2
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Repurposing paclitaxel for the treatment of fibrosis: indication discovery for existing drugs

      1 , 2 , 3

      Drug Design, Development and Therapy

      Dove Medical Press

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Dear editor In the recently published paper by Zhang et al1 in Drug Des Develop Ther, the authors have evaluated the role of signal transducer and activator of transcription 3 (STAT3) in the antifibrotic activity of paclitaxel in vitro and in mice. They have reported that the treatment of paclitaxel at 2–4 μM reduced the level of phosphorylated STAT3 at Tyr705 in a dose- and time-dependent manner, and downregulated the expression of fibronectin, α-smooth muscle actin (α-SMA), and collagen I in cultured rat renal interstitial fibroblast NRK-49F cells derived from normal kidney. Treatment of the cells with the selective STAT3 inhibitor S3I-201 at 50 mM suppressed the expression of fibronectin, α-SMA, and collagen I in NRK-49F cells. However, S3I-201 treatment increased the expression of phosphorylated STAT1 but did not affect that of phosphorylated STAT5M. The immunoprecipitation assay has revealed that paclitaxel inhibited the STAT3 activity by disrupting the binding of STAT3 with tubulin independently of the effect on STAT3 phosphorylation and by inhibiting STAT3 nucleus translocation.1 Furthermore, paclitaxel treatment by intraperitoneal injection at 0.3 mg/kg twice a week ameliorated renal interstitial fibrosis by inhibiting the expression of fibronectin, α-SMA, and collagen I in a male C57 mouse model of unilateral ureteral obstruction. Paclitaxel administration also suppressed the infiltration of macrophages and neutrophils and production of tumor necrosis factor (TNF)-α, interleukin (IL)-1β, transforming growth factor (TGF)-β, and intercellular adhesion molecule 1 (ICAM-1) by inhibition of STAT3 activity in mouse obstructive nephropathy.1 These findings indicate that paclitaxel suppresses renal interstitial fibrosis via inhibition of STAT3-mediated pathway and production of proinflammatory cytokines. The findings from this study indicate that in addition to being a clinically used anticancer agent, paclitaxel may represent a new agent that manages renal fibrosis. Through indication discovery or therapeutic switching, drugs that have been approved for clinical use may be used for new indications, and this process is called drug repositioning or drug repurposing.2–7 Drug repositioning is different from drug coincidence or “serendipity”, which arises from unintentional mishaps in the drug discovery process as exemplified by drugs such as sildenafil and thalidomide. Apart from the staggering manufacturing cost and time reduction, drug repositioning facilitates drug discovery that will overcome bottlenecks in the therapeutic development process and prolong patent life, thereby obtaining largest investment return throughout the development process coupled with a significantly higher rate of success and reduced development risk. The benefits of drug repositioning for patients are evident in that newly arising diseases such as severe acute respiratory syndrome and Middle East respiratory syndrome that threaten human beings can be treated by existing drugs with established pharmacokinetic, formulation, and safety data in animals and humans where specific repositioning potential is displayed in the associated references.4,8 As such, drug repositioning may tremendously decrease the overall development time to 3–12 years and decrease total cost and attrition rates. There are increasing numbers of successes in drug repositioning. For example, colesevelam as a bile acid sequestrant was originally developed as an adjunct to diet and exercise to decrease elevated low-density lipoprotein cholesterol in patients with primary hyperlipidemia as monotherapy, but it has also gained approval from the Food and Drug Administration (FDA) to treat type 2 diabetes mellitus with unknown mechanism of action.9–11 Gabapentin and pregabalin were both originally developed as antiepileptic agents; they have been approved by the FDA to treat anxiety disorders and neuropathic pain.12–14 There are multiple technical approaches for drug repositioning. The disease- and drug-derived approaches employ available data related to diseases and knowledge of how drugs interact with the biological systems at molecular and cellular levels to identify potential new indications for existing drugs.2,6,7 Computational methods have been widely applied to explore drug–protein interactomes, drug off-targets, and adverse drug effects that can provide clues of new indications. Furthermore, genome-wide association studies (GWAS), medical genetics, and data from systems biological approaches have been used to conduct drug repositioning.15–21 GWAS data provide insights into the biology and pathology of diseases via bioinformatic network analysis, which may be translated into potential new therapeutic targets that can be hit by approved drugs. Since pathologies are often shared between diseases, existing drugs against known targets can be retested for possible new indications. Genomic expression data in combination with in vitro drug screening and target verification studies provide insights into the mechanisms of action of drugs and thus have become widely used in drug repositioning.22–25 Recently, we have explored prediction of adverse drug reactions (ADRs) based on the drug-induced gene-expression profiles from cultured human cells in the Connectivity Map (CMap) database.26 The results show that drugs inducing comparable ADRs generally lead to similar CMap expression profiles. On the basis of such ADR–gene expression association, we have established prediction models for various ADRs, including severe myocardial and infectious events. Drugs with FDA boxed warnings of safety liability have been identified. We, therefore, suggest that drug-induced gene expression change in combination with computational methods may offer a new way to facilitate systematic drug safety evaluation and drug repositioning. In another study, we have demonstrated that the on-target and off-target effects of drugs could be characterized by drug-induced in vitro genomic expression data in CMap.24 In some cases, a family of ligands for the same target tends to interact with common off-targets, which may help increase the efficiency of indication discovery and explain the complicated mechanisms of action for some drugs. We propose that CMap expression similarity is a new indicator of drug–target interactions, which can be used to increase the productivity during drug repositioning process. Finding novel therapeutics to treat and cure diseases is a fundamental challenge in biomedical research due to the high cost and failure rate. Drug repositioning is considered a promising strategy to revitalize the slowing drug discovery pipeline due to shorter development time and lower failure and toxicity risks. Although drug repositioning has its intrinsic limitations, it represents an alternative pathway to achieve therapeutic success in the postgenomic era.

