ES2387841T3 - Protein-protein interactions for pharmacological profiling - Google Patents

Abstract

A method for effecting the removal of a test compound with undesirable properties, said method comprising the analysis of said test compound having undesirable properties against a panel of cell-based protein fragmentation complementation tests, said panel comprising two or more complementation assays of high protein fragments.

Description

Protein-protein interactions for pharmacological profiling

Background of the invention

US 2004/063088 discloses procedures for analyzing the effect of a biologically active agent 5 on cellular pathways to establish biomap data sets, which comprise the use of cell-based ELISAs in a panel of assay combinations.

The concept of selective drugs has dominated the discovery and development of new therapeutic agents during the last century. A molecule can be a potentially useful therapeutic agent if it can be demonstrated that it selectively effects a change in cell physiology by acting in a desired manner on a target within

10 of the biochemical routes that underlie the processes of interest. However, even an extremely selective chemical compound that binds to a therapeutic target can have completely unexpected or “out of route” effects when it comes into contact with a living cell. Such effects can result in expensive preclinical and clinical failures. For the purposes of the present invention, the inventors define "out of route" activity as any activity of a compound in a route other than the intended target of the compound.

15 The identification of mechanisms of action of drugs and their off-route activities cannot be achieved with enzymatic assays because it is not feasible to prepare an assay for each of the tens of thousands of proteins that represent the biological environment. To assess the mode of action of a drug within the complex biochemical pathways that make up a living cell, a means is needed to explore the routes directly. Therefore, the inventors sought a general strategy for profiling

20 pharmacological. In particular, the inventors sought to determine whether quantitative measures of protein-protein interactions could be used to perform large-scale pharmacological activity profiles.

The background for the present invention is as follows. The binding of agonists to receptors induces a cascade of intracellular events mediated by other signaling molecules. Conceptually such signaling cascades involve the regulated association and dissociation of proteins within complexes

25 macromolecular In addition, the assembly and disassembly of protein-protein complexes occurs dynamically after the addition of an agonist, antagonist or inhibitor of a route. Finally, the assembly and disassembly of specific protein-protein complexes occurs in particular subcellular locations such as the cell membrane, cytoplasm, nucleus, mitochondria or other compartments within the cell.

Most of the cell-based pharmacological profiles to date have been performed with microarrays of

30 DNA (gene microplates or gene chips). DNA microarrays have originated the field of toxicogenomics, which involves the use of complex populations of mRNA to understand toxicity. Cells, or whole animals, are treated with drugs; messenger RNA is isolated from the cell or tissue; and the mRNA gene expression patterns in the absence and presence of a drug are compared. Such realization of transcriptional profiles may reveal differences between compounds, when the compounds affect the activity.

Ultimately transcriptional of one or more routes. The identification of specific routes that are stimulated or repressed in response to specific conditions or treatments is a useful way to begin to unravel the cellular mechanisms of disease and drug response. However, changes in the level of individual mRNA molecules will not always correlate directly with the level of activity of the corresponding protein at a single time point. In addition, many proteins undergo numerous modifications.

40 post-translational and protein interactions, which can affect the functions and activities of proteins within a tissue or cell. Therefore, simply identifying all the mRNA species present and the levels at which they are present at a particular time may not produce the full picture of a particular drug. Finally, a target drug can affect the transcription of dozens of genes, making the interpretation of the results of gene microplate experiments an arduous task.

45 The regulation of cellular responses is mediated by a certain "threshold" number of molecular interactions that eventually reach the cell nucleus and induce the cell's gene apparatus to synthesize new expression proteins in response to initial stimuli. Indicator gene assays couple the biological activity of a target with the expression of an easily detected enzyme or protein indicator, allowing control of cellular events associated with signal transduction and gene expression. Based on the merger of

50 transcriptional control elements with a variety of indicator genes, these systems "indicate" the effects of a cascade of signaling events on gene expression within cells. Synthetic repeats of a particular response element can be inserted upstream of the reporter gene to regulate its expression in response to signaling molecules generated by activation of a specific pathway in a living cell. Such trials have been shown to be useful in primary and secondary exploration of chemical libraries and

55 drug candidates for their biological effects and transcription indicator test panels have been used to perform the pharmacological activity profile (Bionaut, Inc.). Although transcription indicator assays have the ability to provide information on the response of a route for chemical agents, such assays only measure the consequence of the activation or inhibition of the route, and not the site of action of the compound.

Direct measures of specific events within the signaling pathway would eliminate the problems associated with the interpretation of transcription profiles. Unlike transcription indicator assays, the information obtained by controlling a protein-protein interaction reflects the effect of a drug on a particular branch or node of a cell signaling path, not its endpoint. For example, stimulation of a route by an agonist could lead to an increase in the association of an intracellular protein (such as a kinase) with a related binding partner (such as a substrate). Stimulation results in substrate phosphorylation by the kinase. The pharmacological effect could therefore be tested by quantifying substrate phosphorylation or the amount or location of the kinase / substrate complex in the absence and presence of the agonist. In this example the kinase / substrate complex acts as a sentinel of the activity of the route. A drug that acts at the beginning of the path (such as a receptor antagonist) or that acts on another target downstream of the receptor could alter the amount or location or state of modification of the kinase / substrate complex within the cell. Therefore, evaluation of the kinase / substrate complex in the absence or presence of a chemical compound would reveal whether or not the chemical compound acts in that route. Ideally such changes would be measured in intact cells. That is, cells of interest would be treated with the test compound for a defined period of time and the route sentinel or interaction would be evaluated in the intact cell (live or fixed).

High content trials are particularly promising since they are able to differentiate events that occur at the subcellular level. The routes are often organized to a large extent in the subcellular space, with membrane receptors that receive signals in the plasma membrane and transmit those signals to the cell nucleus, resulting in activation or repression of gene transcription. With high content assays, fluorescence signals in the membrane, cytosol, nucleus and other subcellular compartments can be differentiated. Such tests can be performed by automatic microscopy allowing the production of hundreds of microwell plates per day, making the practical approach on a moderate to large scale.

With the exception of the work of the inventors cited below and in the references herein (Michnick et al.) The prior art does not mention the use of protein-protein interactions for pharmacological profiling. In addition, the prior art does not mention the use of high content assays for pharmacological profiling and candidate withdrawal.

Several procedures are available for the quantification of protein-protein interactions. For the purposes of the present invention, the inventors have focused on procedures that allow quantification and / or localization of protein-protein complexes in living cells while recognizing that additional procedures, such as fluorescence polarization, are suitable for measuring union events. Available cell-based procedures involve fluorescent or bioluminescent assays of protein-protein interactions and include resonance energy transfers (FRET or BRET), enzyme subunit complementation and protein-fragment complementation assays (PCA). It will be understood by one skilled in the art that the present invention is not limited to the exact test methodology that is selected, to the detection procedure or to particular instrumentation. The present invention teaches that protein-protein interactions can be used to perform pharmacological profiles regardless of the particular technology or instrumentation applied as long as the technology is sufficiently sensitive and robust for the purposes of the assay.

The most extended protein-protein interaction assays based on more widespread fluorescent cells are based on the phenomenon of fluorescence resonance energy transfer (FRET) or bioluminescence resonance energy transfer (FRET). In a FRET assay the genes of two different fluorescent indicators, capable of experiencing FRET were fused separately with genes encoding interest, and the fusion proteins are co-expressed in living cells. When a protein complex is formed between the proteins of interest, fluorophores approach if the two proteins have overlapping emission and excitation, emission of photons by a first "donor" fluorophore, results in the effective absorption of photons emitted by the second "acceptor" fluorophore. The FRET pair fluoresces with a unique combination of excitation and emission wavelengths that can be distinguished from those of each fluorophore alone in living cells. As specific examples, a variety of GFP mutants have been used in FRET assays, including cyan, citrin, enhanced green and enhanced blue fluorescent proteins. With BRET a luminescent protein is used, for example the Renilla luciferase enzyme (RLuc), as a donor and a green fluorescent protein (GFP) is used as an acceptor molecule. After the addition of a compound that acts as the substrate for Rluc, the FRET signal is measured by comparing the amount of blue light emitted by Rluc with the amount of green light emitted by GFP. The ratio of green and blue increases as the two proteins get closer. New procedures are being developed to allow the deconvolution of fret draft and autofluorescence. In addition, fluorescence life time image capture microscopy (FLIM) eliminates many of the artifacts associated with simple FRET intensity quantification.

Various assays have been constructed based on the activity of wild-type beta-galactosidase or the phenomenon of alpha or omega complementation. Beta-gal is a multimeric enzyme that forms tetramers and octomeric complexes of up to 1 million Daltons. Beta-gal subunits undergo auto-oligomerization that leads to activity. This phenomenon of natural origin has been used to develop a variety of homogeneous in vitro tests that are the subject of more than 30 patents. The alpha or omega beta-gal complementation that was first introduced in 1965, has been used to develop assays for the detection of interactions

antibody-antigen, drug-protein, protein-protein and other biomolecular. Background activity due to self-oligomerization has been partially overcome by the development of low affinity mutant subunits with a reduced or insignificant ability to complement naturally, allowing various assays including for example the detection of activity dependent on EGF receptor ligand in living cells.

PCA represents a particularly useful procedure for measurements of the association, dissociation or localization of protein-protein complexes within the cell. PCA allows the determination and quantification of the amount and subcellular localization of protein-protein complexes in living cells. With PCA, proteins are expressed as fusions with polypeptide fragments obtained by genetic engineering, in which the polypeptide fragments themselves (a) are not fluorescent or luminescent moieties; (b) they are not of natural origin; and (c) are generated by fragmentation of an indicator. Michnick et al. (US 6,270,964) taught that any indicator protein of interest can be used for PCA, including any of the indicators described above. Therefore, suitable indicators for PCA include, but are not limited to any of several enzymes and fluorescent, luminescent or phosphorescent proteins. Small monomeric proteins for PCA are preferred, including monomeric enzymes and monomeric fluorescent proteins, resulting in small fragments (150 amino acids). Since any indicator protein can be fragmented using the principles established by Michnick et al., Assays can be adapted to the particular demands of the cell type, target, signaling process and instrumentation of choice. Finally, the ability to choose from a wide series of indicator fragments allows the construction of fluorescent, luminescent, phosphorescent or otherwise detectable signals; and the selection of high content or high performance assay formats, allowing various assays including for example the detection of ligand-dependent activation of the erythropoietin receptor in living cells.

Fluorescent assays of protein-protein interactions have been used individually to detect pharmacological effects on individual targets, including receptors and other intracellular targets. For example, the inventors have used PCA to build high performance fluorescent scanning assays based on

cells based on the NFkappaB complex (p65 / p50) (Yu et al.). However, the prior art does not mention the use of panels of such assays for the realization of pharmacological profiles.

In the process of carrying out the present invention the inventors tested three hypotheses. The first hypothesis was that high-content assays, and in particular quantitative dynamic measurements of specific protein-protein complexes within specific routes would allow an evaluation of the activation and inhibition of those routes by a chemical compound or agent. The second hypothesis was that two types of dynamic events could occur in response to route activation: an increase or reduction in the amount of a specific complex and / or the translocation of a particular protein-protein complex from one subcellular compartment to another. The third hypothesis was that the quantification and location of a number and diversity of different protein complexes within living cells would allow the realization of a pharmacological profile on a global scale, throughout the cell.

In carrying out the present invention, cell-based assays were used to control the dynamic association and dissociation of proteins in the absence or presence of chemical compounds. Although any of the above-mentioned fluorescent or luminescent procedures is suitable for use in conjunction with the present invention, the inventors chose to use a fragment-protein complementation assay (PCA) strategy. The inventors created quantitative fluorescent assay panels for different protein-protein interactions in living cells and tested the activities of more than 100 known drugs against the test panels. The inventors demonstrate that the pattern of responses or "pharmacological profiles" detected by changes in intensity and / or physical location of the pair of sentinels is related to the mechanism of action, specificity and out-of-route effects of the drug being tested.

Objects and advantages of the invention

It is the object of the present invention to allow the withdrawal of pharmacological candidates with toxic or undesirable effects.

Related to this is the establishment of pre-clinical safety profiles for new pharmacological candidates, to improve the effectiveness of drug discovery by identifying adverse, toxic or other off-route effects before clinical trials to improve the safety of first-category drugs by identifying adverse, toxic or other off-route effects before clinical trials, to provide adequate procedures for the development of "designer drugs" with pre-determined properties to allow the identification of new therapeutic indications for known drugs, to allow identification of the biochemical routes that underlie the pharmacological toxicity, or to allow the identification of the biochemical routes that underlie the pharmacological efficacy for a wide range of diseases.

The present invention has the advantage of being widely applicable to any route, gene, gene library, pharmacological target class, indicator protein, detection mode, synthetic or natural product, chemical entity, assay format, automatic instrumentation or cell type of interest .

Summary of the invention

The present invention seeks to meet the aforementioned needs of pharmaceutical discovery. The present invention teaches that cell-based assays can be used to identify the mechanism of action, selectivity and adverse or out-of-path effects of pharmacologically active agents. The present invention provides a general strategy for carrying out pharmacological analysis and realization of pharmacological profiles with cell-based assays, in particular protein-protein interaction assays. In addition, the present invention provides a wide range of useful procedures and compositions to accomplish these objectives.

Cellular responses are mediated by a complex network of proteins that reside within the subcellular compartments. Cell proliferation, cell death (apoptosis), chemotaxis, metastasis, etc., are all controlled at the level of proteins that participate in protein-protein interactions. The object of the present invention is a methodology that allows quantitative analysis of the effects of chemical compounds in biochemical networks. The present invention allows an analysis of the effects of any chemical compound regardless of the drug class or the target class.

The new methodology of the present invention allows: (1) Direct visualization of the molecular architecture of specific cellular responses at the level of discrete protein-protein interactions that allow said cellular architecture; (2) Direct and quantitative analysis of pharmacological effects in cellular signaling networks in a way never before possible; and (3) The creation of quantitative and predictive pharmacological profiles of candidate compounds and drugs regardless of their mechanism of action.

