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ScienceDirect Editorial overview: Cancer: From target discovery to targeted therapy: the risky business of target validation Francisco Cruzalegui Current Opinion in Pharmacology 2014, 17:iv–vi For a complete overview see the Issue Available online 15th September 2014 http://dx.doi.org/10.1016/j.coph.2014.08.004 1471-4892/# 2014 Elsevier Ltd. All right reserved.
Francisco Cruzalegui Translational Science, Oncology iMED, AstraZeneca, Alderley Park, Macclesfield, Cheshire, SK10 4TG, UK e-mail: [email protected]
Francisco Cruzalegui has a degree in biochemical engineering from the University of Louvain and a PhD from Baylor College of Medicine. He carried out post-doctoral training at CRUK London and at the MRC Laboratory of Molecular Biology in Cambridge, UK. Following a lectureship at the University of Nottingham, he worked for 12 years in the Oncology Drug Discovery group of Servier, Paris. Since 2013 he is an associate director in Oncology Translational Science at AstraZeneca.
Target validation for cancer drug discovery, either in industry or academia, is a crucial step in the long process leading to a new medicine. The investment and strategy used for target validation vary but some elements tend to be conserved throughout. One essential concept is that targeted drug discovery requires clear identification of a key intervention point in a biochemical pathway and that disruption or inhibition will lead to a cellular phenotype downstream. This is particularly true for small molecule approaches where the therapeutic agent is able to access either intracellular or membrane targets and interfere with their biochemical activity. Examples of these are enzymes such as protein kinases, ion channels or G-protein coupled receptors. A different rationale may apply to biologicals (i.e. antibodies and recombinant proteins) because targets for these entities must be exposed at the cell membrane. Depending on the specific format of therapeutic antibody, its action could mainly stem from its ability to block the function of the target. Alternatively, some antibody formats can trigger antibodydirected cell cytotoxicity (ADCC). In this case, tumor-specific target expression becomes the main criteria for target choice since the therapeutic agent serves essentially to recruit cell-killing T-lymphocytes . In the cancer field, the ultimate phenotypic endpoint is tumor cell death without affecting viability of normal cells. Nevertheless, in the post-cytotoxic era, the direct outcome of many or most of targeted therapies developed so far are cell cycle arrest, angiogenesis inhibition, induction of differentiation and metabolic changes. These effects can indirectly result in tumor cell apoptosis by pushing cancer cells ‘over the edge’ taking advantage of their oncogenic load. Directly targeting the apoptosis machinery in order to induce cell death has proven more challenging with the first generation of such agents reaching the clinic now. In addition, a new era of rational combinations of novel targeted therapies has now begun, helped by an unprecedented access to a wealth of genomic, transcriptomic and proteomic data. In addition to robust data about the mechanistic rationale for a potential target, other important factors come into play such as druggability, redundancy of mechanism, dependence for tumor cell survival, potential on-target and off-target side effects and competitive landscape . The comprehensive assessment of all these parameters is an obligatory step to trigger the kind of financial investment needed to generate a lead compound and later a drug candidate. All this makes the target validation decision one of the most risky steps of the process of drug discovery. Druggability or the potential for interfering with the target’s function by the action of a small or large molecule is addressed in the review by Blagg and Workman in this issue. Examples of druggable targets include protein kinases, metabolic enzymes,
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Editorial overview Cruzalegui v
GPCRs and more recently epigenetic regulators. A more challenging category includes protein-protein interactions in which a critical interaction has been identified either by peptide-mimetic approaches or via tool compounds. Examples of these are the Bcl2 family proteins  and epigenetic readers . Finally, extremely difficult targets are those where protein–protein interactions are involved but the interaction is extensive and lacks a definable critical point. Examples of the latter are transcription factors and interaction domains such as coiledcoil or leucine zippers. One potential first pitfall in the process of target validation can be the robustness of the original data supporting the idea. Many exciting and novel findings about potential targets originate from using defined preclinical models and experimental tools as well as laboratory conditions that may not be reproducible in other settings and may be far removed from the reality of human disease. This is particularly true in cancer research. Preclinical models are fraught with pitfalls such as the use of cell lines growing in two-dimensional cultures or animal models based on xenografts of human cell lines in immune-deprived mice. In the past few years, there have been several reports written by experienced target validation experts in the pharmaceutical industry [2,5] about the risks of triggering investment in drug discovery base only on published data. Despite this problem, as mentioned above, pharmaceutical companies may be willing to risk launching drug discovery projects with little inhouse validation. The benefit of this approach is to accelerate the start of drug development and increase chances of being first-in-class. In other cases, extensive in-house validation is done before any further investment in drug discovery. This approach has the risk of lagging behind high-risk-taking competitors or of starting a drug discovery effort when all the knowledge on the target is public and the competitive advantage may be eroded. An intermediate balanced approach is to set up a minimal biochemical and cellular assays in order to generate tool compounds that will help to validate the target. The review by Blagg and Workman, in this issue, describes the importance of tool compound generation for target validation and presents examples of this approach. Linked to the use of tool compounds to interrogate cellular mechanisms, tool compounds are also important to explore the druggability of new classes of targets. Enzymes such as protein kinases have been at the forefront of cancer targeted therapies. Although initially it was thought that selectivity of kinase inhibitors would be a major issue, the availability of a large number of tool inhibitors and actual drugs as well as the increasing structural information on kinase catalytic pockets and inhibitor binding modes has allowed a growing understanding of how to target this family with an acceptable degree of selectivity. Nevertheless, as described in the www.sciencedirect.com
