Novel oncogenic protein kinase inhibitors for cancer therapy

Small-molecule drug discovery for cancer therapy is making extraordinary progress within the realm of advancing novel oncogenic protein kinase inhibitor lead compounds of significant impact to both basic research and clinical testing. In this perspective, structure- and mechanism-based drug design are highlighted relative to such progress. Also, evolving concepts in novel oncogenic protein kinase inhibitor drug discovery is highlighted relative to therapeutic target selectivity, including the recent identification of oncogenic kinase mutants effecting drug-resistance or enhanced drug susceptibility to small-molecule inhibitors.     

Structural approaches to obtain kinase selectivity

One of the grand challenges in kinase drug discovery is the design of small-molecule inhibitors with selectivity profiles that will ultimately be efficacious in the clinic. Current medicinal chemistry strategies make heavy use of structural, biophysical and computational approaches to achieve this multi-faceted goal. Here we review structure-based approaches underlying the development of several molecules that are currently in clinical trials, including the cMet inhibitor ARQ197 and the Bcr-Abl inhibitor ponatinib. We highlight the challenge posed by the emergence of resistance mutants and discuss promising lead generation strategies to obtain selective inhibitors of protein and lipid kinases such as targeting of specific sites, the use of fragment-based approaches and new chemical probes based on metal complexes.     

Protein kinase inhibitors: structural insights into selectivity

Protein kinases are involved in many diseases like cancer, inflammation, cardiovascular disease, and diabetes. They have become attractive target classes for drug development, making kinase inhibitors as important class of therapeutics. The success of small-molecule ATP-competitive kinase inhibitors such as Gleevec, Iressa, and Tarceva has attracted much attention in the recent past. Kinases make use of ATP for phosphorylation of a specific residue(s) on their protein substrates. More than 400 X-ray structures of about 70 different kinases are publicly available. These structures provide insights into selectivity and mechanisms of inhibition. However, prediction of binding specificity of kinase inhibitors based on structural information alone appears to be insufficient. Here, we will review these observations to gain insights into the rules that govern protein kinase inhibitor selectivity.     

Evaluation of kinase inhibitor selectivity by chemical proteomics

Small-molecule inhibitors of protein kinases constitute a novel class of drugs for therapeutic intervention in a variety of human diseases. Most of these agents target the relatively conserved ATP-binding site of protein kinases and have only been tested against a rather small subset of all human protein kinases. Therefore, the selectivity of protein kinase inhibitors has remained a widely underestimated, but highly important issue in drug development programs. In this review, we focus on the recent advancement of chemical proteomic methods to evaluate drug selectivity in an unbiased, comprehensive way. Efficient affinity purification procedures using immobilized kinase inhibitors combined with the sensitivity of mass spectrometry detection permit the mapping of drug targets on a proteome-wide scale. Data from this type of assessment can be used to set up tailor-made selectivity panels, which guide compound development in the context of the most relevant off-targets during lead optimization. In cases in which identified alternative targets are of validated clinical relevance, chemical proteomics provides the opportunity to repeatedly exploit a once established kinase inhibitor principle for additional target kinases and can thereby dramatically shorten the time toward highly selective, preclinical candidates. Moreover, the identification of alternative targets for preclinical or clinical drugs can provide new insights into their cellular modes of action, which might help to define those disease settings in which the most beneficial therapeutic effect is likely to occur.     

酪氨酸激酶抑制剂类新药的研发现状

蛋白激酶在多种疾病,特别是肿瘤发生发展过程中起重要作用,以其为药物靶点的激酶抑制剂则成为近年药物研发的热点。目前已有11个小分子激酶抑制剂上市,其中9个为酪氨酸激酶抑制剂。激酶抑制剂具有高选择性、副作用少的特点。已上市药物已经在慢性粒细胞白血病、非小细胞肺癌、肾细胞癌等多种疾病的治疗中显示出其较传统治疗药物的优越性,部分已成为治疗肿瘤的一线用药。本文对小分子酪氨酸激酶抑制剂类新药的研发现状进行综述。     

Cancer metastasis therapeutic targets and drug discovery: emerging small-molecule protein kinase inhibitors