A preferred embodiment of the invention uses a gene or genes encoding specific proteins of interest; preferably as full length cDNAs characterized. The methodology is not limited, however, to full-length clones since partial cDNAs or protein domains can also be used. The cDNAs, labeled with an indicator or indicator fragment that allow the measurement of a protein-protein interaction, are inserted into a suitable expression vector and the fusion proteins are expressed in a cell of interest. However, endogenous cellular genes can be used by marking the genome with indicators or indicator fragments, for example by non-homologous recombination. In the latter case, native proteins are expressed together with the selected indicator markers allowing the detection of native protein protein complexes.

The procedure requires a procedure to measure protein-protein interactions and / or a test procedure of high equivalent content for a route sentinel. In a preferred embodiment, protein interactions are measured within a cell. Such procedures may include but are not limited to FRET, BRET, two-hybrid or three-hybrid procedures, enzymatic subunit complementation procedures and protein-fragment complementation (PCA). In alternative embodiments, the interactions are measured in tissue sections, cell lysates or cell extracts or biological extracts. In the latter cases, a wide variety of analytical procedures can be employed for the measurement of protein-protein complexes, for example, immunohistochemistry, western blotting, immunoprecipitation followed by two-dimensional gel electrophoresis; mass spectroscopy; ligand binding; quantum dots or other probes; or other biochemical procedures to quantify specific protein-protein complexes. Such procedures are well known to those skilled in the art. It should be emphasized that it is not necessary to apply simple technology to the measurement of different protein-protein interactions. Any variety or type of quantitative assays can be combined for use with the present invention.

Preferred embodiments for the present invention are the methods of complementing enzymatic fragments and complementation of protein fragments. These procedures allow quantification and subcellular localization of protein-protein complexes in living cells.

With the complementation of enzyme fragments, proteins are expressed as fusions with enzyme subunits such as naturally occurring subunits or beta / galactosidase alpha / beta mutants. With PCA, proteins are expressed as fusions with synthetic polypeptide fragments, in which the polypeptide fragments themselves (a) are not fluorescent or luminescent moieties; (b) they are not of natural origin; and (c) are generated by fragmentation of an indicator. Michnick et al. (US 6,270,964) taught that any indicator protein of interest can be used in PCA, including any of the indicators described above. Therefore, suitable indicators for PCA include, but are not limited to, any of several enzymes and fluorescent, luminescent or phosphorescent proteins. Small monomeric proteins for PCA including monomeric enzymes and monomeric fluorescent proteins are preferred, which results in small fragments (150 amino acids). Since any indicator protein can be fragmented using the principles established by Michnick et al., Assays can be adapted to the particular needs of the cell type, target, signaling procedure and instrumentalization of choice. Finally, the ability to choose from a wide series of indicator fragments allows the construction of fluorescent, luminescent, phosphorescent or otherwise detectable signals; and the selection of high content or high performance assay formats.

As shown above, the genetically engineered polypeptide fragments for PCA are not individually fluorescent or luminescent. This characteristic of PCA distinguishes it from other inventions that involve labeling proteins with fluorescent molecules or luminophores, such as US 6,518,021 (Thastrup et al.) In which the proteins are labeled with GFP or other luminophores. A fragment of PCA is not a luminophore and does not allow control of the redistribution of an individual protein. On the contrary, what is measured with PCA is the formation of a complex between two proteins.

The present invention is not limited to the type of cell, biological fluid or extract selected for analysis. The cell type can be a mammalian cell, human cell, bacteria, yeast, fungus or any other cell type of interest. The cell can also be a cell line, or a primary cell, such as a hepatocyte. The cell can be a component of an intact or animal tissue, or in the whole body, such as in an explant or xenotransplant; or it can be isolated from a biological fluid or organ. For example, the present invention can be used in bacteria to identify antibacterial agents that block key pathways; In fungal cells to identify antifungal agents that block key pathways, the present invention can be used in mammalian or human cells to identify agents that block disease-related pathways and have no adverse or out-of-path effects. The present invention can be used together with drug discovery for any disease of interest including cancer, diabetes, cardiovascular disease, inflammation, neurodegenerative diseases and other chronic or acute diseases that afflict the human being.

The present invention can be used in living cells or tissues in any medium, context or system. This includes cells in culture, organs in culture, and in living organisms. For example, the present invention can be used in model organisms such as Drosophilus or zebrafish. The present invention can also be used in nude mice, for example, human cells that express labeled proteins, such as with "PCA inside," can be treated as xenotransplants in nude mice and a drug or other compound administered to the mouse. The cells can then be removed again from the implant or whole mouse images can be taken using live animal image capture systems such as those provided by Xenogen (Alameda, California). In addition, the present invention can be used in transgenic animals in which the protein fusions that represent the protein-protein interactions to be analyzed reside in the genome of the transgenic animal.

Any type of chemical compound can be analyzed with the procedures provided herein. Such compounds include synthetic molecules, natural products, combinatorial libraries, known or potential drugs, ligands, antibodies, peptides, small interfering RNAs (siRNA), or any other chemical agent whose activity is desired to be tested. Successful exploration campaigns of combinatorial library or other high-performance exploration campaigns may be used in conjunction with the present invention. The invention can be used to identify compounds with more desirable properties compared to compounds with less desirable properties. Therefore the present invention is suitable for use in optimization and / or withdrawal of candidate compounds with unexpected, undesirable or toxic attributes.

The preferred embodiment is a high content assay format. In the case of an increase or reduction in the amount of a protein-protein complex in response to a chemical agent, the crude fluorescent or luminescent signal can be quantified. In the case of a shift in the subcellular location of a protein-protein complex in response to drug, images of individual cells are taken and the signal that arises from the protein-protein complex and its subcellular location are detected. Multiple examples of these events are provided herein. Some procedures and indicators will be better adapted to different situations. With PCA, a selection of indicators allows quantification and localization of protein-protein complexes. Particular indicators may be more or less optimal for different cell types and for different protein-protein complexes.

One skilled in the art will realize that, in many cases, the amount of a protein-protein complex will increase or decrease as a result of an increase or decrease in the amounts of individual proteins in the complex. Similarly, the subcellular location of a protein-protein complex may change as a result of a shift in the subcellular location of individual proteins in the complex. In such cases, the complex or individual components of the complex can be evaluated and the results will be equivalent. High content assays can be constructed for individual path sentries (proteins) by labeling the proteins with a fluorophore or a luminophore, such as a green fluorescent protein (GFP) that is operably linked with the protein of interest; or by newer self-labeling procedures that include SNAP markers and Halo markers (Invitrogen, BioRad); or applying immunofluorescence procedures, which are well known to those skilled in the field of cell biology, using protein specific or modification specific antibodies provided by Cell Signaling Technologies, Becton Dickinson, and many other providers. Such procedures and reagents may be used in conjunction with the protein-protein interactions provided herein. In particular, individual proteins, including those listed in Tables 1-2, can be used to construct high content assays for pharmacological profiling in accordance with the present invention.

Strategies and procedures for detecting the effects of test compounds on cell-modulable pathways are also provided herein. The route modulation strategy can be applied to the realization of pharmacological profiles together with any cell type and with any measurable parameter or format

of testing. While the test compounds may not have a significant effect at baseline conditions, their effects can be detected by treating a cell with the test compound and then with a route modulator. This strategy improves the sensitivity of the invention. For example, in some cases a test compound may not have effects in basal conditions, but it may have a pronounced effect in conditions in which a route is activated or suppressed. Examples of the route modulation strategy are shown herein for GPCR routes (+ isoproterenol), cytokine routes (+ TNF) and DNA damage response routes (+ camptothecin). Any variety of cellular routes can be activated or suppressed by known modulators, which can be used to improve the sensitivity of pharmacological profiles.

The procedures and assays provided herein may be performed in multiwell formats, in microtiter plates, in multiple point formats, or in matrices, allowing flexibility in the assay format and miniaturization. The selections of assay formats and detection modes are determined by the biology of the process and the functions of the proteins within the complex under analysis. It should be noted that in any of the cases the compositions that are the object of the present invention can be read with any instrument that is suitable for detecting the signal generated by the selected indicator. Luminescent, fluorescent or bioluminescent signals are easily detected and quantified with any one of a variety of high performance and / or automatic instrumentation systems including fluorescent multiwell plate readers, fluorescence activated cell separators (FACS) and control systems. image capture based on automatic cells that provide special signal resolution. A variety of instrumentation systems have been developed to automate HCS including automatic microscopy and automatic fluorescence imaging systems developed by Cellomics, Amersham, TTP, Q3DM (Beckman Coulter), Evotec, Universal Imaging (Molecular Devices) and Zeiss . Fluorescence recovery after photobleaching (FRAP) and time-lapse fluorescence microscopy have also been used to study protein mobility in living cells. The present invention can also be used in conjunction with the procedures described in US 5,989,835 and US 6,544,790.

Brief description of the drawings

Figure 1 illustrates the purpose of the present invention. Biochemical networks that control cellular behavior are represented as a circuit diagram. Drugs and chemical compounds have both known (intended) and unknown (not intended) effects within cells. Protein-protein interactions, or complexes, represent binary elements within biochemical networks that can be explored to identify known or unknown effects of drugs and candidate compounds. Figure 2 provides examples of intracellular proteins that form protein-protein complexes. Virtually every protein in the cell forms complexes with one or more other proteins or other macromolecules, and exerts its activity in the context of these macromolecular complexes. Examples of complex-forming proteins, for which assays can be constructed, include receptors (membrane, cytosolic or nuclear), ion channels, adapter proteins, kinases, phosphatases, proteases, nucleotide binding proteins and nucleotide exchange factors, Biosynthetic or catabolic enzymes, chaperones, exchange factors and transport proteins, apoptotic, cytoskeletal and cell cycle machinery, frameworks and structural proteins, the cytoskeleton and other structural elements and transcription factors. Figure 3 shows the intercellular networks related to the concept for pharmacological effects and for pharmacological profiles. (A) Biochemical signaling networks are comprised of modules or groups of functionally related protein-protein interactions. Three theoretical modules are represented as sphere and bar diagrams (Modules A, B and C). The spheres represent protein interactions that are connected between modules by lines. The signal transmission between Module A and Module B occurs through the complex shaded in blue. Each protein in a complex constitutes a potential assay to control upstream pharmacological activity. The red and green tones represent selected trials to explore the module's response to A-C drugs. Protein complexes that are reduced in response to a particular drug treatment are shaded in red and those that increase the response to a particular drug treatment are shaded in green. (B) A matrix that represents the quantitative patterns resulting from inhibition (relative shades of red) and activation (relative shades of green) for various drugs (on the x-axis) versus individual trials (on the y-axis). Drugs in category A (DA) have an inhibitory effect on module A that is transmitted to module B. Therefore, the four trials that represent module A as well as those that represent module B reduced activity. Drugs in category B (DB) have an inhibitory effect in module B trials only, while drugs in category C (DC) have a stimulatory effect in trials of module C only. Figure 4 represents the basic stages in the realization of pharmacological profiles. Figure 5 shows a high performance system for performing pharmacological profiles. The entire procedure can be automated in microtiter plate formats or matrices.

The drugs can be analyzed at different time points after their addition to cells, to obtain a study of the pharmacological action dynamics. The images are converted to numerical results and the numerical data can be stored in a relational database and analyzed by any variety of graphical, numerical or statistical routines.

Figure 6A is a published scheme of GPCR routes. G-protein coupled receptors and their downstream interactors form protein-protein complexes that can be explored in intact cells. Figure 6B shows examples of protein complexes in GPCR pathways in intact human cells as assessed by PCA. The fluorescence signal reflects the amount and subcellular location of each protein-protein complex (green); the nuclei were stained with Hoechst (blue). Refer to Tables 1-3 and the experimental protocol for details of the trial shown here. Such assays can be used in the realization of pharmacological profiles. Figure 7A is a published scheme of interactions in the ubiquitin-proteasome and cell cycle pathways. Such routes can be explored with protein-protein interactions. Figure 7B shows examples of protein complexes in the route of ubiquitilation, sumoylation and / or proteasome. Direct interactions of proteins with ubiquitin or SUMO are shown, as well as interactions of E3 and SUMO ligases with substrates as assessed by PCA. Such assays can be used to detect the effects of chemical compounds in these routes and can be constructed for many other proteins. Figure 7C shows that the proteasome inhibitor, ALLN, causes the accumulation of protein-protein complexes that are known to be controlled by the proteasome; This ALLN effect is therefore an expected or "en route" effect of the agent. Agents that inhibit, activate or potentiate these routes can be identified with these assays. Figure 8A is a published scheme of interactions in signaling pathways, including interactions of various kinases with other proteins. Figure 8B shows examples of interactions between kinases and other proteins in intact cells as evaluated by PCA. Such assays can be constructed for many other signaling kinases and proteins and can be used to detect the effects of new or known chemical compounds in these routes. Figure 8C shows expected effects of "upstream" kinase inhibitors or "downstream" complexes. Wortmanin and Ly294002 are inhibitors of PI3K (phosphatidylinositol-3-kinase) that is upstream of PDK1 and AKT1. AKT1 is a substrate of PDK1 and forms a complex with PDK1. Cellular treatment with PI3K inhibitors results in loss of PDK1 / AKT1 in the cell membrane and relocation of the complex to the cytosol, as shown in the images. Changes occur within a period of minutes from the addition of the drug; images were taken at 90 minutes. The terms "upstream" and "downstream" are commonly used to refer to signaling cascades, in which events occur in a time sequence, preceding a "upstream" event to a "downstream" event. Figure 8D shows the effects of two different non-selective kinase inhibitors, indirubin-3'monoxima and BAY11-7082, on Ras / Raf-1 complexes in HEK293 cells. The number of complexes is reduced in a period of minutes from the pharmacological treatment; The images were taken at 30 minutes. Figure 9A is a published scheme of selected interactions of chaperones and co-chaperones with each other and with client proteins. Figure 9B shows that treatment of cells with known HSP inhibitors, geldanamicin or 17-allylamino-geldanamicin (17-AAG) causes a loss of client-protein complexes. Aktl is a client protein for HSP90. Effects are shown for Akt1 / p27. Such assays can be used to evaluate the effects of new or known compounds on these targets and routes. Figure 10A is a published scheme of interactions involving nuclear hormone receptors. The agonist, rosiglitazone, induces the formation of complexes between its receptor, PPARgamma, and a nuclear coactivator, in this case RXRalfa. Figure 10B shows examples of several interactions involving nuclear hormone receptors. As in the other examples shown herein, assays were constructed using PCA. Such protein-protein interactions can be used to evaluate the effects of known or new compounds in these routes. Figure 11A shows a published scheme of interactions in the mitochondrial pathway of apoptosis. The photomicrographs show the real interaction of Bad / Bcl-xL in the mitochondria; The PCA signal (green) is colocalized with a mitochondrial stain (red) which demonstrates that the protein complexes are correctly located in the mitochondria. Figure 11B shows the dynamic effects of staurosporine, an apoptosis inducing agent, in four different protein interactions. Fluorescence photomicrographs show the location and intensity of the complexes and the histograms show the quantification of the signal based on quantitative image analysis. Such protein-protein interactions can be used to evaluate the effects of new or known compounds in these routes. Figure 12 (A-D) shows images of different protein-protein interactions with representative pharmacological effects. Details of the proteins and drugs used are in Table 1 and Table 4, respectively. Fluorescence images of Hoechst (blue) and YFP (green) (40X objective) are shown. For each column, the left side panel shows the control (vehicle only) and names the complex in white text, and the right side panel shows the drug / time effect. Before completion of the test, the "+ CPT" labeled assays were stimulated with 500 nM camptothecin for 960 minutes, the "+ ISO" labeled assay was stimulated with 2 micromolar isoproterenol for 30 minutes, and the "+ TNFalfa" labeled assay. ”50 ng / ml was stimulated with human TNFalpha for 20 minutes. The p50: p65 assay is shown without nuclear Hoechst images to allow visualization of pharmacological effects on the subcellular location of the complex. The use of camptothecin, isoproterenol and TNFalfa to induce routes