review by Knapp and Sundstro¨m, only about 10% of kinases encoded in the genome have been exploited as cancer targets, many of the remaining 90% lacking data on their link to cancer biology. Tyrosine kinases were the first to become targets cancer therapy, in particular SRC and EGFR and most of cancer targets remain in this subfamily of protein kinases. Although SRC was the first tyrosine kinase to be identified and its ability to transform mouse fibroblasts was described in the 70s, the clinical efficacy of targeting this kinase has been disappointing and all known Src targeting efforts have been stopped. On the other hand, EGFR inhibitors have been approved and they are part of the arsenal used in combination. As described by Frigault and Barrett in this issue, later generations of EGFR inhibitors target mutated isoforms emerging from treatment with first generation inhibitors. As mentioned above, tool compounds are also of great value in order to explore new target classes. Aided by structural biology, these can be used to gain insight into potential binding modes and chemical space. Examples of target classes in need to novel chemical tools are protein phosphatases, deubiquitinases and E3-ligases. One particular area undergoing a renaissance is targeting tumor metabolism. For many years, tumor cells have been known to use alternative metabolic pathways to obtain energy. Glycolysis is the main source of ATP for many tumors, instead of oxidative phosphorylation . Targeting these tumor-specific changes in metabolism has been challenging since potential targets are not mutated in tumors and other tissues have also use these pathways. The renewed interest in tumor metabolism stems from the emergence of tumor genomics as routine for analysis of clinical samples and tumor cell lines. This has allowed researchers to identify mutations in metabolic pathways having unexpected consequences in tumor-specific oncogenic proteins. Ross and Critchlow nicely describe three examples of this in their review on tumor metabolism in this issue. As indicated above, the accumulated genomic data and easy access to next-generation sequencing for analysis of tumor samples in clinical trials have revolutionized target discovery and validation. Today, it is possible to interrogate genomic databases to search for genomic alterations in potential targets. In addition, bioinformatics tools are now available allowing network analysis of mutational and gene expression data, facilitating our understanding of the impact of a given mutation on cellular mechanisms. Combination of genomic profiles of cancer cell models and their pharmacological responses has allowed the generation of hypothesis about the genetic determinants of sensitivity or resistance to targeted therapies and potential mechanism of resistance. Finally, in addition to genomic data issued from patient samples, it is important to add the generation of tumor Current Opinion in Pharmacology 2014, 17:iv–vi
banks and tissue microarrays. These resources have enabled the interrogation of new targets in relation to their expression, their activation state in real tumors and their linkage to a pathway rationale. Immuno-histochemistry, reverse-phase protein arrays and capillary electrophoresis are powerful techniques allowing pathway analysis in small amounts of tumor samples. This increasingly allows researchers to understand the prevalence of the targeted mechanism in human tumors and to validate pharmacodynamic biomarkers. As reviewed by Frigault and Barrett these markers allow to evaluate the level of target inhibition needed in human tumors and not only validate the mechanism but the amplitude and duration of inhibition needed. In addition, patient samples have also served to generate complex in vitro models and mouse models where the effect of tool compounds on biomarkers of target activity can be assessed. Unfortunately many aspects of cancer target validation are not covered by this issue. Preclinical in vitro and in vivo models and their limitations for target validation is one. This is a major issue that both academia and
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industry, together are tackling via pre-competitive consortia such as the EU/EFPIA -funded Innovative Medicines Initiative (http://www.predect.eu/about/). Clearly one way forward is collaborative work for improving validation models since the risks of getting the target wrong is detrimental not only for industry but far more importantly for patients.
Beck A, Wurch T, Bailly C, Corvaia N: Strategies and challenges for the next generation of therapeutic antibodies. Nat Rev Immunol 2010, 10:345-352.
Gashaw I, Ellinghaus P, Sommer A, Asadullah K: What makes a good drug target? Drug Discov Today 2012, 17 Suppl:S24-S30.
Czabotar PE, Lessene G, Strasser A, Adams JM: Control of apoptosis by the BCL-2 protein family: implications for physiology and therapy. Nat Rev Mol Cell Biol 2014, 15:49-63.
Dawson MA, Kouzarides T: Cancer epigenetics: from mechanism to therapy. Cell 2012, 150:12-27.
Prinz F, Schlange T, Asadullah K: Believe it or not: how much can we rely on published data on potential drug targets? Nat Rev Drug Discov 2011, 10:712.
Kroemer G, Pouyssegur J: Tumor cell metabolism: cancer’s Achilles’ heel. Cancer Cell 2008, 13:472-482.