Abstract
Cancer drug development is leading the way in exploiting molecular biological and genetic information to develop 'personalized' medicine. The new paradigm is to develop agents that target the precise molecular pathology driving the progression of individual cancers. Drug developers have benefited from decades of academic cancer research and from investment in genomics, genetics and automation; their success is exemplified by high-profile drugs such as Herceptin (trastuzumab), Gleevec (imatinib), Tarceva (erlotinib) and Avastin (bevacizumab). However, only 5% of cancer drugs entering clinical trials reach marketing approval. Cancer remains a high unmet medical need, and many potential cancer targets remain undrugged. In this review we assess the status of the discovery and development of small-molecule cancer therapeutics. We show how chemical biology approaches offer techniques for interconnecting elements of the traditional linear progression from gene to drug, thereby providing a basis for increasing speed and success in cancer drug discovery.
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Main
As in other therapeutic areas, success in innovative small-molecule cancer drug discovery depends on the creative interaction between chemistry and biology. At the heart of the classical drug discovery process are iterative cycles linking chemical synthesis and biological evaluation. Hypotheses generated about therapeutic targets lead to the production of new chemical matter, which is then evaluated in appropriate biological assays. The results generate new hypotheses and stimulate further rounds of synthetic refinement and testing until a compound with the required, predefined properties can be selected for clinical development. Notwithstanding the enormous technical advances made across many fronts, drug discovery remains a rationally driven but essentially empiric process. An important way to improve productivity is to decrease the timescale between hypothesis and feedback. These timescales can be relatively short, as in lead optimization (which may now be completed in as little as 12 to18 months), to very long, as in clinical trials (which can take many years). In this Review, we show how powerful new technologies can be combined to reduce the cycle times at all stages, so that success or failure is reached faster. It is a concern that despite the increasing number of targeted molecular therapeutics, attrition rates for oncology drugs in the clinic are worse than those for other disease areas1 (Box 1). It is therefore important that drugs and molecular targets that will not make it are identified earlier in the drug discovery process, thereby saving enormous late-stage development costs and allowing resources to be focused on the drugs most likely to succeed. Most critical in this regard is improving the suitability and robustness of the agents that enter the clinic.
Introduction of new technologies has been extremely important—particularly various high-throughput genomic approaches for target and biomarker discovery2, high-throughput screening (HTS) for hit identification3,4 and structure-based design5, which we will discuss in detail. Although many individual developments are enhancing speed and quality, it is important to emphasize that most of the clinical successes so far have resulted from close integration of different technologies and disciplines (chemistry, biology and experimental medicine), and from the application of 'joined-up thinking', in particular to address the issues that lead to failure of drugs in the clinic1.
Chemical biology offers experimental techniques for linking together elements from all stages of what was previously viewed as a linear progression from gene to drug (Fig. 1). These techniques allow us to look ahead on the path to the clinic using probe compounds to refine the target pharmacological profile and select the best models to guide drug development6. They also allow us to define the appropriate subject population for clinical trial by identifying biomarkers for drug action7. Chemical biology facilitates evaluation of compounds on a genome- or proteome-wide scale through interaction screens that examine many biological systems simultaneously in a well-defined manner, as in the elucidation of kinase inhibitor selectivity profiles8,9 and the identification of desirable polypharmacies and combination therapies through detection of synthetic lethality10,11.
Against this background, our review will focus on the discovery of new small-molecule anticancer drugs for relevant protein targets, highlighting particularly but not exclusively those acting on kinases and the molecular chaperone heat shock protein 90 (HSP90). These are areas of drug discovery in which chemical biology techniques are clearly making a significant impact. We assess the progress and current challenges of cancer drug discovery, and finally we indicate ways forward to enhance the discovery and development of cancer therapeutics.
The molecular basis of cancer: drugging the cancer genome
Efforts to elucidate the molecular basis of cancer are not new. They date back to the characterization of animal cancer viruses in the 1960s and 1970s, the identification of the first cancer-causing oncogenes and tumor suppressor genes in the 1970s and 1980s and the discovery of the ways cancer genes subvert signal transduction pathways in the 1990s12. What has been radically different in the last five to ten years is a profound cultural change, in which cancer drug discovery has embraced molecular oncology as a source of disease-causing targets for hypothesis-driven, mechanism-based drug discovery13,14.
The first generation of effective cancer drugs were the cytotoxics that still form the basis of most treatment regimens14. Many were discovered by screening for compounds that kill tumor cells. The concept underlying the development of these agents was that cancer cells replicate their DNA and divide more frequently than healthy cells. This somewhat naive notion underpinned the development of DNA-damaging agents, antimetabolites that inhibit DNA synthesis, and microtubule inhibitors such as Taxol (paclitaxel) that block the mechanics of cell division. Though this first era of cancer drug development did not deliberately exploit the genetic basis of cancer, many of the agents were nevertheless 'molecularly targeted'. For example, antifolate thymidylate synthase inhibitors were rationally designed according to principles of modern medicinal chemistry, which involved structure-activity relationships (SARs) and structural biology15. The term “targeted molecular therapeutics” and similar tags are now used to describe small-molecule agents that are not only rationally designed but also act on disease-causing oncogenic targets. The increasing number of approved agents, together with the therapeutic antibodies that act on similar targets (Table 1 and Box 1), demonstrates that we are in a second golden era of cancer drug development14.
The development of molecular cancer therapeutics is founded on an understanding of the types of genes involved (Fig. 2a). The process of exploiting cancer genes to develop both molecular therapeutics and molecular biomarkers is now well established (Fig. 2b). The integration of these forms the basis for the development of personalized cancer medicine13,14. Activation of oncogenes and inactivation of tumor suppressor genes—often facilitated by inactivation of DNA repair genes, which causes genetic instability—leads to hijacking of signal transduction pathways and hence to the various well-defined phenotypic hallmark traits of cancer16,17. These traits include not only loss of cell cycle control and the unrestricted proliferation referred to above, but also independence from positive and negative homeostatic regulatory factors, inappropriate survival, decreased apoptosis, immortalization, and stimulation of invasion, angiogenesis and metastasis. All these processes present targets for therapeutic intervention. Oncogene products themselves may be good targets, but other proteins downstream in a key pathway may also be suitable; for example, MAP-kinase kinase 1 (MEK1) and MEK2 in the RAS-RAF-MEK-ERK signaling pathway and mammalian target of rapamycin (mTOR) in the phosphatidylinositol-3-OH kinase (PI(3)K) pathway. In addition, the use of chemical probes has shown that oncogenic support processes such as protein chaperoning (for example, HSP90) and chromatin regulation (for example, histone deacetylase (HDAC)) can provide valuable drug targets18,19.
A major expectation for targeted molecular cancer therapeutics is that they show good efficacy and low toxicity, and this is certainly true of the poster child Gleevec20. An important theory underpinning this selectivity is that of “oncogene dependence” or “addiction”21. Although it requires further experimental validation, this concept proposes that cancer cells undergo selection to become driven by, but also dependent on, key oncogenic pathways.
