrays are set to have an important impact on drug discovery. Protein arrays, an emerging yet very promising technology, are now being used to examine enzyme–substrate, DNA–protein and protein–protein interactions [10]. By profiling the differential expression of proteins using antibody arrays and correlating the changes to a disease phenotype, putative targets (and biomarkers) to a particular disease can be identified [11], Current Opinion in Chemical Biology 2004, 8:371–377
although to date, such microarrays have not been used to their full potential because of difficulties with the technology [12]. One example using protein microarrays made up of 83 different antibodies enables monitoring alterations of the protein levels in hepatocellular carcinomas (HCCs) and non-neoplastic liver tissue. Further analysis of altered proteins was performed using western blot analysis and tissue microarrays containing 210 HCC specimens and corresponding liver tissue. This approach revealed differential expression between HCC and normal liver of 32 of the 83 proteins examined: 21 of these were up-regulated and 11 down-regulated [13]. Another very interesting example is the use of fluorescent substrate compounds for protein microarrays [14]. Various enzymes immobilized on microarray slides are used to screen fluorescently labeled enzyme substrates, which are small molecules. More importantly, this strategy is based on the mechanism of the reaction between the ligands and proteins, thus demonstrating the approach as www.sciencedirect.com
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an activity-based and high-throughput method. Further application of this method may lie on targeting known drugs or biological active compounds. Tissue and cell microarrays
Almost all published tissue microarray studies have been related to the analysis of tumors [13,15]. An alternative to the use of whole-tissue specimens is the use of live cell microarrays, which can be used to identify potential drug targets by functionally characterizing large numbers of gene products in cell-based assays. In one example [13], a tissue microarray was constructed using 210 human HCCs and corresponding normal liver, and followed by immunohistochemical analysis to test the clinical value of the proteins that have been identified.
Bioinformatics Besides being used as an essential step in analyzing highthroughput experiments such as DNA microarrays, bioinformatics can contribute to the processes of target identification and validation by providing functional information of target candidates and positioning information on the biological networks. Numerous public biological databases are warehousing and providing a great amount of functional information for genes or proteins, and many useful bioinformatics tools are continuously developed. Enriched information and the bioinformatics tools have enabled in silico cloning of target candidates. SNAIL3, a potent target of pharmacogenomics in the field of oncology and regenerative medicine, is an example for in silico isolation by proper combination of available bioinformatics tools and information [16]. The gene was isolated by a similarity search of a known database and the characteristics of the sequence, such as chromosomal location, phylogeny and in silico expression analysis, were investigated by commonly available tools and databases. In-depth analysis of experimental data using bioinformatics can give new insight into the prediction of target validity in the clinical stage. The supervised machine learning algorithm was applied for the analysis of B-cell lymphoma (DLBCL) array data to select rational targets and to predict DLBCL treatment outcome. The classprediction strategy successfully classified two categories of patients with very different five year overall survival rates. The genes involved in B-cell receptor signaling, critical serine/threonine phosphorylation pathways and apoptosis, were selected as featured genes of DLBCL outcome [17]. The application of bioinformatics to SNPs (single nucleotide polymorphisms), one of the key factors for personalized medicine, and systems biology make it possible to investigate the relationship between sequence variation and physiological function in silico. The effects of SNPs in the two key enzymes, glucose-6-phosphate dehydrogenwww.sciencedirect.com
ase (G6PD) and pyruvate kinase, on human red blood metabolism were assessed through the in silico model. In the case of G6PD, the predicted result of overall cell behavior correlated with the severity of the clinical conditions. This kind of study should be useful for the development of personalized drugs and may reduce the time and cost at the clinical stage by pre-investigation of the individual’s genotype [18].
