QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics

CD45 inhibition in myeloid leukaemia cells sensitizes cellular responsiveness to chemotherapy

Myeloid malignancies are a group of blood disorders characterized by the proliferation of one or more haematopoietic myeloid cell lineages, predominantly in the bone marrow, and are often caused by aberrant protein tyrosine kinase activity. The protein tyrosine phosphatase CD45 is a trans-membrane molecule expressed on all haemopoietic blood cells except that of platelets and red cells. CD45 regulates various cellular physiological processes including proliferation, apoptosis, and lymphocyte activation. However, its role in chemotherapy response is still unknown; therefore, the aim of this study was to investigate the role of CD45 in myeloid malignancies in terms of cellular growth, apoptosis, and response to chemotherapy. The expression of CD45 on myeloid leukaemia primary cells and cell lines was heterogeneous with HEL and OCI-AML3 cells showing the highest level. Inhibition of CD45 resulted in increased cellular sensitivity to cytarabine and ruxolitinib, the two main therapies for AML and MPN. Bioinformatics analysis identified genes whose expression was correlated with CD45 expression such as JAK2, ACTR2, THAP3 Serglycin, and PBX-1 genes, as well as licensed drugs (alendronate, allopurinol, and balsalazide), which could be repurposed as CD45 inhibitors which effectively increases sensitivity to cytarabine and ruxolitinib at low doses. Therefore, CD45 inhibition could be explored as a potential therapeutic partner for treatment of myeloid malignancies in combination with chemotherapy such as cytarabine especially for elderly patients and those showing chemotherapy resistance.

The Potential of a Stratified Approach to Drug Repurposing in Alzheimer's Disease

Alzheimer's disease (AD) is a complex neurodegenerative condition that is characterized by the build-up of amyloid-beta plaques and neurofibrillary tangles. While multiple theories explaining the aetiology of the disease have been suggested, the underlying cause of the disease is still unknown. Despite this, several modifiable and non-modifiable factors that increase the risk of developing AD have been identified. To date, only eight AD drugs have ever gained regulatory approval, including six symptomatic and two disease-modifying drugs. However, not all are available in all countries and high costs associated with new disease-modifying biologics prevent large proportions of the patient population from accessing them. With the current patient population expected to triple by 2050, it is imperative that new, effective, and affordable drugs become available to patients. Traditional drug development strategies have a 99% failure rate in AD, which is far higher than in other disease areas. Even when a drug does reach the market, additional barriers such as high cost and lack of accessibility prevent patients from benefiting from them. In this review, we discuss how a stratified medicine drug repurposing approach may address some of the limitations and barriers that traditional strategies face in relation to drug development in AD. We believe that novel, stratified drug repurposing studies may expedite the discovery of alternative, effective, and more affordable treatment options for a rapidly expanding patient population in comparison with traditional drug development methods.

Transcriptional Profiling of a Patient-Matched Cohort of Glioblastoma (IDH-Wildtype) for Therapeutic Target and Repurposing Drug Identification

Glioblastoma (GBM) is the most prevalent and aggressive adult brain tumor. Despite multi-modal therapies, GBM recurs, and patients have poor survival (~14 months). Resistance to therapy may originate from a subpopulation of tumor cells identified as glioma-stem cells (GSC), and new treatments are urgently needed to target these. The biology underpinning GBM recurrence was investigated using whole transcriptome profiling of patient-matched initial and recurrent GBM (recGBM). Differential expression analysis identified 147 significant probes. In total, 24 genes were validated using expression data from four public cohorts and the literature. Functional analyses revealed that transcriptional changes to recGBM were dominated by angiogenesis and immune-related processes. The role of MHC class II proteins in antigen presentation and the differentiation, proliferation, and infiltration of immune cells was enriched. These results suggest recGBM would benefit from immunotherapies. The altered gene signature was further analyzed in a connectivity mapping analysis with QUADrATiC software to identify FDA-approved repurposing drugs. Top-ranking target compounds that may be effective against GSC and GBM recurrence were rosiglitazone, nizatidine, pantoprazole, and tolmetin. Our translational bioinformatics pipeline provides an approach to identify target compounds for repurposing that may add clinical benefit in addition to standard therapies against resistant cancers such as GBM.

