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(12) Patent Application Publication (10) Pub. No.: US 2009/0269772 A1 Califano Et Al

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(12) Patent Application Publication (10) Pub. No.: US 2009/0269772 A1 Califano Et Al US 20090269772A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2009/0269772 A1 Califano et al. (43) Pub. Date: Oct. 29, 2009 (54) SYSTEMS AND METHODS FOR Publication Classification IDENTIFYING COMBINATIONS OF (51) Int. Cl. COMPOUNDS OF THERAPEUTIC INTEREST CI2O I/68 (2006.01) CI2O 1/02 (2006.01) (76) Inventors: Andrea Califano, New York, NY G06N 5/02 (2006.01) (US); Riccardo Dalla-Favera, New (52) U.S. Cl. ........... 435/6: 435/29: 706/54; 707/E17.014 York, NY (US); Owen A. (57) ABSTRACT O'Connor, New York, NY (US) Systems, methods, and apparatus for searching for a combi nation of compounds of therapeutic interest are provided. Correspondence Address: Cell-based assays are performed, each cell-based assay JONES DAY exposing a different sample of cells to a different compound 222 EAST 41ST ST in a plurality of compounds. From the cell-based assays, a NEW YORK, NY 10017 (US) Subset of the tested compounds is selected. For each respec tive compound in the Subset, a molecular abundance profile from cells exposed to the respective compound is measured. (21) Appl. No.: 12/432,579 Targets of transcription factors and post-translational modu lators of transcription factor activity are inferred from the (22) Filed: Apr. 29, 2009 molecular abundance profile data using information theoretic measures. This data is used to construct an interaction net Related U.S. Application Data work. Variances in edges in the interaction network are used to determine the drug activity profile of compounds in the (60) Provisional application No. 61/048.875, filed on Apr. Subset of compounds. The drug activity profiles are used to 29, 2008, provisional application No. 61/061,573, form a filter set of compound combinations from the subset of filed on Jun. 13, 2008. compounds. 34 'Y Wide area network 10 - Powcr Source CPU Circuitry 24 22 2O 36 --- 30 Operating System File system Compound library 1 Compound library - - - - - - - - - Cell based activity screen assay data (single compound exposure) |Cell type, compound, and assay result Cell type, compound, and assay result N MAP data store Cell line Compound ------------- - - - - - Cell line Compound Abundance value for cellular constituent - - - - - - - - - Abundance value for cellular constituent N Mixed-interaction network for targct phenotype Filter compound combination list Cell based activity screen assay data (compound combination exposures) compoundCell type, compound dosages, and combination, assay result 1. Cell type, compound combination, compound dosages, and assay result M Patent Application Publication Oct. 29, 2009 Sheet 1 of 5 US 2009/0269772 A1 34 '' Communications Circuitry 20 36 - - - Operating System Compound library 1 Compound library X Cell based activity screen assay data (single compound exposure) Cell type, compound, and assay result 1 Cell type, compound, and assay result N MAP data store MAP 1 Cell line Compound Controller Abundance value for cellular constituentl Abundance value for cellular constituent N MAP M Cell line Compound Abundance value for cellular constituent Abundance value for cellular constituent N Mixed-interaction network for target phenotype Filter compound combination list Cell based activity screen assay data (compound combination exposures) Cell type, compound combination, compound dosages, and assay result Cell type. compound combination. compound dosages, and assay result M Patent Application Publication Oct. 29, 2009 Sheet 2 of 5 US 2009/0269772 A1 - 202 ------ f Perform cell based activity screen assay using a plurality of compounds. Test each compound in the plurality of compounds against a panel of cell types that includes normal cells and malignant cells. Optionally test compounds at different concentrations and at different time delays. Identify compounds that have best end-point phenotype in malignant cells versus normal cells (e.g. apoptosis, also called programmed cell death) and that are selective against the phenotype of interest. After the readout, select the top compounds (e.g., top 500-1,000) with the highest activity (e.g., the greatestability to reduce viability in malignant cells) and sufficient selectivity for further testing thereby achieving a large-fold (e.g. 10) search space reduction (e.g. from one million to one thousand compounds). - 204 V ? Obtain a molecular abundance map (MAP) 52 for each of the active compounds from Step 202. For each respective compound tested, one or more cell lines that represent the phenotype of interest (e.g., disease Subtype of interest) are treated with the respective compound and then the abundance values of the cellular constituent for a plurality of cellular