A key driver in selecting cancer drug targets is the identification of distinguishing features of cancer cells, which may arise by mutations or gene rearrangements, inherited epigenetic changes or cell lineage legacies. While contributing to cancer development, these genetic, epigenetic or metabolic features can create “dependencies”—weaknesses that can be exploited according to four types or “tracks,” as classified by Benson et al.22: (i) the genetics track, based on oncogene addiction and exemplified by Gleevec in BCR-ABL–positive leukemia and MEK1 and MEK2 inhibitors in BRAF-driven melanoma models23; (ii) the synergy track, founded on the concept of synthetic lethality and illustrated by the selective killing by poly(ADP-ribose) polymerase (PARP) inhibitors of cells with BRCA gene defects10; (iii) the lineage track, another form of addiction, based on gene expression profiles showing that cancers from a given tissue or cell of origin share many common molecular features of that origin, and exemplified by antihormonal therapies in breast and prostate cancer, and potentially by the amplified oncogene microphthalmia-associated transcription factor (MITF) in aggressive melanomas24; and (iv) the host track, arising from recognition that tumor-host cell interactions and microenvironmental and physiological factors related to tumor hypoxia are critical for cancers, and exemplified by the vascular endothelial growth factor (VEGF)-targeted antibody Avastin and small-molecule inhibitors of VEGF receptor tyrosine kinases such as Nexavar (sorafenib) and Sutent (sunitinib). Together with the view of molecular cancer therapeutics discussed above (Fig. 2), the concept of cancer dependencies provides a valuable framework for thinking about oncology drug targets, selecting an individual target to work on, or building a portfolio of targets to tackle the disease in different ways.
There is certainly no shortage of potential drug targets. More than 350 cancer genes have been catalogued (www.sanger.ac.uk/genetics/CGP/Census/)25. New cancer genes continue to be found, particularly by high-throughput systematic methods such as genome resequencing26 (as used to discover BRAF27) and array-based DNA copy number and expression-profiling analysis (as used to discover MITF24). A short hairpin RNA (shRNA) barcode screen has been used to identify genes that affect sensitivity to the nutlin inhibitors of p53-binding MDM2 (ref. 28), and a small interfering RNA (siRNA)-based screen has identified kinases that cooperate with AKT (protein kinase B)11. High-throughput RNA interference (RNAi) technology is a powerful tool for gene discovery29. Validation and prioritization of the best targets is now a critical activity. This can be done using a combination of human genetics and genomics; functional validation, especially by overexpression or knockdown by RNAi; and transgenic animals and model organisms22. Extremely important in triaging targets is their druggability. For example, with current technology, enzymes such as kinases and small-domain-size protein-protein interactions are druggable, but many potential targets are not, as with the mutant oncoprotein RAS and mutant tumor suppressor p53.
Genes hijacked in cancer have important homeostatic roles in development and normal physiology. Despite cancer dependencies, toxicity to healthy cells is possible. For example, epidermal growth factor receptor (EGFR)-targeted agents cause mechanism-based skin rash. In retrospect, skin toxicity might have been predicted from the phenotype of EGFR knockout mice, was seen in the preclinical toxicology of Iressa (gefitinib) and Tarceva (http://www.accessdata.fda.gov/scripts/cder/drugsatfda/), and can now be understood in terms of the receptor tyrosine kinase ERRB family biology30. However, therapeutic index remains difficult to predict accurately purely from knowledge of the molecular action of drugs and is probably still best estimated from animal models of toxicity and efficacy during preclinical development.
Lead generation: finding the diamond in the rough
The next major step after target selection is lead generation (Fig. 1). Chemical starting points for drug discovery may come from the structures of endogenous ligands, existing drugs, biologically active natural products, high-throughput or focused screening of synthetic-compound libraries and, increasingly, design and screening using structural biology information. Biochemical HTS against isolated cancer targets has been effective for several protein classes, including kinases3 and HSP9018. For kinases, such screens can be biased toward identifying competitive inhibitors of phosphate transfer from ATP by the active enzyme. In contrast, in vitro biochemical recapitulation of signal transduction cascades (for example, the RAF-MEK-ERK pathway31) may identify compounds having a wider range of well-defined mechanisms of action.
Phenotypic screening in intact cells or organisms, such as Caenorhabditis elegans or zebrafish embryos, is applicable to target identification and lead generation4. This extends the reach of small-molecule screening to targets that are incompatible with in vitro biochemical assays and allows many molecular targets to be probed simultaneously in the cellular environment, as in the discovery of an inhibitor of oncogenic WNT/β-catenin signaling32. Postscreening mechanistic deconvolution of active compounds is needed to define their precise molecular targets33. For noncovalent inhibitors this may involve gene expression and protein array profiling, affinity chromatography of cell lysates, or yeast chemical genetic screens, as used to find the targets of antiproliferative phenylaminopyrimidines related to Gleevec34. Chemically reactive inhibitors can be used directly as labels to isolate their targets, as shown for inhibitors of breast cancer cell proliferation35. Alternatively, imaging-based high-content screens interrogate the phenotype directly at the molecular level. Imaging of protein recruitment to the cell membrane has identified inhibitors of AKT kinase activity36 and has determined the effects of selective PI(3)K inhibitors on the response to growth factors37. Observation of subcellular localization of the transcription factor FOXO1A has identified small molecules targeting the PI(3)K-AKT pathway and nuclear transport machinery38.
Sensitive biophysical techniques such as NMR and X-ray diffraction can detect the weak binding of much smaller compounds (fragments) than those comprising typical HTS libraries. Analysis of fragment binding modes may suggest how to link the fragments to generate more traditional lead structures39, as shown for p38 MAP-kinase inhibitors40. Combining biochemical HTS for low-affinity compounds and medium-throughput biophysical methods, especially protein-ligand cocrystallography, is a powerful and efficient lead-generation strategy41.
Computational chemistry increasingly contributes to screening strategies. Empirical parameters describing appropriate physicochemical properties of fragments, leads and drugs should be routinely incorporated into new compound library design42,43,44,45,46,47 (Box 2). Some structural motifs confer promiscuous nonspecific activity, or are associated with toxicities, and hence can also be eliminated from screening sets48,49. Virtual screening50 of library structures for their fit with specific three-dimensional pharmacophores can enrich HTS hit rates, as shown for inhibitors of checkpoint kinase 1 (ref. 51). Pharmacophores may come from protein-inhibitor cocrystal structures, or they may be derived from SARs.
The most efficient screening strategy depends on the molecular target and what is known when the drug discovery program starts. The various approaches may require compound libraries of quite different designs. At one extreme, large collections of highly diverse structures are useful for biochemical HTS against targets for which there is little chemical biology information. Libraries biased with compounds having a relevant biological pedigree increase the hit rates in phenotypic screens, as with RAS signaling pathway inhibitors52. When structural biology or known ligands define a pharmacophore, smaller, focused libraries based on a few structural motifs may be useful. The close structural similarity of the ATP cofactor binding site across the kinome enables the development of privileged structures or molecular master keys (molecular scaffolds with a good fit and therefore high probability of binding), which provide starting points for new inhibitor discovery and allow target hopping within a chemical class53,54. One potential weakness is the increased probability of finding kinase cross-reactive 'frequent hitters' and the consequent need to engineer selectivity into compounds during drug development.
The clinical anticancer activity of complex natural products—for example, camptothecins, vinca alkaloids and epothilones—suggests that such structures occupy a pharmaceutically valid chemical space55. Natural-product core scaffolds may be a source of diverse new chemical materials that serve as starting points for developing targeted therapeutics. Biology-oriented56 and diversity-oriented synthesis57 are emerging paradigms for guiding the generation of compound libraries that mimic the structural complexity of natural products, outside the arguably restricted chemical space occupied by synthetic drugs58. However, the extent to which highly complex structures are compatible with drug-like physicochemical properties is not fully determined59. It will also be important to distinguish molecularly targeted biological activities from general cytotoxic activity when natural products serve as inspiration for anticancer drug discovery.
Structure-based approaches: designer drugs
As mentioned before, the engine of medicinal chemistry continues to be the iterative cycles of design, chemical synthesis and biological evaluation that establish SARs. Observation of the interaction of ligands and proteins through cocrystallography informs and accelerates the process and has been very successful in kinase, HSP90 and HDAC inhibitor design. Crystallographic identification of potential small-molecule binding sites embedded in the larger surfaces that usually mediate protein-protein interactions has been important in discovering proapoptotic agents that inhibit interactions of MDM2-p53 and BCL260.