particular is an example of the route modulation strategy provided in the present invention. Figure 13A shows examples of quantitative pharmacological effects in protein-protein interactions. 17 different drugs were tested against four different protein-protein interactions at multiple time points. Representative microphotographs, showing subcellular localization of fluorescent protein-protein complexes, are shown on the left. Histograms reveal the quantitative results of the pharmacological effects seen in the images that are marked with asterisks. Figure 13B shows examples of pharmacological effects in protein-protein interactions. 17 different drugs were tested against four different protein-protein interactions at multiple time points. Representative microphotographs, showing the subcellular localization of fluorescent protein proteins, are shown on the left. The histograms show the quantitative results of the pharmacological effects (with asterisk). Figure 14A illustrates the route modulation strategy for conducting pharmacological profiles. The cells are treated with a test compound followed by a route modulator of any type or chemical composition. With this strategy, test compounds can be detected or block the effects of the modulator on cell pathways. Figure 14B shows the usefulness of the route modulation strategy for conducting pharmacological profiles. Camptothecin was used to induce DNA damage, which leads to the activation of DNA damage response pathways, as shown for Chk1 / Cdc25C. Agents that enhance or block the effects of camptothecin can then be identified, as shown in the matrix. A similar test strategy can be taken with any cell route and with any route modulator. Figure 15A shows activation of a GPCR route by its known agonist and inhibition by antagonist. Isoproterenol induced the formation of complexes between the beta-adrenergic receptor (a GPCR) and betaarrestine. Pre-treatment with propanolol blocked the effect of isoproterenol. Therefore, in the absence of isoproterenol, route activators can be detected; while in the presence of isoproterenol antagonists or route inhibitors can be detected. A similar strategy can be applied to any cellular receptor including GPCR, membrane receptors and nuclear receptors. Figure 15B an unintended or undesirable "out of route" effect of a candidate compound on a GPCR route. The compound was a patented, selective and potent kinase inhibitor with antiproliferative activity, which was intended for cancer treatment and was not intended or expected to have activity in GPCR pathways. The effect of the agent was observed in the presence of isoproterenol, which exemplifies the usefulness of the route modulation strategy. Since the agent does not bind directly with GPCR, this unintended effect could be the result of inhibition of a GRK (G protein receptor kinase) or other regulatory protein in this path. Since GPCRs regulate cardiovascular function, this “out of route” activity is undesirable and this test result allowed the removal of this compound. Other candidate compounds within the same chemical series did not show this activity and could therefore be investigated. Figure 16A shows that mechanism-based toxicity can be detected in cell-based assays. The pronounced effects of a known toxic substance, cadmium chloride (10 micromolar) on four different protein interactions in human cells are shown in the images and histograms. Images are taken 8 hours after cell treatment with cadmium chloride. Figure 16B with the use of assays for protein interactions, the activity profile of any new agent can be compared with the profile of a known toxic substance, such as cadmium chloride, to determine if the new agent has cellular activity similar to that of the toxic sustance. In the example shown, four different tests were processed in parallel. Green indicates an increase in the test parameter and red indicates a reduction. In the profiles shown, two patented compounds (candidate 101 and 102) had profiles similar to those of cadmium and were subsequently documented to produce liver toxicity in rodents. Other candidate compounds in this series did not share this toxic profile. Figure 17A The activities of 107 different known drugs were evaluated against a panel of 53 different protein interactions, each interaction being tested at one or more time points in HEK293 cells with PCA. The result of each test was coded as no effect (black), increased test signal (green) or reduced test signal (red). In the resulting matrix, the drugs are on the x-axis and the tests are on the y-axis. Each drug gave a characteristic pharmacological identification (profile). The numerical test results of each trial / time / pretreatment combination were grouped as described in the experimental protocol, to assess similarities and differences between drugs based on their activity profiles. Figure 17B The x-axis drug groups of Figure 17A are shown herein with drug names attached to the dendrogram. Known drugs were grouped based on their similar route activities, validating that the strategy faithfully captures large-scale en route activities. There were also differences in selectivity (off-road activity) between closely related compounds, which highlights the ability of the strategy to capture off-road activities of a wide range of chemical compounds and targets. Figure 18 is a matrix showing the profiles of pharmacological candidates, representing several different classes of different drugs, in a panel of tests of different protein interactions. The color coding was as in Figure 16A. Drugs that were nonselective could easily be distinguished from those that were more selective in the test panel. Two compounds that proved to be hepatotoxic showed extensive out-of-path activity that suggested a general lack of selectivity in human cells. Therefore, protein interaction assays can be used in stages during

Candidate optimization to develop candidates that are “cleaner” (more selective) in human cells. In the example shown, each drug candidate was tested at a concentration that was 3x his cellular IC50; Dose response curves can also be used to compare potencies along any test panel.

Detailed description of the invention

Identification of effects in route and out of route of drugs (realization of pharmacological profiles)

All drugs exert their effects by acting on proteins within living cells. The typical drug discovery procedure involves selecting a protein target and establishing an in vitro assay for that target of interest. The discovery target selection phase is followed by identification, exploration or development of a chemical compound that exerts the desired effect on that target. This is achieved by high performance scanning of a chemical compound or other library of compounds; by crystallization of the target and in silico or wet procedures to design a compound that conforms to a binding site on a target; or by a combination of these and similar procedures. Regardless of the procedure used, there is always a known target, activity or effect of the compound. However, in almost all cases, drugs and drug candidates exert unknown effects when they come into contact with living cells. Such unknown effects are the result of the lack of specificity of the molecule in the context of the thousands of proteins that make up the biochemical medium of the living cell. In some cases, these unknown effects may result in adverse biological consequences.

or toxicity that is not seen until the drug is given to hundreds or thousands of patients.

The central challenge of the pharmaceutical industry is to develop drugs that are both safe and effective in man. The balance between safety and efficacy is usually difficult to achieve as demonstrated by the 75% failure rate of drugs in clinical trials. Frequently, a drug is completely safe but has insufficient efficacy in a particular condition to provide any appreciable benefit to the patient population for which it is intended. In other cases, a drug is effective but has long-term adverse effects that are not apparent until a large number of patients have been studied for long periods of time. Acute and chronic toxicities can lead to withdrawal of a drug from the market, and there are numerous examples such as troglitazone (Rezulin) and other drugs. Therefore, the pharmaceutical industry employs substantial research and development money in an attempt to understand the selectivity and potential adverse effects of drugs, before clinical trials. However, current procedures for performing drug selectivity profiles are extremely limited. Therefore, the inventors are looking for a procedure to allow the rapid identification of route and out-of-route effects of drugs on a genomic scale.

Examples of intracellular proteins that form complexes

It is believed that each cell protein comes into contact with numerous other proteins. It is also believed that there are many routes in the cell, which represent organized collections of proteins that act in concert to respond to conditions or to exert a particular cellular phenotype. Figure 2 provides examples of the kinds of proteins that participate in protein-protein interactions. Some of these intracellular protein-protein interactions are constitutive, that is, they do not respond to external stimuli, environmental conditions or other modulators. By "responding" the inventors understand that a particular protein-protein interaction increases, decreases or undergoes a change in subcellular distribution or pattern in response to a disturbance. However, many, if not most, of the interactions are modulated under certain conditions in living cells. For example, certain protein-protein interactions are induced by the binding of an agonist, hormone or growth factor with a receptor that induces a signaling cascade or by a small molecule that activates an intracellular protein or enzyme. Other interactions can be inhibited, for example by binding an antagonist or an antibody with a receptor thereby blocking a signaling cascade; by an siRNA, which silences a gene that encodes a protein that is critical for a route; or by a drug that inhibits a particular protein within a route. These examples are intended to illustrate the breadth of the invention and are not limiting to the practice of the invention.

Procedure for conducting pharmacological profiles

An overview of the pharmacological analysis procedure is shown in Figure 4.

Stage 1 involves selecting the chemical compounds, pharmacological candidates or drugs whose profile is to be performed.

Stage 2 involves selecting protein-protein interactions to test. These may be new or known interaction proteins. Interaction proteins can be identified, or rationally selected, for example, by prior knowledge of a route or a pair of interaction proteins, or empirically. For example, interaction proteins can be identified by one or more procedures that include bait scan against library or peer interaction mapping (gene to gene). In addition, an unlimited number of protein-protein interaction assays can simply be constructed randomly and empirically tested for their sensitivity to any variety of drugs or chemical compounds and

Results can be combined in a pharmacological profile. There is practically no limit to the types, numbers or identities of protein-protein interactions that can be used in the realization of pharmacological profiles. There are probably hundreds of thousands of protein-protein interactions that occur in mammalian cells. However, some will be constitutive; and others will be redundant. A constitutive interaction, which does not respond to drugs, agonists or other disturbing compounds, will not be useful in the realization of pharmacological profiles. Fortunately, it can be determined empirically if a specific protein-protein interaction is useful in profiling, simply by constructing an assay for the interaction, using one of the many types of assay analyzed herein, and testing it with respect to sensitivity to a series of drugs or other compounds. A redundant interaction is one that provides information that is not different from the information provided by a different interaction. Since interactions between proteins are mapped gradually over time it will be possible to construct a panel of fully predictive assays based on the procedures provided herein. Such a panel will allow testing any compound to determine its full spectrum of activities against all routes in the living cell and determine any off-route activity that suggests adverse consequences.

Stage 3 of the realization of pharmacological profiles involves constructing the assays for two or more protein-protein interactions. Suitable procedures for constructing such assays are widespread and have been described in detail in the background of the invention; such procedures, which include two hybrid and FRET procedures as well as immune capture procedures, are well documented in the literature and can simply be adapted for the present invention if the interaction protein pairs are appropriately selected and the procedure is sufficiently sensitive. to detect and quantify changes in signal strength or location due to the chemical compounds or drugs of interest.

In the case of fragment complementation tests, the tests are constructed according to the following scheme. To study the signaling network, each of the members of a selected protein-protein pair is cloned as a phase gene fusion (3 'or 5') with indicator fragments encoding a PCA indicator molecule in an appropriate vector so that after transfection in an appropriate cell line the encoded protein that is subsequently expressed in the cell carries a protein fragment (polypeptide) at the amino terminus

or terminal carboxyl of the protein encoded in the selected original cDNA. Assays can be constructed using transient transfections or stable cell lines or infection, such as with retroviruses or adenoviruses designed to express the proteins of interest and to allow detection of the interactions of interest.

Stage 4 of the realization of pharmacological profiles, each chemical compound or drug is tested against each protein-protein interaction at times and at specific pharmacological concentrations. Each pharmacological result is compared with control values (without treatment). (Positive and negative controls can be processed for each trial, at each time point and stimulus condition). In Stage 5, the test results are combined to establish a pharmacological profile for each compound. The results can be presented in various ways. In the examples provided herein, the inventors have used a matrix presentation and / or a histogram to represent the pharmacological profiles. In the case of a matrix, the results of each scan can be represented in a color-coded matrix in which red indicates a reduction in signal strength or location while green indicates an increase. Different shades of red and green can be used to represent the intensity of the change. In this way the presentation is similar to that of a microarray experiment.

Suitable software for route analysis can be used to create route maps, in which the results of the pharmacological compound are translated into a diagram showing upstream and downstream events and linking drugs to those events. Proper route analysis software is marketed by Ingenuity Systems, GeneGo, and many other distributors.

Experimental protocol

To apply the present invention on a large scale, the inventors constructed numerous assays for routes that control key cellular processes, including apoptosis, the cell cycle, response to DNA damage, GPCR signaling, molecular chaperone interactions, cytoskeletal regulation, proteosomal degradation, mitogenesis, inflammation and nuclear hormone receptor pathways. Protein protein complexes were selected to represent interactions at different hierarchical levels within well characterized routes. Examples of explored routes, and examples of tests for various targets, are provided in Figures 7-15 of the present invention together with examples of expected ("en route") and unexpected effects of the drugs, toxic substances and candidate compounds. The inventors covered a wide range of routes, proteins, drugs and target classes to highlight the universality of the approach.

