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Feature Articles : Jun 1, 2011 ( )
Kinase Inhibitor Obstacles Falling
Structural Modeling and Improved Assays Propel Advances in Field
Protein kinases drive many critical cellular events including proliferation, metabolism, apoptosis, and differentiation. They catalyze the transfer of the terminal phosphate from ATP to tyrosine, serine, or threonine residues of the kinase itself or another protein substrate. Therapeutic inhibition of protein kinases has revolutionized the treatment of certain cancers.
New inhibitors are also targeting a host of other conditions from atherosclerosis to neurodegenerative diseases. Although the first FDA-approved drug was an antibody that targets the extracellular domain of the epidermal growth factor receptor (1998, Herceptin®/Genentech), several small molecule kinase inhibitors are now in clinical use with many more in the pipeline. The field continues to experience expansive growth. The global market for kinase inhibitors is expected to reach $20.2 billion by 2014.
Recent conferences—Informa’s Protein Kinases Congress and GTC’s conference on “Protein Kinases in Drug Discovery”—highlighted new developments in the field that include novel paradigms for drug development, improved inhibitor profiling and selectivity strategies, and expanding targets (e.g., Alzheimer disease and traumatic brain injury).
Protein kinases have both active and inactive states. While the structure of the active state is well studied across the kinome, this is not the case for the inactive state. Kinases employ diverse mechanisms to control their activation with a variety of structurally different conformations. “These differences may be exploitable for structure-guided drug design,” indicated Mark A. Ashwell, Ph.D., vp of chemistry at ArQule.
“The current thinking in the field is to develop a new kinase inhibitor paradigm that will combine a better understanding of structure and function and a knowledge-driven design approach. The understanding in the field is growing and it has taken some time to distill, especially how knowledge of the plasticity of kinases can be translated into improved inhibitor design. We are making strides to develop in silico processes for the design of inhibitors that take advantage of changes that take place upon activation.”
Dr. Ashwell and his team are targeting the inactive state of kinases. “This approach mimics nature’s own mechanism for inhibiting kinase activity and maintains the kinase in a state where it isn’t competing for the abundant ATP.
“We performed structural and functional studies on inhibitors of the c-Met receptor tyrosine kinase that is implicated in several cancers. We co-crystallized our small molecule inhibitor, tivantinib (ARQ 197), with inactive c-Met and found a conformation that is distinct from published kinase structures. Our structural analysis showed a cleft lined with nonpolar amino acids that were organized into clusters upon ARQ 197 binding.”
Dr. Ashwell explained that the information they derived from these studies allowed them to generalize to a hypothetical model of other protein kinases.
“We selected the fibroblast growth factor receptor tyrosine kinase family. Utilizing structural information, we identified inhibitor candidates for optimization in a commercial chemical library. Next, we performed biophysical, biochemical, and cell-based assays as well as x-ray co-crystallography. Our results validated the initial hypothesis that nonconserved hydrophobic residues present in the inactive state participated in nonpolar interactions with these novel inhibitors.
“We believe that as we are better able to understand the structure and function of kinases, we can successfully exploit a powerful and new chemical space for inhibitor designs.”
Clinical success for kinase inhibitors has been demonstrated in oncology. However, chronic neurodegenerative diseases (e.g., stroke, Alzheimer disease, multiple sclerosis) represent a large unmet need in the population, said Marcie A. Glicksman, Ph.D., co-director, Laboratory for Drug Discovery in Neurodegeneration (LDDN), Harvard NeuroDiscovery Center.
“For most neurodegenerative diseases, there is no significant disease-modifying agent available on the market. To develop candidates, one not only faces the typical challenges for developing kinase inhibitors (selectivity, efficacy, etc.), but also other issues. For example, in Alzheimer disease, animal models may not replicate key features of the human disease. Also, potential biomarkers have recently been identified but still have to be correlated to disease.”
Analysis & Insights: Protein Kinase Inhibitors for CNS Diseases at the Research and Discovery Phase
While only one kinase inhibitor targeting neurodegenerative disorders has reached the clinic to date, vast efforts to sift through relevant pathways and identify inhibitors of kinases thought to be involved in these diseases are under way. Get the details here.
The LDDN has established a collaborative model for drug discovery that works with academic laboratories in the U.S. and worldwide to create and then license out leads. “We have a library of 150,000 compounds utilized by biologists and chemists who subsequently work on optimizing candidates identified from high-throughput screening. We also have imaging capabilities and the ability to test in animal models.”
This approach is gaining in popularity, Dr. Glicksman noted. “There is a real need to work on more challenging diseases in academic settings. We can take the time to solve issues and take bigger risks whereas big pharma cannot. Academic institutional research has better opportunities for receiving funding for projects with large translational value. Once academic institutions solve some of these challenging issues, we can then work with big pharma to take the next steps.”
The NeuroDiscovery Center has developed lead candidates for multiple kinase targets. One target is the erythropoietin-producing hepatocellular carcinoma receptor that is involved in cell-signaling pathways in discrete areas of the brain. All members of the family have an intracellular tyrosine kinase domain. Inhibitors could be used to modulate cerebral ischemia and traumatic brain injury.