Structural biology has revealed several binding modes for protein kinase inhibitors5,61 (Fig. 3a–d). Behind these is the conserved bilobal tertiary structure defining a binding cleft for the cofactor ATP. Although the sequence and structural similarity of kinases was originally viewed as a potential liability, they are now recognized as classic druggable targets62. The binding site contains conserved features that enable hydrogen bonding to the adenosine, ribose and phosphate components of ATP. These areas are flanked by subsites that are not occupied by cofactor and that vary considerably between kinases. Inhibitors exploit conserved and nonconserved features to achieve potency and selectivity. Additionally, conformational changes (usually triggered by phosphorylation) associated with activation of kinases indicate that each kinase target may exist in more than one form. Drugs such as Iressa and Tarceva compete for occupation of the ATP binding site in the activated enzyme, whereas Gleevec, Nexavar and Tykerb (lapatinib) bind and stabilize an inactive form, thereby preventing activation5,61. Other compounds, such as the MEK1 and MEK2 inhibitor PD318088 (ref. 63), bind remotely and allosterically inhibit enzyme activity. Inhibition may also involve binding to sites that mediate protein-protein interactions, as seen for ligands of the AKT pleckstrin-homology domain64 and the mTOR inhibitor rapamycin65.
The structural biology of kinases has important consequences for inhibitor selectivity and development of resistance. Three-dimensional structure is more obviously conserved between activated kinases than between inactive forms. Thus inhibitors targeting stabilization of inactive enzymes can be more selective5,61. Greater use of interactions within the nonconserved regions of the ATP binding site should confer greater selectivity for inhibitors targeting active forms. However, the potential for drug resistance should also be considered. Mutation of residues essential for binding of drugs to the inactive form may be better tolerated in terms of retaining catalytic function in the active conformation. Likewise, regions of the ATP binding site that are not involved in catalysis or cofactor binding are likely to be hot spots in which mutations do not affect kinase function but may ablate drug binding. The gatekeeper residue, which delineates the size of a buried pocket adjacent to the ATP binding site and is mutationally silent in terms of ATP binding, is one such residue. Gatekeeper and other mutational hot spots have been identified in variants of Gleevec-resistant BCR-ABL66 and Iressa-resistant EGFR kinase67. Deliberate mutation of kinase residues is a valuable tool for target validation and investigation of drug action. Construction of a gatekeeper mutant of EGFR that is insensitive to Iressa paralled the identification of the EGFR T790M Iressa-resistant mutant in clinical trials68. The effect of conditional kinase inhibition on signaling pathways can be probed by kinase mutants that are sensitive to chemically orthogonal inhibitors not affecting the wild type69.
Some kinases contain a cysteine residue in the active site. Starting from the chemical scaffolds of reversible inhibitors such as Iressa, structure-based design has enabled development of irreversible inhibitors, such as the anilinoquinazoline CI-1033, that have low nonspecific chemical reactivity and that covalently attach to the thiol group70. A chemical genetics approach has led to highly selective, irreversible inhibitors through targeting the limited number of kinases in which the gatekeeper residue and a reactive cysteine are close in the ATP site71.
Structure-based design has been valuable in identifying and optimizing HSP90 inhibitors. The structure of the natural products geldanamycin and radicicol bound to the N-terminal ATPase domain of HSP90 revealed the presence of a unique folding pattern in the ATP binding site that includes a network of tightly bound water molecules72 (Fig. 3e). Cocrystallization of the arylpyrazole HTS hit CCT018159 has shown that the resorcinol motif of this small molecule exploits the water network in a manner similar to that of radicicol73 (Fig. 3f). The first synthetic inhibitors of HSP90 ATPase, a series of purines, were developed from modeling based on HSP90-ATP74, though subsequent cocrystallography of the inhibitor PU3 revealed an unexpected change in conformation that creates a new binding pocket75. Iterative reinvestigation of ligand-protein structures during lead optimization is highly valuable for understanding the conformational variability of the target and its consequence for drug design.
Structure determination of a bacterial homolog of HDAC has defined the interaction of the inhibitor suberoylanilide hydroxamic acid (SAHA); it shows the hydroxamate group chelating an enzyme-bound zinc atom at the base of a deep tubular pocket76 (Fig. 3g). Elucidation of human HDAC8 structures has shown that the surface adjacent to the conserved active site pocket is conformationally malleable77, thereby presenting opportunities and challenges for design of HDAC subtype-selective inhibitors78. Chemical genetic screening has unraveled the mechanism and possible polypharmacy of HDAC inhibitors, with different isoforms catalyzing acetylation of histones versus tubulin79.
Multiparameter lead optimization: polishing the diamond
With the widespread use of HTS in many therapeutic areas from the late1980s onwards came the realization that not all small-molecule leads are suitable for optimization to drug candidates (Box 2). Profiling of potential leads against multiple chemical, physicochemical and biological criteria was adopted to select the best chemical starting point and biological test cascade to maximize the probability of clinical success80. Multiparameter profiling continues iteratively throughout lead optimization to identify compounds having an acceptable profile across the whole range of properties needed for an effective drug.
In addition to considerations of physicochemical properties, synthetic tractability of leads is also important, as many structurally diverse analogs are needed during optimization. Fortunately, combinatorial and parallel syntheses used for contemporary screening libraries are usually adaptable for lead optimization81, as exemplified in the development of Nexavar82. Advances in synthetic and medicinal chemistry contribute to the discovery of new biologically active scaffolds, as in the incorporation of boronic acid functionality into the US Food and Drug Administration (FDA)-approved proteasome inhibitor Velcade (bortezomib)83.
Potency against the molecular target is an obvious consideration, with respect to both biochemical and cellular readouts84. It is useful to compare leads by ligand efficiency, a measure of the effectiveness of the interaction of the chemical structure with the target85. A key goal is to establish productive SARs, in which changes to the lead structure elicit corresponding improvements in biological activity.
Determining selectivity for the molecular target is very important and requires a combination of biochemical and cellular approaches. One issue is the extent to which comparison of in vitro measurements of compound activity at various isolated targets accurately reflects selectivity in cells, even with proteins from the same family, such as kinases86. Inherent differences in kinase expression levels, enzyme kinetics and responsiveness of downstream effectors along individual pathways may lead to selective phenotypic outcomes from apparently unselective inhibitors. For example, despite a 20-fold in vitro selectivity for inhibition of cyclin-dependent kinase 1 (Cdk1), GW297361 elicits a response in yeast cells indicative of selective inhibition of pho85 over Cdk187. Nevertheless, in vitro selectivity profiling using panels of isolated kinases88, or other enzymes and receptors, remains a valuable tool for comparing overall specificity. Other methods of kinase selectivity profiling parallel proteomic and chemical genetic techniques for identifying targets of phenotypic screens. Affinity chromatography has revealed the cellular targets of Iressa89 and hymenialdisine90, and a yeast chemical genetic screen has identified the targets of CDK inhibitors8. Maps of the interactions of clinical kinase inhibitors with 113 kinases have been assembled based on competitive binding of free inhibitors and immobilized, unselective probe inhibitors to bacteriophage-expressed kinases9 (Fig. 4).