Cancer metastasis is a significant problem and a tremendous challenge to drug discovery relative to identifying key therapeutic targets as well as developing breakthrough medicines. Recent progress in unravelling the complex molecular circuitry of cancer metastasis, including receptors, intracellular proteins and genes, is highlighted. Furthermore, recent advances in drug discovery to provide novel proof-of-concept ligands, in vivo effective lead compounds and promising clinical candidates, are summarised. Such drug discovery efforts illustrate the integration of functional genomics, cell biology, structural biology, drug design, molecular/cellular screening and chemical diversity (e.g., small molecules, peptides/peptidomimetics, natural products, antisense, vaccines and antibodies). Promising therapeutic targets for cancer metastasis have been identified, including Src, focal adhesion kinase, the integrin receptor, the vascular endothelial growth factor receptor, the epidermal growth factor receptor, Her-2/neu, c-Met, Ras/Rac GTPases, Raf kinase, farnesyl diphosphate synthase (i.e., amino-bisphosphonate therapeutic target) and matrix metalloproteases within the context of their implicated functional roles in cancer growth, invasion, angiogenesis and survival at secondary sites. Clinical and preclinical drug discovery is described and emerging small-molecule inhibitors of protein kinases are highlighted.     

Chemogenomics: structuring the drug discovery process to gene families

In the post-genomic era, if all proteins in a gene family can putatively be identified, how can drug discovery effectively tackle so many novel targets that might lack structural and small-molecule inhibitory data? In response, chemogenomics, a new approach that guides drug discovery based on gene families, has been developed. By integrating all information available within a protein family (sequence, SAR data, protein structure), chemogenomics can efficiently enable cross-SAR exploitation, directed compound selection and early identification of optimum selectivity panel members. This review examines recent developments in chemogenomics technologies and illustrates their predictive capabilities with successful examples from two of the major protein families: protein kinases and G-protein-coupled receptors.     

Identification of binding specificity-determining features in protein families

We present a new approach for identifying features of ligand-protein binding interfaces that predict binding selectivity and demonstrate its effectiveness for predicting kinase inhibitor specificity. We analyzed a large set of human kinases and kinase inhibitors using clustering of experimentally determined inhibition constants (to define specificity classes of kinases and inhibitors) and virtual ligand docking (to extract structural and chemical features of the ligand-protein binding interfaces). We then used statistical methods to identify features characteristic of each class. Machine learning was employed to determine which combinations of characteristic features were predictive of class membership and to predict binding specificities and affinities of new compounds. Experiments showed predictions were 70% accurate. These results show that our method can automatically pinpoint on the three-dimensional binding interfaces pharmacophore-like features that act as "selectivity filters". The method is not restricted to kinases, requires no prior hypotheses about specific interactions, and can be applied to any protein families for which sets of structures and ligand binding data are available.     

Human kinome drug discovery and the emerging importance of atypical allosteric inhibitors

Importance of the field: Protein kinases are important targets for drug discovery because they possess critical roles in many human diseases. Several protein kinase inhibitors have entered clinical development with others having already been approved for treating a host of diseases. However, many kinase inhibitors suffer from non-selectivity because they interact with the ATP binding region which has similar structures amongst the protein kinases and this non-selectivity sometimes can cause side effects. As a consequence, there is much interest in developing drugs that inhibit kinases through non-classical mechanisms with the hope of avoiding the side effects of previous kinase drugs. Areas covered in this review: This review covers emerging information on kinase biology and discusses new approaches to design selective inhibitors that do not compete with ATP. What the reader will gain: The reader will gain a better understanding of the importance of the field of allosteric inhibitor drug discovery and how this has required the adoption of a new generation of high-throughput screening techniques. Take home message: Discovery and development of allosteric modulators will result in a family of novel kinase therapies with greater selectivity and more varied ways to control activity of disease causing kinase targets.     