Details of particular proteins that are explored in Tables 1-3 are found below. It is important to note that the invention can be used with any protein interaction as long as a dynamic assay can be constructed for that interaction. In addition, particular elements of the invention, including the route modulation strategy for cell-based assays of the inventors, can be applied to any measurement parameter of a protein complex or even an individual protein in a cell.

To assess the pharmacological effects in human signal transduction networks, the inventors analyzed the pharmacological effects with assays of high content in a human cell line (HEK293 cells) the assays allow the exploration of the activity of specific signaling nodes under different conditions and activity can be controlled at temporal and spatial levels within a route network. To apply this strategy on a large scale, the inventors constructed numerous assays for routes that control key cellular processes, including apoptosis, the cell cycle, DNA damage response, GPCR signaling, molecular chaperone interactions, cytoskeletal regulation, proteosomal degradation, mitogenesis , inflammation and nuclear hormone receptor pathways. Analyzing the response of various signaling nodes that represent multiple classes of target and routes, the inventors were able to specify pharmacological activity and selectivity in route and out of route, general and mechanism-based toxicity and pharmacological relationships.

PCA scans employ complementary fragments (F [1] and F [2]) of an indicator protein that will fold in an active form only if fused with two interacting proteins. Protein-protein complexes were selected (Table 1) to represent known interactions that occur at different hierarchical levels within well-characterized pathways. For the examples, the inventors organized tests based on fragments of an intensely fluorescent mutant of YFP (Remy and Michnick, 2001; Remy and Michnick, 2004; Remy et al., 2004), which matures rapidly (9 minutes (Nagai et al. , 2002)), which allows the visualization, quantification and localization of complexes formed between full-length proteins expressed at low levels in human cells. The objective of minimizing expression was to minimize potential disturbances of endogenous pathways by exogenous proteins (Yu, 2003).

Fragment synthesis and construction preparation

Indicator fragments for PCA were generated by oligonucleotide synthesis (Blue Heron Biotechnology, Bothell, WA). First, oligonucleotides encoding YFP [1] and YFP [2] polypeptide fragments (corresponding to amino acids 1-158 and 159-239 of YFP) were synthesized. Next, PCR mutagenesis was used to generate the IFP [1] and IFP [2] mutant fragments. The IFP fragment [1] corresponds to YFP [1] - (F46L, F64L, M153T) and the IFP fragment [2] corresponds to YFP [2] - (V163A, S175G). These mutations have been shown to increase the fluorescence intensity of the intact YFP protein (Nagai et al., 2002). The YFP [1], YFP [2], IFP [1] and IFP [2] fragments were amplified by PCR to incorporate restriction sites and a linker sequence, described later, in configurations that would allow the fusion of a gene of interest with the 5'- or 3'- end of each indicator fragment. The linker-indicator fragment cassettes were subcloned into a mammalian expression vector (pcDNA3.1Z, Invitrogen) that had been modified to incorporate the origin of replication (oriP) of the Epstein Barr virus (EBV). OriP allows episomic replication of these modified vectors in cell lines that express the EBNA1 gene, such as HEK293E cells (293-EBNA, Invitrogen). Additionally, these vectors still retain the SV40 origin, which allows episomic expression in cell lines that express the SV40 large T antigen (for example, HEK293T, Jurkat or COS). The integrity of the mutated indicator fragments and the new origin of replication were confirmed by sequencing.

PCA fusion constructs were prepared for a large number of proteins that are known to participate in a diverse series of cell pathways (Table 1). The complete coding sequence for each gene of interest was amplified by PCR from a full length cDNA with modified sequence. The resulting PCR products were column purified (Centricon), digested with appropriate restriction enzymes to allow directional cloning and fused in phase with the 5 'or 3' end of YFP [1], YFP [2], IFP [1] or IFP [2] through a linker encoding a flexible 10 amino acid peptide (Gly.Gly.Gly.Gly.Ser) 2. The flexible crimp ensures that the orientation / arrangement of the fusions is optimal for placing the indicator fragments in close proximity (Pelletier et al., 1998). Recombinants in host strains DH5-alpha (Invitrogen, Carlsbad, CA) or XL1 Blue MR (Stratagene, La Jolla, CA) were screened by colony PCR, and clones containing inserts of the correct size were subjected to end sequencing to confirm the presence of the gene of interest and phase fusion with the appropriate indicator fragment. A subset of fusion constructs was selected for complete insert sequencing by primer advance. DNA was isolated using Qiagen MaxiPrep kits (Qiagen, Chatsworth, CA). PCR was used to assess the integrity of each fusion construct, combining the appropriate gene-specific primer with a specific indicator primer to confirm that the correct gene fusion and the correct size was present without internal deletions.

All of the above-mentioned fluorescent protein fragments are the subject of United States Application of the same applicant serial number 10 / 724,178 filed on December 1, 2003.

Transient transfections

HEK293 cells were maintained in MEM alpha medium (Invitrogen) supplemented with 10% FBS (Gemini Bio-Products), 1% penicillin and 1% streptomycin and allowed to grow in a 37 ° C incubator equilibrated at 5% CO2. Approximately 24 hours before the transfections, the cells were seeded on 96-well poly-D-Lysine-coated plates (Greiner) using a Multidrop 384 peristaltic pump system (Thermo Electron Corp., Waltham, Mass) at a density of 7,500 per well. Up to 100 ng of the complementary YFP or IFP fragment fusion vectors were co-transfected using Fugene 6 (Roche) according to the manufacturer's protocol. The list of selected PCA pairs explored in this study, and indicator fragment and gene information

corresponding, they are listed in Table 2. After 24 or 48 hours of expression, the cells were screened in front of a panel of drugs as described below.

Table 1. Proteins, genes and assays for protein interactions for the present invention.

Test Name

Gen _1 Gen Access Number _1 Gen _2 Gen access number _2

to tubulin / HDAC6

TUBA2  NM_006001 HDAC6 NM_006044

to tubulin / HDAC6

TUBA2  HDAC6 NM_006001 NM_006044

a-actin / a-actinine

ACT1  NM_001100 ACTN1 NM_001102

AKL3L / GUK1

 AKL3L NM_016282 GUK1 NM_000858

AKL3L / IPP2

AKL3L NM_016282  PPP1R2 NM_006241

Akt1 / Cdc37

AKT1 NM_005163 CDC37 NM_007065

Akt1 / GSK31

AKT1  NM_005163 GSK3B NM_002093

Akt1 / Cdc37

AKT1 NM_005163 CDC37 NM_007065

Akt1 / GSK31

AKT1  NM_005163 GSK3B NM_002093

Akt1 / p27

AKT1 NM_005163 CDKN1B NM_004064

Akt1 / p70S6K

AKT1 NM_005163 RPS6KB1 NM_003161

Akt1 / Smad3

AKT1 NM_005163 MADH3 NM_005902

AMPK / HMGCR

PRKAA1 NM_206907  HMGCR NM_000859

annex I / FPRL1

ANXA1 NM_000700 FPRL1 NM_001462

ARF1 / gamma-COP

ARF1 NM_001658 COPG1 NM_016128

B2AR / 1-arrestina2

beta2AR  NM_000024 ARRB2 NM_004313

12AR / Gai3

beta2AR  NM_000024 GNAl3 NM_006496

Bad / 14-3-3a

BAD NM_004322 SFN NM_006142

Bad / Bd-xL

BAD NM_004322 BCL2L1 NM_138578

Bag1 / Hsp70L1

BAG1 NM_004323 HSP70L1 NM_005527

Bax / Bid

BAX NM_138761 IDB NM_001196

Bcl-xL / Bik

BCL2L1 NM_138578 BIK NM_001197

1-arrestina2 / Erk2

ARRB2 NM_004313 MAPK1 NM_002745

1-arrestina2 / Ub

ARRB2  NM_004313 UBB NM_021009 (nt 69..296)

1-catenin / CBP

CTNNB1 NM_001904 CREBBP S66385 (nt 1..2313)

1-catenin / Pin1

CTNNB1 NM_001904 PIN1 NM_006221

1-TrCP / 1-catenin

BTRC  NM_033637 CTNNB1 NM_001904

1-TrCP / ROC1

RBX1 NM_033637 BTRC NM_014248

c-Jun / CBP

 JUN NM_002228 CREBBP S66385 (nt 1..2313)

c-Jun / Fos

JUN NM_002228 FOS NM_005252

(continuation)

Test Name

Gen _1 Gen Access Number _1 Gen _2 Gen access number _2

c-Src / Grk2

CSK NM_004383 ADRBK1 NM_001619

Calcineurin A / Bad

PPP3CA NM_005605  BAD  NM_004322

CaM / Calcineurin A

CALM2 NM_001743 PPP3CA NM_005605

CaM / DAPK2

CALM2 NM_001743  DAPK2 NM_014326

CaM / MEF2C

CALM2 NM_001743 MEF2C NM_002397

Caspasa 3 / Cdc6

CASP3 NM_004346  CDC6  NM_001254

Caspasa 3 / MST4

CASP3 NM_004346  MST4  NM_016542

Caspasa 3 / 1CAD

CASP3 NM_004346 DFFA NM_004401

CBP / ATF-1

CREBBP S66385 (nt 1..2313)  ATF1 NM_005171

CBP / CBP

CREBBP S66385 (nt 1..2313) CREBBP S66385 (nt 1..2313)

CBP / CREB1

CREBBP S66385 (nt 1..2313)  CREB1  NM_134442

CCR5 / PKCa

CCR5  NM_000579 PRKCA NM_002737

Cdc2 / CiclinaB

 CDC2 NM_001786 CCNB1 NM_031966

Cdc25A / Cdc2

 CDC25A NM_ 001789 CDC2 NM_001786

Cdc25C / 14-3-3C

 CDC25C NM_001790 YWHAZ NM_003406

Cdc25C / Cdc2

 CDC25C NM_001790 CDC2 NM_001786

Cdc42 / Jnk2

 CDC42 NM_001791  MAPK9 NM_002752

Cdc42 / Pak4

 CDC42 NM_001791  PAK4 NM_005884

Cdc42 / WASP

CDC42 NM_001791  WASP NM_000377

Cdk2 / 14-3-30

 CDK2 NM_001798 SFN NM_006142

Cdk2 / Axina

 CDK2 NM_001798 Axin1 NM_009733

Cdk2 / Cdc6

 CDK2 NM_001798 CDC6 NM_001254

Cdk2 / CiclinaE

 CDK2 NM_001798 CCNE1 NM_001238

Cdk2 / Mcm4

CDK2 NM_001798  MCM4 NM_005914

Cdk4 / l Cyclin D1

CDK4 NM_000075 CCND1 NM_053056

Cdk4 / Rb

 CDK4 NM_000075 RB1 NM_000321

Ghk1 / Cdc25A

 CHEK1 NM_001274 CDC25A NM_001789

Chk1lCdc25C

 CHEK1 NM_001274 CDC25C NM_001790

Chk1 / Ku70

 CHEK1 NM_001274 G22P1 NM_001469

Chk1 / p53

 CHEK1 NM_001274 TP53 NM_000546

Chk2 / Chk2

 CHEK2 NM_007194 CHEK2 NM_007194

Chk2 / p53

 CHEK2 NM_007194 TP53 NM_000546

(continuation)

Test Name

Gen _1 Gen Access Number _1 Gen _2 Gen access number _2

Crk / RalA

 CRK NM_005206 RALA NM_005402

DGKa / c-Src

DGKA NM_001345 CSK NM_004383

Dnmt1 / Histona H3A

Dnmt1 NM_010066 H3F3A NM_002107

E2F1 / CEBPs

E2F1 NM_005225 CEBPE  NM_001805

E6 / E6AP

 E6 NC_001526 E6AP ------

E6 / p53

 E6 NC_001526 TP53 NM_000546

EDG2 / Gy4

EDG2 NM_001401 GNG4 NM_004485

EGFR / CaM

EGFR NM_00522827 CALM2 NM_001743

EGFR / Grb2

EGFR 005228 GRB2 NM_002086

elF4E / 4E-BP2

 EIF4E NM_001968 EIF4BP2 NM 004096

elF4E / elF4A

 EIF4E NM_001968 EIF4A1 NM 001416

elF4EietF4G

 EIF4E NM_001968 EIF4G1 NM 198244

Elk1 / SRF

ELK1 NM_005229 SRF NM 003131

ERa / RXRa

ESR1  NM_000125 RXRA NM 002957

ER / SRC-1

ESR1 NM_000125 SRC-1 U40396 (nt 624..1256)

Erk2 / Elk1

MAPK1 NM_002745  ELK1 NM_005229

Erk2 / Mnk1

MAPK1 NM_002745  MKNK1 NM_003684

Erk2 / p27

MAPK1 NM_002745  CDKN1B  NM_004064

FAK1 / Socs-3

 FAK1 NM_005607 Socs3 NM_007707

FXR / RXRa

Nr1h4  NM_009108 RXRA NM_002957

FXR / SRC-1

Nr1h4 NM_009108 SRC-1 U40396 (nt 624..1256)

Fz-4 / Ga (o)

FZD4 NM_012193 Gnao1 NM_010308

Fz-4 / Grk2

FZD4 NM_012193 ADRBK1 NM_001619

Fz-4 / RGS2

FZD4 NM_012193 RGS2 NM_002923

Gai / G11

GNAI1 NM_002069 GNB1 NM_008142

GLUT4 / Sumo-1

GLUT4 NM_001042 SUMO1 NM_003352

Grk2 / Ga (o)