As mentioned earlier, adopting strategies to evaluate pharmacokinetic properties as early as possible in drug discovery has substantially reduced clinical failure due to inadequate bioavailability1. In contrast to cytotoxic compounds, molecular cancer therapeutics may generally require chronic, and therefore oral, dosing. Lead profiling and optimization therefore need to concentrate on the absorption, distribution, metabolism, excretion and toxicity (ADMET) of compounds. This is facilitated by medium- and high-throughput in vitro models, although these require validation for each structural class to ensure that they accurately reflect in vivo behavior. Compound bioavailability depends on two contradictory physicochemical requirements—aqueous solubility and lipid membrane permeability—which must be balanced in an effective pharmaceutical. High-throughput assays for passive diffusion across artificial lipid membranes and turbidity assays for solubility are valuable91. Measurement of transport across Caco-2 cell monolayers92 informs on permeability, active transport and, importantly, susceptibility to drug efflux pumps, which can compromise bioavailability and are a key factor in tumor resistance to chemotherapies93. Compound metabolism in microsomal preparations or hepatocytes identifies structures likely to be rapidly metabolized in vivo, whereas metabolite identification by mass spectrometry may suggest modifications to block this94. Avoiding the potential for compounds to interfere with therapeutic concentrations of other drugs by inhibition or induction of the drug-metabolizing cytochrome P450 enzymes is also important95. In vivo cassette dosing, in which small sets of compounds are dosed simultaneously, reduces animal usage and provides faster feedback of pharmacokinetic information, as illustrated by recent experience with purine CDK2 inhibitors and arylpyrazole HSP90 inhibitors96,97.
The goal of lead optimization is a drug candidate for clinical evaluation. Demonstrating efficacy and a therapeutic window in relevant in vivo models is therefore very important. Compounds with potent biochemical and cellular activity, and adequate pharmacokinetic properties, are progressed to in vivo models. Initial studies should focus on recapitulating a cellular pharmacodynamic response in the intact tumor and correlating this with tumor concentrations of compounds to establish pharmacokinetic-pharmacodynamic relationships98. In some cases, poor pharmacokinetics and low exposure to compound may be compensated for by long duration of action (slow off-rate) at the specific target84, thereby leading to a viable pharmacodynamic response. However, inherent poor pharmacokinetic properties leave no room for maneuver during clinical development and therefore represent a considerable risk to the eventual success of the compound1. Prioritized compounds that pass through a pharmacokinetic-pharmacodynamic filter are generally evaluated for efficacy in tumor growth inhibition in molecularly characterized models (for example, human tumor xenografts in rodents)99. Transgenic models are also valuable for proof of concept but are generally less convenient and reproducible for lead optimization100. In addition to tolerability and toxicity in animals, many specific off-target activities causing toxicological issues in humans can now be investigated by in vitro screening101. Some of these are general problems, such as inhibition of the HERG cardiac ion channel, whereas others may be associated with particular chemical scaffolds, for example the rich pharmacology of purine-based ligands102.
Selected case histories: chemical biology in action
Development of molecular-targeted agents for kinases, HSP90 and HDACs illustrates both lead optimization and the importance of feedback from clinical studies to the early drug discovery process. Formation of the BCR-ABL fusion protein is a specific oncogenic event for chronic myeloid leukemia (CML). Highly selective BCR-ABL inhibitors were generated from a series of anilinopyrimidine protein kinase C (PKC) inhibitors, an example of target hopping. Lead optimization to yield Gleevec focused on improving pharmacokinetic properties20 (Scheme 1a). The success of Gleevec in clinical trials for CML provided an important proof of concept for the development of molecular-targeted small-molecule drugs in oncology. The observation that Gleevec inhibits a limited number of other kinases, notably the oncoprotein c-KIT, has led to successful clinical trials of the drug for the treatment of gastrointestinal stromal tumor (GIST), which is often driven by a mutant kinase103. The emergence of Gleevec-resistant kinase mutants in people with CML has prompted the development of second-generation inhibitors such as Sprycel (dasatinib), which targets the more conserved active form of the kinase and inhibits many of the Gleevec-resistant mutants as well as other targets such as the nonreceptor tyrosine kinase SRC104.
Nexavar exemplifies the use of combinatorial chemistry for lead generation and optimization from a privileged structure, the diarylurea82 (Scheme 1b). Although developed as a targeted CRAF inhibitor, Nexavar was subsequently recognized as having useful receptor tyrosine kinase polypharmacology (particularly in inhibiting VEGFR, platelet-derived growth factor receptor (PDGFR), c-KIT and FMS-related tyrosine kinase 3 (FLT3)), which led to its approval for the treatment of renal cell cancers105. Single-agent activity was not, however, seen in melanoma, despite the fact that Nexavar does have activity on BRAF. Selectively targeting oncogenic BRAF is one focus for research on second-generation RAF inhibitors31.
The search for small-molecule inhibitors of HSP90 was given impetus by successful proof-of-concept clinical trials with 17-AAG, a derivative of the natural product geldanamycin18, which showed both the molecular signature of target inhibition in tumor tissue and evidence of activity in individuals with melanoma, but which has solubility, formulation and metabolic liabilities106. The optimization of the 3-(2,4-dihydroxyphenyl)pyrazole HTS hit CCT018159 (ref. 72) exemplifies the way in which protein structure information guides the choice and positioning of extra functionality to improve inhibitor affinity. The amidopyrazole VER49009 is one of the first small-molecule HSP90 inhibitors to be described that demonstrates the required potency to become a clinical candidate107 (Scheme 1c).
Another area in which early clinical trials of a natural-product inhibitor, Zolinza (vorinostat, SAHA), have informed subsequent small-molecule drug discovery is the development of HDAC inhibitors108. Zolinza has been approved for use in percutaneous T-cell lymphoma but has suboptimal pharmacokinetics. The HTS hit NVP-LAK974 contains a novel cinnamyl hydroxamate as the essential zinc-chelating group common to the inhibitor class (Scheme 1d). In this example, lead optimization concentrated on improving in vivo efficacy and tolerability, thereby leading to the clinical candidate NVP-LAQ824 (ref. 109).
Biomarkers: the pharmacologic audit trail
Molecular diagnostics are required to identify individuals most likely to benefit from molecularly targeted therapy110. Molecular biomarkers are also needed for proof of concept of target inhibition and for optimizing dosing schedules7. Biomarkers are used to make clinical trials more intelligent and informative, and to make decision making more rational and effective110,111. We have developed the concept of the 'pharmacologic audit trail', which offers a logical and practical framework for tracking the performance of a drug during both preclinical and clinical development7,13,112,113. It also provides a rational basis for assessing the risk of failure and for making important decisions on project progression. The pharmacologic audit trail consists of a series of hierarchically or sequentially arranged questions (Fig. 5).
Ideally data should be collected at each level. If the answer is “no” at any point then immediate action should be taken, with one possibility being termination of the project. As a drug progresses through the hierarchy of questions the risk of failure decreases. It is essential to have a series of robust, validated assays available for molecular biomarkers and pharmacokinetic behavior. Advances in genomic technologies have enhanced biomarker discovery114. The development of minimally invasive methods based on positron emission tomography and NMR spectroscopy or imaging is particularly important115. An important point in biomarker-driven decision making is that quantitative information is frequently lacking concerning exactly how hard a target or pathway needs to be hit to obtain a relevant degree of therapeutic benefit. This needs to be better defined in preclinical models using quantitative technologies, such as ELISA116.
Future prospects: toward bespoke cancer medicine
So how far have we come—and where are we going—with small-molecule cancer therapeutics? We argue here that the glass is half full. There have been spectacular successes, led by Gleevec and Herceptin, that demonstrate proof of concept that major clinical benefit can be gained from targeting the driving oncogenic abnormalities responsible for particular types of cancer in particular subject populations. Will these drugs be a model to follow? In a way, yes, because the principle of the rational, targeted approach is now proven. But cancer is a formidably complex disease, and significant challenges have already been identified.
