Modeling of non-steroidal anti-inflammatory drug effect within signaling pathways and miRNA-regulation pathways

In silico screening, synthesis, characterization and biological evaluation of novel anticancer agents as potential COX-2 inhibitors

BACKGROUND

Cyclooxygenase enzyme is frequently overexpressed in various types of cancer and found to play a crucial role in poor prognosis in cancer patients. In current research, we have reported the new COX-2 inhibitors for cancer treatment using computer-aided drug design and experimental validation.

METHODS

A total of 12,795 compounds from the different databases were used to screen against the COX-2 enzyme. It perceived three new compounds with better binding affinity to the enzyme. Afterwards, physicochemical properties and in silico bioactivity were assessed for efficacy, safety, and structural features required for binding. The molecules were synthesized and confirmed by spectroscopic techniques. Later on, molecules were evaluated for their anti-cancer activity using MCF-7, MDA-MB-231 and SiHa cancer cell lines.

RESULTS

Compound ZINC5921547 and ZINC48442590 (4a, and 4b) reduced the MCF-7, MDA-MB-231, and SiHa cells proliferation potently than parent compounds. The PG-E2 estimation shown, both compounds act through the COX-2 PGE2 axis. Compound 4a and 4b block the cell cycle at G1-S phase and induce cancer cell death.

CONCLUSIONS

We concluded that compounds 4a and 4b effectively promotes cancer cell death via COX-2 PGE2 axis, and further in vivo studies can be evaluated for development in both compounds as anticancer agents. The compilation of this information will help us to generate better outcome through robust computational methods. The high-quality experimental results may pave the way for identifying effective drug candidates for cancer treatment.

Systems biology of angiogenesis signaling: Computational models and omics

Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases.

This article is categorized under:

Cardiovascular Diseases > Computational Models

Cancer > Computational Models

Prediction of Radiation Pneumonitis Using Genome-Scale Flux Analysis of RNA-Seq Derived From Peripheral Blood

Purpose: Radiation pneumonitis (RP) frequently occurs during a treatment course of chest radiotherapy, which significantly reduces the clinical outcome and efficacy of radiotherapy. The ability to easily predict RP before radiotherapy would allow this disease to be avoided.

Methods and Materials: This study recruited 48 lung cancer patients requiring chest radiotherapy. For each participant, RNA sequencing (RNA-Seq) was performed on a peripheral blood sample before radiotherapy. The RNA-Seq data was then integrated into a genome-scale flux analysis to develop an RP scoring system for predicting the probability of occurrence of RP. Meanwhile, the clinical information and radiation dosimetric parameters of this cohort were collected for analysis of any statistical associations between these parameters and RP. A non-parametric rank sum test showed no significant difference between the predicted results from the RP score system and the clinically observed occurrence of RP in this cohort.

Results: The results of the univariant analysis suggested that the tumor stage, exposure dose, and bilateral lung dose of V5 and V20 were significantly associated with the occurrence of RP. The results of the multivariant analysis suggested that the exposure doses of V5 and V20 were independent risk factors associated with RP and a level of RP ≥ 2, respectively. Thus, our results indicate that our RP scoring system could be applied to accurately predict the risk of RP before radiotherapy because the scores were highly consistent with the clinically observed occurrence of RP.

Conclusion: Compared with the standard statistical methods, this genome-scale flux-based scoring system is more accurate, straightforward, and economical, and could therefore be of great significance when making clinical decisions for chest radiotherapy.