ADRBK1  NM_001619 Gnao1 NM_010308

Grk2 / Gy2

ADRBK1  NM_001619 GNG2 XM_495987

Grk2 / PKCa

ADRBK1  NM_001619 PRKCA NM_002737

Grk2 / PKC�

ADRBK1  NM_001619 PRKCZ NM_002744

GRM3 / Gy4

GRM3 NM_000840 GNG4 NM_004485

GSK31 / PP2A

GSK3B NM_002093 PPP2CA  NM_004156

HDAC1 / 14-3-3c

HDAC1  NM_004964 SFN NM_006142

(continuation)

Test Name

Gen _1 Gen Access Number _1 Gen _2 Gen access number _2

HDAC1 / HDAC1

 HDAC1 NM_004964 HDAC1 NM_004964

HDAC1 / Stat1

 HDAC1 NM_004964 STAT1 NM_007315

Histone H3 / HDAC1

H3F3A NM_002107 HDAC1 NM_004964

Histona H3 / Histona H3

H3F3A NM_002107 H3F3A NM_002107

H-Ras / Raf1

HRAS NM_005343 RAF1 NM_002880

Hsc70 / p53

HSC70 NM_006597 TP53 NM_000546

Hsp901 / Akt1

HSPCB  NM_007355 AKT1 NM_005163

Hsp901 / Bid

HSPCB  NM_007355 IDB NM_001196

Hsp901 / Cdc37

HSPCB  NM_007355 CDC37 NM_007065

Hsp901 / Chk1

HSPCB  NM_007355 CHEK1 NM_001274

Hsp901 / Eef2k

HSPCB  NM_007355 Eef2k NM_007908

Hsp901 / Mek1

HSPCB  NM_007355 Map2k1 Z16415

Hsp901 / Mek2

HSPCB  NM_007355 Map2k2 D14592

Hsp901 / Wee1

HSPCB  NM_007355 Wee1 NM_009516

ICAD / CAD

DFFA NM_004401  DFFB NM_004402

IkBa / p65

NFKB1A NM_020529 Rela NM_009045

IKK1 / IKKy

IKBKB NM_001556 IKBKG  NM_003639

IKKy / p27

IKBKG  NM_003639 CDKNIB  NM_004064

IPP2 / RIPK2

PPP1R2 NM_006241  RIPK2 NM_003821

ITGa5 / ITG11

ITGA5  NM_002211 ITGB1 NM_002205

Jnk1 / c-Jun

MAPK8 NM_002750  JUN NM_002228

JNK2 / ATF-2

 MAPK9 NM_002752  ATF2 NM_001880

Jnk2 / Cdc25C

 MAPK9 NM_002752  CDC25C NM_001790

Jnk2 / Cdk2

MAPK9 NM_002752  CDK2 NM_001798

Ku70 / Ku80

 G22P1 NM_001469 XRCC5 NM_021141

Limk2 / cofilina1

 LIMK2 NM_005569 CFL1 NM_005507

LXR1 / RXRa

NR1H2  NM_007121 RXRA NM_002957

Map3k8 / Clk1

 Map3k8 NM_005204 CLK1 NM_004071

Map3k8 / PP1-r1a

Map3k8  NM_005204 PPP1R1A NM_006741

MAPKAPK-2 / Eef2k

 MAPKAPK2 NM_032960  Eef2k NM_007908

Mdm2 / NPM1

MDM2 NM_002392  NPM1 NM_002520

Mdm2 / p53

 MDM2 NM_002392 TP53 NM_000546

Mek1 / Erk2

 Map2k1 Z16415 MAPK1 NM_002745

(continuation)

Test Name

Gen _1 Gen Access Number _1 Gen _2 Gen access number _2

Mek2 / Jnk

 Map2k2 D14592 MAPK8 NM_002750

Mevalonate

 MVK NM_000431 MVK NM_000431

MKK6 / p38a

MAP2K6 NM_002758 MAPK14 NM_001315

Mnk1 / elF4E

 MKNK1 NM_003684 ElF4E NM_001968

Myc / Max

MYC NM_002467 MAX NM_002382

NLK / c-Myb

 NLK NM_016231 My B NM_010848

eNos / CaM

Nos3 NM_008713 CALM2 NM_001743

NURR1 / RXRa

NR4A2  NM_006186 RXRA NM_002957

Orc1 / Orc2

ORC1L NM_004153 ORC2L NM_006190

p21 / Cdc2

 CDKN1A NM_000389 CDC2 NM_001786

p21 / CyclinB

 CDKN1A NM_000389 CCNB1 NM_031966

p22 (phox) / p47 (phox)

 CYBA NM_000101 NOXO2 NM_000265

p27 / CRM1

 CDKN1B NM_004064 XPO1 NM_003400

P2Y2r / Ga15

P2RY2  NM_002564 GNA15 NM_002068

P2Y2r / Gy4

P2RY2  NM_002564 GNG4 NM_004485

p38a / cdc25B

MAPK14 NM_001315 CDC25B  NM_021873

p38a / Cdk2

MAPK14 NM_001315 CDK2 NM_001798

p38a / Elk1

MAPK14 NM_001315 ELK1 NM_005229

p38a / MAPKAPK-2

MAPK14 NM_001315 MAPKAPK2 NM_032960

p38a / Max

MAPK14 NM_001315 MAX NM_002382

p38a / Mnk1

MAPK14 NM_001315 MKNK1 NM_003684

p38a1 / Rad9

MAPK14 NM_001315 RAD9 NM_004584

p53 / p53

 TP53 NM_000546 TP53 NM_000546

p65 / CBP

Challenge NM_009045 CREBBP S66385 (nt 1..2313)

p65 / p50

 Challenge NM_009045 NFKB1 NM_003998

p70S6K / Map3k8

 RPS6KB1 NM_003161 Map3k8 NM_005204

p70S6K / RIPK2

 RPS6KB1 NM_003161 RIPK2 NM_003821

Pak4 / Bad

PAK4 NM_005884  BAD NM_004322

Pak4 / Cofilina

PAK4 NM_005884  CFL1 NM _005507

Parvina-beta / ILK

PARVB ------ ILK NM_004517

Pcna / Pol8 (p50)

PCNA  NM_002592 POLD2 NM_006230

PCNA / Xrcc1

 PCNA NM_002592 Xrcc1 NM_009532

Pdk1 / Akt

PDPK1 NM_002613  AKT1 NM_005163

(continuation)

Test Name

Gen _1 Gen Access Number _1 Gen _2 Gen access number _2

PFK-2 / IPP2

PFKFB1 NM_002625  PPP1R2 NM_006241

PIAS1 / Mdm2

 PIAS1 NM_016166 MDM2 NM_002392

PIASy / p53

 PIASy NM_015897 TP53 NM_000546

PIASy / Smad3

 PIASy NM_015897 MADH3 NM_005902

Pin1 / Cdc25C

 PIN1 NM_006221 CDC25C NM_001790

Pin1 / c-Jun

 PIN1 NM_006221 JUN NM_002228

Pin1 / p53

 PIN1 NM_006221 TP53 NM_000546

Pin1 / p70S6K

 PIN1 NM_006221 RPS6KB1 NM_003161

PKCa / p47Nox

PRKCA  NM_002737 NOXO1 NM_144603

PPIc1 / HDAC1

PPP1CB  NM_002709 HDAC1 NM_004964

PP1-r1a / PP-1B

PPP1R1A  NM_006741 PPP1CB  NM_002709

PP2A / Cdk2

PPP2CA NM_002715  CDK2 NM_001798

PP2A / Mek1

 PPP2CA NM_002715 Map2k1 Z16415

PPARy / RXRa

PPARG NM_138712 RXRA  NM_002957

2PARy / SRC-1

PPARG NM_138712 SRC-1 U40396 (nt 624..1256)

Pthr1 / Ga15

Pthr1  NM_011199 GNA15 NM_002068

PTPa / c-Src

PTPRA NM_080840 CSK NM_004383

PTPa / Grb2

PTPRA NM_080840 GRB2 NM_002086

PXR / RXRa

PXR  NM_022002 RXRA NM_002957

PXR / SRC-1

PXR NM_022002 SRC-1 U40396 (nt 624..1256)

Rac1 / Pak1

RAC1 NM_006908  PAK1 NM_002576

Rac1 / Pak6

RAC1 NM_006908  PAK6 NM_020168

Rad1 / Rad17

 RAD1 NM_002853 RAD17 NM_133338

Rad9 / HDAC1

 RAD9 NM_004584 HDAC1 NM_004964

Rad9 / Hus1

 RAD9 NM_004584 HUS1 NM_004507

Rad9 / p53

 RAD9 NM_004584 TP53 NM_000546

Raf1 / 14-3-3s

RAF1 NM_002880 SFN NM_006142

Raf1 / Cdc37

 RAF1 NM_002880 CDC37 NM_007065

Raf1 / Mek1

 RAF1 NM_002880 Map2k1 Z16415

Raf1 / Mek2

 RAF1 NM_002880 Map2k2 D14592

RalB / RalBP1

 RALB NM_002881 RALBP1 NM_006788

RARB / RXRa

RARB  NM_000965 RXRA NM_002957

Rb / Cyclophilin A

 RB1 NM_000321 PPIA NM_021130

(continuation)

Test Name

Gen _1 Gen Access Number _1 Gen _2 Gen access number _2

RGS2 / Gai2

RGS2 NM_002923 GNAI2 NM_002070

SCAP / Insig-1

 SCAP NM_012235 INSIG1 NM_ 005542

Smad3 / HDAC1

MADH3 NM_005902 HDAC1 NM_004964

Smurf1 / Smad1

 SMURF1 NM_181349 MADH1 NM_005900

Speedy1 / p27

 SPDY1 NM_182756 CDKN1B NM_004064

SRF / Sumo-1

 SRF NM_003131 SUMO1 NM_003352

SSTR2 / G11

SSTR2  NM_001050 GNB1 NM_008142

Stat1 / Stat1

STAT1 NM_007315  STAT1 NM_007315

Stat5a / Stat5b

 STAT5A NM_003152  STAT5B NM_012448

STK15 / Guk1

STK15 NM_003600 GUK1 NM_000858

Syntaxin: Sinaptabrevin2

STX4A NM_004604 VAMP2 NM_U14232

TFCOUP2 / SRC-1

Nr2f2 NM_009697 SRC-1 U40396 (nt 624..1256)

TF-COUP3 / TFIIB

Nr2f6 NM_010150 GTF2B NM_001514

THR a / RXRa

THRA NM_003250 RXRA NM_002957

Tlk1 / Asf1

 TLK1 NM_012290 ASF1 NM_014034

TNFR1 / FADD

TNFR1 NM_001065 FADD NM_003824

TNFR1 / TNFR1

TNFR1 NM_001065 TNFR1 NM_001065

TRAF2 / p65

TRAF2 NM_021138 Rela NM_009045

TRAF2 / lkBa

 TRAF2 NM_021138 NFKB1A NM_020529

Vav / Cdc42

 VAV NM_005428 CDC42 NM _001791

VIPR2 / G11

 VIPR2 NM_003382 GNB1 NM_008142

WASP / Grb2

WASP NM_000377 GRB2 NM_002086

Wee1 / Cdc2

 WEE1 NM_ 009516 CDC2 NM_001786

Xrcc1 / Ape1

 Xrcc1 NM_009532  JAPE1 NM_001641

Table 2. Description of some of the proteins used in the construction of the assay

Gene / Protein

 Other names Gene / Protein Other names Gene / Protein Other names

ACT1

 actin alpha 1 Ephb4 HtX, MOK2, Myk1, Tyrol1 PAK4 P21 activated kinase (COKN1A) - 4

ACTN1

actinine, alpha 1 Ephb6 PCTK1, isoform a PCTAIRE 1, PCTGAIRE

ACVR18

EPOR erythropoietin receptor PCTK1, isoform b PCTA1RE1, PCTGAIRE

Acvit1

activin A. receptor similar to type 11 ESR1 ER. ESR. It was, ESRA. NR3A1 (estrogen receptor 1 (alpha)) PCTK2 PCTAIRE 2 protein kinase

(continuation)

Gene / Protein

 Other names Gene / Protein Other names Gene / Protein Other names

ADPRT

PARP FADD PDGFRB Platelet-derived growth factor receptor, beta polypeptide

AKT1

PKB (protein kinase B) FAF1 hFAF 1, CGI-03, HFAFis; factor associated with FAS- 1 Pdk1 protein kinase associated with death 2

AKT2

PKB (protein kinase B) FAK1 FAK, PTK2 PDK2 Pyruvate Dehydrogenase Kinase, Isoenzyme 2

APC

adenomatous colon polyposis FASTK Fas-activated serine / threonine kinase PDK4 Pyruvate Dehydrogenase Kinase, Isoenzyme 4

ARAF1

PKS2. A-RAF. RAFA1 FGFR4 fibroblast growth factor receptor 4 PDPK1

ARF

FGR SRC2, c-fgr. p55ctnr PED

ARHA

 RhoA FKBP FK506 binding protein PHKG2 phosphorylase kinase, gamma 2

ARRB2

ARB2, ARR2: arrestine, beta 2 FKHRL1 PIk3ca mPI3k p110

ARR82

beta-arrestina-2, beta arr2 FKSGB1 PIK3R1 PI3X p85

AT1

AT1AR. AGTR1 FOS PIM1 PIM1 oncogene

Arin

FRB FKBP binding domain of murine FRAP / TOR PIM2 proto-oncogene Pim2 (serine threonine kinase)

AXL

AXL receptor tyrosine kinase FRK GTK RAK: methylated kinase Pim3

BEAM

FRS2 SNT, SNT1. FRS2A, SNT-1. FRS2alfa: neurotrophic factor target associated with suc1 (FGFR signaling adapter) Pkmyt1 membrane-associated tyrosine and threonine-specific cdc2 inhibitory kinase

BAK

BAK1, CDN1, BCL2L7: BCL2 1 death agent / antagonist: 1 cell death inhibitor G22P1  Ku70 PLCG PLCG1: PLC1, PLC-11, PLC18; PLC-gamma-1

BAX

GAB1 binding protein associated with GRB2-1 PLK PLK1, STPK13: pole type kinase

BC033012

GADD45A PPARG Gamma peroxisome proliferator activated receptor

BCL2

B 2 lymphocyte lymphoma protein GADD45B growth arrest and inducible by DNA damage, beta PPP2CA PP2A, catalytic subunit