Many important cancer targets (for example, mutant p53, RAS and MYC) and oncogenic pathways (such as WNT) remain undrugged. Although drugging the cancer kinome is clearly achievable, many other target classes, notably phosphatases and most protein-protein interactions, have proved technically intractable so far. Nevertheless, success with MDM2 binding agents such as the nutlins117 and with BCL2 antagonists such as ABT-737 (ref. 118) shows that at least some protein-protein interactions can be drugged.
What else? The 95% attrition rate for oncology drugs in the clinic is unacceptably high. We must prevent the expensive, late-stage casualties such as the matrix metalloprotease (MMP) inhibitors, the farnesyltransferase inhibitors and some receptor tyrosine kinase inhibitors. In the case of MMP inhibitors, there was a good rationale to develop these as anti-invasive agents and they did show activity in preclinical models. It is likely that they are casualties of being among the first of the new generation of molecularly targeted agents to enter the clinic, particularly in terms of their evaluation in late-stage disease rather than in earlier-stage cancer, in which their activity may have been revealed more readily. However, it could be argued that the hurdle for therapeutic activity may be generally set too low and that we should be more rigorous in the level of preclinical activity required for entry into the clinic. On the other hand, given the uncertain predictiveness of our animal models99,100, it would be difficult at this time to set quantitative criteria for regression or cytostasis, particularly for first-in-class agents. But this is an area that requires close attention and highlights the need to integrate early clinical experience with ongoing preclinical drug discovery to reevaluate the optimum path into and through clinical development.
Cycle times in preclinical and clinical discovery and development generally need to be compressed. Also, it is clear that the age-old problem of drug resistance will still apply to the new molecular cancer therapeutics. Because of this and the multiple abnormalities contributing to very many cancers, a combinatorial therapeutic approach will be essential, as with cytotoxic cancer therapy and HIV-AIDS treatment.
The application and integration of new and existing techniques will shorten cycle times. High-throughput genomic, molecular and biochemical technologies will help to elucidate the complete range of mutational repertoires and hierarchies driving different cancers. This will help us in the molecular detection, classification, monitoring, treatment and, potentially, prevention of cancer. Both genetic and (increasingly) epigenetic changes will be important to understand. Better methods are needed to validate, select and prioritize new drug targets, including the use of high-throughput RNAi platforms. Understanding and exploiting oncogene addiction and other cancer dependencies will remain important. Greater emphasis on achieving synthetic lethal therapies (for example by exploiting DNA repair abnormalities, as with BRCA mutants) is justified10.
Emphasis so far has been on targets involved in cell cycle control and proliferation, and more recently in angiogenesis. The other hallmark traits of cancer need to be addressed more fully. Mechanism-based inducers of apoptosis are now entering the clinic. Telomerase inhibitors may be used to block immortalization. We need to revisit mechanism-based inhibition of invasion and evaluate this in more appropriate and careful trials than was the case for MMP inhibitors, particularly with regard to selection of subjects and stage of disease. Though it is an important goal, clinical evaluation of specifically targeted metastasis inhibitors is challenging because of the long timescales that may be required to obtain a meaningful end point.
In the broad sense, as stressed in this article, chemical biology methods will help to enhance the traditional linear process of drug development by providing means to interconnect the disparate stages. Lead generation will benefit from extracting more information from screening119 and from refined compound-selection criteria that take account of all the potential hurdles a drug must clear. Evaluation of efficacy, selectivity, ADMET, appropriate combinations and resistance liability for compounds at the earliest stages will provide a multidimensional SAR that will focus and speed up lead optimization, thereby providing rapid routes to the clinic. This may require further development of expert systems to correlate the very large amount of cheminformatic data generated as multiple properties are determined for increasing numbers of compounds120,121. Prototype small molecules and clinical agents will serve as tools to reinvestigate the underlying biological hypotheses, to discover modified or new strategies for targeted therapy, and to interrogate cancer models. Ongoing improvement of animal models122 and critical review of their clinical predictiveness is essential. Prospective analysis of the predictiveness of animal models may allow us to raise the preclinical hurdle for entry into the clinic.
Clinical feedback needs to be obtained more quickly, but also more intelligently. This can be done using better and more quantitative end points, and particularly by using molecular and imaging biomarkers. Clinical trial design needs to reflect the genetics and molecular cell biology of the target and the pharmacology of the drug; it should also reflect the expected outcome, which is often disease stabilization rather than rapid regression123. Biomarkers will be used increasingly to select people most likely to respond to a molecular cancer therapeutic, and to show proof of concept, monitor therapy and design optimal schedules. The pharmacologic audit trail can be used to aid decision making and manage risk.
Mechanisms of drug resistance can now be predicted and overcome by molecular and chemical biology techniques. Combinatorial multitarget inhibition will be essential in most cases. This can be achieved by using rationally selected combinations of highly targeted agents, according to the precise genetic and epigenetic makeup of the particular cancer, or by using intrinsic polypharmacy agents such as multitargeted kinase inhibitors or drugs affecting multiple downstream targets (for example, molecular chaperones or chromatin-modifying enzymes). Identifying the best drug combinations is difficult. There may be value in modulating the same target with different agents, hitting the same biological pathway at different levels, or inhibiting distinct pathways or hallmark traits simultaneously. Rational choice of effective combinations may be based on molecular knowledge. Use of chemical inhibitors alongside high-throughput RNAi technology can identify effective combinatorial targets11,28. Systematic HTS of drug pairs124 and systems-biology approaches are underway125. High-throughput inhibitor-selectivity profiling methods will be important for developing both highly selective and multitargeted agents9.
Much greater emphasis should be placed on understanding the complex signal transduction networks that are hijacked by malignant cells, rather than erroneously considering these as simple linear, textbook pathways. Better understanding of feedback and feedforward loops and of network robustness and sensitivity is needed. Mathematical models must be developed to predict signaling-network behavior and optimal points for intervention, and these must then be evaluated experimentally126.
An excellent example of the importance of feedback control was provided recently with the use of rapamycin-based mTOR inhibitors. mTOR is an important target for cancer therapy, but clinical results have been disappointing, except in renal cell cancer, in which the activity may be due to an antiangiogenic effect. Activity may be limited by a feedback loop involving the downstream ribosomal protein S6 kinase and the upstream adaptor insulin receptor substrate 1 (IRS1)127, thereby leading to activation of AKT. In a recent chemical biology approach in which a library of PI(3)K inhibitors was used to identify biological functions of various isoforms, it was shown that this problem can be overcome by the pyridofuropyrimidine inhibitor PI103, which simultaneously inhibits mTOR and the PI(3)K p110α, thereby causing a more complete blockade of the pathway and in particular preventing feedback activation6,128.
A concept of cancer biology that is likely to be important for targeted molecular therapeutics is that of tumor heterogeneity, and in particular the presence of tumor stem cells129. Such cells are by definition capable of repopulating the whole tumor, and therapies that are able to eradicate them will be critical.
We are in an exciting era in which there is great potential to develop rational, hypothesis-driven, mechanism-based molecular therapeutics for cancer. There are many challenges, but they can be addressed by the powerful techniques of genomics, molecular biology and chemical biology. The concept of a chemical probe for every protein encoded by the genome130 can now be extended to the vision of achieving a molecularly targeted drug for all oncogenic proteins encoded by the cancer genome, or at least for every oncogenic pathway involved in the disease. There are excellent prospects for prolonging life and even curing people with cancer. A progressive advance toward the development of truly personalized cancer medicine can be predicted over the next five to ten years.
References
Kola, I. & Landis, J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov. 3, 711–715 (2004).
Dalton, W.S. & Friend, S.H. Cancer biomarkers-an invitation to the table. Science 312, 1165–1168 (2006).
