Search

ATAD2 is an epigenetic bromodomain-containing target which is overexpressed in many cancers and has been suggested as a potential oncology target. While several small molecule inhibitors have been described in the literature, their cellular activity has proved to be underwhelming. In this work, we describe the identification of a novel series of ATAD2 inhibitors by high throughput screening, confirmation of the bromodomain region as the site of action, and the optimization campaign undertaken to improve the potency, selectivity, and permeability of the initial hit. The result is compound 5 (AZ13824374), a highly potent and selective ATAD2 inhibitor which shows cellular target engagement and antiproliferative activity in a range of breast cancer models.

Proteins of the bromodomain and extraterminal (BET) family, in particular bromodomain-containing protein 4 (BRD4), are of great interest as biological targets. BET proteins contain two separate bromodomains, and existing inhibitors bind to them monovalently. Here we describe the discovery and characterization of probe compound biBET, capable of engaging both bromodomains simultaneously in a bivalent, in cis binding mode. The evidence provided here was obtained in a variety of biophysical and cellular experiments. The bivalent binding results in very high cellular potency for BRD4 binding and pharmacological responses such as disruption of BRD4-mediator complex subunit 1 foci with an EC 50 of 100 pM. These compounds will be of considerable utility as BET/BRD4 chemical probes. This work illustrates a novel concept in ligand design-simultaneous targeting of two separate domains with a drug-like small molecule-providing precedent for a potentially more effective paradigm for developing ligands for other multi-domain proteins.

The bromodomain and extraterminal (BET) protein BRD4 regulates gene expression via recruitment of transcriptional regulatory complexes to acetylated chromatin. Pharmacological targeting of BRD4 bromodomains by small molecule inhibitors has proven to be an effective means to disrupt aberrant transcriptional programs critical for tumor growth and/or survival. Herein, we report AZD5153, a potent, selective, and orally available BET/BRD4 bromodomain inhibitor possessing a bivalent binding mode. Unlike previously described monovalent inhibitors, AZD5153 ligates two bromodomains in BRD4 simultaneously. The enhanced avidity afforded through bivalent binding translates into increased cellular and antitumor activity in preclinical hematologic tumor models. In vivo administration of AZD5153 led to tumor stasis or regression in multiple xenograft models of acute myeloid leukemia, multiple myeloma, and diffuse large B-cell lymphoma. The relationship between AZD5153 exposure and efficacy suggests that prolonged BRD4 target coverage is a primary efficacy driver. AZD5153 treatment markedly affects transcriptional programs of MYC, E2F, and mTOR. Of note, mTOR pathway modulation is associated with cell line sensitivity to AZD5153. Transcriptional modulation of MYC and HEXIM1 was confirmed in AZD5153-treated human whole blood, thus supporting their use as clinical pharmacodynamic biomarkers. This study establishes AZD5153 as a highly potent, orally available BET/BRD4 inhibitor and provides a rationale for clinical development in hematologic malignancies. Mol Cancer Ther; 15(11); 2563-74. ©2016 AACR., (©2016 American Association for Cancer Research.)

Here we report the discovery and optimization of a series of bivalent bromodomain and extraterminal inhibitors. Starting with the observation of BRD4 activity of compounds from a previous program, the compounds were optimized for BRD4 potency and physical properties. The optimized compound from this campaign exhibited excellent pharmacokinetic profile and exhibited high potency in vitro and in vivo effecting c-Myc downregulation and tumor growth inhibition in xenograft studies. This compound was selected as the development candidate, AZD5153. The series showed enhanced potency as a result of bivalent binding and a clear correlation between BRD4 activity and cellular potency.