Identification of Homoharringtonine as a potent inhibitor of glioblastoma cell proliferation and migration

We previously demonstrated that Annexin A2 (ANXA2) is a pivotal mediator of the pro-oncogenic features displayed by glioblastoma (GBM) tumors, the deadliest adult brain malignancies, being involved in cell stemness, proliferation and invasion, thus negatively impacting patient prognosis. Based on these results, we hypothesized that compounds able to revert ANXA2-dependent transcriptional features could be exploited as reliable treatments to inhibit GBM cell aggressiveness by hampering their proliferative and migratory potential. Transcriptional signatures obtained by the modulation of ANXA2 activity/levels were functionally mapped through the QUADrATiC bioinformatic tool for compound identification. Selected compounds were screened by cell proliferation and migration assays in primary GBM cells, and we identified Homoharringtonine (HHT) as a potent inhibitor of GBM cell motility and proliferation, without affecting their viability. A further molecular characterization of the effects displayed by HHT, confirmed its ability to inhibit a transcriptional program involved in cell migration and invasion. Moreover, we demonstrated that the multiple antitumoral effects displayed by HHT are correlated to the inhibition of a platelet derived growth factor receptor α (PDGFRα)-dependent intracellular signaling through the impairment of Signal transducer and activator of transcription 3 (STAT3) and Ras homolog family member A (RhoA) axes. Our results demonstrate that HHT may act as a potent inhibitor of cancer cell proliferation and invasion in GBM, by hampering multiple PDGFRα-dependent oncogenic signals transduced through the STAT3 and RhoA intracellular components, finally suggesting its potential transferability for achieving an effective impairment of peculiar GBM hallmarks.

Identifying Gene Signatures for Cancer Drug Repositioning Based on Sample Clustering

Drug repositioning is an important approach for drug discovery. Computational drug repositioning approaches typically use a gene signature to represent a particular disease and connect the gene signature with drug perturbation profiles. Although disease samples, especially from cancer, may be heterogeneous, most existing methods consider them as a homogeneous set to identify differentially expressed genes (DEGs)for further determining a gene signature. As a result, some genes that should be in a gene signature may be averaged off. In this study, we propose a new framework to identify gene signatures for cancer drug repositioning based on sample clustering (GS4CDRSC). GS4CDRSC first groups samples into several clusters based on their gene expression profiles. Second, an existing method is applied to the samples in each cluster for generating a list of DEGs. Then a weighting approach is used to identify an intergrated gene signature from all the lists of DEGs. The integrated gene signature is used to connect with drug perturbation profiles in the Connectivity Map (CMap)database to generate a list of drug candidates. GS4CDRSC has been tested with several cancer datasets and existing methods. The computational results show that GS4CDRSC outperforms those methods without the sample clustering and weighting approaches in terms of both number and rate of predicted known drugs for specific cancers.

Human Protein Complex-Based Drug Signatures for Personalized Cancer Medicine

Disease signature-based drug repositioning approaches typically first identify a disease signature from gene expression profiles of disease samples to represent a particular disease. Then such a disease signature is connected with the drug-induced gene expression profiles to find potential drugs for the particular disease. In order to obtain reliable disease signatures, the size of disease samples should be large enough, which is not always a single case in practice, especially for personalized medicine. On the other hand, the sample sizes of drug-induced gene expression profiles are generally large. In this study, we propose a new drug repositioning approach (HDgS), in which the drug signature is first identified from drug-induced gene expression profiles, and then connected to the gene expression profiles of disease samples to find the potential drugs for patients. In order to take the dependencies among genes into account, the human protein complexes (HPC) are used to define the drug signature. The proposed HDgS is applied to the drug-induced gene expression profiles in LINCS and several types of cancer samples. The results indicate that the HPC-based drug signature can effectively find drug candidates for patients and that the proposed HDgS can be applied for personalized medicine with even one patient sample.