Wesche, H., Xiao, S. & Young, S.W. High-throughput screening for protein kinase inhibitors. Comb. Chem. High Throughput Screen. 8, 181–195 (2005).
Clemons, P.A. Complex phenotypic assays in high-throughput screening. Curr. Opin. Chem. Biol. 8, 334–338 (2004).
Noble, M.E.M., Endicott, J.A. & Johnson, L.N. Protein kinase inhibitors: insights into drug design from structure. Science 303, 1800–1805 (2004).
Fan, Q.W. et al. A dual PI3 kinase/mTOR inhibitor reveals emergent efficacy in glioma. Cancer Cell 9, 341–349 (2006).
Workman, P. How much gets there and what does it do?: the need for better pharmacokinetic and pharmacodynamic endpoints in contemporary drug discovery and development. Curr. Pharm. Des. 9, 891–902 (2003).
Becker, F. et al. A three-hybrid approach to scanning the proteome for targets of small molecule kinase inhibitors. Chem. Biol. 11, 211–223 (2004).
Fabian, M.A. et al. A small molecule–kinase interaction map for clinical kinase inhibitors. Nat. Biotechnol. 23, 329–336 (2005).
Farmer, H. et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434, 917–921 (2005).
Morgan-Lappe, S. et al. RNAi-based screening of the human kinome identifies Akt-cooperating kinases: a new approach to designing efficacious multi-targeted kinase inhibitors. Oncogene 25, 1340–1348 (2006).
Varmus, H. The new era in cancer research. Science 312, 1162–1165 (2006).
Workman, P. Drugging the cancer kinome: progress and challenges in developing personalized molecular cancer therapeutics. Cold Spring Harb. Symp. Quant. Biol. 70, 499–515 (2005).
Workman, P. Genomics and the second golden era of cancer drug development. Mol. Biosyst. 1, 17–26 (2005).
Marsham, P.R. et al. Design and synthesis of potent non-polyglutamatable quinazoline antifolate thymidylate synthase inhibitors. J. Med. Chem. 42, 3809–3820 (1999).
Vogelstein, B. & Kinzler, K.W. Cancer genes and the pathways they control. Nat. Med. 10, 789–799 (2004).
Hanahan, D. & Weinberg, R.A. The hallmarks of cancer. Cell 100, 57–70 (2000).
McDonald, E., Workman, P. & Jones, K. Inhibitors of the HSP90 molecular chaperone: attacking the master regulator in cancer. Curr. Med. Chem. 6, 1091–1107 (2006).
Minucci, S. & Pelicci, P.G. Histone deacetylase inhibitors and the promise of epigenetic (and more) treatments for cancer. Nat. Rev. Cancer 6, 38–51 (2006).
Capdeville, R., Buchdunger, E., Zimmerman, J. & Matter, A. Glivec (ST571, imatinib), a rationally developed, targeted anticancer drug. Nat. Rev. Drug Discov. 1, 493–502 (2002).
Weinstein, I.B. Cancer. Addiction to oncogenes-the Achilles heal of cancer. Science 297, 63–64 (2002).
Benson, J.D. et al. Validating cancer drug targets. Nature 441, 451–456 (2006).
Solit, D. et al. BRAF mutation predicts sensitivity to MEK inhibition. Nature 439, 358–362 (2006).
Garraway, L. et al. Integrative genomic analyses identify MITF as a lineage survival oncogene amplified in malignant melanoma. Nature 436, 117–122 (2005).
Futreal, P.A. et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).
Thomas, R.K. et al. Sensitive mutation detection in heterogeneous cancer specimens by massively parallel picoliter reactor sequencing. Nat. Med. 12, 852–855 (2006).
Davies, H. et al. Mutations of the BRAF gene in human cancer. Nature 417, 949–954 (2002).
Brummelkamp, T.R. et al. An shRNA barcode screen provides insight into cancer cell vulnerability to MDM2 inhibitors. Nat. Chem. Biol. 2, 202–206 (2006).
Chatterjee-Kishore, M. & Miller, C.P. Exploring the sounds of silence: RNAi-mediated gene silencing for target identification and validation. Drug Discov. Today 10, 1559–1565 (2005).
Lacouture, M.E. Mechanisms of cutaneous toxicities to EGFR inhibitors. Nat. Rev. Cancer 6, 803–812 (2006).
Newbatt, Y. et al. Identification of inhibitors of the kinase activity of oncogenic V600E BRAF in an enzyme cascade high-throughput screen. J. Biomol. Screen. 11, 145–154 (2006).
Park, S. et al. Hexachlorophene inhibits Wnt/beta-catenin pathway by promoting Siah-mediated beta-catenin degradation. Mol. Pharmacol. 70, 960–966 (2006).
Hart, C.P. Finding the target after screening the phenotype. Drug Discov. Today 10, 513–519 (2005).
Luesch, H. et al. A genome-wide overexpression screen in yeast for small-molecule target identification. Chem. Biol. 12, 55–63 (2005).
Evans, M.J., Saghatelian, A., Sorensen, E.J. & Cravatt, B.F. Target discovery in small-molecule cell-based screens by in situ proteome reactivity profiling. Nat. Biotechnol. 23, 1303–1307 (2005).
Lundholt, B.K. et al. Identification of Akt pathway inhibitors using redistribution screening on the FLIPR and the IN cell 3000 analyzer. J. Biomol. Screen. 10, 20–29 (2005).
Wolff, M. et al. Automated high content screening for phosphoinositide 3 kinase inhibition using an AKT 1 redistribution assay. Comb. Chem. High Throughput Screen. 9, 339–350 (2006).
Kau, T.R. et al. A chemical genetic screen identifies inhibitors of regulated nuclear export of a Forkhead transcription factor in PTEN-deficient tumor cells. Cancer Cell 4, 463–476 (2003).
Rees, D.C., Congreve, M., Murray, C.W. & Carr, R.A.E. Fragment-based lead discovery. Nat. Rev. Drug Discov. 3, 660–672 (2004).
Gill, A.L. et al. Identification of novel p38alpha MAP kinase inhibitors using fragment based lead generation. J. Med. Chem. 48, 414–426 (2005).
Card, G.L. et al. A family of phosphodiesterase inhibitors discovered by cocrystallography and scaffold-based drug design. Nat. Biotechnol. 23, 201–207 (2005).
Lipinski, C.A., Lombardo, F., Dominy, B.W. & Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 46, 3–26 (2001).
Lumley, J.A. Compound selection and filtering in library design. QSAR Comb. Sci. 24, 1066–1075 (2005).
Oprea, T.I., Davis, A.M., Teague, S.J. & Leeson, P.D. Is there a difference between leads and drugs? A historical perspective. J. Chem. Inf. Comput. Sci. 41, 1308–1315 (2001).
Veber, D.F., Johnson, S.R., Cheng, H., Ward, K.W. & Kopple, K.D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 45, 2615–2623 (2002).
Vieth, M. et al. Characteristic physical properties and structural fragments of marketed oral drugs. J. Med. Chem. 47, 224–232 (2004).
Lu, J.J. et al. Influence of molecular flexibility and polar surface area metrics on oral bioavailability in the rat. J. Med. Chem. 47, 6104–6107 (2004).
McGovern, S.L., Caselli, E., Grigorieff, N. & Schoichet, B.K. A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. J. Med. Chem. 45, 1712–1722 (2002).
Rishton, G.M. Nonleadlikeness and leadlikeness in biochemical screening. Drug Discov. Today 8, 86–96 (2003).
Kitchen, D.B., Decornez, H., Furr, J.R. & Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov. 3, 935–949 (2004).