(continuation)

Gene / Protein

 Other names Gene / Protein Other names Gene / Protein Other names

BCLG

apoptosis regulator BCL-G GADD45G growth arrest and inducible by DNA damage, gamma gwtuta PPP2R1B

BCL-X

GNAI3 PRKACA protein kinase, cAMP dependent, catalytic, alpha

beta2AR

beta2 adrenergic receptor, adrb2 GNAS1 Guanine nucleotide binding protein (G protein), alpha 1 stimulatory activity polypeptide PRKCA

IDB

BH3 interaction domain death agonist GNB1 Guanine 1, beta nucleotide binding protein PRKCD Protein kinase C. delta

BIK

BP4, NBK, BBC1, BIP1; BCL2 interaction death agent GNG5 Guanine nucleotide binding protein (G protein), gamma 5 PRKCE

BIM1

GPR34 Prkch protein kinase C, eta

BIRC2

API1, MIHB, cIAP1, HIAP2, RNF48 GPRK5 PRKCI protein kinase C, iota

BLK

tyrosine kinase lymphoid B GPRK6 Prkx X-linked protein kinase

BMX

BMX non-receptor tyrosine kinase Gprk7 G 7 protein coupled receptor PTEN

BRCA1

breast cancer 1, early onset GPRK7 G 7 protein coupled receptor PTPN11 protein tyrosine phosphatase, non-receptor type 11

BTRC

 b-TrCP GR82 PYK2 PTK2, FAK, FADK, FAK1, pp125FAK

BTRC

 b-TrCP GRB7 RAC1 Rho family. Small GTP binding protein Rac1

BUB1B

SUBR1, Bub1A, MAO3L; SUB1 germination uninhibited by benzimidazoles 1 beta homologue (yeast) GS3955 TRB2: tribbles counterpart 2 RAD1 HRAD1: Hrad1 cell cycle control point protein

CAMK1

calmodulin / calcium dependent protein kinase 1 GSK3B Rad17 cell cycle control point protein (RAD17)

CAMK2G

1 2G calmodulin / calcium-dependent protein kinase H2AX histone H2AFX: H2AX Rad51 RAD51 homolog (RecA homolog, E. coli) (S. cerevisiae)

CAMK4

calmodulin / calcium-dependent protein kinase 4 HCK JTK9; hemopoietic cell kinase RAD9 RAD9A; RAD9 homolog (S. pombe) cell cycle control protein

Camkk1

calmodulin / calcium dependent protein kinase 1 HDAC1 histone deacetylase 1 RAF1

(continuation)

Gene / Protein

 Other names Gene / Protein Other names Gene / Protein Other names

CAMKKB1

HIF1A hypoxia-1 inducible factor, alpha RALA

CASP9

HRAS ralbp1 ralA 1 binding protein

d-CATENIN

delta-catenin Hri titration factor regulated by heme 2-alpha kinase RAP1 RAPGA1; KREV-1, SMGP21, RAP1GAP, KIAA0474; RAP1, GTPase 1 activating protein

CBP

Binding protein with cyclic AMP response element HSPA1L type hsp70 Weird alpha retinoid acid receptor

c-CBL

HSPB1 HSP27, HSP28, Hsp25. HS.76087; 27 kD in 1 thermal shock protein RATP130CAS

CCNB1

cyclin B1 HSPCB hsp90, beta RB1 retinoblastoma

CCND1

 cyclin D1 Hus1 HUS1 control point counterpart (S. pombe)  Rb2, p130

CCND2

cyclin D2 IKBKAP IKAP Challenge p65, p65 NFkB

Table 3: Description of individual trials and route modulation (“stimulation”) when used

PCA description

Stimulation Genbank Gen 01 Indicator Fusion Orientation Genbank Gen 02 Indicator Fusion Orientation

one

14-3-zeta: CDC25C + CPT 500 nM CPT; 4 pm BC003623 C NM_001790 C

2

Akt1: p27 NM_005163 N NM_004064 C

3

Akt1: PDPK1 NM_005163 N NM_002613 C

4

BAD: IDB NM_004322 N NM_001196 C

5

bARR2: b2AR NM_004313 N NM_000024 C

6

bARR2: b2AR + ISO Isoproterenol 2! M; 30 min NM_004313 N NM_000024 C

7

Bcl-xL: Bad NM_138578 C M_004322 N

8

Bcl-xL: BIK NM_138578 N NM_001197 N

9

Cdc2: Cdc25A + CPT 500 nM CPT; 4 pm NM_001786 N NM_001789 C

10

Cdc2: CDC25C NM_001786 N NM_001790 C

(continuation)

PCA description

Stimulation Genbank Gen 01 Indicator Fusion Orientation Genbank Gen 02 Indicator Fusion Orientation

eleven

Cdc2: CDC25C + CPT 500 nM CPT; 4 pm NM_001786 N NM_001790 C

12

Cdc2: Wee1 NM_001786 N NM_009516 N

13

CDC42: PAK4 NM_001791 N NM_005884 C

14

Chk1: CDC25A + CPT 500 nM CPT; 4 pm NM_001274 N NM_001789 C

fifteen

Chk1: CDC25C NM_001274 N NM_001790 C

16

Chk1: CDC25C + CPT 500 nM CPT; 4 pm NM_001274 N NM_001790 C

17

Cofilina: LIMK2 NM_005507 C NM_005569 N

18

CyclinB: Cdc2 NM_031966 N NM_001786 C

19

CiclinaD: Cdk4 NM_053056 N NM_001791 C

twenty

CiclinaE: Cdk2 NM_001238 N NM_001798 N

twenty-one

E6: E6AP AJ386069 N NM_130839 (CDS 1729 .. 2028) C

22

E6: p53 AJ388069 N NM_000546 N

2. 3

Elk1: MAPK1 NM_005229 C NM_002745 C

24

ESR1: SRC-1 NM_000125 N U40396 (CDS 624..1256) N

25

Gai: b2AR NM_006496 C NM_000024 C

26

H-Ras: Raf NM_005343 N NM_002880 C

27

Hsp90: CDC37 NM_007355 C NM_007065 N

28

Hsp90: Eef2k NM_007355 C NM_007908 N

29

Hsp90: MEK1 NM_007355 C Z16415 N

30

MAPK9: ATF2 L31951 N NM_001880 N

31

Mdm2: p53 BC009893 N NM_000546 C

(continuation)

PCA description

Stimulation Genbank Gen 01 Indicator Fusion Orientation Genbank Gen 02 Indicator Fusion Orientation

32

MKNK1: MAPK1 NM_003684 C NM_002745 C

33

MAX: MYC NM_002382 C NM_002467 C

3. 4

NtCBP: JUN S66385 (CDS 1..2313) N NM_002228 C

35

NtCBP: p65 S66385 (CDS 1..2313) N NM_009045 N

36

Cdc2: p21 NM_001786 N NM_000389 N

37

P27: Ubiquitin C NM_004064 N NM_021009 (CDS 69..296) N

38

p50: p65 NM_003998 N NM_009045 N

39

p53: Chk1 NM_000546 C NM_001274 N

40

p53: Chk1 + CPT 500 nM CPT; 4 pm NM_000546 C NM_001274 N

41

p53: p53 NM_000546 C NM_000546 C

42

p53: p53 + CPT 500 nM CPT; 4 pm NM_000546 C NM_000546 C

43

PAK4: Cofilina NM_005884 C NM_005507 C

44

Pin1: CDC25C NM_006221 C NM_001790 C

Four. Five

Pin1: JUN NM_006221 C NM_002228 C

46

Pin1: p53 NM_006221 C NM_000546 C

47

PXR: RXRa BC017304 C NM-002957 C

48

RAD9: p38a NM_004584 C NM_001315 N

49

RAD9: p53 NM_004584 C NM_000546 N

fifty

Raf1: Map2k2 NM_002880 C D14592 N

51

RB1: CDK4 BC040540 C NM_001791 C

52

RXRa: PPARg NM-002957 C NM_138711 C

53

Smad3: HDAC NM_005902 N NM_004964 C

Table 4: Drugs and concentrations used in the example of carrying out a pharmacological profile

Drug

Source Concentration  Drug Source Concentration  Drug Source Concentration

(S) - (+) - Camptotecina

Sigma 500 nM GGTI-2133 Calbiochem 5 microM Quetiapine Sequoia 2 microM

17-AAG

Tocris 5 microM Gleevec Novartis 10 microM Raloxifene LKT Labs, Inc. 500 nM

Acetyl Ceramide

Sigma 10 microM Gó 6976 Calbiochem 100.00 nM Rapamycin Calbiochem 250 nM

ALLN

Calbiochem 25 microM GSK-3 Inh. II Calbiochem 1 microM Risperidone Sequoia 2 microM

Aminoglutethimide

Microsource 30 microM (GW1929 Alexis 3 microM Rofecoxib Sequoia 10 microM

Angiogenin

Sigma 100 ng / ml H-89 Calbiochem 2 microM Rolipram Calbiochem 25 microM

Angiotensin II

Calbiochem 300 nM HA14-1 Tocris 2 microM Roscovitine Calbiochem 5 microM

Apigenin

Calbiochem 50 microM HDAC Inh. I Calbiochem 10 microM Rosiglitazone LKT Labs, Inc. 15 microM

Arsenic Oxide (lll)

Sigma 5 microM lndirrubin-3'-Monoxima Calbiochem 10 microM Rosuvastatin Sequoia 30 microM

ATRA

Sigma 5 microM Isoproterenol Sigma 2 microM Rotenone Sigma 300 nM

BAY 11-7082

Calbiochem 10 microM Ketoconazole Sigma 30 microM Salbutamol Sigma 2 microM

Bicalutamide

Sequoia 500 nM L-744.832 Sigma 10 microM Sarafotoxin S6b Calbiochem 100 nM

Brefeldina A

Sigma 50 mg / ml Leptomycin B Sigma 10.00 ng / ml SB 203580 Calbiochem 25 microM

Caffeine

Sigma 50 microM Letrozole Sequoia 1.50 microM SC-560 Calbiochem 250 nM

Caliculin A

Calbiochem 2nM Lithium chloride Sigma 1000 microM Sildenafil Sequoia 1 microM

Celecoxib

Sequoia 10 microM Lovastatin Calbiochem 30 microM Simvastatin Calbiochem 30 microM

Cerivistatin

Sequoia 30 microM LPA Sigma 5 microM Tadalafil Sequoia 1 microM

Ciglitazone

Calbiochem 15 microM LY 294002 Calbiochem 25 microM Tamoxifen Calbiochem 500 nM

Cilostazol

Sigma 2 microM Mevastatin Calbiochem 30 microM Taxol Calbiochem 2.5 microM

Ciprofibrate

sigma 30 microM MG 132 Tocris I microM Thalidomide Calbiochem 250 microM

Clenbuterol

Sigma 2 microM Milrinona Sigma 200 nM Toremifene Sequoia 500 nM

(continuation)

Drug

Source Concentration  Drug Source Concentration  Drug Source Concentration

Clofibrate

Sigma 30 microM Olanzapine Sequoia 2 microM TRAIL Sigma 50.00 ng / ml

Clozapine

Sequoia 2 microM Paroxetine Sequoia 10 microM Trichostatin A Calbiochem 5 microM

DBH

Calbiochem 5 microM Patulina Sigma 10 microM Troglitazone Calbiochem 15 microM

Dexamethasone

Sigma 500 nM PD 158780 Calbiochem 1 microM Tirfostina AG 1296 Calbiochem 5 microM

Epothilone A

Calbiochem 100 nM PD 98059 Calbiochem 20 microM Tirfostina AG 1433 Calbiochem 25 microM

Estrogen

Calbiochem 500 nM PD153035 Calbiochem 200 nM Valdecoxib Sigma 10 microM

Exemestane

Sequoia 1.50 microM Pertussis toxin Sigma 100 ng / ml Vardenafil Sequoia 1 microM

Fluvastatin

Calbiochem 30 microM Pifitrina-a Calbiochem 50 microM Wortmanina Calbiochem 500 nM

Fulvestrant

Tocris 500 nM Pioglitazone Calbiochem 15 microM Y-27632 Calbiochem 25 microM

Geldanamicin

Calbiochem 2.5 microM Pravastatin Calbiochem 30 microM Ziprasidone Sequoia 2 microM

Gemfibrozil

Sigma 30 microM Propanolol Calbiochem 2 microM ZM 336372 Calbiochem 5 microM

Genistein

Calbiochem 12.5 microM PTPBS Calbiochem 500 nM

Stable cell lines

For several interactions, stable cell lines were generated. For p50 / p65 and IkBa / p65, HEK293 cells were transfected with the YFP [2] -p65 fusion vector and stable cell lines were selected using 100 µg / ml hygromycin B (Invitrogen). The selected cell lines were subsequently transfected with YFP [1] -p50 or IKBa-YFP

[1] and cell lines expressing YFP [1] -p50 / YFP [2] -p65 and IKBa-YFP [1] / YFP [2] -p65 were isolated after double antibiotic selection with hygromycin B 50 µg / ml and zeocin 500 µg / ml. For B2AR / B-arrestin, HEK293 cells were transfected with the arrestin 1-YFP fusion vector [1] and stable cell lines were selected using 1000 µg / ml zeocin. Selected cell lines were subsequently transfected with the beta2-AR-YFP fusion vector [2] and stable cell lines expressing YFP [1] -beta-arrestin / beta2-AR-YFP [2] were isolated following the selection of double antibiotic with hygromycin B 200 µg / ml and zeocin 500 µg / ml. For Akt1 / PDK1, HEK293 cells were co-transfected with the YFP [1] -Akt1 and PDK1-YFP [2] fusion vectors and stable cell lines were selected using 1000 µg / ml zeocin. For all cell lines, fluorescence signals were stable for at least 25 passes (data not shown). Approximately 24 hours before drug treatments, the cells were seeded in 96-well poly-D-lysine coated plates (Greiner) using a Multidrop 384 peristaltic pump system (Thermo Electron Corp., Waltham, Mass).