Repurposing FDA approved drugs as radiosensitizers for treating hypoxic prostate cancer

Background

The presence of hypoxia is a poor prognostic factor in prostate cancer and the hypoxic tumor microenvironment promotes radioresistance. There is potential for drug radiotherapy combinations to improve the therapeutic ratio. We aimed to investigate whether hypoxia-associated genes could be used to identify FDA approved drugs for repurposing for the treatment of hypoxic prostate cancer.

Methods

Hypoxia associated genes were identified and used in the connectivity mapping software QUADrATIC to identify FDA approved drugs as candidates for repurposing. Drugs identified were tested in vitro in prostate cancer cell lines (DU145, PC3, LNCAP). Cytotoxicity was investigated using the sulforhodamine B assay and radiosensitization using a clonogenic assay in normoxia and hypoxia.

Results

Menadione and gemcitabine had similar cytotoxicity in normoxia and hypoxia in all three cell lines. In DU145 cells, the radiation sensitizer enhancement ratio (SER) of menadione was 1.02 in normoxia and 1.15 in hypoxia. The SER of gemcitabine was 1.27 in normoxia and 1.09 in hypoxia. No radiosensitization was seen in PC3 cells.

Conclusion

Connectivity mapping can identify FDA approved drugs for potential repurposing that are linked to a radiobiologically relevant phenotype. Gemcitabine and menadione could be further investigated as potential radiosensitizers in prostate cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-021-00856-x.

Transcriptional perturbation of protein arginine methyltransferase-5 exhibits MTAP-selective oncosuppression

We hypothesized that small molecule transcriptional perturbation could be harnessed to target a cellular dependency involving protein arginine methyltransferase 5 (PRMT5) in the context of methylthioadenosine phosphorylase (MTAP) deletion, seen frequently in malignant pleural mesothelioma (MPM). Here we show, that MTAP deletion is negatively prognostic in MPM. In vitro, the off-patent antibiotic Quinacrine efficiently suppressed PRMT5 transcription, causing chromatin remodelling with reduced global histone H4 symmetrical demethylation. Quinacrine phenocopied PRMT5 RNA interference and small molecule PRMT5 inhibition, reducing clonogenicity in an MTAP-dependent manner. This activity required a functional PRMT5 methyltransferase as MTAP negative cells were rescued by exogenous wild type PRMT5, but not a PRMT5E444Q methyltransferase-dead mutant. We identified c-jun as an essential PRMT5 transcription factor and a probable target for Quinacrine. Our results therefore suggest that small molecule-based transcriptional perturbation of PRMT5 can leverage a mutation-selective vulnerability, that is therapeutically tractable, and has relevance to 9p21 deleted cancers including MPM.

Lost in application: Measuring hypoxia for radiotherapy optimisation

The history of radiotherapy is intertwined with research on hypoxia. There is level 1a evidence that giving hypoxia-targeting treatments with radiotherapy improves locoregional control and survival without compromising late side-effects. Despite coming in and out of vogue over decades, there is now an established role for hypoxia in driving molecular alterations promoting tumour progression and metastases. While tumour genomic complexity and immune profiling offer promise, there is a stronger evidence base for personalising radiotherapy based on hypoxia status. Despite this, there is only one phase III trial targeting hypoxia modification with full transcriptomic data available. There are no biomarkers in routine use for patients undergoing radiotherapy to aid management decisions, and a roadmap is needed to ensure consistency and provide a benchmark for progression to application. Gene expression signatures address past limitations of hypoxia biomarkers and could progress biologically optimised radiotherapy. Here, we review recent developments in generating hypoxia gene expression signatures and highlight progress addressing the challenges that must be overcome to pave the way for their clinical application.

Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases

The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by 'borrowing' treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different 'waves' of repurposing. Early drug repurposing occurred in academia and was based on serendipitous observations or perceived disease similarity, often driven by the availability and popularity of drug classes. Since the 1990s, most biologic therapies have been developed for one or several RAIDs and then tested among the others, with varying levels of success. The past two decades have seen data-driven repurposing characterized by signature-based approaches that rely on molecular biology and genomics. Additionally, many data-driven strategies employ computational modelling and machine learning to integrate multiple sources of data. Together, these repurposing periods have led to advances in the treatment for many RAIDs.

Connectivity Mapping: Methods and Applications

Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.

Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data

Background

Although in silico drug discovery is necessary for drug development, two major strategies, a structure-based and ligand-based approach, have not been completely successful. Currently, the third approach, inference of drug candidates from gene expression profiles obtained from the cells treated with the compounds under study requires the use of a training dataset. Here, the purpose was to develop a new approach that does not require any pre-existing knowledge about the drug–protein interactions, but these interactions can be inferred by means of an integrated approach using gene expression profiles obtained from the cells treated with the analysed compounds and the existing data describing gene–gene interactions.

Results

In the present study, using tensor decomposition-based unsupervised feature extraction, which represents an extension of the recently proposed principal-component analysis-based feature extraction, gene sets and compounds with a significant dose-dependent activity were screened without any training datasets. Next, after these results were combined with the data showing perturbations in single-gene expression profiles, genes targeted by the analysed compounds were inferred. The set of target genes thus identified was shown to significantly overlap with known target genes of the compounds under study.

Conclusions

The method is specifically designed for large-scale datasets (including hundreds of treatments with compounds), not for conventional small-scale datasets. The obtained results indicate that two compounds that have not been extensively studied, WZ-3105 and CGP-60474, represent promising drug candidates targeting multiple cancers, including melanoma, adenocarcinoma, liver carcinoma, and breast, colon, and prostate cancers, which were analysed in this in silico study.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2395-8) contains supplementary material, which is available to authorized users.

Impact of Variable RNA-Sequencing Depth on Gene Expression Signatures and Target Compound Robustness: Case Study Examining Brain Tumor (Glioma) Disease Progression

Purpose

Gene expression profiling can uncover biologic mechanisms underlying disease and is important in drug development. RNA sequencing (RNA-seq) is routinely used to assess gene expression, but costs remain high. Sample multiplexing reduces RNA-seq costs; however, multiplexed samples have lower cDNA sequencing depth, which can hinder accurate differential gene expression detection. The impact of sequencing depth alteration on RNA-seq–based downstream analyses such as gene expression connectivity mapping is not known, where this method is used to identify potential therapeutic compounds for repurposing.

Methods

In this study, published RNA-seq profiles from patients with brain tumor (glioma) were assembled into two disease progression gene signature contrasts for astrocytoma. Available treatments for glioma have limited effectiveness, rendering this a disease of poor clinical outcome. Gene signatures were subsampled to simulate sequencing alterations and analyzed in connectivity mapping to investigate target compound robustness.

Results

Data loss to gene signatures led to the loss, gain, and consistent identification of significant connections. The most accurate gene signature contrast with consistent patient gene expression profiles was more resilient to data loss and identified robust target compounds. Target compounds lost included candidate compounds of potential clinical utility in glioma (eg, suramin, dasatinib). Lost connections may have been linked to low-abundance genes in the gene signature that closely characterized the disease phenotype. Consistently identified connections may have been related to highly expressed abundant genes that were ever-present in gene signatures, despite data reductions. Potential noise surrounding findings included false-positive connections that were gained as a result of gene signature modification with data loss.

Conclusion

Findings highlight the necessity for gene signature accuracy for connectivity mapping, which should improve the clinical utility of future target compound discoveries.

In vitro and clinical data analysis of Osteopontin as a prognostic indicator in colorectal cancer

Osteopontin (OPN) has been shown to promote colorectal cancer (CRC) progression; however, the mechanism of OPN-induced CRC progression is largely unknown. In this study, we found that OPN overexpression led to enhanced anchorage-independent growth, cell migration and invasion in KRAS gene mutant cells but to a lesser extent in KRAS wild-type cells. OPN overexpression also induced PI3K signalling, expression of Snail and Matrix metallopeptidase 9 (MMP9), and suppressed the expression of E-cadherin in KRAS mutant cells. In human CRC specimens, a high-level expression of OPN significantly predicted poorer survival in CRC patients and OPN expression was positively correlated with MMP9 expression, and negatively correlated with E-cadherin expression. Furthermore, we have found that 15 genes were co-upregulated in OPN highly expression CRC and a list of candidate drugs that may have potential to reverse the secreted phosphoprotein 1 (SPP1) gene signature by connectivity mapping. In summary, OPN is a potential prognostic indicator and therapeutic target for colon cancer.