          Related collections

          Most cited references 21

          • Record: found
          • Abstract: found
          • Article: not found

          The value of drug repositioning in the current pharmaceutical market.

          Drug repositioning is the process of developing new indications for existing drugs or biologics. Increasing interest in drug repositioning has occurred due to sustained high failure rates and costs involved in attempts to bring new drugs to market. It has been estimated that it may cost more than USD 800 million to develop a new drug de novo. In addition, due to regulatory requirements regarding safety, efficacy and quality, the time required to develop a new drug de novo has been estimated to be 10 to 17 years. De novo drug discovery has failed to efficiently supply pharmaceutical company pipelines. A rational approach to drug repositioning may include a cross-disciplinary focus on the elucidation of the mechanisms of disease, allowing matching of disease pathways with appropriately targeted therapeutic agents. Repurposed drugs or biologics have the advantage of decreased development costs and decreased time to launch due to previously collected pharmacokinetic, toxicology and safety data. For these reasons, repurposing should be a primary strategy in drug discovery for every broadly focused, research-based pharmaceutical company. Copyright 2009 Prous Science, S.A.U. or its licensors. All rights reserved.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            DiseaseConnect: a comprehensive web server for mechanism-based disease–disease connections

            The DiseaseConnect (http://disease-connect.org) is a web server for analysis and visualization of a comprehensive knowledge on mechanism-based disease connectivity. The traditional disease classification system groups diseases with similar clinical symptoms and phenotypic traits. Thus, diseases with entirely different pathologies could be grouped together, leading to a similar treatment design. Such problems could be avoided if diseases were classified based on their molecular mechanisms. Connecting diseases with similar pathological mechanisms could inspire novel strategies on the effective repositioning of existing drugs and therapies. Although there have been several studies attempting to generate disease connectivity networks, they have not yet utilized the enormous and rapidly growing public repositories of disease-related omics data and literature, two primary resources capable of providing insights into disease connections at an unprecedented level of detail. Our DiseaseConnect, the first public web server, integrates comprehensive omics and literature data, including a large amount of gene expression data, Genome-Wide Association Studies catalog, and text-mined knowledge, to discover disease–disease connectivity via common molecular mechanisms. Moreover, the clinical comorbidity data and a comprehensive compilation of known drug–disease relationships are additionally utilized for advancing the understanding of the disease landscape and for facilitating the mechanism-based development of new drug treatments.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding

              Introduction Understanding the complex responses of the human body to drug treatments is vitally important to address the efficacy and safety-related issues of compounds in later stages of drug development and, thus, to reduce high attrition rates in clinical trials (Kola and Landis, 2004). The fundamental challenge towards this goal lies in the selection and thorough characterization of model systems that can accurately recapitulate the drug response of human physiology for diverse drug-screening projects (Jones and Diamond, 2007; Sharma et al, 2010; Dow and Lowe, 2012). One way of obtaining unbiased, large-scale readouts from model systems is genome-wide expression profiling of the transcriptional response to various drug treatments (Feng et al, 2009; Iskar et al, 2011). This has first been systematically explored in model organisms, such as Saccharomyces cerevisiae, with the aim to elucidate drug mechanism of action (MOA) based on their transcriptional effects (Hughes et al, 2000; Ihmels et al, 2002; di Bernardo et al, 2005). Simultaneously, coexpression analysis and transcriptional modules of the yeast data allowed the inference of functional roles for genes that respond coherently to these perturbations (Hughes et al, 2000; Ihmels et al, 2002; Wu et al, 2002; Segal et al, 2003; Tanay et al, 2004). Recently, the Connectivity Map (CMap) successfully extended the concept of large-scale gene expression profiling of drug response to human cell lines (Lamb et al, 2006; Lamb, 2007). In parallel, drug-induced expression changes have been profiled at a large scale in animal models, such as rat liver (Ganter et al, 2005; Natsoulis et al, 2008). Computational advances in mining these data have improved signature comparison methods leading to novel drug–drug (Subramanian et al, 2005; Lamb et al, 2006; Iorio et al, 2009, 2010) and drug–disease (Hu and Agarwal, 2009; Sirota et al, 2011; Dudley et al, 2011; Pacini et al, 2012) connections based on their (anti)correlated transcriptional effects (Qu and Rajpal, 2012; Iorio et al, 2012). However, these mammalian transcriptional readouts still need to be utilized for uncovering the underlying gene regulatory networks and for predicting gene function and delineating pathway membership. Along those lines, we used a biclustering approach that is well-suited for revealing the modular organization of transcriptional responses to drug perturbation (Ihmels et al, 2002; Prelić et al, 2006), as it can group coregulated genes with the drugs they respond to (technically, each bicluster consists of both a gene and a drug subset). We applied it to large-scale transcriptome resources for three human cell lines and rat liver to generate, for the first time, a large compendium of mammalian drug-induced transcriptional modules. We extensively characterized these modules in terms of functional roles of the genes and the bioactivities of the drugs they contain, in order to gain insights into both, drug MOA and the perturbed cellular systems (Figure 1). Comparing drug-induced transcriptional modules generated independently for each of the three human cell types and rat liver allowed us to assess their conservation across tissue types and organisms. Although it has been noted earlier that particular transcriptional changes along developmental trajectories, in different growth conditions, stress or disease can be conserved between species (Stuart et al, 2003; van Noort et al, 2003), it remains an open question, how well the modular organization of the transcriptome is conserved across tissues and organisms (Miller et al, 2010; Zheng-Bradley et al, 2010; Dowell, 2011). In this context, our results on the conservation of drug-induced transcriptional modules contribute to a better understanding of the degree to which cell line models recapitulate the biological processes and signaling pathways taking place in the human physiological context (Jones and Diamond, 2007; Sharma et al, 2010). Results and discussion Identification of drug-induced modules in human cell lines and rat liver To identify and compare drug-induced transcriptional modules from human cell lines as well as rat liver tissue, we exploited data from the following two public resources: (i) the CMap (Lamb et al, 2006), which contains 6100 expression profiles of several human cancer cell lines treated with 1309 drug-like small molecules (hereafter simply referred to as ‘drugs') (Supplementary Figure 1) and (ii) the DrugMatrix resource, a large data set of 1743 expression profiles from liver tissue of drug-treated rats (Natsoulis et al, 2008). Raw microarray data were subjected to quality control and preprocessing procedures to improve data consistency and reduce batch effects (Iskar et al, 2010). For CMap, this resulted in a usable set of expression measurements of 8964 genes in three human cell lines (HL60, MCF7 and PC3, see Materials and Methods), each treated with the same set of 990 drugs. From the rat data set, only genes with orthologous human genes present in CMap were considered. This yielded expression profiles for 3618 genes in response to treatments with 344 distinct drugs (Supplementary Figure 1). Drug-induced transcriptional modules were detected and tested for statistical significance separately in each of the four matrices of expression data using an unsupervised biclustering approach that has previously been shown to maintain high accuracy even with noisy input data (Iterative Signature Algorithm (ISA); Bergmann et al, 2003; Ihmels et al, 2004; Prelić et al, 2006). Each resulting bicluster (hereafter referred to as ‘module') consists of a subset of genes and a subset of drugs that coherently regulate these genes. To ensure comprehensiveness and robustness of this analysis, we explored a wide range of parameter settings for the ISA workflow (Supplementary Figure 2 and Supplementary Tables 1 and 2), and in addition, applied a size threshold to exclude small, likely spurious modules as motivated by previous studies (Langfelder and Horvath, 2008, and see Materials and Methods). As a result, we identified robust, drug-induced transcriptional modules in each system individually: 25 in MCF7 cells, 28 in PC3 cells, 29 in HL60 cells and 43 in rat liver (Figure 2A and Supplementary