Rapid computational identification of the targets of protein kinase inhibitors

This review focuses primarily on computational methods for predicting the selectivity of small-molecule inhibitors of protein kinases. A detailed discussion is presented of two computational studies that have attempted to make inhibitor selectivity predictions on a kinome-wide scale, and studies describing other methodologies that might potentially be applied to this area are also outlined. As the availability of reliable experimental data measuring inhibitor binding affinities (as opposed to the degree of inhibition of the kinase reaction) is important for the development and validation of most computational methods, recent advances in the large-scale experimental acquisition of selectivity data are also discussed, and a comparison of recently published computational selectivity predictions against a large-scale experimental screen is provided.     

Design strategies for protein kinase inhibitors

Deregulation of protein kinases due to mutation or overexpression is a hallmark of several diseases, for example, cancer, diabetes, inflammation and cardiovascular disorders. Consequently, there has been a growing interest in the discovery of protein kinase inhibitors as novel drugs. Here, we focus on key milestones and advances in protein kinase inhibitor drug discovery over the last year, with an emphasis on novel strategies such as targeting of single and multiple binding sites, inactive conformations of kinases, allosteric sites, regulatory domains and heat shock proteins. We will highlight recent developments for specific inhibitors in clinical studies for growth factor receptors, non-receptor tyrosine kinases and serine/threonine kinases.     

Recent advances in designing substrate-competitive protein kinase inhibitors

Protein kinases play central roles in cellular signaling pathways and their abnormal phosphorylation activity is inseparably linked with various human diseases. Therefore, modulation of kinase activity using potent inhibitors is an attractive strategy for the treatment of human disease. While most protein kinase inhibitors in clinical development are mainly targeted to the highly conserved ATP-binding sites and thus likely promiscuously inhibit multiple kinases including kinases unrelated to diseases, protein substrate-competitive inhibitors are more selective and expected to be promising therapeutic agents. Most substrate-competitive inhibitors mimic peptides derived from substrate proteins, or from inhibitory domains within kinases or inhibitor proteins. In addition, bisubstrate inhibitors are generated by conjugating substrate-competitive peptide inhibitors to ATP-competitive inhibitors to improve affinity and selectivity. Although structural information on protein kinases provides invaluable guidance in designing substrate-competitive inhibitors, other strategies including bioinformatics, computational modeling, and high-throughput screening are often employed for developing specific substrate-competitive kinase inhibitors. This review focuses on recent advances in the design and discovery of substrate-competitive inhibitors of protein kinases.     

High-throughput kinase profiling as a platform for drug discovery

To fully exploit the potential of kinases as drug targets, novel strategies for the efficient discovery of inhibitors are required. In contrast to the traditional, linear process of inhibitor discovery, high-throughput kinase profiling enables a parallel approach by interrogating compounds against hundreds of targets in a single screen. Compound potency and selectivity are determined simultaneously, providing a choice of targets to pursue that is guided by the quality of lead compounds available, rather than by target biology alone.     

选择性的激酶ATP竞争性抑制剂设计研究进展

激酶在细胞的生命过程中起着至关重要的作用，其功能异常会导致包括肿瘤在内的许多重大疾病的发生。针对疾病相关的激酶靶标，研发ATP竞争性的小分子激酶抑制剂成为当前的热点。但激酶催化域的结构和序列高度保守，使得许多激酶抑制剂的选择性都比较低。近年来，随着结构生物学和计算机辅助药物分子设计技术的进步，选择性的ATP竞争性激酶抑制剂的研发取得了很大的进展，本文拟就近年来选择性的激酶抑制剂设计研究进展作一综述。     

Maximizing discovery efficiency with a computationally driven fragment approach

A reliable and accurate method for the computational design of novel drug candidates has been a passionate pursuit of the pharmaceutical industry. Such technology would dramatically improve the efficiency of drug discovery by quickly and inexpensively providing potent molecules that can be further prioritized for synthesis based on characteristics such as patentability, specific protein-ligand interactions, ease of chemical synthesis, protein selectivity and pharmacological considerations. Described herein is the progress made at Locus Pharmaceuticals Inc toward achieving this ideal with a fragment-driven, computationally directed approach to small-molecule discovery. Specific lead identification examples from Locus Pharmaceuticals discovery programs demonstrate the efficiency and cost-effectiveness realized by such an approach.     