Antisense technology A key strategy in target validation is to determine what happens, with respect to phenotype and/or the expression of other genes in cells or model organisms, if a gene of interest is either deleted or its activity is inhibited. Gene knockout mimics the activity of a drug that completely inhibits the normal function of the gene’s product. A temporary knockout, a so called knockdown, is another popular alternative for real-time analysis of the gene function. Several important strategies for gene knockdown involve the use of specific RNAs and/or RNA or DNA analogues [19]. Antisense oligonucleotides
Complementary to a portion of a target mRNA molecule, oligonucleotides are the original type of molecule used for blocking protein synthesis of the target mRNA, and thus achieving the knockdown of the target gene [20]. One example is the identification of COX17 as a therapeutic target for non-small cell lung cancer (NSCLC) [21]. Following the observation of overexpression of the COX17 transcript on a cDNA microarray, increased expression of COX17 in all of eight primary NSCLCs and in 11 of 15 NSCLC cell lines examined was documented using semiquantitative reverse transcriptionPCR, by comparison with normal lung tissue. Treatment of NSCLC cells with antisense S-oligonucleotides or vector-based small interfering RNAs of COX17 suppressed expression of COX17 and also suppressed growth of the cancer cells. Combined with other data, this suggests that selective suppression of COX17 expression could form a promising new strategy for treating lung cancers. RNA interference
Named by Science as the ‘Breakthrough of the year’ for 2002 [22], RNA interference (RNAi), another type of technology involving sequence-specific RNA for use in gene knockdown, has attracted even greater interest in the scientific community. The mechanism of RNAi has been reviewed in several very good articles [23–25]. The long double-stranded RNAs (dsRNAs) that are introduced are recognized and processed to small pieces of 21–25 nt dsRNA (i.e. small interfering RNA; siRNA). The siRNAs contain two perfectly complementary RNA strands, which can guide the RNA-induced silencing protein complex (RISC) to the target mRNAs and induce their destruction through cleaving the mRNA in Current Opinion in Chemical Biology 2004, 8:371–377
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the middle of the target region via an as-yet unknown nuclease. This guidance of RISC to target mRNA is highly sequence specific and, thus, target specific. RNAi coupled with large-scale cell-based screens holds great promise for target identification. In one example, libraries were prepared containing dsRNAs corresponding to all kinases and phosphatases, which equates to approximately 43% of the genes predicted from the completed Drosophila genome sequence. Coupled with a quantitative cultured cell assay, two gene products reported to function in Wingless signaling were identified as Hedgehog pathway components. This approach avoided such limits as selectivity of mutational targeting, complexities of anatomically based phenotypic analysis, or difficulties in subsequent gene identification, and should make possible the systematic identification of components within each pathway, thus leading to potential therapeutic targets [26]. Another example concerns in vivo RNAi target validation. Synthetic siRNA duplexes were delivered into mouse hepatocytes in vivo by hydrodynamic tail vein injection of Cy5-labedled Fas siRNA. Fas mRNA and protein expression in hepatocytes was measured by RNase protection assay and immunoblotting. The results show that Fas siRNA reduced Fas mRNA expression eight- to ten-fold. Moreover, specificity was confirmed since Fas siRNA treatment did not affect the expression of other Fas-related genes [27]. Furthermore, the antisense oligonucleotides or siRNAs can also be used as therapeutics. Mivirsen of ISIS recently received the FDA’s approval, and several others are under clinical trial [25]. In this way, the target and hit compound can be discovered and validated at the same time. As a further role, in this case mRNA serves as the target rather than the protein, which shows significant advantages.
Zinc finger proteins Zinc finger proteins (ZFPs) have remarkable versatility for recognizing different sequences of DNA, and variations in the amino acid sequence of the C2H2 domains allow them to be targeted to different locations in the genome. Each zinc finger is a short stretch of 30 amino acids, containing two conserved cysteines and two conserved histidines. These proteins have been used as the DNA-binding domains of novel transcription factors (ZFP TFs). ZFP TFs can be applied to potential new drug target validation in two ways. One direct way involves designing ZFPs to validate the phenotypic effects of activating or repressing a gene. Alternatively, libraries of ZFP TFs might be used to screen cells for desired phenotypic changes [28]. An example shows that designed ZFP TFs can knock down the mRNA expression of a predetermined target gene, while providing single gene specificity when 16 000 human genes were analyzed [29]. A ZFP TF repressor that binds an 18-bp Current Opinion in Chemical Biology 2004, 8:371–377
recognition sequence within the promoter of the endogenous CHK2 gene gives a >10-fold reduction in CHK2 mRNA and protein. Moreover, the extent of repression achieved by this highly specific engineered TF was sufficient to abolish CHK2 function in two different assays and cell types, while recent data using RNAi or siRNA targeted to CHK2 in human cells reduced CHK2 protein by only 60–75% [30]. Thus, ZFP TFs were demonstrated as precise tools for target validation, and even for clinical therapeutics.
Haplotype analysis Recently, haplotype analysis has attracted more and more interest. The phenomenon of haploinsufficiency, in which loss of function of just one gene copy leads to an abnormal phenotype, has led to the assumption that, regardless of their frequency, haploinsufficient loci define a set of genes whose dosage and function are critical to the organism. Building on this idea, lowering the dosage of a single gene may result in a heterozygote that is sensitized to any drug that acts on the product of this gene. This haploinsufficient phenotype thereby identifies the gene product of the heterozygous locus as the likely drug target [31]. One example of its use in target identification is the work of Lum et al. [32] in which a genome-wide pool of tagged heterozygotes is used to assess the cellular effects of 78 compounds in Saccharomyces cerevisiae. A mixture of isogenic yeast mutants was generated by pooling 3503 heterozygous deletion strains engineered with strain-specific molecular barcode tags. Competitive growth of the mutant pool was carried out for 20 generations in the presence of selected compounds. Genomic DNA was isolated from cultures before and after outgrowth. Barcode tags were amplified and labeled by PCR and later hybridized onto DNA microarrays. Of the 78 compounds analyzed, 18 resulted in no drug-specific fitness changes, 56 resulted in a small number of significant outliers, and four resulted in widespread fitness changes, thus proving this process to be a powerful tool for understanding drug activities and leading to target identification.