Non-steroidal anti-inflammatory drugs (NSAIDs) and organ damage: A current perspective

Owing to the efficacy in reducing pain and inflammation, non-steroidal anti-inflammatory drugs (NSAIDs) are amongst the most popularly used medicines confirming their position in the WHO's Model List of Essential Medicines. With escalating musculoskeletal complications, as evident from 2016 Global Burden of Disease data, NSAID usage is evidently unavoidable. Apart from analgesic, anti-inflammatory and antipyretic efficacies, NSAIDs are further documented to offer protection against diverse critical disorders including cancer and heart attacks. However, data from multiple placebo-controlled trials and meta-analyses studies alarmingly signify the adverse effects of NSAIDs in gastrointestinal, cardiovascular, hepatic, renal, cerebral and pulmonary complications. Although extensive research has elucidated the mechanisms underlying the clinical hazards of NSAIDs, no review has extensively collated the outcomes on various multiorgan toxicities of these drugs together. In this regard, the present review provides a comprehensive insight of the existing knowledge and recent developments on NSAID-induced organ damage. It precisely encompasses the current understanding of structure, classification and mode of action of NSAIDs while reiterating on the emerging instances of NSAID drug repurposing along with pharmacophore modification aimed at safer usage of NSAIDs where toxic effects are tamed without compromising the clinical benefits. The review does not intend to vilify these 'wonder drugs'; rather provides a careful understanding of their side-effects which would be beneficial in evaluating the risk-benefit threshold while rationally using NSAIDs at safer dose and duration.

AutoAnalyze in Systems Biology

AutoAnalyze is a highly customizable framework for the visualization and analysis of large-scale model graphs. Originally developed for use in the automotive domain, it also supports efficient computation within molecular networks represented by reaction equations. A static analysis approach is used for efficient treatment-condition-specific simulation. The chosen method relies on the computation of a global network data-flow resulting from the evaluation of individual genetic data. The approach facilitates complex analyses of biological components from a molecular network under specific therapeutic perturbations, as demonstrated in a case study. In addition to simulating the complex networks in a stable and reproducible way, kinetic constants can also be fine-tuned using a genetic algorithm and built-in statistical tools.

Mechanistic Computational Models of MicroRNA-Mediated Signaling Networks in Human Diseases

MicroRNAs (miRs) are endogenous non-coding RNA molecules that play important roles in human health and disease by regulating gene expression and cellular processes. In recent years, with the increasing scientific knowledge and new discovery of miRs and their gene targets, as well as the plentiful experimental evidence that shows dysregulation of miRs in a wide variety of human diseases, the computational modeling approach has emerged as an effective tool to help researchers identify novel functional associations between differential miR expression and diseases, dissect the phenotypic expression patterns of miRs in gene regulatory networks, and elucidate the critical roles of miRs in the modulation of disease pathways from mechanistic and quantitative perspectives. Here we will review the recent systems biology studies that employed different kinetic modeling techniques to provide mechanistic insights relating to the regulatory function and therapeutic potential of miRs in human diseases. Some of the key computational aspects to be discussed in detail in this review include (i) models of miR-mediated network motifs in the regulation of gene expression, (ii) models of miR biogenesis and miR⁻target interactions, and (iii) the incorporation of such models into complex disease pathways in order to generate mechanistic, molecular- and systems-level understanding of pathophysiology. Other related bioinformatics tools such as computational platforms that predict miR-disease associations will also be discussed, and we will provide perspectives on the challenges and opportunities in the future development and translational application of data-driven systems biology models that involve miRs and their regulatory pathways in human diseases.

Efficacy and Safety of Different Aceclofenac Treatments for Chronic Lower Back Pain: Prospective, Randomized, Single Center, Open-Label Clinical Trials

PURPOSE

Nonsteroidal anti-inflammatory drugs are a mainstay for medical treatment of chronic lower back pain (CLBP). Increased dose intervals for medication have been associated with increased patient adherence to prescriptions. The purpose of this clinical trial was to compare the efficacy and safety of a once daily dose of aceclofenac controlled release (CR) and a twice daily dose of aceclofenac for CLBP management.

MATERIALS AND METHODS

A prospective, randomized, single center, open-label clinical trial was performed to compare the efficacy and safety of aceclofenac CR (200 mg once daily) to aceclofenac dose (100 mg twice daily). Fifty patients in each group were enrolled for the study. The primary end point was Visual Analogue Scale (VAS) change at baseline to that at 2 weeks after medication and safety profiles. Also, change in quality of life measured by EuroQoL 5D (EQ-5D) and Oswestry Disability Index (ODI) functional score for the lumbar spine were also assessed.