constituents in the onc or more cell lines is obtained (c.g., measured) using MAP arrays. —V M Obtain a MAP 52 for each of the compounds in a reserve library of compounds, such as drugs approved by the United States Food and Drug Administration, regardless of the performance of Such drugs in Step 202. MM M -----|-- Use MAPs 52 from steps 204 and 206 to construct a cellular network for the phenotype of interest. The cellular network comprises the identity of the proteins in the cell lines that have been tested (nodes) and the set of molecular interactions between these proteins (edges). Each edge represents a protein protein interaction, a protein-DNA interaction, or a protein that post translationally modifies other proteins. Each edge is either directed or undirected. A directed edge represents an interaction for which there is a molecule that is an activator or a modulator and a molecule that is regulated target of the modulator (e.g., a protein-DNA interaction, or a protein that post translationally modifies other proteins). An undirected edge represents proteins that bind to each other to form a complex (e.g., a protein-protein interaction). Integrate protein-DNA interactions (e.g., from ARACNc) and transcription factor modulatory interactions (e.g., from MINDy) and optionally protein protcin interactions (e.g., from curated databascs obtained by data mining) into a mixed-interaction network using a Bayesian cvidence integration framework. --- v (To 212 ) ----- Fig. 2A Patent Application Publication Oct. 29, 2009 Sheet 3 of 5 US 2009/0269772 A1 212 Perform interaction set enrichment analysis to determine the drug activity profile of each of the compounds tested in steps 204 and 206 against the mixed-interaction network thereby obtaining a drug activity profile for each respective compound tested in steps 204 and 206. Filter compounds to form a filter set of compound combinations by seeking compounds that (i) form compound pairs or compound triplets (or Some higher ordered compound combination) whose respective drug activity profiles involve genes that are in Synergistic pathways rather than the same pathways and (ii) target specific pathways rather than having a pleiotropic effect. Compound combinations in the filtering set are therefore depleted of combinations where each of the compounds in the combinations affect identical pathways that may not bypass the cells redundancy mechanisms and are likely only to produce an additive effect, identical to using a larger dose of a single compound are eliminated in the filtering step. Eliminating Such compound combinations thereby enriches the filtered compound combination list for compounds combinations affecting independent pathways with the same end-point phenotype that produce a Synergistic effect, thus allowing to more effectively defeat a target disease's defenses. v Among all the possible compound combinations from the filtered list of step 214, screen a top number of the most synergistic combinations (e.g. 1,000 to 10,000 combinations) against the phenotype of interest as well as background cell types using, for example, the experimental assay used in Step 202, to assess their synergistic behavior in implementing the desired end-point phenotype. In these Screens, the compounds are Stratified against disease cells and normal background cells at Various concentrations. Compound combinations achieving optimal selectivity in disease versus normal tissue are then screened in vivo for synergistic behavior. In some embodiments, at the end of this step, the original set 1,000,000 potential compound combination will have been reduced to about 10,000 highest priority combinations based on the aforementioned steps. Fig. 2B Patent Application Publication Oct. 29, 2009 Sheet 5 of 5 US 2009/0269772 A1 US 2009/0269772 A1 Oct. 29, 2009 SYSTEMS AND METHODS FOR safer and highly effective when administered in combination IDENTIFYING COMBINATIONS OF (combinatorial therapy). Specific drug combinations, in fact, COMPOUNDS OF THERAPEUTIC INTEREST can have minimal side effects on normal cells as they affect molecular targets that are cancer cell-specific. Furthermore, CROSS REFERENCE TO RELATED combinatorial therapies constitute a direct and unique oppor APPLICATIONS tunity to implement personalized medicine strategies, as the 0001. This application claims benefit, under 35 U.S.C. S ability to selectively modulate the key pathways involved in 119(e), of U.S. Provisional Patent Application No. 61/048, pathogenesis provides great flexibility to address disease het 875, filed on Apr. 29, 2008, which is hereby incorporated by erogeneity and population-specific effects. Some promising reference herein in its entirety. This application also claims examples of combination therapy are already starting to benefit, under 35 U.S.C. S 119(e), of U.S. Provisional Patent emerge, including for instance
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