Chemical-driven random mutagenesis Much of the above discussion concerned experiments in silico and in vitro, rather than whole-organism physiology. These studies still cannot predict the integrated response of a potential drug as accurately as work in living systems, thus in vivo testing is a somewhat more reliable method for target identification and validation [33]. Random mutagenesis of the mouse, driven by N-ethyl-N-nitrosourea, is important for large-scale drug target identification and validation. The technique is used in both reverse and forward genetics to characterize genes and dissect the molecular basis of generated phenotypes, respectively [34]. Several large-scale programs are still underway, and their results are crucial for unraveling the real character of a target in complex biological systems. www.sciencedirect.com
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Chemical genomics and proteomics Rather than finding drugs for targets in the conventional pharmaceutical approach, forward chemical genomics, in a sense, finds targets for known drugs. Its goal is to discover the specific molecular targets and pathways that are modulated by particular chemical molecules (i.e. study the biochemistry underling the phenotype changes induced by chemicals). Tagged library approach
Various kind of chemicals have been used to generate novel chemotypes and once an effect is found, the next step is to identify the biological target using an affinity matrix made of the immobilized hit compound. However, the synthesis of an efficient affinity matrix without the hit compound’s activity loss has been shown to be challenging, or sometimes totally impossible, due to the difficulties of adequate linker attachment (Figure 2). Some efforts have been put to this point, and promising results are debuting using the tagged library approach [35]. In this approach, a tagged library was synthesized through combinatorial methods and screened in the presence of the tag in phenotypic assays. Thus, selected active library compounds were directly connected to the resin beads, or to an affinity moiety, without need for a further structure– activity relationship study utilizing the already-existing tag. This approach dramatically accelerated the connection of the functional screening to the affinity matrix step by reducing the time needed from months to days. Another way to overcome this problem is by displacement affinity chromatography screening. One example used g-phosphate-linked ATP-sepharose as the affinity matrix,
to isolate the entire purine-binding proteome from an animal or cell lysate. The purine-binding proteomes were then screened by diluting the matrix with quinoline drugs, resulting in the identification of two human proteins, aldehyde dehydrogenase 1 and quinone reductase 2 [36]. High-throughput NMR-based screening is also utilized for fast identification of drug–target interaction [37]. Integrated object-oriented pharmacoengineering (IOPE) is a three-step technology to build focused combinatorial libraries of potential inhibitors for enzymes, using cogent NMR data derived from representatives of these protein families. Within days, the NMR structurally oriented library valency engineering data used to design these libraries are gathered. In this approach, one can screen many compounds in a given subfamily of proteins, such as kinases or dehydrogenases. Although no specific example is provided, it is implied that this method can be used for most of the members of the subfamily at the same time and can also provide a specific protein–ligand pair, which can be further optimized to targets and hits.
Activity-based protein profiling The activity of proteins is regulated by widespread posttranslational regulation in vivo, thus protein abundance may not directly correlate with protein activity. Accordingly, methods for activity-based protein profiling (ABPP) may serve as a valuable complement to conventional abundance variations based approaches [38]. Since it was hypothesized that chemical probes capable of directly reporting on the integrity of enzyme active sites in complex proteomes might serve as effective activity-based
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profiling tools, ABPP probes were designed to contain at least a reactive group for binding and covalently modifying the active sites of enzymes and one or more reporter groups (tags), such as biotin and/or a fluorophore, for the rapid detection and isolation of probe-labeled enzymes. Recently groups such as azides have been used to substitute the bulky tag groups, followed by so-called click chemistry to label the proteins. A cluster of proteases, lipases, and esterases that distinguished cancer lines across a panel of human breast and melanoma cancer cells have been identified by this method [39].
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Novel therapeutic target identification and validation is a highly complex and resource-intensive process, which requires an integral use of various tools, approaches and information. It will be largely affected by the genomic and proteomic tools in the post-genome era, which may speed up the whole biological/chemical/medical community, and lead to the high-throughput, low cost, and the means to save time and energy. Currently, microarray technology continues to rapidly identify novel transcriptional cascades, biological processes and disease markers. This technology represents one of the first functional genomics platforms that exploit genome sequence data to analyze a biological process (gene transcription) on a gene-by-gene basis. However, this tool is also facing some problems such as consistency of experimental results [40], standardization in protocols [41], and also data comparison obtained from different platforms [42]. siRNAs can be synthesized in large quantities and thus can be used robotically to analyze a large number of sequences emerging from genome projects in a costeffective manner. However, the major obstacle to the use of siRNAs and also antisense oligonucleotides as therapeutics is the difficulty involved in effective in vivo delivery, although some efforts have been made to address these problems [43]. It is also essential for target validation to construct a proper animal model, since there have already been several misled cases [44,45]. As the experiments proceed, numerous databases are warehousing data, yet further mining of these databases could also be a bottleneck in the whole process of drug development, which requires the further development of bioinformatics. Although we are facing some growing pains in the whole development process, the retrospective analysis of our time 20 years from now will remark on the challenges today as a leading force that attracts our interests and drives us forward.
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