Interaction profiles of protein kinase-inhibitor complexes and their application to virtual screening

A major challenge facing structure-based drug discovery efforts is how to leverage the massive amount of experimental (X-ray and NMR) and virtual structural information generated from drug discovery projects. Many important drug targets have large numbers of protein-inhibitor complexes, necessitating tools to compare and contrast their similarities and differences. This information would be valuable for understanding potency and selectivity of inhibitors and could be used to define target constraints to assist virtual screening. We describe a profile-based approach that enables us to capture the conservation of interactions between a set of protein-ligand receptor complexes. The use of profiles provides a sensitive means to compare multiple inhibitors binding to a drug target. We demonstrate the utility of profile-based analysis of small molecule complexes from the protein-kinase family to identify similarities and differences in binding of ATP, p38, and CDK2 compounds to kinases and how these profiles can be applied to differentiate the selectivity of these inhibitors. Importantly, our virtual screening results demonstrate superior enrichment of kinase inhibitors using profile-based methods relative to traditional scoring functions. Interaction-based analysis should provide a valuable tool for understanding inhibitor binding to other important drug targets.     

Kinase inhibitors as drugs for chronic inflammatory and immunological diseases: progress and challenges

At the time of writing, there are seven marketed kinase inhibitor drugs. The first kinase inhibitor, imatinib mesilate (Gleevec, Novartis), came to market in 2001, an inhibitor of the breakpoint cluster region (BCR)/Abelson murine leukemia oncogene homolog (ABL) fusion, platelet-derived growth factor (PDGF) receptor, and c-kit kinases. The most recent kinase inhibitor to come to market, disatinib (Sprycel, Bristol-Myers Squibb), acts on c-SRC, ABL and Bruton's tyrosine kinase. To date, kinase inhibitor drugs are approved for oncology and demonstrate that it is possible to develop compounds with relative selectivity for the target kinase against the broader kinome. However, the use of kinase inhibitors in chronic inflammatory and immunologic diseases may require greater selectivity for the target kinase. This review addresses the opportunities and challenges of kinase inhibition as a therapeutic approach in chronic immune and inflammatory disease.     

Structural basis of Src tyrosine kinase inhibition with a new class of potent and selective trisubstituted purine-based compounds

The tyrosine kinase pp60src (Src) is the prototypical member of a family of proteins that participate in a broad array of cellular signal transduction processes, including cell growth, differentiation, survival, adhesion, and migration. Abnormal Src family kinase (SFK) signaling has been linked to several disease states, including osteoporosis and cancer metastases. Src has thus emerged as a molecular target for the discovery of small-molecule inhibitors that regulate Src kinase activity by binding to the ATP pocket within the catalytic domain. Here, we present crystal structures of the kinase domain of Src in complex with two purine-based inhibitors: AP23451, a small-molecule inhibitor designed to inhibit Src-dependent bone resorption, and AP23464, a small-molecule inhibitor designed to inhibit the Src-dependent metastatic spread of cancer. In each case, a trisubstituted purine template core was elaborated using structure-based drug design to yield a potent Src kinase inhibitor. These structures represent early examples of high affinity purine-based Src family kinase-inhibitor complexes, and they provide a detailed view of the specific protein-ligand interactions that lead to potent inhibition of Src. In particular, the 3-hydroxyphenethyl N9 substituent of AP23464 forms unique interactions with the protein that are critical to the picomolar affinity of this compound for Src. The comparison of these new structures with two relevant kinase-inhibitor complexes provides a structural basis for the observed kinase inhibitory selectivity. Further comparisons reveal a concerted induced-fit movement between the N- and C-terminal lobes of the kinase that correlates with the affinity of the ligand. Binding of the most potent inhibitor, AP23464, results in the largest induced-fit movement, which can be directly linked to interactions of the hydrophenethyl N9 substituent with a region at the interface between the two lobes. A less pronounced induced-fit movement is also observed in the Src-AP23451 complex. These new structures illustrate how the combination of structural, computational, and medicinal chemistry can be used to rationalize the process of developing high affinity, selective tyrosine kinase inhibitors as potential therapeutic agents.