Abstract: Lung cancer is one of the major cause of cancer-related deaths worldwide. The poor prognosis and resistance to both radiation and chemotherapy urged the development of potential targets for lung cancer treatment. In this study, using a network-based cellular signature bioinformatics approach, we repurposed a clinically approved mTOR inhibitor for renal cell carcinomans, temsirolimus, as the potential therapeutic candidate for lung adenocarcinoma. The PI3K-AKT-mTOR pathway is known as one of the most frequently dysregulated pathway in cancers, including non-small-cell lung cancer. By using a well-documented lung adenocarcinoma mouse model of human pathophysiology, we examined the effect of temsirolimus on the growth of lung adenocarcinoma in vitro and in vivo. In addition, temsirolimus combined with reduced doses of cisplatin and gemcitabine significantly inhibited the lung tumor growth in the lung adenocarcinoma mouse model compared with the temsirolimus alone or the conventional cisplatin-gemcitabine combination. Functional imaging techniques and microscopic analyses were used to reveal the response mechanisms. Extensive immunohistochemical analyses were used to demonstrate the apparent effects of combined treatments on tumor architecture, vasculature, apoptosis, and the mTOR-pathway. The present findings urge the further exploration of temsirolimus in combination with chemotherapy for treating lung adenocarcinoma.

Abstract: A quantitative analysis of dose-response relationships is essential in preclinical and clinical drug development in order to optimize drug efficacy and safety, respectively. However, there is a lack of quantitative understanding about the dynamics of pharmacological drug-target interactions in biological systems. In this study, a quantitative systems pharmacology (QSP) approach is applied to quantify the drug efficacy of cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) inhibitors by coupling physiologically based pharmacokinetic models, at the whole-body level, with affected biological networks, at the cellular scale. Both COX-2 and 5-LOX are key enzymes in the production of inflammatory mediators and are known targets in the design of anti-inflammatory drugs. Drug efficacy is here evaluated for single and appropriate co-treatment of diclofenac, celecoxib, zileuton, and licofelone by quantitatively studying the reduction of prostaglandins and leukotrienes. The impact of rifampicin pre-treatment on prostaglandin formation is also investigated by considering pharmacokinetic drug interactions with diclofenac and celecoxib, finally suggesting optimized dose levels to compensate for the reduced drug action. Furthermore, a strong correlation was found between pain relief observed in patients as well as celecoxib- and diclofenac-induced decrease in prostaglandins after 6 h. The findings presented reveal insights about drug-induced modulation of cellular networks in a whole-body context, thereby describing complex pharmacokinetic/pharmacodynamic behavior of COX-2 and 5-LOX inhibitors in therapeutic situations. The results demonstrate the clinical benefit of using QSP to predict drug efficacy and, hence, encourage its use in future drug discovery and development programs.

Abstract: Drug repositioning for available medications can be preferred over traditional drug development, which requires substantially more effort to uncover new insights into medications and diseases. Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS) are two complimentary methods for finding novel associations between genes and diseases. We hypothesize that discoveries from these studies could be leveraged to find new targets for existing drugs. Thus, we propose a framework to learn opportunities for inferring such relationships via overlapped genes between disease-associated genes (e.g. GWAS and PheWAS findings) and drugtargeted genes. We use drug indications found in Medication Indication Resource (MEDI) as a gold standard to evaluate if drug indications learned from GWAS and PheWAS findings have clinical indications. We examined 151,011 pairs from 987 drugs across 153 diseases and 763 pairs were statistically significant. Out of these 763 pairs, 16 of them were found to have clinical indications.

Abstract: Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features or in case of visualization methods uncover underlying patterns in the feature space. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neural network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.