Fulvestrant is an estrogen receptor (ER) antagonist administered to breast cancer patients by monthly intramuscular injection. Given its present limitations of dosing and route of administration, a more flexible orally available compound has been sought to pursue the potential benefits of this drug in patients with advanced metastatic disease. Here we report the identification and characterization of AZD9496, a nonsteroidal small-molecule inhibitor of ERα, which is a potent and selective antagonist and downregulator of ERα in vitro and in vivo in ER-positive models of breast cancer. Significant tumor growth inhibition was observed as low as 0.5 mg/kg dose in the estrogen-dependent MCF-7 xenograft model, where this effect was accompanied by a dose-dependent decrease in PR protein levels, demonstrating potent antagonist activity. Combining AZD9496 with PI3K pathway and CDK4/6 inhibitors led to further growth-inhibitory effects compared with monotherapy alone. Tumor regressions were also seen in a long-term estrogen-deprived breast model, where significant downregulation of ERα protein was observed. AZD9496 bound and downregulated clinically relevant ESR1 mutants in vitro and inhibited tumor growth in an ESR1-mutant patient-derived xenograft model that included a D538G mutation. Collectively, the pharmacologic evidence showed that AZD9496 is an oral, nonsteroidal, selective estrogen receptor antagonist and downregulator in ER(+) breast cells that could provide meaningful benefit to ER(+) breast cancer patients. AZD9496 is currently being evaluated in a phase I clinical trial. Cancer Res; 76(11); 3307-18. ©2016 AACR., (©2016 American Association for Cancer Research.)

A series of tetrahydroisoquinoline phenols was modified to give an estrogen receptor downregulator-antagonist profile. Optimization around the core, alkyl side chain, and pendant aryl ring resulted in compounds with subnanomolar levels of potency. The phenol functionality was shown to be required to achieve highly potent compounds, but unusually this was compatible with obtaining high oral bioavailabilities in rat.

The discovery of an orally bioavailable selective estrogen receptor downregulator (SERD) with equivalent potency and preclinical pharmacology to the intramuscular SERD fulvestrant is described. A directed screen identified the 1-aryl-2,3,4,9-tetrahydro-1H-pyrido[3,4-b]indole motif as a novel, druglike ER ligand. Aided by crystal structures of novel ligands bound to an ER construct, medicinal chemistry iterations led to (E)-3-(3,5-difluoro-4-((1R,3R)-2-(2-fluoro-2-methylpropyl)-3-methyl-2,3,4,9-tetrahydro-1H-pyrido[3,4-b]indol-1-yl)phenyl)acrylic acid (30b, AZD9496), a clinical candidate with high oral bioavailability across preclinical species that is currently being evaluated in phase I clinical trials for the treatment of advanced estrogen receptor (ER) positive breast cancer.

Removal of the basic piperazine nitrogen atom, introduction of a solubilising end group and partial reduction of the triazolopyridazine moiety in the previously-described lead androgen receptor downregulator 6-[4-(4-cyanobenzyl)piperazin-1-yl]-3-(trifluoromethyl)[1,2,4]triazolo[4,3-b]pyridazine (1) addressed hERG and physical property issues, and led to clinical candidate 6-(4-{4-[2-(4-acetylpiperazin-1-yl)ethoxy]phenyl}piperidin-1-yl)-3-(trifluoromethyl)-7,8-dihydro[1,2,4]triazolo[4,3-b]pyridazine (12), designated AZD3514, that is being evaluated in a Phase I clinical trial in patients with castrate-resistant prostate cancer., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Chemical starting points were investigated for downregulation of the androgen receptor as an approach to treatment of advanced prostate cancer. Although prototypic steroidal downregulators such as 6a designed for intramuscular administration showed insufficient cellular potency, a medicinal chemistry program derived from a novel androgen receptor ligand 8a led to 6-[4-(4-cyanobenzyl)piperazin-1-yl]-3-(trifluoromethyl)[1,2,4]triazolo[4,3-b]pyridazine (10b), for which high plasma levels following oral administration in a preclinical model compensate for moderate cellular potency., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Mitochondria are principal mediators of apoptosis and thus can be considered molecular targets for new chemotherapeutic agents in the treatment of cancer. Inhibitors of mitochondrial complex I of the electron transport chain have been shown to induce apoptosis and exhibit antitumor activity. In an effort to find novel complex I inhibitors which exhibited anticancer activity in the NCI's tumor cell line screen, we examined organized tumor cytotoxicity screening data available as SOM (self-organized maps) (http://www.spheroid.ncifcrf.gov) at the developmental therapeutics program (DTP) of the National Cancer Institute (NCI). Our analysis focused on an SOM cluster comprised of compounds which included a number of known mitochondrial complex I (NADH:CoQ oxidoreductase) inhibitors. From these clusters 10 compounds whose mechanism of action was unknown were tested for inhibition of complex I activity in bovine heart sub-mitochondrial particles (SMP) resulting in the discovery that 5 of the 10 compounds demonstrated significant inhibition with IC50's in the nM range for three of the five. Examination of screening profiles of the five inhibitors toward the NCI's tumor cell lines revealed that they were cytotoxic to the leukemia subpanel (particularly K562 cells). Oxygen consumption experiments with permeabilized K562 cells revealed that the five most active compounds inhibited complex I activity in these cells in the same rank order and similar potency as determined with bovine heart SMP. Our findings thus fortify the appeal of mitochondrial complex I as a possible anticancer molecular target and provide a data mining strategy for selecting candidate inhibitors for further testing.