RESULTS

Within groups at pre- and post-treatment, there were significant VAS reductions for aceclofenac CR and aceclofenac (p=0.028). EQ-5D increased significantly in both groups (p=0.037). ODI scores decreased significantly in both groups (p=0.012). However, there were no significant differences between aceclofenac CR and aceclofenac at pre- and post-treatment. Patients with aceclofenac CR showed significant increases in heartburn and indigestion and adverse gastrointestinal effects, compared to aceclofenac.

CONCLUSION

In patients with CLBP, aceclofenac CR and aceclofenac demonstrated significant symptomatic pain relief, improvement in quality of life and functional scores. Aceclofenac CR slightly increased gastrointestinal adverse effects, such as heartburn and indigestion.

Comparing Patient-Derived Xenograft and Computational Response Prediction for Targeted Therapy in Patients of Early-Stage Large Cell Lung Cancer

PURPOSE

Targeted therapy (TT) provides highly effective cancer treatment for appropriately selected individuals. A major challenge of TT is to select patients who would benefit most.

EXPERIMENTAL DESIGN

The study uses cancer material from 25 patients primarily diagnosed with non-small cell lung cancer (NSCLC). Patient-derived xenografts (PDXs) are treated with cetuximab and erlotinib. Treatment response is measured by tumor shrinkage comparing tumor volume at day 25 (V25) with tumor volume at baseline (V0). Shrinkage below 40% is considered as treatment response: V25/V0 < 0.4 (<40%). Furthermore, RNA-seq data from each tumor sample are used to predict tumor response to either treatment using an in silico molecular signaling map (MSM) approach.

RESULTS

PDX response was 40% (10/25; 95% CI [21.13%, 61.34%]) under cetuximab and 20% (5/25; 95% CI [6.83%, 40.70%]) under erlotinib. MSM predicted response was 48% (12/25; 95% CI [27.8%, 68.7%]) under cetuximab and 40% (10/25; 95% CI [21.13%, 61.34%]) under erlotinib. Agreement between PDX and MSM response prediction is substantial under cetuximab and erlotinib: 84% (21/25, P = 0.001) and 80% (20/25, P = 0.003). A total of 5 from the 25 patients have been treated with cetuximab showing a clinical response identical to both predictions.

CONCLUSIONS

For NSCLC patients, this proof-of-concept study shows a considerable agreement in response prediction from MSM and PDX approaches, but MSM saves time and laboratory resources. Our result indicates the potential of MSM-based approach for clinical decision making when selecting cancer TTs. Clin Cancer Res; 22(9); 2167-76. ©2015 AACR.

A microRNA molecular modeling extension for prediction of colorectal cancer treatment

Background

Several studies show that the regulatory impact of microRNAs (miRNAs) is an essential contribution to the pathogenesis of colorectal cancer (CRC). The expression levels of diverse miRNAs are associated with specific clinical diagnoses and prognoses of CRC. However, this association reveals very little actionable information with regard to how or whether to treat a CRC patient. To address this problem, we use miRNA expression data along with other molecular information to predict individual response of CRC cell lines and CRC patients.

Methods

A strategy has been developed to join four types of information: molecular, kinetic, genetic and treatment data for prediction of individual treatment response of CRC.

Results

Information on miRNA regulation, including miRNA target regulation and transcriptional regulation of miRNA, in integrated into an in silico molecular model for colon cancer. This molecular model is applied to study responses of seven CRC cell lines from NCI-60 to ten agents targeting signaling pathways. Predictive results of models without and with implemented miRNA information are compared and advantages are shown for the extended model. Finally, the extended model was applied to the data of 22 CRC patients to predict response to treatments of sirolimus and LY294002. The in silico results can also replicate the oncogenic and tumor suppression roles of miRNA on the therapeutic response as reported in the literature.

Conclusions

In summary, the results reveal that detailed molecular events can be combined with individual genetic data, including gene/miRNA expression data, to enhance in silico prediction of therapeutic response of individual CRC tumors. The study demonstrates that miRNA information can be applied as actionable information regarding individual therapeutic response.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1437-0) contains supplementary material, which is available to authorized users.