Pharmacological study

HEK293 cells (7,500 per well) were co-transfected with 100 ng of the complementary fusion vectors using Fugene 6 (Roche) according to the manufacturer's protocol. 24 or 48 hours after transfection, cells were screened against drugs in duplicate wells as described below. For pharmacological studies with p50: p65, betaArr2: beta2AR and Aktl: Pdkl, stable cell lines were seeded 24 hours before drug treatment.

107 different drugs were tested (names and doses are listed in Table 4) versus 49 different trials in duplicate at one or more time points. Pharmacological concentrations were selected at approximately three times (3 X) the published cell IC50s and tested to ensure lack of toxicity in HEK293 cells. All liquid handling steps were performed using a Biomek FX (Beckman Instruments, Fullerton, CA). Cells expressing the PCA pairs were incubated in culture medium containing drugs for 30 minutes, 90 minutes and 8 hours or, in the case of DNA damage response pathways, for 18 hours. For certain routes, the cells were first treated with drugs and then stimulated with an agonist, (CPT, TNFalfa or isoproterenol) to induce the route. In this way, drugs that block an agonist-dependent route could be identified. After pharmacological treatments, the cells were stained simultaneously with Hoechst 33342 33 µg / ml (Molecular Probes) and wheat germ agglutinin conjugated with TexasRed 15 µg / ml (WGA; Molecular Probes), and fixed with 2% formaldehyde (Ted Pella) for 10 minutes. The cells were subsequently rinsed with HBSS (Invitrogen) and kept in the same buffer during image acquisition.

Fluorescence image analysis

Cellular images were acquired with a Discovery-1 automatic fluorescence camera (Molecular Devices, Inc.) equipped with a robotic arm (CRS Catalyst Express; Thermo Electron Corp., Waltham, Mass). The following filter sets were used to obtain images of 4 non-overlapping cell populations per well: excitation filter 480 +/- 40 nm, emission filter 535 +/- 50 mm (YFP); excitation filter 360 +/- 40 nm, emission filter 465 +/- 30 nm (Hoechst); excitation filter 560 +/- 50 nm, emission filter 650 +/- 40 nm (Texas Red; pharmacological study). Four scans were acquired for each well representing 150-300 cells each scan. A constant exposure time was used for each wavelength to acquire all the images for a given test.

Unprocessed images in 16-bit gray scale TIFF format were analyzed using modules from the ImageJ API library (http://rsb.info.nih.gov/ij/, NIH, MD). First, images of each fluorescence channel (Hoechst, YFP and Texas Red; the last only for pharmacological study) were normalized using the ImageJ built-in rolling sphere algorithm. (Sternberg, 1983). Since each PCA generates a signal in a specific subcellular compartment or organelle, and treatment with a drug can effect a change in the location of the signal complex or intensity, different algorithms were required to precisely quantify fluorescent signals located in the membrane, nucleus or cytosol. Each assay was categorized according to the subcellular location of the fluorescent signal and changes in the signal strength throughout each sample population were quantified using one of four automatic image analysis algorithms.

Algorithm 1: Changes in the average fluorescence throughout the entire cell were quantified after the generation of masks based on YFP (PCA signal) images, Texas network (WGA; membrane staining) or Hoechst (nuclear staining) with autoumbral. A histogram obtained from the YFP image without threshold (YI) was used to define the overall background (the average of the lowest intensity peak). The superposition of the three masks defined individual particles (which approximate the cells). Positive pixel samples were taken within the YI to calculate the mean pixel intensity for all positive particles within a scan (positive particle average, PPM).

Algorithm 2: To measure changes in the fluorescence restricted to the nucleus, samples of all pixels above a user-defined selection value that were within the nuclear mask of the YI were taken to determine the average nuclear fluorescence and nuclear fluorescence total (normalized to Hoechst area) resulting in Sum of Nuc or Average Nuc.

Algorithm 3: To measure population changes of total fluorescence, samples of all pixels were taken above a user-defined selection value of the YI to determine the mean total fluorescence and total fluorescence (normalized to Hoechst area) giving as a result sum of volume or volume average.

Algorithm 4: To measure changes in fluorescence in the membrane, a mask based on the image of Texas Red with threshold (WGA) was used to sample pixels within the membrane region. Samples of membrane-associated pixels were taken above a manually set selection value to determine the mean membrane fluorescence.

For all algorithms, the data was corrected with respect to the global fund before calculating the average values. For each test, test sets of the images were generated. Each set included images of cells treated with vehicle only (control) and images of cells treated with an identification compound (known modulator of the assay). Fluorescence in the images was quantified with each algorithm, and the results were compared with manual image scores to identify the most appropriate algorithm to quantify differences between the treated sample and the control.

The effect of each treatment in each trial was compared with the result for vehicle control (PBS for isoproterenol and propranolol; DMSO for all other drugs). For data generated by algorithm 1, the PPM was grouped for each data point (PCA pair / stimulus / drug) of at least 8 scans. An external filter was applied to exclude particles that were outside the range of the group (mean ± 3DT) before calculating the PPM for the entire sample. For algorithms 2 -4, the sum of Nuc, sum of volume or sum of Mem for each sample represents the average of at least 8 drug scans after application of a 2DT filter to exclude scans with fluorescent devices. The results of the pharmacological study were repeated in at least three separate experiments for "distinctive" drugs and showed agreement between repeated experiments.

Matrices and grouping

In the matrices, each row represents a unique drug and each column represents a unique assay at a particular time point in the absence or presence of a route modulator such as TNFalfa, CPT or isoprotenerol. Each data point was formed by taking the logarithm of the sample and control relationship. Each row and column has the same weight. The hierarchical clustering algorithm of Ward (Ward, 1963) and Euclidean distance metric (http // www.r-project.org /) were used to group the treatments. For presentation purposes, each data point within the matrix is color-coded to illustrate relative differences within an essay. There is an increase in relation to the control value in green and a reduction in red is presented. Each color is further divided into two levels: level 1 (2x. CV) or level 2 (1.5 x CV). Level one is presented as the lightest tone and level 2 as the darkest tone.

Results

Scope of the invention

The inventors have shown that the present invention provides a universal method for performing pharmacological profiles and drug discovery, which can be applied to any test compound, drug, toxic substance, pharmacological target, route or cell of interest. Numerous examples are shown in Figures 6-16 of a wide range of proteins and routes.

The invention allows the identification of effects in route and out of route of drugs and enables the improvement of pharmacological candidates through candidate optimization and selective withdrawal. The invention can be applied in steps to evaluate the properties of pharmacological candidates, modify those properties and retest the modified candidates. With this approach, cell activity-structure studies are possible in which chemical compound structures are linked to particular test results and pharmacological profiles, allowing the identification of desired and unwanted chemical constituents. Thus, the invention is an example of chemical genomics, since it allows the detection of chemical effects on a genomic scale in a biological system. The invention can be used with cell-based or in vitro assays, although cell-based assays, as used herein, require less manipulation.

En route and out of route effects of drugs, toxic substances and candidate compounds

Figure 6 (A-B) shows examples of interactions in GPCR routes. Figure 7 (A-C) shows examples of interactions in the ubiquitin / sumo / proteasome pathways and pharmacological effects on those routes. Figure 8 (A-D) shows examples of interactions in kinase signaling pathways and pharmacological effects on those routes. Figure 9 (A-B) shows examples of interactions in heat shock protein pathways, and pharmacological effects on those routes. Figure 10 (A-B) shows examples of interactions in nuclear hormone receptor pathways, and pharmacological effects on those routes. Figure 11 (A-B) shows examples of interactions in apoptotic routes and pharmacological effects on those routes. Figure 12 shows additional examples of pharmacological effects (in the absence or presence of route modulators) on various routes. Figure 13 (A-B) shows pharmacological profiles represented as histograms. Figure 14 illustrates the route modulation strategy with results for DNA damage response pathways. Figure 15 (A-B) illustrates the route modulation strategy with results for a GPCR route. These examples were selected to represent target classes of known drugs; These examples, and the interactions listed in Table 1, are provided to illustrate the breadth of the invention, and are not intended to limit the scope or use of the invention. The invention can be applied to any protein in any route. Examples of protein interactions and pharmacological effects are analyzed below.

With this strategy, pharmacological effects can be seen in a period of minutes or hours of pharmacological treatment, which allows the determination of both short-term and long-term effects. Figure 12 (A-D) shows representative images for 17 known protein complexes that are modulated by known mechanisms, including post-translational modifications, protein degradation or stabilization or protein translocation. Each drug shown caused an increase, reduction or a shift in the subcellular location of the signal for a particular complex at a particular time of treatment compared to the appropriate vehicle control. The images of various complexes show predominantly membrane, nuclear and cytoplasmic locations of the respective complexes, highlighting the high content nature of these assays and the ability to resolve subcellular localization of signals with automatic microscopy.

For example, many kinases interact with chaperones such as Hsp90 and Cdc37 (Perdew et al, 1997; Stancato et al, 1993, Arlander et al, 2003). Images of such interactions were shown in Figure 12 (A). Pharmacological inhibitors of HSP (geldanamicin or 17-allylamino-geldanamicin [17-AAG]) caused the loss of numerous complexes involving client proteins. For example, Akt1: Hsp90beta, Akt1: p27, Cdc37: Aktl and Akt1: Pdk1 were all sensitive to geldanamicin and 17AAG as were H-Ras: Raf-1, Chkl: Cdc25C, Cdc2: Cdc25C; Cdc2: Weel; and Chk1: p53. Examples of the effects of geldanamicin and 17AAG on Aktl complexes are shown in Figure 12 (A) and in Figure 9B. Therefore the physical and functional associations between targets, as well as specific pharmacological effects on the targets, can be determined by treating live cells with drugs and controlling protein complexes within routes of interest.

In another example, the beta-adrenergic receptor (beta2AR) interacts with beta-arrestine (beta-ARR2) after ligand-induced phosphorylation of the receptor by GPCR kinases (GRK) (Lin et al, 2002). In the absence of an agonist, there were no visible betaArr2: beta2AR complexes (Figure 15 left panel). The treatment of cells with the adrenergic agonist, isoprotenerol, caused the formation of numerous betaArr2: beta2AR complexes in a period of minutes (Figure 15 right panel). Fluorescence photomicrograph in Figure 15 shows a punctuated cytoplasmic pattern that is consistent with the temporal course of arrestine binding with the recipient and subsequent internalization by clathrin-coated depressions (Zhang et al, 1996). Pretreatment of cells with the inverse agonist, propranolol, prevented the interaction induced by isoproterenol and internalization of betaArr2: beta2AR as predicted (Zhang et al, 1996). These results highlight the ability of this approach to detect temporary effects of agonists and antagonists directly on their known targets. Since many GPCRs are coupled to beta-arrestine, these and any other downstream interactions can be explored with this strategy.

A key feature of the invention is the ability to distinguish effects of different drugs at different levels of route hierarchy, as illustrated in the following examples. In proliferating cells, phosphatidylinositol-3-kinase (PI3K) recruits Pdk and Akt to the cell membrane (Anderson et al, 1998). Wortmanina and LY294002 are known inhibitors of PI3K (Vlahos et al, 1994), which prevents the synthesis of phosphoinositide membrane receptors (PIP3) for Akt, thus preventing the translocation of Akt to the membrane in which it is phosphorylated and activated by phosphatidylinositol 3-dependent kinase (Pdkl). The inventors studied the effects of drugs on routes involving Akt by evaluating the complex between Aktl and phosphatidylinositol 3-dependent kinase (Pdkl) and between Aktl and the cyclin-dependent kinase inhibitor, p27 (CDKN1B; Kipl), which plays a role in cell cycle regulation. In control wells, the Akt1: Pdk1 complexes were predominantly located in the cell membrane (Figure 8C) while the Aktl: p27 complexes were concentrated in the cell nucleus (Figure 9B). Wortmanina and LY294002 caused a rapid relocation of Ak1t: Pdk1 from the membrane to the cytoplasm and a reduction of the total Akt1: Pdk1 complexes (Figure 8C) but had no significant effect on Aktl: p27 (not shown). However, both complexes were sensitive to geldanamicin and 17-AAG. These results suggest that the group of Akt1: p27 complexes in the nucleus is insensitive to drugs that act at an early stage of Akt signaling. The defined locations and responses of Akt1: Pdk1 and Aktl: p27 illustrate that each assay reports on the biology of a particular Akt complex instead of the total Akt cell group, a crucial feature for detecting unique effects of drugs on defined cell processes and compartments. . Upstream kinase inhibitors can be detected by exploring downstream interactions. This is shown in Figure 12D, in which LY294002 induced p53: p53 complexes in the presence of CPT and in Figure 8D, in which two different non-specific kinase inhibitors reduced the H-Ras1: Raf-1 complexes. These results both validate the usefulness of the network profile realization strategy presented in the diagram in Figure 3 and show that effects can be detected in route as well as out of route with the invention.

G-protein coupled receptors (GPCRs) represent a class of target proteins for major drugs, such as growth factor / cytokine receptors, and nuclear hormone receptors. Nuclear hormone receptors modulate the effects of steroids such as estrogen and testosterone, the effects of retinoids and synthetic agents such as thiazolidinediones and fibrates. Nuclear hormone receptor agonists are marketed for a variety of indications including the treatment of metabolic disease, diabetes and hyperlipidemia. Figure 10A illustrates some of the interactions on these routes and the effects of a known agonist, rosiglitazone (marketed as Avandia) on its recipient target, PPARgamma. Such assays can be used to explore any variety of known or orphaned receptors and to determine whether test compounds activate or inhibit these interactions. In addition, known agonists such as rosiglitazone may be used in conjunction with the route modulation strategy presented in the diagram in Figure 14A to identify agents that block these activated routes.