An integrated, bioinformatic analysis of three databases comprising tumor-cell-based small molecule screening data, gene expression measurements, and PDB (Protein Data Bank) ligand-target structures has been developed for probing mechanism of drug action (MOA). Clustering analysis of GI50 profiles for the NCI's database of compounds screened across a panel of tumor cells (NCI60) was used to select a subset of unique cytotoxic responses for about 4000 small molecules. Drug-gene-PDB relationships for this test set were examined by correlative analysis of cytotoxic response and differential gene expression profiles within the NCI60 and structural comparisons with known ligand-target crystallographic complexes. A survey of molecular features within these compounds finds thirteen conserved Compound Classes, each class exhibiting chemical features important for interactions with a variety of biological targets. Protein targets for an additional twelve Compound Classes could be directly assigned using drug-protein interactions observed in the crystallographic database. Results from the analysis of constitutive gene expressions established a clear connection between chemo-resistance and overexpression of gene families associated with the extracellular matrix, cytoskeletal organization, and xenobiotic metabolism. Conversely, chemo-sensitivity implicated overexpression of gene families involved in homeostatic functions of nucleic acid repair, aryl hydrocarbon metabolism, heat shock response, proteasome degradation and apoptosis. Correlations between chemo-responsiveness and differential gene expressions identified chemotypes with nonselective (i.e., many) molecular targets from those likely to have selective (i.e., few) molecular targets. Applications of data mining strategies that jointly utilize tumor cell screening, genomic, and structural data are presented for hypotheses generation and identifying novel anticancer candidates., (Copyright 2005 Wiley-Liss, Inc.)

Motivation: Data mining tools are proposed to establish mechanistic connections between chemotypes and specific cellular functions. Drawing on a previous study that classified the cellular response patterns of growth inhibition measurements log( GI(50)) from the National Cancer Institute's (NCI's) anticancer screen, we have examined additional data for mRNA expression, sets of known molecular targets and mutational status against these same tumor cell lines to relate chemosensitivity more precisely to biochemical pathways., Results: Our analysis finds that gene expression levels do not, in general, correlate with log(GI(50)) measurements, instead they reflect a generic toxic condition. Within the remaining set of non-generic conditions, examples were found where a correlation suggesting a biochemical basis for cellular cytotoxicity could be supported. These included reconfirmation of previously observed associations between mutant and wild-type status of p53, and chemosensitivity to alkylating agents, while extending these results to reveal associations with gamma-induced expressions of MDM2, WAF1 and GADD45, signals that were not apparent in measurements of basal mRNA expression levels for any of these genes. Additional examinations revealed that mRNA expression levels directly correlated with paclitaxel chemosensitivity to mitosis, while also identifying additional chemotypes as P-glycoprotein substrates. Our analysis revealed well-known direct associations between p16 mutant status and chemotypes implicated in cell cycle control, and extended these results to include expression levels for three additional tyrosine kinase proteins (TEK, transgelin and hCdc4). Links were also found that suggested associations between chemosensitivity and the endocrine, paracrine ligand-receptor loops, via expression of the adrenergic receptor, calcium second messenger pathways via expression levels of carbonic anhydrase and cellular communication pathways via fibrillin.