Screening of kinase inhibitors is often outsourced to a CRO due to the number of assays that are involved. Laurie LeBrun, Ph.D., principal scientist, biochemistry, Celgene, described the company’s in-house kinase selectivity strategy.
“We wanted to develop an internal screening system that had a faster turn-around time than we can get via CROs. The advantage is that the selectivity of our screening hits can be assessed early on in the process. This provides the medicinal chemists additional data to assist in the prioritization of the screening hits and aids in the selection of the best leads.”
Dr. LeBrun said that the Caliper Life Sciences’ ProfilerPro kinase selectivity assay quickly and easily provided the selectivity snapshots they were seeking. “We could quickly and easily screen early target chemical series and get selectivity results within one day. The 48 kinases included in kits one and two provide good coverage of the kinome.”
Each plate can profile 12 different compounds against 24 kinases. The enzymes and fluorophore-labeled peptide substrates are predispensed into 384-well microtiter plates. Each profiling kit provides a predictable conversion of substrate (nonphosphopeptide) to product (the phosphopeptide) at the apparent ATP Km, Dr. LeBrun explained. The assays are read using the Lab-on-a-Chip mobility shift assay technology, and she reported that she used the EZ Reader II.
“When we compared our internal data with that obtained through a CRO, we found a good correlation for the kinase selectivity that we observed internally.”
Simple Assay for Optimization
There are more than 500 protein kinases encoded in the human genome. An inhibitor may target one or more. Thus, delineating activity and potential cross-reactivity can help optimize lead discovery of kinase inhibitors. According to John Moffat, Ph.D., scientist, small molecule biochemical pharmacology at Genentech, there are two key questions that arise after identifying cross-reactivity to other kinases: “Does this activity translate to a functional effect in cells?” and “What is the window (if any) between on-target and off-target functional activity?”
“High-content screening assays can provide answers to both of these questions,” Dr. Moffat said. “It is a simple way to assess activity in living cells that can complement data obtained from other types of screening assays. We have established a high-content assay that employs 384-well plates screened using only a fluorescent dye for nuclear staining. The images from the fluorescent microscope subsequently are analyzed with software. This gives cell-cycle information similar to flow cytometry, but provides another layer of information such as cell health and nuclear morphology.”
This approach also allows a better definition of the on-target versus off-target effects of a candidate. “We are working on an oncology program that targets kinases with known or predicted effects on cell-cycle progression. We receive a large number of compounds from our medicinal chemists to profile. For example, we may screen for compounds that are expected to arrest the cell in its G1 cycle. Our screening not only validates this activity, but also helps quantitate the window between on- and off-target effects.”
Dr. Moffat concluded that “a lot of information can be mined from a relatively simple imaging assay as to the cellular effects of kinase inhibitors that may be missed by other assays. Early identification of the most useful candidates to pursue saves time and money.”
Entropy and Selectivity
Most scientists would understand entropy as a thermodynamic property. However, applying “information entropy” to kinase-inhibitor profiling is a new way to solve the old problem of making sense out of the large amounts of data, according to Joost Uitdehaag, Ph.D., senior research scientist at Merck.
“Today, researchers are able to identify hits from new libraries of compounds using high-throughput screening. There is a lot of debate as to how to improve selectivity of these hits in the process and to determine when your compound is sufficiently selective. But, it all starts with quantification, with being able to compare actual values. This is what the entropy score for selectivity brings to the field.”
Dr. Uitdehaag said this process allows one to very quickly choose the best compound to take forward into further testing. “We have proposed a way to calculate a single value from a set of IC50 data to quantify selectivity profiling from panel profiling. It is a powerful way to study molecular mechanisms of kinase inhibitor selectivity.
“Often, other methods are utilized such as dotting a kinome tree, heat maps or a radius plot, but these only provide a qualitative comparison. For quantitative approaches, others have developed a selectivity score based on kinase-profiling data. But this doesn’t provide sufficient sensitivity. Other common methods include using the so-called Gini score or a partition index. None of these measures are fully adequate.”
How do these new equations work? They are based on the principle that an inhibitor candidate will assume a Boltzmann distribution across the various targets when confronted by multiple kinases. “This distribution has calculated entropy. If it is, for example, 2.2, which is an average measure of selectivity, the compound has average selectivity. If the calculation ends up to be 1, however, this indicates that the compound is a much more selective inhibitor. One can use this information to quickly select the best candidates after screening. The nice thing is that this method gives consistent values across profiling experiments, so it’s really general.”
Dr. Uitdehaag also noted that selectivity entropy can be used to study the success of candidates in clinical trials. “We assessed clinically tested inhibitors and determined their selectivity scores. We found that the most successful compounds actually are those with more broad selective profiles. These findings indicate that selective candidates have less of a chance for surviving early clinical trials.”
Indicating the selectivity of an inhibitor should ultimately be just as commonplace as indicating its IC50, advised Dr. Uitdehaag. “I think, for instance, the selectivity values of inhibitors should be reported when people do biological validation experiments with them. It would make a lot of sense.”
Although great advances have been made over the last decade in measuring and predicting kinase inhibitor selectivity, a number of issues remain. Advances in structural-guided modeling and in enhancing the selectivity of assays should provide critical improvements for future drug development and therapeutic target expansion.
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