Data set 1). On average, these modules contain 70 genes in the human cell lines and 50 genes in the rat liver, induced by an average of 29 drug treatments in both data sets. Within each data set, modules can overlap (on average, 7% of the genes and 34% of the drugs were contained in multiple modules), reflecting the effects of polypharmacological drugs that modulate multiple targets (Hopkins et al, 2006; Keiser et al, 2009), which in turn can lead to perturbation of multiple pathways by the same drug. Conservation of drug-induced modules across cell types and organisms As appropriate animal models that accurately recapitulate the human drug response are crucial in drug discovery and development, we assessed the conservation of drug responses at the transcriptome level by comparing drug-induced transcriptional modules across cell lines and organisms. This resulted in a network of module similarity calculated using a reciprocal best-hit approach, which linked modules from different cell lines and rat liver to each other if their gene members significantly overlapped between data sets (Supplementary Table 3). In addition, we assessed the drug overlap of modules linked across cell lines (see Materials and Methods). For 58 out of 82 modules (71%) from human cell lines, we identified a corresponding module in at least 1 other cell line, which yielded a total of 23 nonredundant, conserved drug-induced transcriptional modules (CODIMs; Figure 2A and Supplementary Figure 3). A conservation level of 71% between cell lines is in line with a previous study that assessed the conservation of constitutive coexpression networks across human brain regions (Oldham et al, 2008). In addition to similarity across cell lines, we found considerable (statistically significant) conservation of human modules in rat liver ranging from 3 to 5 out of 25 to 29 individual modules per cell line (15% average ratio, permutation-based P-value 0.6, see Supplementary Table 2 for robustness analysis with respect to the actual parameters used). Second, we discarded very small, likely spurious modules and retained only biclusters with a minimum of 20 genes and 5 drugs (10 for rat liver). Finally, we applied the standard ISA redundancy removal to reduce the number of very similar biclusters that are the result of many randomly initialized runs converging to highly similar result sets (Csárdi et al, 2010). Before this removal step, all transcriptional modules were sorted by gene and drug thresholds aiming to preferentially retain medium-sized modules (first prioritizing gene threshold values from 4 to 3 over ones from 5 to 3.2 and finally 2.8 to 2 for cell line data, and prioritizing values from 3 to 2 over ones from 5 to 3.2 for rat liver data; second, prioritizing drug threshold values from 3 to 2 over 4 to 3.2 in both cell line and rat liver data followed by values from 1.8 to 1 in cell line data). On the basis of this prioritization, redundant modules were filtered in sequential order following the developers' recommendations (Csárdi et al, 2010), using a correlation threshold of 0.3 to determine redundant biclusters (see Supplementary Table 1 for a parameter robustness analysis). Lastly, in addition to ISA redundancy removal, modules from the same cell line showing significant gene overlap with a module of higher priority (hypergeometric test, P-value 0.4), while excluding methodologically analogous coexpression associations (von Mering et al, 2005). Similarly, 11 988 reliable associations between 1869 rat proteins were retrieved from STRING having a combined evidence score over 0.4 and again excluding analogous coexpression associations. For each module, we calculated the proportion of functionally associated gene pairs (excluding self-relations) within the module and compared this against the distribution obtained for the proportion of functionally associated pairs in 1000 random gene sets of matched sizes. Characterization of drug-induced transcriptional modules We characterized both individual transcriptional modules along with their unified CODIMs in detail by mining information for both gene and drug members using several annotation resources. We used the Database for Annotation, Visualization and Integrated Discovery (DAVID knowledgebase; Huang et al, 2009a, 2009b) to test for enriched gene function and to identify biological themes among these. For each module, the DAVID web service provided a ranked list (enrichment score >1.3) of functionally relevant annotation clusters that represent a summary of several annotation categories, including GO (Ashburner et al, 2000), KEGG (Kanehisa et al, 2012) and BioCarta pathways (Nishimura, 2001). In this analysis, gene reference background was set to all genes for CMap, and rat liver data set provided as an input to the ISA algorithm. The analysis of gene functions was complemented with a second approach, exploiting drug annotation resources for module characterization. For this, we extracted the ATC classification code (Andersen and Hvidberg, 1981; Pahor et al, 1994) for 677 approved drugs from the set of chemicals in CMap and 4331 chemical–protein interactions for 493 CMap compounds from the STITCH database (Kuhn et al, 2011), considering only reliable database and experimental evidence with high confidence (any of the two scores >0.7). In addition, 23 458 associations between 1106 side effects and 373 drugs contained in CMap were obtained from the SIDER database (Kuhn et al, 2010). Lastly, a library of chemical fragments (>6 atoms) generated by exhaustive molecular fragmentation of 892 CMap drugs (93%) ( 1 and FDR-corrected P-value <0.01; Supplementary Table 6). Data availability Supplementary Data such as drug-induced modules and CODIMs are available from http://codim.embl.de in human and machine readable formats. The web resource also provides functionality for drug and gene search. Supplementary Material Supplementary Information Supplementary figures 1–10, Supplementary tables 1, 2, 4, 5 and 6 Supplementary Table 3 Comparison of gene and drug members of drug-induced modules across human cell lines and organisms Supplementary Data set 1 Characterization of gene and drug members of drug-induced modules Review Process File
                Bookmark

