Pharmacogenomic Drug–Target Network Analysis Reveals Similarity Profiles Among FDA–Approved Cancer Drugs

The Wnt/beta-catenin signaling pathway plays diverse roles in embryonic development and in maintenance of organs and tissues in adults. Activation of this signaling cascade inhibits degradation of the pivotal component beta-catenin, which in turn stimulates transcription of downstream target genes. Over the past two decades, intensive worldwide investigations have yielded considerable progress toward understanding the cellular and molecular mechanisms of Wnt signaling and its involvement in the pathogenesis of a range of human diseases. Remarkably, beta-catenin signaling is aberrantly activated in greater than 70% of colorectal cancers and to a lesser extent in other tumor types, promoting cancer cell proliferation, survival and migration. Accordingly, beta-catenin has gained recognition as an enticing molecular target for cancer therapeutics. Disruption of protein-protein interactions essential for beta-catenin activity holds immense promise for the development of novel anti-cancer drugs. In this review, we focus on the regulation of beta-catenin-dependent transcriptional activation and discuss potential therapeutic opportunities to block this signaling pathway in cancer.

Heat shock protein 90 as a molecular target for cancer therapeutics

Regulators of Mitotic Arrest and Ceramide Metabolism Are Determinants of Sensitivity to Paclitaxel and Other Chemotherapeutic Drugs

Successful Investigational New Drug Preparation without Reinventing the Wheel

Organization of Scaffolds.- A-Kinase Anchoring Proteins as the Basis for cAMP Signaling.- Arrestins as Multi-Functional Signaling Adaptors.- Role of Ena/VASP Proteins in Homeostasis and Disease.- Scaffold/Matrix Attachment Regions (S/MARs): Relevance for Disease and Therapy.- Clathrin/AP-2-Dependent Endocytosis: A Novel Playground for the Pharmacological Toolbox?.- Scaffolding Proteins and Cellular Signalling.- PDE4 Associates with Different Scaffolding Proteins: Modulating Interactions as Treatment for Certain Diseases.- G-Protein-Coupled Receptor-Signaling Components in Membrane Raft and Caveolae Microdomains.- Protein Scaffolds, Lipid Domains and Substrate Recognition in Protein Kinase C Function: Implications for Rational Drug Design.- Compartmentalised MAPK Pathways.- Dynamic Protein Complexes Regulate NF-?B Signaling.- An Oncogenic Hub: ?-Catenin as a Molecular Target for Cancer Therapeutics.- A Toolkit for Real-Time Detection of cAMP: Insights into Compartmentalized Signaling.- Cell Type-Specific Anchoring.- Scaffolding Proteins in Cardiac Myocytes.- Molecular Architecture of Signal Complexes Regulating Immune Cell Function.- Scaffolding Proteins at the Postsynaptic Density: Shank as the Architectural Framework.- Interference with Protein-Protein Interaction Sites as a New Pharmacological Concept.- Domains Mediate Protein-Protein Interactions and Nucleate Protein Assemblies.- Proline-Rich Sequence Recognition Domains (PRD): Ligands, Function and Inhibition.- Chemical Inhibition Through Conformational Stabilization of Rho GTPase Effectors.- Pharmacological Interference with Protein-Protein Interactions Mediated by Coiled-Coil Motifs.- Direct AKAP-Mediated Protein-Protein Interactions as Potential Drug Targets.

DRUG labeling is of vital importance in guiding the safe and effective use of approved drugs. Drug labels represent the most visible expression of months or years of scientific review by physicians and scientists at the U.S. Food and Drug Administration (FDA), and they are also fundamental to the purpose and mission of the FDA. Creation of the FDA dates to the 1906 passage of the Food and Drugs Act, which prohibited the manufacture and interstate shipment of adulterated and misbranded foods and drugs.† A 1937 disaster, in which more than 100 people died after ingestion of Elixir Sulfanilamide, precipitated the Federal Food, Drug, and Cosmetic Act of 1938, which, for the first time in U.S. history, required demonstration of safety before marketing new drugs. Elixir Sulfanilamide contained diethylene glycol and had never been tested for safety. In 1960, a marketing application for the drug thalidomide was submitted to the FDA. Withstanding enormous pressure from the applicant, FDA reviewers, including Frances Kelsey, M.D., Ph.D., a medical officer at the Center for Drug Evaluation and Research at the FDA (Washington D.C.),‡ determined that inadequate data were available to support the safety of the drug product despite its already widespread use throughout the rest of the world. The application was not approved. After thousands of children in 46 countries were born with deformities as a consequence of thalidomide use, leaving the United States relatively unscathed, a political movement for tighter drug controls in the United States gained popular support. The Drug Amendments of 1962 were the first to require demonstration of effectiveness before marketing, recognizing that the assessment of safety must also consider benefit. Since 1962, more than a thousand prescription drugs have had their labeling changed or have been taken off the market to reflect the scientific evidence (or lack thereof) documenting their safety and/or effectiveness.§ Section 505 of the Federal Food, Drug, and Cosmetic Act (21 USC 355) currently specifies that approved drugs must be safe and effective for use under the conditions prescribed, recommended, or suggested in the labeling. Current regulations stipulate the following labeling requirements:

Long non-coding RNAs (lncRNAs) form a new class of RNA molecules implicated in various aspects of protein coding gene expression regulation. To study lncRNAs in cancer, we generated expression profiles for 1707 human lncRNAs in the NCI60 cancer cell line panel using a high-throughput nanowell RT-qPCR platform. We describe how qPCR assays were designed and validated and provide processed and normalized expression data for further analysis. Data quality is demonstrated by matching the lncRNA expression profiles with phenotypic and genomic characteristics of the cancer cell lines. This data set can be integrated with publicly available omics and pharmacological data sets to uncover novel associations between lncRNA expression and mRNA expression, miRNA expression, DNA copy number, protein coding gene mutation status or drug response