A review of connectivity map and computational approaches in pharmacogenomics

Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.

A review of connectivity map and computational approaches in pharmacogenomics

Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.

An integrated meta-analysis approach to identifying medications with potential to alter breast cancer risk through connectivity mapping

BACKGROUND

Gene expression connectivity mapping has gained much popularity in recent years with a number of successful applications in biomedical research testifying its utility and promise. A major application of connectivity mapping is the identification of small molecule compounds capable of inhibiting a disease state. In this study, we are additionally interested in small molecule compounds that may enhance a disease state or increase the risk of developing that disease. Using breast cancer as a case study, we aim to develop and test a methodology for identifying commonly prescribed drugs that may have a suppressing or inducing effect on the target disease (breast cancer).

RESULTS

We obtained from public data repositories a collection of breast cancer gene expression datasets with over 7000 patients. An integrated meta-analysis approach to gene expression connectivity mapping was developed, which involved unified processing and normalization of raw gene expression data, systematic removal of batch effects, and multiple runs of balanced sampling for differential expression analysis. Differentially expressed genes stringently selected were used to construct multiple non-joint gene signatures representing the same biological state. Remarkably these non-joint gene signatures retrieved from connectivity mapping separate lists of candidate drugs with significant overlaps, providing high confidence in their predicted effects on breast cancers. Of particular note, among the top 26 compounds identified as inversely connected to the breast cancer gene signatures, 14 of them are known anti-cancer drugs.

CONCLUSIONS

A few candidate drugs with potential to enhance breast cancer or increase the risk of the disease were also identified; further investigation on a large population is required to firmly establish their effects on breast cancer risks. This work thus provides a novel approach and an applicable example for identifying medications with potential to alter cancer risks through gene expression connectivity mapping.

Systems healthcare: a holistic paradigm for tomorrow

Systems healthcare is a holistic approach to health premised on systems biology and medicine. The approach integrates data from molecules, cells, organs, the individual, families, communities, and the natural and man-made environment. Both extrinsic and intrinsic influences constantly challenge the biological networks associated with wellness. Such influences may dysregulate networks and allow pathobiology to evolve, resulting in early clinical presentation that requires astute assessment and timely intervention for successful mitigation. Herein, we describe the components of relevant biological systems and the nature of progression from at-risk to manifest disease. We illustrate the systems approach by examining two relevant clinical examples: Alzheimer's and cardiovascular diseases. The implications of systems healthcare management are examined through the lens of economics, ethics, policy and the law. Finally, we propose the need to develop new educational paradigms to enhance the training of the health professional in an era of systems medicine.

Natural killer-like signature observed post therapy in locally advanced rectal cancer is a determinant of pathological response and improved survival

Around 12-15% of patients with locally advanced rectal cancer undergo a pathologically complete response (tumor regression grade 4) to long-course preoperative chemoradiotherapy; the remainder exhibit a spectrum of tumor regression (tumor regression grade 1-3). Understanding therapy-related transcriptional alterations may enable better prediction of response as measured by progression-free and overall survival, in addition to aiding the development of improved strategies based on the underlying biology of the disease. To this end, we performed high-throughput gene expression profiling in 40 pairs of formalin-fixed paraffin-embedded rectal cancer biopsies and matched resections following long-course preoperative chemoradiotherapy (discovery cohort). Differential gene expression analysis was performed contrasting tumor regression grades in resections. Enumeration of the tumor microenvironment cell population was undertaken using in silico analysis of the transcriptional data, and real-time PCR validation of NCR1 undertaken. Immunohistochemistry and survival analysis was used to measure CD56+ cell populations in an independent cohort (n=150). Gene expression traits observed following long-course preoperative chemoradiotherapy in the discovery cohort suggested an increased abundance of natural killer cells in tumors that displayed a clinical response to CRT in a tumor regression grade-dependent manner. CD56+ natural killer-cell populations were measured by immunohistochemistry and found to be significantly higher in tumor regression grade 3 patients compared with tumor regression grade 1-2 in the validation cohort. Furthermore, it was observed that patients positive for CD56 cells after therapy had a better overall survival (HR=0.282, 95% CI=0.109-0.729, χ2=7.854, P=0.005). In conclusion, we have identified a novel post-therapeutic natural killer-like transcription signature in patients responding to long-course preoperative chemoradiotherapy. Furthermore, patients with a higher abundance of CD56-positive natural killer cells post long-course preoperative chemoradiotherapy had better overall survival. Therefore, harnessing a natural killer-like response after therapy may improve outcomes for locally advanced rectal cancer patients. Finally, we hypothesize that future assessment of this natural killer-like response in on-treatment biopsy material may inform clinical decision-making for treatment duration.