                Author and article information

                Journal
                Drug Des Devel Ther
                Drug Des Devel Ther
                Drug Design, Development and Therapy
                Drug Design, Development and Therapy
                Dove Medical Press
                1177-8881
                2015
                07 September 2015
                : 9
                : 4869-4871
                Affiliations
                [1 ]Department of General Surgery, The First People’s Hospital of Shunde, Southern Medical University, Shunde, Foshan, Guangdong, People’s Republic of China
                [2 ]School of Medicine, Deakin University, Waurn Ponds, Victoria, Australia
                [3 ]Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, FL, USA
                Author notes
                Correspondence: Xiao-Wu Chen, Department of General Surgery, The First People’s Hospital of Shunde, Southern Medical University, 1 Penglai Road, Shunde, Foshan 528300, Guangdong, People’s Republic of China, Tel +86 757 2231 8555, Fax +86 757 2222 3899, Email drchenxiaowu@ 123456163.com
                Shu-Feng Zhou, Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, 12901 Bruce B Downs Boulevard, Tampa, FL 33612, USA, Tel +1 813 974 6276, Fax +1 813 974 9885, Email szhou@ 123456health.usf.edu
                Article
                dddt-9-4869
                10.2147/DDDT.S87771
                4567211
                © 2015 Chen et al. This work is published by Dove Medical Press Limited, and licensed under Creative Commons Attribution – Non Commercial (unported, v3.0) License

                The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.

                Categories
                Editorial

                Pharmacology & Pharmaceutical medicine

                Comments

                Comment on this article