Abstract: The explosive growth of high-throughput experimental methods and resulting data yields both opportunity and challenge for selecting the correct drug to treat both a specific patient and their individual disease. Ideally, it would be useful and efficient if computational approaches could be applied to help achieve optimal drug-patient-disease matching but current efforts have met with limited success. Current approaches have primarily utilized the measureable effect of a specific drug on target tissue or cell lines to identify the potential biological effect of such treatment. While these efforts have met with some level of success, there exists much opportunity for improvement. This specifically follows the observation that, for many diseases in light of actual patient response, there is increasing need for treatment with combinations of drugs rather than single drug therapies. Only a few previous studies have yielded computational approaches for predicting the synergy of drug combinations by analyzing high-throughput molecular datasets. However, these computational approaches focused on the characteristics of the drug itself, without fully accounting for disease factors. Here, we propose an algorithm to specifically predict synergistic effects of drug combinations on various diseases, by integrating the data characteristics of disease-related gene expression profiles with drug-treated gene expression profiles. We have demonstrated utility through its application to transcriptome data, including microarray and RNASeq data, and the drug-disease prediction results were validated using existing publications and drug databases. It is also applicable to other quantitative profiling data such as proteomics data. We also provide an interactive web interface to allow our Prediction of Drug-Disease method to be readily applied to user data. While our studies represent a preliminary exploration of this critical problem, we believe that the algorithm can provide the basis for further refinement towards addressing a large clinical need.

Abstract: Background & Aims: Primary tumors of colorectal carcinoma (CRC) with liver metastasis might gain some liver-specific characteristics to adapt the liver micro-environment. This study aims to reveal potential liver-like transcriptional characteristics associated with the liver metastasis in primary colorectal carcinoma. Methods: Among the genes up-regulated in normal liver tissues versus normal colorectal tissues, we identified "liver-specific" genes whose expression levels ranked among the bottom 10% ("unexpressed") of all measured genes in both normal colorectal tissues and primary colorectal tumors without metastasis. These liver-specific genes were investigated for their expressions in both the primary tumors and the corresponding liver metastases of seven primary CRC patients with liver metastasis using microdissected samples. Results: Among the 3958 genes detected to be up-regulated in normal liver tissues versus normal colorectal tissues, we identified 12 liver-specific genes and found two of them, ANGPTL3 and CFHR5, were unexpressed in microdissected primary colorectal tumors without metastasis but expressed in both microdissected liver metastases and corresponding primary colorectal tumors (Fisher's exact test, P < 0.05). Genes co-expressed with ANGPTL3 and CFHR5 were significantly enriched in metabolism pathways characterizing liver tissues, including "starch and sucrose metabolism" and "drug metabolism-cytochrome P450". Conclusions: For primary CRC with liver metastasis, both the liver metastases and corresponding primary colorectal tumors may express some liver-specific genes which may help the tumor cells adapt the liver micro-environment.

Abstract: A significant portion of biomedical literature is represented in a manner that makes it difficult for consumers to find or aggregate content through a computational query. One approach to facilitate reuse of the scientific literature is to structure this information as linked data using standardized web technologies. In this paper we present the second version of Biotea, a semantic, linked data version of the open-access subset of PubMed Central that has been enhanced with specialized annotation pipelines that uses existing infrastructure from the National Center for Biomedical Ontology. We expose our models, services, software and datasets. Our infrastructure enables manual and semi-automatic annotation, resulting data are represented as RDF-based linked data and can be readily queried using the SPARQL query language. We illustrate the utility of our system with several use cases. Our datasets, methods and techniques are available at http://biotea.github.io.

Abstract: In this work we aim to show how Genome Scale Metabolic Models (GSMMs) can be used as tools for drug design. By comparing the chemical structures of human metabolites (obtained using their KEGG indexes) and the compounds contained in the DrugBank database, we have observed that compounds showing Tanimoto scores higher than 0.9 with a metabolite, are 29.5 times more likely to bind the enzymes metabolizing the considered metabolite, than ligands chosen randomly. By using RNA-seq data to constrain a human GSMM it is possible to obtain an estimation of its distribution of metabolic fluxes and to quantify the effects of restraining the rate of chosen metabolic reactions (for example using a drug that inhibits the enzymes catalyzing the mentioned reactions). This method allowed us to predict the differential effects of lipoamide analogs on the proliferation of MCF7 (a breast cancer cell line) and ASM (airway smooth muscle) cells respectively. These differential effects were confirmed experimentally, which provides a proof of concept of how human GSMMs could be used to find therapeutic windows against cancer. By using RNA-seq data of 34 different cancer cell lines and 26 healthy tissues, we assessed the putative anticancer effects of the compounds in DrugBank which are structurally similar to human metabolites. Among other results it was predicted that the mevalonate pathway might constitute a good therapeutic window against cancer proliferation, due to the fact that most cancer cell lines do not express the cholesterol transporter NPC1L1 and the lipoprotein lipase LPL, which makes them rely on the mevalonate pathway to obtain cholesterol.