Role of mathematical medicine in gastrointestinal carcinoma: Current status and perspectives

Mathematical medicine has already played an important role in clinical and basic research as a major interdisciplinary branch of medicine. Mathematical medicine has an important role not only in imaging diagnosis, image storage and transmission in gastrointestinal (GI) cancer, but also in tumor precision therapy. Specifically, in the field of minimally invasive treatment such as precise ablation, 3-dimension modeling, navigation, and surgical simulation significantly improve the therapeutic safety and efficiency in GI cancer. In addition, in the era of big data, data analysis and individualized therapy using mathematical medicine will become a trend in the future, offering an effective method for diagnosing and treating GI cancer and promoting clinical and scientific research.

KRAS mutant colorectal cancer gene signatures identified angiotensin II receptor blockers as potential therapies

Colorectal cancer (CRC) is a life-threatening disease with high prevalence and mortality worldwide. The KRAS oncogene is mutated in approximately 40% of CRCs. While antibody based EGFR inhibitors (cetuximab and panitumumab) represent a major treatment strategy for advanced KRAS wild type (KRAS-WT) CRCs, there still remains no effective therapeutic course for advanced KRAS mutant (KRAS-MT) CRC patients.In this study, we employed a novel and comprehensive approach of gene expression connectivity mapping (GECM) to identify candidate compounds to target KRAS-MT tumors. We first created a combined KRAS-MT gene signature with 248 ranked significant genes using 677 CRC clinical samples. A series of 248 sub-signatures was then created containing an increasing number of the top ranked genes. As an input to GECM analysis, each sub-signature was translated into a statistically significant therapeutic drugs list, which was finally combined to obtain a single list of significant drugs.We identify four antihypertensive angiotensin II receptor blockers (ARBs) within the top 30 significant drugs indicating that these drugs have a mechanism of action that can alter the KRAS-MT CRC oncogenic signaling. A hypergeometric test (p-value = 6.57 × 10-6) confirmed that ARBs are significantly enriched in our results. These findings support the hypothesis that ARB antihypertensive drugs may directly block KRAS signaling resulting in improvement in patient outcome or, through a reversion to a KRAS wild-type phenotype, improve the response to anti-EGFR treatment. Antihypertensive angiotensin II receptor blockers are therefore worth further investigation as potential therapeutic candidates in this difficult category of advanced colorectal cancers.

Challenges for drug discovery and development in China

INTRODUCTION

The drug development industry is restructuring worldwide in terms of the research and development process. As with pharmaceuticals in the west, China faces major challenges for drug discovery and development. Areas covered: In this review, the authors discuss anti-cancer, anti-allergy, anti-infectious, and proprietary Chinese Medicines (pCM) for various chronic diseases (such as the allergic diseases: eczema, asthma and allergic rhinitis), which remain the contemporary therapeutic strategies that are being explored and developed. Drug transporters, disease specific biomarkers, pharmacophores, bioactive natural products and pharmacogenetics are some aspects of research technologies. Proprietary Chinese medicine remains one of the most popular strategies. There is however the issue of good research documentation of efficacy versus adverse effects. China has a complex healthcare system involving a large patient pool. Expert opinion: Various factors can impact drug development in China including the concurrent use of both western and Chinese medicines, pharmacogenetic variances, lack of multidisciplinary team impact on disease management and drug safety. China may adopt the current development of big data analysis in other countries such as UK and US to build and centralize a nationwide database for better monitoring and clinical evaluation to provide more efficient care at a lower cost.