In addition to the routes mediated by chaperone, GPCR, nuclear hormone and kinase discussed above, Figure 12 shows examples of complexes involved in dynamic GTPase-mediated processes, cytoskeleton, cell cycle, apoptosis, DNA damage response pathways and interactions of the nuclear hormone receptor. Representative effects of drugs in these routes are described herein as detected by these complex indicators.

GTPase interactions with effector proteins are recognized as key molecular switching. Figure 8D and Figure 13A illustrate a kinase effector complex: Prototypic GTPase (H-Ras: Raf1) that was sensitive to non-selective kinase inhibitors, including BAY 11-7082. BAY 11-7082, originally identified as an IKK inhibitor, also blocked the translocation of p50: p65 (NFkappaB) from the cytoplasm to the nucleus in response to TNFalfa, as expected (Figure 12B) (Thevenod et al, 2000). With respect to routes that control the cytoskeleton morphology, LIM kinase 2 (Limk2) inactivates the actin depolymerization factor cofilin (Sumi et al, 1999). The cofilin: Limk2 complexes were almost completely removed by the indirubin-3'monoxima kinase inhibitor (Figure 12C). Cell cycle proteins that were studied included Cdc25C, Cdk4, cyclin D1 and p53. Treatment with the proteasome inhibitor, ALLN, resulted in the gradual accumulation of cyclin D1: Cdk4 in the nucleus (Figure 12B and 13B), consistent with the regulation of cyclin D1 levels by the 26S proteasome (Diehl et al, 1997 ). Isoprotenerol induced Mnkl: Erk2, a result that is consistent with previous reports of MAPK activity induced by adrenergic agonist (Figure 12D) (Daaka et al, 1997; Muhlenbeck et al, 1998). As mentioned above, LY294O02 increased p53: p53, which is consistent with reports that Akt phosphorylates and activates Mdm2, enhancing the ubiquitination and degradation of p53 (Ogawara et al, 2002). Finally, the transcription factor complexes activated by ligand, PPARgamma: SRC-1 (Figure 12B) and PPARgamma: RXRalfa. (Figure 10A) increased in response to treatment with the known PPARgamma agonist, rosiglitazone, as predicted (Nolte et al.) 1998.

The inventors observed interesting effects of particular drugs that are new but are consistent with published route organization reports. TRAIL cytokine protein, which is a TNFalfa family ligand (Wiley et al, 1995) increased Bcl-xL: Bad (Figure 12B). In addition, the protein kinase inhibitor, Gö6976, increased the complexes of PAK4 with the cytoskeletal protein cofilin (Figure 12D). In yeast, homologs of Cdc42 and PAK are activated and cytoskeletal change occurs, as cells progress to the G2 / M phase of the cell cycle (Richman et al, 1999). Gö6976 is a potent control point kinase inhibitor (Chk) (Kohn et al, 2003) and facilitates progression to G2 / M. Therefore, the activation mediated by Gö6976 of downstream processes of Cdc42 / PAK, such as PAK4: cofilin, is not surprising. The inventors also demonstrate for the first time images of a nuclear complex that contains a transcription factor (c-Jun) and a prolyl isomerase (Pinl). Pinl binds to Jun, after phosphorylation and activation by the JNK kinase (Wulf et al, 2001). The inventors observed strong inhibition of Pinl: Jun complexes after cell treatment with clofibrate (Figure 12D). It has been shown that clofibrate, a PPARalfa agonist, increases the association of PPARalfa with its transcriptional co-regulators and reduces the transcriptional activity of c-Jun (Yokoyama et al., 1993) which leads to the suggestion by the present authors of a direct interaction of PPARgamma and c-Jun. In addition, PPARgamma agonists have been shown to inhibit the activity of the JNK pathway (Irukayama-Tomobe et al., 2004), which would lead to a reduction of Pinl: Jun complexes, as the inventors observed.

In summary, both expected and unexpected pharmacological effects could be detected in a wide range of target classes and cellular processes at short and / or long time points controlling protein complexes and their responses to drug treatment.

Chemically related drugs had common activities in the cell assay panel

The examples presented above illustrate that the individual effects of drugs on cell processes and pathways can be determined by analyzing the spatial and temporal dynamics of protein complexes. The inventors extended this approach to the realization of pharmacological profiles of 107 different drugs (Table 4) that addressed 6 therapeutic areas (cancer, inflammation, cardiovascular disease, diabetes, neurological disorders, infectious diseases) or that act as general modulators of mechanisms cell phones (for example, protein transport). The inventors focused on physiologically relevant drug concentrations to avoid generalized out-of-target effects frequently observed with compound dosing above the physiological one. The drugs were tested at multiple time points ranging from 30 minutes to 16 hours to capture early temporary effects on cell pathways. As an example, some compounds induce a continuous increase in test signals over time while others cause a biphasic change in signal intensity and other pharmacological treatments result in reductions in signal intensity over time.

Quantitative data for all test / drug results are presented in a color-coded matrix grouped for both drugs and for individual trial time points (Figure 17A) and an expanded view of the pharmacological group dendrogram is shown in Figure 17B. Notably, the drugs were mainly grouped into target classes of known structure, despite the fact that the trials were not intentionally selected to report routes to which the drugs are directed. The results suggest that the shared pharmacological effects in cellular processes could be elucidated by evaluating the dynamics of protein complexes within highly branched cellular signaling networks. Structurally related drug groups, with similar or identical cell targets reported, generated functional groups (Fig. 17B). These included proteasome inhibitors ALLN and MG-132; GPCR beta adrenergic agonists, isoproterenol, clenbuterol and salbutamol; HSP90, 17-AAG and geldanamicin inhibitors; and the agonists of PPARgamma, pioglitazone, rosiglitazone and troglitazone. In these and other examples, the similarities of the pharmacological profiles, which led to the observed groups, were associated with shared structural characteristics within pharmacological groups.

Known toxic substances may be used in conjunction with the present invention, both to study mechanisms underlying toxicity and to establish pharmacological profiles related to specific toxic mechanisms. The candidate compounds and other test agents can then be compared with the known toxic substances to determine if the test compound has similar activities. For example, cadmium chloride has pronounced effects in multiple routes, as shown in Figure 16A. Some of these are known or expected, such as the effect of cadmium on NFkappaB (p50: p65) and both the unexpected and expected effects of cadmium could be specified in these tests. Candidate compound profiles were compared with cadmium profiles, as shown in Figure 16B. Two patented chemical candidates had profiles that were similar to cadmium. It was subsequently documented that these compounds produced liver toxicity in rodents. Carrying out pharmacological profiles of the candidates according to the present invention would have identified these activities before animal studies, saving time and money in preclinical trials.

Some drug activities are desired in certain contexts, but these are undesirable in other contexts. The invention can be used to identify these activities. Figure 15B shows that a patented candidate compound, originally designed as a kinase inhibitor for the cancer clinic, had a potent effect on the beta adrenergic receptor. This activity was unwanted in view of the intended indication and allowed to leave this compound. Subsequent studies showed that this compound was in fact cardiotoxic, thereby validating the usefulness of the invention and the protein-protein interactions provided herein and the methods for its use.

This result also highlights an important aspect and general strategy of the present invention that the inventors call a "route modulation strategy" (Figure 14A). Many cell routes have low activity in a basal or non-stimulated state, but can be activated with one or more agonists or activators. If a cell is treated first with a test compound and then treated with an agonist or activator, the ability of the test compound to block route activation can be determined. This is the case for the candidate compound studied in Figure 15B, which blocked the beta adrenergic receptor; an effect that could only be seen in the presence of isoprotenerol. (In the absence of isoprotenerol, this route is completely closed, as shown in the upper left image of Figure 15B). In addition, agents that block the TNF-dependent pathway could be seen dealing with the agent and then stimulating cells with TNF (Figure 12).

The route modulation strategy is further exemplified in Figure 14B, in which cells were treated with test compounds (drugs) and then with camptothecin. Camptothecin (CPT) induces DNA damage over a period of time, and induces various interactions in damage response pathways, as shown in the images in Figure 14B. The evaluation of the effects of various compounds in the presence of CPT allowed the detection of agents that blocked the effects of CPT or potentiated the effects of CPT. Some of these effects were desired or unwanted depending on the context. For example, control point kinase inhibitors are being developed for use in the cancer clinic along with DNA damage agents. For such agents, synergistic effects with CPT are desired and can be detected with this strategy. Taxol and terfenadine activated many of these routes, as shown in the matrix. In contrast, geldanamicin, 17-AAG and Y27632 had opposite effects on several of these routes.

As shown in Figure 14B, terfenadine activated several DNA damage response pathways in a manner similar to that found for paclitaxel (taxol) a known and effective antineoplastic agent. This prompted an evaluation of the potential antiproliferative activities of terfenadine. Studies of terfenadine in a prostate carcinoma cell line showed that, consistent with its pharmacological profile as determined herein, it had an antiproliferative activity (data not shown). Therefore, the realization of pharmacological profiles of known drugs according to the present invention can be used to quickly identify potential new uses, indications and combinations of known drugs for human therapeutic agents. This is achieved by performing pharmacological profiles with a known drug and comparing the resulting profile with that of other agents with known therapeutic properties. If the test agent has a profile similar to that of an agent with a known / desired profile then the test agent can be evaluated in functional tests to determine if it has the same functions as the comparator drug.

While some cell pathways are inactivated in the basal state but can be activated by treating cells with route agonists, other cell pathways may be activated in the basal state but can be inactivated by treating the cells with antagonists or inhibitors. For example, Akt is concentrated in the membrane, and Akt1: Pdk1 is activated, in HEK293 cells grown in the presence of serum, as shown by the robust membrane signal for Akt1: Pdk1 at baseline (Figure 8C left panel). Therefore, cells expressing Akt1: Pdk1 could be treated with antagonists or inhibitors, such as wortmanin or Ly294002, together with the test agent of interest. If the test agent is able to reverse the effect of the antagonist or inhibitor then it would restore the signal of Akt1: Pdk1 to the membrane (basal) state. The invention therefore enables the treatment of cells with a test compound of interest together with another agent. The additional agent (route modulator) can be applied before, after or simultaneously with the application of the test compound of interest, the optimal protocol being determined empirically for each route and test. It will be appreciated by one skilled in the art that the route modulation strategy for the realization of pharmacological profiles is a unique component of the invention that is not limited to the measurement of a protein-protein interaction but can be applied to other measurements of drug activity. route, including as the amount or subcellular localization of any individual protein or its post-translational modification status.

One skilled in the art will realize that the protein kinase, Aktl, experiences the same subcellular redistribution pattern in response to wortmanin or Ly294002 as the inventors observed for the Akt1: PDK1 complex. Therefore, assays capable of measuring the location and subcellular redistribution of Akt can be used as an alternative to tests that measure the subcellular location and redistribution of the Akt1: Pdk1 complex. Assays for the redistribution of Akt from BioImage (Denmark) are commercially available and can be used in the realization of pharmacological profiles together with the present invention. Similarly, assays for redistribution of the p65 subunit of the NFkappaB complex may be used in conjunction with the present invention, as an alternative to measuring the p50: p65 complex, and will provide substantially similar results. Such assays are available from BioImage, in which the assay is based on the expression of a p65 protein labeled with GFP, or Cellomics Inc, in which the assay is based on immunofluorescence with a specific p65 antibody. In a final example, the subcellular location of beta arrestine shifts to endosomes in response to beta-adrenergic agonists and antagonists; These assays are known as TransFluor assays (Norak / Molecular Devices) and can be used as an alternative to receptor measurement: arrestine complex shown in Figure 15A. The present invention enables the use of high content test panels for the realization of pharmacological profiles and such panels may include high content assay for the redistribution of individual proteins in addition to high content or high performance assays for protein complexes or interactions.

The present invention also addresses several limitations of previously used pharmacological profiling strategies such as gene microplates. A feature of the invention is the ability to capture both short-term and long-term effects at multiple points within a route. Part of the test dynamics observed were responses to functional or secondary cellular activities, such as apoptosis or cell cycle progression, particularly at longer time points (8 hours or 16 hours), while shorter time points report effects more immediate compounds. By exploring signage at multiple time points, and at multiple levels within the route hierarchies, inventors can identify changes that directly or near the target of interest imply.

Approaches such as this are necessary to clarify the functional and biochemical roles of particular proteins, and to identify the most promising targets for the development of human therapeutic agents. A rapid exclusion of compounds with potential deleterious effects (the so-called “fail-safe” strategy) is equally important. In addition, understanding the specific biochemical nature of the off-target activity would allow the refinement of a chemical structure to address desirable or undesirable functional attributes of a molecule. The strategy described herein provides an effective means to label compounds for exclusion from subsequent studies, or to redirect development to other therapeutic areas.

Reference is made to the following patents and publications:

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Although the many forms of the invention disclosed herein constitute the preferred embodiments at present, many others are possible and the additional details of the preferred embodiments and other possible embodiments should not be construed as limitations. It is understood that the terms used in this document are merely descriptive and not limiting.

Claims (6)

1. A method for effecting the removal of a test compound with undesirable properties, said method comprising the analysis of said test compound having undesirable properties against a panel of complementation assays of cell-based protein fragments, said comprising 5 panel two or more complementation assays of high protein fragments. 2. A method for effecting the removal of a test compound with undesirable properties, said method comprising the analysis of said test compound having undesirable properties against a panel of protein fragmentation complementation tests, said panel comprising high test content for two or more protein-protein interactions. The method of claim 1 or 2, wherein the protein fragmentation complementation assay is used in a cell, biological fluid or extract. 4. The method of claim 3, wherein the cell is a cell line, cell in culture, component of an intact or animal tissue, organ in culture or a non-human living organism. 5. The method of claim 3 or 4, wherein the cell is a mammalian, bacterial, yeast, plant or fungus cell. 6. The method of claim 4, wherein the non-human or animal living organism is Drosophila, zebrafish or mouse 7. The method of any one of claims 1 to 6, wherein the test compound is a molecule synthetic, natural product, known or potential drug, ligand, antibody, peptide, small interference RNA or combinatorial library.