Lyne, P.D. et al. Identification of compounds with nanomolar binding affinity for checkpoint kinase-1 using knowledge-based virtual screening. J. Med. Chem. 47, 1962–1968 (2004).
Muller, O. et al. Identification of potent Ras signaling inhibitors by pathway-selective phenotype-based screening. Angew. Chem. Int. Edn Engl. 43, 450–454 (2004).
Prien, O. Target-family-oriented focused libraries for kinases – conceptual design aspects and commercial availability. ChemBioChem 6, 500–505 (2005).
Muller, G. Medicinal chemistry of target family-directed masterkeys. Drug Discov. Today 8, 681–691 (2003).
Mann, J. Natural products in cancer chemotherapy: past, present and future. Nat. Rev. Cancer 2, 143–148 (2002).
Noren-Muller, A. et al. Discovery of protein phosphatase inhibitor classes by biology-oriented synthesis. Proc. Natl. Acad. Sci. USA 103, 10606–10611 (2006).
Tan, D.S. Diversity-oriented synthesis: exploring the intersections between chemistry and biology. Nat. Chem. Biol. 1, 74–84 (2005).
Clardy, J. & Walsh, C. Lessons from natural molecules. Nature 432, 829–837 (2004).
Lipinski, C.A. & Hopkins, A. Navigating chemical space for biology and medicine. Nature 432, 855–861 (2004).
Fry, D.C. & Vassilev, L.T. Targeting protein-protein interactions for cancer therapy. J. Mol. Med. 83, 955–983 (2005).
Liu, Y. & Gray, N.S. Rational design of inhibitors that bind to inactive kinase conformations. Nat. Chem. Biol. 2, 358–364 (2006).
Cohen, P. Protein kinases – the major drug targets of the twenty-first century? Nat. Rev. Drug Discov. 1, 309–315 (2002).
Ohren, J.F. et al. Structures of human MAP kinase kinase 1 (MEK1) and MEK2 describe novel noncompetitive kinase inhibition. Nat. Struct. Mol. Biol. 11, 1192–1197 (2004); erratum 12, 278 (2005).
Barnett, S.F., Bilodeau, M.T. & Lindsley, C.W. The Akt/PKB family of protein kinases: a review of small molecule inhibitors and progress towards target validation. Curr. Top. Med. Chem. 5, 109–125 (2005).
Choi, J., Chen, J., Schreiber, S. & Clardy, J. Structure of the FKBP12-rapamycin complex interacting with the binding domain of human FRAP. Science 273, 239–242 (1996).
Gorre, M.E. et al. Clinical resistance to STI-571 cancer therapy caused by BCR-ABL gene mutation or amplification. Science 293, 876–880 (2001).
Paez, J.G. et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304, 1497–1500 (2004).
Blencke, S., Ullrich, A. & Daub, H. Mutation of threonine 766 in the epidermal growth factor receptor reveals a hotspot for resistance formation against selective tyrosine kinase inhibitors. J. Biol. Chem. 278, 15435–15440 (2003).
Shokat, K. & Velleca, M. Novel chemical genetic approaches to the discovery of signal transduction inhibitors. Drug Discov. Today 7, 872–879 (2002).
Fry, D.W. Site-directed irreversible inhibitors of the erbB family of receptor tyrosine kinase as novel chemotherapeutic agents for cancer. Anticancer Drug Des. 15, 3–16 (2000).
Cohen, M.S., Zhang, C., Shokat, K.M. & Taunton, J. Structural bioinformatics-based design of selective, irreversible kinase inhibitors. Science 308, 1318–1321 (2005).
Roe, S.M. et al. Structural basis for inhibition of of the Hsp90 molecular chaperone by the antitumour antibiotics radicicol and geldanamycin. J. Med. Chem. 42, 260–266 (1999).
Cheung, K.M. et al. The identification, synthesis, protein crystal structure and in vitro biochemical evaluation of a new 3,4-diarylpyrazole class of Hsp90 inhibitors. Bioorg. Med. Chem. Lett. 15, 3338–3343 (2005).
Chiosis, G. et al. A small molecule designed to bind to the adenine nucleotide pocket of Hsp90 causes Her2 degradation and the growth arrest and differentiation of breast cancer cells. Chem. Biol. 8, 289–299 (2001).
Wright, L. et al. Structure-activity relationships in purine-based inhibitor binding to HSP90 isoforms. Chem. Biol. 11, 775–785 (2004).
Finnin, M.S. et al. Structures of a histone deacetylase homologue bound to the TSA and SAHA inhibitors. Nature 401, 188–193 (1999).
Somoza, J.R. et al. Structural snapshots of human HDAC8 provide insights into the class I histone deacetylases. Structure 12, 1325–1334 (2004).
Hildmann, C. et al. Substrate and inhibitor specificity of class 1 and class 2 histone deacetylases. J. Biotechnol. 124, 258–270 (2006).
Koeller, K.M. et al. Chemical genetic modifier screens: small molecule trichostatin suppressors as probes of intracellular histone and tubulin acetylation. Chem. Biol. 10, 397–410 (2003).
Davis, A.M., Keeling, D.J., Steele, J., Tomkinson, N.P. & Tinker, A.C. Components of successful lead generation. Curr. Top. Med. Chem. 5, 421–439 (2005).
Shuttleworth, S.J. et al. Design and synthesis of protein superfamily-targeted chemical libraries for lead identification and optimization. Curr. Med. Chem. 12, 1239–1281 (2005).
Lowinger, T.B., Riedl, B., Dumas, J. & Smith, R.A. Design and discovery of small molecules targeting Raf-1 kinase. Curr. Pharm. Des. 8, 2269–2278 (2002).
Adams, J. et al. Potent and selective inhibitors of the proteasome: dipeptidyl boronic acids. Bioorg. Med. Chem. Lett. 8, 333–338 (1998).
Swinney, D.C. Biochemical mechanisms of drug action: what does it take for success? Nat. Rev. Drug Discov. 3, 801–808 (2004).
Hopkins, A.L., Groom, C.R. & Alex, A. Ligand efficiency: a useful metric for lead selection. Drug Discov. Today 9, 430–431 (2004).
Knight, Z.A. & Shokat, K.M. Features of selective kinase inhibitors. Chem. Biol. 12, 621–637 (2005).
Kung, C., Kenski, D.M., Krukenberg, K., Madhani, H.D. & Shokat, K.M. Selective kinase inhibition by exploiting differential pathway selectivity. Chem. Biol. 13, 399–407 (2006).
Bain, J., McLauchlan, H., Elliott, M. & Cohen, P. The specificities of protein kinase inhibitors: an update. Biochem. J. 371, 199–204 (2003).
Brehmer, D. et al. Cellular targets of gefitinib. Cancer Res. 65, 379–382 (2005).
Wan, Y. et al. Synthesis and target identification of hymenialdisine analogues. Chem. Biol. 11, 247–259 (2004).
Obata, K., Sugano, K., Machida, M. & Aso, Y. Biopharmaceutics classification by high throughput solubility assay and PAMPA. Drug Dev. Ind. Pharm. 30, 181–185 (2004).
Kerns, E.H. et al. Combined application of parallel artificial membrane permeability assay and Caco-2 permeability assays in drug discovery. J. Pharm. Sci. 93, 1440–1453 (2004).
Longley, D.B. & Johnston, P.G. Molecular mechanisms of drug resistance. J. Pathol. 205, 275–292 (2005).
Nassar, A.E., Kamel, A.M. & Clarimont, C. Improving the decision-making process in the structural modification of drug candidates: enhancing metabolic stability. Drug Discov. Today 9, 1020–1028 (2004).