An unsupervised self-organizing map-based clustering strategy has been developed to classify tissue samples from an oligonucleotide microarray patient database. Our method is based on the likelihood that a test data vector may have a gene expression fingerprint that is shared by more than one tumor class and as such can identify datasets that cannot be unequivocally assigned to a single tumor class. Our self-organizing map analysis completely separated the tumor from the normal expression datasets. Within the 14 different tumor types, classification accuracies on the order of approximately 80% correct were achieved. Nearly perfect classifications were found for leukemia, central nervous system, melanoma, uterine, and lymphoma tumor types, with very poor classifications found for colorectal, ovarian, breast, and lung tumors. Classification results were further analyzed to identify sets of differentially expressed genes between tumor and normal gene expressions and among each tumor class. Within the total pool of 1139 genes most differentially expressed in this dataset, subsets were found that could be vetted according to previously published literature sources to be specific tumor markers. Attempts to classify gene expression datasets from other sources found a wide range of classification accuracies. Discussions about the utility of this method and the quality of data needed for accurate tumor classifications are provided.

We propose an integrated application of technologies, computation and statistical methods to design experiments for examination of cellular pathways that are necessary for cell survival and that are candidates for cancer therapy. Our design combines information derived from two very different data sets: tumor screening data from over 36,000 synthetic compounds screened against over 60 tumor cell lines, and replicate microarray gene expression measurements using one cell line and one compound. Data filtering, based on restricted cellular cytotoxicity profiles from chemically similar sets of compounds, has been used to select a class of benzothiazoles for subsequent microarray gene expression measurements in the most chemosensitive tumor cell line. The results confirmed observations that P450 metabolizing isoforms, CYP1A1 and CYP1B1, are overexpressed in MCF-7 tumor cells following treatment with benzothiazole. These results are consistent with the proposed inactivity of the CYP1A1-mediated metabolism of benzothiazole and the antitumor activity of the metabolically resistant halogenated forms.

We have investigated three different microarray datasets of approximately 6 K gene expressions across the National Cancer Institute's panel of 60 tumor cell lines. Initial assessments of reproducibility for gene expressions within each dataset, as derived from sequence analysis of full-length sequences as well as expressed sequence tags (EST), found statistically significant results for no more than 36% of those cases where at least one replicate of a gene appears on the array. Filtering the data based only on pairwise comparisons among these three datasets creates a list of approximately 400 significant concordant expression patterns. The expression profiles of these smaller sets of genes were used to locate similar expression profiles of synthetic agents screened against these same 60 tumor cell lines. A correspondence was found between mRNA expression patterns and 50% growth inhibition response patterns of screened agents for 11 cases that were subsequently verifiable from ligand-target crystallographic data. Notable amongst these cases are genes encoding a variety of kinases, which were also found to be targets of small drug-like molecules within the database of protein structures. These 11 cases lend support to the premise that similarities between expression patterns and chemical responses for the National Cancer Institute's tumor panel can be related to known cases of molecular structure and putative cellular function. The details of the 11 verifiable cases and the concordant gene subsets are provided. Discussions about the prospects of using this approach as a data mining tool are included.

Increasing insight into the genetics and molecular biology of cancer has resulted in the identification of an increasing number of potential molecular targets for anti-cancer drug discovery and development. These targets can be approached through exploitation of emerging structural biology, "rational" drug design, screening of chemical libraries, or a combination of these methods. In this article we discuss the application of high-throughput screening to anti-cancer drug discovery, with special reference to approaches used at the U.S. National Cancer Institute.