Noncoding RNAs, cytokines, and inflammation-related diseases

Chronic inflammation is involved in the onset and development of many diseases, including obesity, atherosclerosis, type 2 diabetes, osteoarthritis, autoimmune and degenerative diseases, asthma, periodontitis, and cirrhosis. The inflammation process is mediated by chemokines, cytokines, and different inflammatory cells. Although the molecules and mechanisms that regulate this primary defense mechanism are not fully understood, recent findings offer a putative role of noncoding RNAs, especially microRNAs (miRNAs), in the progression and management of the inflammatory response. These noncoding RNAs are crucial for the stability and maintenance of gene expression patterns that characterize some cell types, tissues, and biologic responses. Several miRNAs, such as miR-126, miR-132, miR-146, miR-155, and miR-221, have emerged as important transcriptional regulators of some inflammation-related mediators. Additionally, little is known about the involvement of long noncoding RNAs, long intergenic noncoding RNAs, and circular RNAs in inflammation-mediated processes and the homeostatic imbalance associated with metabolic disorders. These noncoding RNAs are emerging as biomarkers with diagnosis value, in prognosis protocols, or in the personalized treatment of inflammation-related alterations. In this context, this review summarizes findings in the field, highlighting those noncoding RNAs that regulate inflammation, with emphasis on recognized mediators such as TNF-α, IL-1, IL-6, IL-18, intercellular adhesion molecule 1, VCAM-1, and plasminogen activator inhibitor 1. The down-regulation or antagonism of the noncoding RNAs and the administration of exogenous miRNAs could be, in the near future, a promising therapeutic strategy in the treatment of inflammation-related diseases.

Protective effects of resveratrol against UVA-induced damage in ARPE19 cells

Ultraviolet radiation, especially UVA, can penetrate the lens, reach the retina, and induce oxidative stress to retinal pigment epithelial (RPE) cells. Even though it is weakly absorbed by protein and DNA, it may trigger the production of reactive oxygen species (ROS) and generate oxidative injury; oxidative injury to the retinal pigment epithelium has been implicated to play a contributory role in age-related macular degeneration (AMD). Studies showed that resveratrol, an abundant and active component of red grapes, can protect several cell types from oxidative stress. In this study, adult RPE cells being treated with different concentrations of resveratrol were used to evaluate the protective effect of resveratrol on RPE cells against UVA-induced damage. Cell viability assay showed that resveratrol reduced the UVA-induced decrease in RPE cell viability. Through flow cytometry analysis, we found that the generation of intracellular H₂O₂ induced by UVA irradiation in RPE cells could be suppressed by resveratrol in a concentration-dependent manner. Results of Western blot analysis demonstrated that resveratrol lowered the activation of UVA-induced extracellular signal-regulated kinase, c-jun-NH₂ terminal kinase and p38 kinase in RPE cells. In addition, there was also a reduction in UVA-induced cyclooxygenase-2 (COX-2) expression in RPE cells pretreated with resveratrol. Our observations suggest that resveratrol is effective in preventing RPE cells from being damaged by UVA radiation, and is worth considering for further development as a chemoprotective agent for the prevention of early AMD.

Unravelling adverse reactions to NSAIDs using systems biology

We introduce the reader to systems biology, using adverse drug reactions (ADRs), specifically hypersensitivity reactions to multiple non-steroidal anti-inflammatory drugs (NSAIDs), as a model. To disentangle the different processes that contribute to these reactions - from drug intake to the appearance of symptoms - it will be necessary to create high-throughput datasets. Just as crucial will be the use of systems biology to integrate and make sense of them. We review previous work using systems biology to study related pathologies such as asthma/allergy, and NSAID metabolism. We show examples of their application to NSAIDs-hypersensitivity using current datasets. We describe breakthroughs in high-throughput technology and speculate on their use to improve our understanding of this and other drug-induced pathologies.

