Polypharmacology of Approved Anticancer Drugs

The major drug discovery efforts in oncology have been concentrated on the development of selective molecules that are supposed to act specifically on one anticancer mechanism by modulating a single or several closely related drug targets. However, a bird's eye view on data from multiple available bioassays implies that most approved anticancer agents do, in fact, target many more proteins with different functions. Here we will review and systematize currently available information on the targets of several anticancer drugs along with revision of their potential mechanisms of action. Polypharmacology of the current antineoplastic agents suggests that drug clinical efficacy in oncology can be achieved only via modulation of multiple cellular mechanisms

p53MutaGene: an online tool to estimate the effect of p53 mutational status on gene regulation in cancer

p53MutaGene is the first online tool for statistical validation of hypotheses regarding the effect of p53 mutational status on gene regulation in cancer. This tool is based on several large-scale clinical gene expression data sets and currently covers breast, colon and lung cancers. The tool detects differential co-expression patterns in expression data between p53 mutated versus p53 normal samples for the user-specified genes. Statistically significant differential co-expression for a gene pair is indicative that regulation of two genes is sensitive to the presence of p53 mutations. p53MutaGene can be used in 'single mode' where the user can test a specific pair of genes or in 'discovery mode' designed for analysis of several genes. Using several examples, we demonstrate that p53MutaGene is a useful tool for fast statistical validation in clinical data of p53-dependent gene regulation patterns

Exploration of individuality in drug metabolism by high-throughput metabolomics: The fast line for personalized medicine

In many cases, individuality in metabolism of a drug is a reliable predictor of the drug efficacy/safety. Modern high-throughput metabolomics is an ideal instrument to track drug metabolism in an individual after treatment. Productivity and low cost of the metabolomics are sufficient to analyse a large cohort of patients to explore individual variations in drug metabolism and to discover drug metabolic biomarkers indicative of drug efficacy/safety. The only potential disadvantage of metabolomics becoming a routine clinical procedure is a need to treat the patient once before making a prognosis. However, in many clinical applications this would not be a limitation. Here, we explore current opportunities and challenges for translating high-throughput metabolomics into the platform for personalized medicine

SynTarget: an online tool to test the synergetic effect of genes on survival outcome in cancer

International audienceThe identification of target combinationswith synergistic effects on cancer is at the leading edge of modern cancer research, especially for the development of combined anticancer therapies. However, at present, the basis for selection of beneficial targetcombinations commonly relies on expert opinion without any systematic rationale. The development of high-throughput technologies has led to the availability of large-scale clinical gene expression data sets.2–4Mining of these data sets for identification of gene combinations with synergetic effects on survival outcome in cancer could provide a systematic rationale for the identification of target combinations with potential therapeutic synergy. ..

Polypharmacology of small molecules targeting the ubiquitin-proteasome and ubiquitin-like systems

Targeting the ubiquitin-proteasome system (UPS) and ubiquitin-like signalling systems (UBL) has been considered a promising therapeutic strategy to treat cancer, neurodegenerative and immunological disorders. There have been multiple efforts recently to identify novel compounds that efficiently modulate the activities of different disease-specific components of the UPS-UBL. However, it is evident that polypharmacology (the ability to affect multiple independent protein targets) is a basic property of small molecules and even highly potent molecules would have a number of "off target" effects. Here we have explored publicly available high-throughput screening data covering a wide spectrum of currently accepted drug targets in order to understand polypharmacology of small molecules targeting different components of the UPS-UBL. We have demonstrated that molecules targeting a given UPS-UBL protein also have high odds to target a given off target spectrum. Moreover, the off target spectrum differs significantly between different components of UPS-UBL. This information can be utilized further in drug discovery efforts, to improve drug efficiency and to reduce the risk of potential side effects of the prospective drugs designed to target specific UPS-UBL components

Perspective on Multi-Target Antiplatelet Therapies: High Content Phenotypic Screening as an Unbiased Source of Novel Polypharmacological Strategies

Platelets play an important role in cardiovascular thrombosis as well as in many other pathological conditions such as inflammation, atherosclerosis and cancer. While multi-target strategies to treat complex diseases are gaining considerable attention, current development of antiplatelet therapies is mostly oriented towards several single targets, arising from our present understanding of the regulation of platelet activation. Limited efforts to develop multi-target agents or multidrug therapies are mostly due to a lack of a systematic basis to define target combinations with synergistic effects. Here we discuss the perspective to use high content phenotypic screening of in vitro models as a potential source for inference of synergetic multi-target strategies to control platelet activation