In an effort to enhance access to information available in the National Cancer Institute's (NCI) anticancer drug-screening database, a new suite of Internet accessible (http://spheroid. ncifcrf.gov) computational tools has been assembled for self-organizing map-based (SOM) cluster analysis and data visualization. A range of analysis questions were initially addressed to evaluate improvements in SOM cluster quality based on the data-conditioning procedures of Z-score normalization, capping, and treatment of missing data as well as completeness of drug cell-screening data. These studies established a foundation for SOM cluster analysis of the complete set of NCI's publicly available antitumor drug-screening data. This analysis identified relationships between chemotypes of screened agents and their effect on four major classes of cellular activities: mitosis, nucleic acid synthesis, membrane transport and integrity, and phosphatase- and kinase-mediated cell cycle regulation. Validations of these cellular activities, obtained from literature sources, found (i) strong evidence supporting within cluster memberships and shared cellular activity, (ii) indications of compound selectivity between various types of cellular activity, and (iii) strengths and weaknesses of the NCI's antitumor drug screen data for assigning compounds to these classes of cellular activity. Subsequent analyses of averaged responses within these tumor panel types find a strong dependence on chemotype for coherence among cellular response patterns. The advantages of a global analysis of the complete screening data set are discussed.

We have devised a Cartesian combination operator and coding scheme for improving the performance of genetic algorithms applied to the protein folding problem. The genetic coding consists of the C alpha Cartesian coordinates of the protein chain. The recombination of the genes of the parents is accomplished by: (1) a rigid superposition of one parent chain on the other, to make the relation of Cartesian coordinates meaningful, then, (2) the chains of the children are formed through a linear combination of the coordinates of their parents. The children produced with this Cartesian combination operator scheme have similar topology and retain the long-range contacts of their parents. The new scheme is significantly more efficient than the standard genetic algorithm methods for locating low-energy conformations of proteins. The considerable superiority of genetic algorithms over Monte Carlo optimization methods is also demonstrated. We have also devised a new dynamic programming lattice fitting procedure for use with the Cartesian combination operator method. The procedure finds excellent fits of real-space chains to the lattice while satisfying bond-length, bond-angle, and overlap constraints.

A way of formulating the protein-folding problem in neural network optimization terms is presented in this paper. This is accomplished by representing the conformation of a protein as an array of the amino acid sequence versus position on a three-dimensional face-centered cubic lattice with an energy function defined in terms of the array variables. The method is called lattice neural network minimization (LNNM). Using the neural network minimization method, the energy function is minimized to locate the global minimum energy for the conformation of the protein. The energy function consisted of site exclusion and bond connectivity penalty terms and a pairwise contact energy potential. The contact energy potential used in the procedure is the united-residue potential of Miyazawa, Jernigan and Covell. The LNNM method found the global minimum for a seven-residue peptide in all of the 15 runs carried out. The time for each run was approximately 30 seconds on one processor of an IBM 3090 computer. For a nine-residue peptide, the global minimum was found in 7 out of 15 runs (47%) in approximately 50 seconds per run. For this peptide, LNNM found the global minimum or the second lowest minimum in 10 of the runs. In the same total CPU times (approximately 750 seconds), a Monte Carlo simulated annealing method found the global minimum or the second lowest minimum in only two runs, demonstrating the superiority of LNNM over the standard Monte Carlo simulated annealing method for this nine-residue peptide. Starting from a uniform array for the protein crambin (46 residues) on the lattice, the energy of the crambin array was minimized and a compact low-energy structure was found in approximately 25 minutes of CPU time. Its energy was much lower than that of the native protein, suggesting that there are inadequacies in the Miyazawa-Jernigan-Covell potential. The LNNM method was applied to the prediction of what was previously called nucleation but more properly called chain-folding initiation sites (CFIS) of a protein. LNNM correctly predicted the CFIS for the two proteins examined, RNase S and T4 lysozyme. The LNNM method was also applied to another chain optimization problem, minimization of the root-mean-square distance error (r.m.s.d.) (a measure similar to r.m.s. deviation) in fitting X-ray structures to a lattice, with good results.