“Black box” 101: How the Food and Drug Administration evaluates, communicates, and manages drug benefit/risk

Cancer therapeutics include an ever-increasing array of tools at the disposal of clinicians in their treatment of this disease. However, cancer is a tough opponent in this battle and current treatments which typically include radiotherapy, chemotherapy and surgery are not often enough to rid the patient of his or her cancer. Cancer cells can become resistant to the treatments directed at them and overcoming this drug resistance is an important research focus. Additionally, increasing discussion and research is centering on targeted and individualized therapy. While a number of approaches have undergone intensive and close scrutiny as potential approaches to treat and kill cancer (signaling pathways, multidrug resistance, cell cycle checkpoints, anti-angiogenesis, etc.), much less work has focused on blocking the ability of a cancer cell to recognize and repair the damaged DNA which primarily results from the front line cancer treatments; chemotherapy and radiation. More recent studies on a number of DNA repair targets have produced proof-of-concept results showing that selective targeting of these DNA repair enzymes has the potential to enhance and augment the currently used chemotherapeutic agents and radiation as well as overcoming drug resistance. Some of the targets identified result in the development of effective single-agent anti-tumor molecules. While it is inherently convoluted to think that inhibiting DNA repair processes would be a likely approach to kill cancer cells, careful identification of specific DNA repair proteins is increasingly appearing to be a viable approach in the cancer therapeutic cache.

With the discovery of different non-coding RNA species, the complexity of the transcriptome and its regulation has increased dramatically. Next to the well-studied small non-coding miRNAs, several thousands of long non-coding RNAs (lncRNAs) have recently been described. Like miRNAs, lncRNAs appear to predominantly function as regulators of gene expression and are implicated in various regulatory networks involving both miRNAs and protein-coding genes. In order to facilitate the search for miRNA-lncRNA-mRNA regulatory networks in cancer we have profiled the expression of each of these RNA information layers using the high-throughput SmartChip RT-qPCR technology on the entire NCI60 cancer cell line panel. In total, three SmartChip Panels with 1050 miRNAs, 1250 cancer-focused mRNAs, and 1718 lncRNAs, respectively, were quantified with a minimum of 3 technical replicates against each cell line. Here, we present the results of an unprecedented integrated analysis aimed at identifying networks of highly co-regulated miRNA-lncRNA-mRNA clusters. In brief, individual clusters are annotated using a pathway enrichment approach whereby network edges are evaluated using miRNA target, lncRNA target, and transcription factor target predictions. MiRNA-lncRNA-mRNA clusters centered around key cancer genes are annotated and the complex interplay is described. This unique and extensive high quality dataset, comprised of three major information layers of the NCI60 cell line transcriptome offers numerous opportunities towards a better understanding of complex regulatory networks in cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2987. doi:1538-7445.AM2012-2987

The recent progress in our understanding of the molecular-genetic mechanisms active in many diseases and the application of new biologically based approaches in therapy are exciting new developments that have emerged following mapping of the human genome. Novel “molecular therapies” have been

Critical Considerations in Anticancer Drug Development and Dosing Strategies: The Past, Present, and Future.

Quantitative structure-activity relationships (QSAR) were developed for nucleoside analogs with anti-HIV activity. These compounds were investigated to determine the correlation of structure and toxicity/activity using molecular similarity analysis and structure-activity maps. A multiple-formula approach was used to perform quantitative molecular similarity analysis (QMSA) and QSAR study. Molecular descriptors such as number of atoms and bonds of a molecule (NAB), maximum common substructure (MaCS), and molecular similarity index (MSI) were used in our structure-activity relationship study. The MaCS of two molecules is defined as the substructure with the greatest NAB value common to both molecules. The MSI of two molecules X and Y is defined as MSI (X,Y) = [MaCS (X,Y) /NAB (X) ] X [MaCS (X,Y) /NAB (Y) ]. MaCS and MSI quantify the similarity between two molecular structures. Structure-activity maps (structure-toxicity map and structure-antiviral map) and QMSA were used to determine the site and type of modification for reduced toxicity and improved activity of new compounds.

11 - Regulatory Considerations in Clinical Trials of Novel Anticancer Drugs

In previous work, we have assessed the structural similarities between marketed drugs (‘drugs’) and endogenous natural human metabolites (‘metabolites’ or ‘endogenites’), using ‘fingerprint’ methods in common use, and the Tanimoto and Tversky similarity metrics, finding that the fingerprint encoding used had a dramatic effect on the apparent similarities observed. By contrast, the maximal common substructure (MCS), when the means of determining it is fixed, is a means of determining similarities that is largely independent of the fingerprints, and also has a clear chemical meaning. We here explored the utility of the MCS and metrics derived therefrom. In many cases, a shared scaffold helps cluster drugs and endogenites, and gives insight into enzymes (in particular transporters) that they both share. Tanimoto and Tversky similarities based on the MCS tend to be smaller than those based on the MACCS fingerprint-type encoding, though the converse is also true for a significant fraction of the comparisons. While no single molecular descriptor can account for these differences, a machine learning-based analysis of the nature of the differences (MACCS_Tanimoto vs MCS_Tversky) shows that they are indeed deterministic, although the features that are used in the model to account for this vary greatly with each individual drug. The extent of its utility and interpretability vary with the drug of interest, implying that while MCS is neither ‘better’ nor ‘worse’ for every drug–endogenite comparison, it is sufficiently different to be of value. The overall conclusion is thus that the use of the MCS provides an additional and valuable strategy for understanding the structural basis for similarities between synthetic, marketed drugs and natural intermediary metabolites.