Hutzler, J.M., Messing, D.M. & Wienkers, L.C. Predicting drug-drug interactions in drug discovery: where are we now and where are we going? Curr. Opin. Drug Discov. Devel. 8, 51–58 (2005).
Raynaud, F.I. et al. Cassette dosing pharmacokinetics of a library of 2,6,9-trisubstituted purine cyclin-dependent kinase 2 inhibitors prepared by parallel synthesis. Mol. Cancer Ther. 3, 353–362 (2004).
Smith, N.F. et al. Preclinical pharmacokinetics and metabolism of a novel diaryl pyrazole resorcinol series of heat shock protein 90 inhibitors. Mol. Cancer Ther. 5, 1628–1637 (2006).
Banerji, U. et al. Pharmacokinetic-pharmacodynamic relationships for the heat shock protein 90 molecular chaperone inhibitor 17-allylamino, 17-demethoxygeldanamycin in human ovarian cancer xenograft models. Clin. Cancer Res. 11, 7023–7032 (2005).
Sausville, E.A. & Burger, A.M. Contributions of human tumour xenografts to anticancer drug development. Cancer Res. 66, 3351–3354 (2006).
Becher, O.J. & Holland, E.C. Genetically engineered models have advantages over xenografts for preclinical studies. Cancer Res. 66, 3355–3359 (2006).
Whitebread, S., Hamon, J., Bojanic, D. & Urban, L. In vitro safety pharmacology profiling: an essential tool for successful drug development. Drug Discov. Today 10, 1421–1433 (2005).
Haystead, T.A. The purinome, a complex mix of drug and toxicity targets. Curr. Top. Med. Chem. 6, 1117–1127 (2006).
Judson, I. Gastrointestinal stromal tumours (GIST): biology and treatment. Ann. Oncol. 13, 287–289 (2002).
Shah, N.P. et al. Overriding imatinib resistance with a novel ABL kinase inhibitor. Science 305, 399–401 (2004).
Strumberg, D. Preclinical and clinical development of the oral multikinase inhibitor sorafenib in cancer. Drugs Today (Barc). 41, 773–784 (2005).
Banerji, U. et al. Phase I pharmacokinetic and pharmacodynamic study of 17-allylamino, 17-demethoxygeldanamycin in patients with advanced malignancies. J. Clin. Oncol. 23, 4152–4161 (2005).
Dymock, B.W. et al. Novel, potent small-molecule inhibitors of the molecular chaperone Hsp90 discovered through structure-based design. J. Med. Chem. 48, 4212–4215 (2005).
Kelly, W.K. & Marks, P.A. Drug insight: histone deacetylase inhibitors–development of the new targeted anticancer agent suberoylanilide hydroxamic acid. Nat. Clin. Pract. Oncol. 2, 150–157 (2005).
Remiszewski, S.W. The discovery of NVP-LAQ824: from concept to clinic. Curr. Med. Chem. 10, 2393–2402 (2003).
Sawyers, C.L. Opportunities and challenges in the development of kinase inhibitor therapy for cancer. Genes Dev. 17, 2998–3010 (2003).
Frank, R. & Hargreaves, R. Clinical biomarkers in drug discovery and development. Nat. Rev. Drug Discov. 2, 566–580 (2003).
Workman, P. Challenges of PK/PD measurements in modern drug development. Eur. J. Cancer 38, 2189–2193 (2002).
Workman, P. Auditing the pharmacological accounts for Hsp90 molecular chaperone inhibitors: unfolding the relationship between pharmacokinetics and pharmacodynamics. Mol. Cancer Ther. 2, 131–138 (2003).
Workman, P. & Johnston, P.G. Genomic profiling of cancer: what next? J. Clin. Oncol. 23, 7253–7256 (2005).
Workman, P. et al. Minimally invasive pharmacokinetic and pharmacodynamic technologies in hypothesis-testing clinical trials of innovative therapies. J. Natl. Cancer Inst. 98, 580–598 (2006).
Garrett, M.D. et al. Novel isoquinoline-5-sulfonamides as biochemical and cellular inhibitors of PKB/AKt. Eur. J. Cancer Suppl. 2, 98 (2004).
Vassilev, L.T. et al. In vivo activation of the p53 pathway by small-molecule antagonists of MDM2. Science 303, 844–888 (2004).
Oltersdorf, T. et al. An inhibitor of Bcl-2 family proteins induces regression of solid tumours. Nature 435, 677–681 (2005).
Inglese, J. et al. Quantitative high-throughput screening: a titration-based approach that efficiently identifies biological activities in large chemical libraries. Proc. Natl. Acad. Sci. USA 103, 11473–11478 (2006).
Paolini, G.V. et al. Global mapping of pharmacological space. Nat. Biotechnol. 24, 805–815 (2006).
Shoemaker, R. The NCI60 human tumour cell line anticancer drug screen. Nat. Rev. Cancer 6, 813–823 (2006).
Sharpless, N.E. & DePinho, R.A. The mighty mouse: genetically engineered mouse models in cancer drug development. Nat. Rev. Drug Discov. 5, 741–754 (2006).
Ratain, M.J. & Eckardt, S.G. Phase II studies of modern drugs directed against new targets: if you are fazed, too, then resist RECIST. J. Clin. Oncol. 22, 4442–4445 (2004).
Borisy, A.A. et al. Systematic discovery of multicomponent therapeutics. Proc. Natl. Acad. Sci. USA 100, 7977–7982 (2003).
Fitzgerald, J.B., Schoeberl, B., Nielsen, U.B. & Sorger, P.K. Systems biology and combination therapy in the quest for clinical efficacy. Nat. Chem. Biol. 2, 458–466 (2006).
Alves, R., Antunes, F. & Salvador, A. Tools for kinetic modeling of biochemical networks. Nat. Biotechnol. 24, 667–672 (2006).
Harrington, L.S. et al. The TSC1–2 tumor suppressor controls insulin-PI3K signaling via regulation of IRS proteins. J. Cell Biol. 166, 213–223 (2004).
Workman P., Clarke, P.A., Guillard, S. & Raynaud, F.I. Drugging the PI3 kinome. Nat. Biotechnol. 24, 794–796 (2006); corrigendum 24, 1033 (2006).
Clarke, M.F. & Fuller, M. Stem cells and cancer: two faces of eve. Cell 124, 1111–1115 (2006).
Schreiber, S.L. Stuart Schreiber: biology from a chemist's perspective. Interview by Joanna Owens. Drug Discov. Today 9, 299–303 (2004).
Kassel, D.B. Applications of high-throughput ADME in drug discovery. Curr. Opin. Chem. Biol. 8, 339–345 (2004).
Acknowledgements
This article is dedicated to our late friend and colleague F.T. (Tom) Boyle, who spent most of his successful career working on the medicinal chemistry of cancer drugs. The authors' work (http://www.icr.ac.uk/) is funded primarily by Cancer Research UK [CUK] Programme Grant C309/A2187, and P. Workman is a Cancer Research UK Life Fellow. We thank our many colleagues and collaborators for stimulating discussions.
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The authors have received research funding and/or have collaboration/licensing arrangements with Vernalis, Astex Therapeutics, Chroma Therapeutics, PIramed, Sareum, Cyclacel, Novartis, AstraZeneca, Genentech and GlaxoSmithKline. P.W. holds stock/options in Chroma Therapeutics, PIramed and Avalon Pharmaceuticals and is a consultant for Chroma Therapeutics, PIramed, Avalon Pharmaceuticals and Novartis.
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Collins, I., Workman, P. New approaches to molecular cancer therapeutics. Nat Chem Biol 2, 689–700 (2006). https://doi.org/10.1038/nchembio840
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DOI: https://doi.org/10.1038/nchembio840
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