Abstract: Over recent years, typhoid fever has gained increasing attention with several cases reporting treatment failure due to multidrug resistant (MDR) strains of Salmonella enterica serovar Typhi. While new drug development strategies are being devised to combat the threat posed by these MDR pathogens, drug repurposing or repositioning has become a good alternative. The latter is considered mainly due to its capacity for saving sufficient time and effort for pre-clinical and optimization studies. Owing to the possibility of an unsuccessful repositioning, due to the mismatch in the optimization of the drug ligand for the changed biochemical properties of "old" and "new" targets, we have chosen a "targeted" approach of adopting a combined chemical moiety-based drug repurposing. Using small molecules selected from a combination of earlier approved drugs having phenalenone and furanone moieties, we have computationally delineated a step-wise approach to drug design against MDR Salmonella. We utilized our network analysis-based pre-identified, essential chaperone protein, SicA, which regulates the folding and quality of several secretory proteins including the Hsp70 chaperone, SigE. To this end, another crucial chaperone protein, Hsp70 DnaK, was also considered due to its importance for pathogen survival under the stress conditions typically encountered during antibiotic therapies. These were docked with the 19 marketed anti-typhoid drugs along with two phenalenone-furanone derivatives, 15 non-related drugs which showed 70% similarity to phenalenone and furanone derivatives and other analogous small molecules. Furthermore, molecular dynamics simulation studies were performed to check the stability of the protein-drug complexes. Our results showed the best binding interaction and stability, under the parameters of a virtual human body environment, with XR770, a phenaleno-furanone moiety based derivative. We therefore propose XR770, for repurposing for therapeutic intervention against emerging and significant drug resistance conferred by pathogenic Salmonella strains.

Abstract: Background: Accumulating evidences indicate that the apoptosis of proximal tubular epithelial cells (PTECs) play a vital role in the progression of the diabetic kidney disease (DKD). This study aimed to explore the therapeutic potential of salidroside (SAL) in DKD and its underlying mechanism in anti-apoptosis of PTECs. Methods: Twenty-eight male Wistar rats were allocated into four groups: sham-operated, uninephrectomy (unx), diabetes with uninephrectomy (DKD) and DKD treated with SAL (DKD + SAL). SAL (70 mg/kg) was gavage administered for 8 weeks. 24-h albuminuria and serum creatinine (SCr), blood urea nitrogen (BUN), renal histological changes were examined. The silico analysis was used to identify the main therapeutic targets and pathways of SAL involved in DKD treatment. Apoptosis was determined by TUNEL and Annexin V-FITC/PI double staining in vivo and in vitro, respectively. The expression of BIM, BAX, and cleaved caspase-3 were evaluated by western blot and immunostaining. Results: Treatment with SAL significantly attenuated diabetic kidney injury via inhibiting 24-h albuminuria, SCr, BUN, glomerular mesangial dilatation and tubular injury in DKD rats. The silico analysis identified the intrinsic apoptotic pathway as an important pathway responsible for the nephroprotective properties of SAL. Our data validated that SAL effectively inhibited the apoptosis of PTECs induced by high-glucose (HG), both in vitro and in vivo. Silence of BIM by shRNA in HK-2 cells prevented HG-induced apoptosis. The up-regulated BIM and its downstream targets (BAX and cleaved caspase-3) were also inhibited by SAL. Conclusion: In summary, SAL significantly relieved DKD. And the possible mechanisms might be partially attributed to inhibiting apoptosis of proximal renal tubular cells. The apoptotic protein BIM could be an important target of SAL in this process.