Effects of small interfering RNA interference of connexin 37 on subcutaneous gastric tumours in mice

The present study aimed to investigate the effects of small interfering (si)RNA interference of connexin 37 (Cx37) on subcutaneous gastric tumours in mice. Constructed lentiviruses carrying siRNA against Cx37 significantly knocked down Cx37 mRNA and protein expression in vitro. A total of 60 mice with gastric cancer were randomly divided into the Cx37 siRNA group, the mock‑siRNA group and the control group. Cx37 siRNA, mock‑siRNA and saline were separately injected (with the lentiviruses transfected into the gastric cancer cells). Following six weeks, the Cx37 mRNA expression, Cx37 protein expression and tumor apoptosis were detected using semiquantitative reverse transcription‑polymerase chain reaction, western blot analysis and terminal deoxynucleotidyl transferase‑mediated dUTP nick end labelling, respectively. Six weeks following lentiviral transfection, the Cx37 mRNA levels in the Cx37 siRNA group, mock‑siRNA group and saline group decreased to 42, 63 and 67%, respectively (P<0.05). The mock‑siRNA group demonstrated no significant change in Cx37 levels compared with the control group. Western blot analysis revealed lower Cx37 protein levels in the Cx37‑RNAi group than in the other groups (0.21±0.07 vs. 0.65±0.06 vs. 0.54±0.07), and that the apoptotic index of the Cx37‑RNAi group was higher than those of the mock‑siRNA and control groups (19.7±5.1 vs. 9.8±6.4 vs. 10.5±7.2%, 11.1±6.9; P<0.05). In conclusion, it was demonstrated that Cx37 siRNA is correlated with gastric cancer. Interference of Cx37 effectively reduces Cx37 mRNA and protein expression and promotes tumour apoptosis.

Preoperative low dose NSAID treatment influences the genes for stemness, growth, invasion and metastasis in colorectal cancer

Preclinical data, and an increasing list of clinical investigations, show anti-inflammatory agents to favourably influence the biology of colorectal tumor. We have earlier reported on re-expression of activated immune cells after three days preoperative treatment of patients with colorectal carcinoma, randomized to receive oral NSAID (indomethacin or celebrex). Antisecretory prophylaxis (esomeprasol) was provided to all patients and served as sham treatment. Concomittant to MHC locus activation, Prominin1/CD133, a marker associated with stemness and poor prognosis in several solid tumors, was downregulated. The aim of the present study was to evaluate expression of additional regulators belonging to the stem cell niche, OCT4, SOX2 and BMP7, as well as some microRNAs, reported to act as tumor suppressors or oncomiRs. Peroperative tumor biopsies were analyzed by microarrays, quantitative real-time PCR and immunohistochemistry (IHC). The stem cell master regulator SOX2 was increased by NSAIDs (p<0.01), as well as the tumor suppressor miR-630 (p<0.01), while BMP7, a marker for poor prognosis in CRC, was downregulated by NSAID (indomethacin, p<0.02). The upregulation of SOX2, but not of its heterodimer binding partner OCT4, could imply a negative feed-back loop, with a switch‑off for stemness preservation of tumor cells. This is supported by the overall evaluation of gene expression profiles with subsequent events, indicating less aggressive tumors following NSAID treatment.

A molecular signaling map and its application

Cancer research over the past decades has revealed a number of molecular, biochemical, and cellular events that reflect progressive transformation of normal human cells into their malignant derivatives. These findings help to better understand the complexity of human tumorigenesis. In our study, molecular information is organized to chart a comprehensive map of the signaling network for human cancer. It includes transcriptional and translational regulation and diverse feedback-control loops. It is demonstrated that applying this signaling network map allows predicting the effect of targeted therapy before it can be applied into practice to reduce clinical trial risks. Hence, the proposed map with prognosticating potential effect might become part of drug discovery programs for targeted therapy. Applied in individual patient care it helps to reduce the current reliance of cancer treatment on chemotherapies with low therapeutic indices. This study also demonstrates that continuing elucidation of tumorigenesis will not only need heterotypic organ culture systems in vitro and increasingly refined animal models in vivo, but also computationally calculable virtual cell models in silico.