Combinatorial Chemistry & High Throughput Screening - Volume 20, Issue 10, 2017

Aims & Scope: Computational drug repositioning emerges as a new idea of drug discovery and development. Contrary to conventional routines, computational drug repositioning encompasses low risk and high safety. Some successful cases demonstrated its advantage. Therefore, a large number of computational drug repositioning approaches have been developed over the past decades. We summarized briefly these methods and classified them into target-based, geneexpression- based, phenome-based and multi-omics-based categories according to strategies of drug repositioning. Conclusion: We reviewed some representatives of computational drug repositioning methods in each category, with emphasis on detail of techniques and finally discussed developing trends of computational drug repositioning.

Objective: Drug-induced liver injury (DILI) is a major cause of drug withdrawal. The chemical properties of the drug, especially drug metabolites, play key roles in DILI. Our goal is to construct a QSAR model to predict drug hepatotoxicity based on drug metabolites. Materials and Methods: 64 hepatotoxic drug metabolites and 3,339 non-hepatotoxic drug metabolites were gathered from MDL Metabolite Database. Considering the imbalance of the dataset, we randomly split the negative samples and combined each portion with all the positive samples to construct individually balanced datasets for constructing independent classifiers. Then, we adopted an ensemble approach to make prediction based on the results of all individual classifiers and applied the minimum Redundancy Maximum Relevance (mRMR) feature selection method to select the molecular descriptors. Eventually, for the drugs in the external test set, a Bayesian inference method was used to predict the hepatotoxicity of a drug based on its metabolites. Results: The model showed the average balanced accuracy=78.47%, sensitivity =74.17%, and specificity=82.77%. Five molecular descriptors characterizing molecular polarity, intramolecular bonding strength, and molecular frontier orbital energy were obtained. When predicting the hepatotoxicity of a drug based on all its metabolites, the sensitivity, specificity and balanced accuracy were 60.38%, 70.00% and 65.19%, respectively, indicating that this method is useful for identifying the hepatotoxicity of drugs. Conclusions: We developed an in silico model to predict hepatotoxicity of drug metabolites. Moreover, Bayesian inference was applied to predict the hepatotoxicity of a drug based on its metabolites which brought out valuable high sensitivity and specificity.

Aim and Objective: Breast cancer is one of the major causes of cancer deaths in women worldwide. Therefore, it is necessary to discover novel drugs or design effective treatments for this disease. However, the research and development of drugs by using only experimental methods is always time-consuming and expensive. With the development of computer science, some advanced computational methods can make full use of known knowledge to design candidate drugs, thereby reducing the cost and time of experimental testing. Materials and Methods: A computational method was proposed to identify novel candidates for breast cancer. The approved drugs and genes of breast cancer were taken as the input of the method. The chemical-chemical interactions and chemical-protein interactions were adopted to extract possible candidates from large numbers of existing chemicals. The method included three stages, termed searching stage, filtering stage and selecting stage. In the searching stage, chemicals that have associations with approved drugs were extracted. Then, these chemicals were screened in the filtering stage to discard those that have no relationships with breast cancer related genes. Finally, a clustering algorithm, termed as EM clustering algorithm, was employed to identify the potential candidates in the selecting stage. Results: An extensive analysis of twenty-one chemicals related to the same category with approve drugs indicated that multiple selected candidates were confirmed to have anti-breast cancer activities by retrieving literature. Conclusion: This method can provide some valuable instructions for drug repositioning.

Aim and Objective: Cancer is one of the serious diseases that cause several human deaths every year. Up to now, we have spent lots of time and money to investigate this disease, thereby designing effective treatments. Previous studies mainly focus on studying genetic background of different subtypes of cancer and neglect another important factor, i.e. environmental factor. Carcinogenic chemical is one of the types of environmental factor; the exposure of such chemical may definitely initiate and promote the tumorigenesis. In this study, we tried to partly describe the differences between carcinogenic and non-carcinogenic chemicals using gene ontology (GO) terms and KEGG pathways. Material and Methods: The carcinogenic and non-carcinogenic chemicals that were retrieved from Carcinogenic Potency Database (CPDB) were encoded into numeric vectors using the enrichment theories of GO terms and KEGG pathways. Then, the minimal redundancy maximal relevance (mRMR) method was adopted to analyze all the features, resulting in some important GO terms and KEGG pathways. Results and Conclusion: The extensive analysis of the identified GO terms and KEGG pathways indicates that they all play roles during tumorigenesis, inducing that they can be the key indicator for the identification of carcinogenic chemicals.

Background: Epigallocatechin-3-gallate (EGCG) is an important pharmacological component in tea, and various effects, including anti-tumor, anti-inflammation, anti-aging, antiobesity, anti-diabetes, cardiovascular disease prevention and protection, immunoregulation, and neuroprotection, of this component have been confirmed. However, EGCG has been rarely used in clinical applications because of its poor stability and low bioavailability. Objective: The work summarizes the characteristics about EGCG, describes its pharmacological mechanisms, explores the clinical availability of EGCG, and establishes a basis for EGCG preparations in clinical applications. Conclusion: In addition to altering dosage forms or synthesizing analogs to overcome losses during absorption, inducing gauxiliary effect and enhancing chemosensitivity can be achieved by EGCG in a combined medication. The pharmacological action, pharmacology network, including mutation of signaling receptor and modulation of intracellular signaling pathway, and combination treatment strategy of EGCG are clarified and distinguished. Possible targets and combined applications based on the characteristics of EGCG are also systematically summarized. EGCG may be a candidate compound that maintains balance of the patient's disease progression and becomes suitable for clinical use in combination treatment.

Background: The post-translational modifications (PTMs) on the side chains of conserved lysine (Lys) residues play important roles in myriad cellular processes, such as modification of the structures and activities of histones, protein degradation and turnover, and the regulation of DNA damage responses. To date, several computational methods have been developed to identify different PTMs on Lys residues. However, most of these methods focused on identifying one particular PTM regardless of other types of PTMs. Method: In this study, we first conducted a computational investigation of three types of PTMs (acetylation, sumoylation, and ubiquitination) at the same time by analyzing the protein structure and sequence factors surrounding the substrate Lysresidues in these types of PTMs. To fully extract the structural and sequence information around the Lysresidues, six types of features were used to encode the peptide segments containing the substrates. Next, through a feature selection method, i.e., maximum relevance minimum redundancy (mRMR), two feature lists, i.e., MaxRel feature list and mRMR feature list, were obtained. For the mRMR feature list, it was applied to extract the optimal features of the random forest algorithm for distinguishing three types of PTMs. Results: An optimal classification model with an overall accuracy of 0.989 was built. For the MaxRel feature list, we investigated the top-ranked features to uncover the site-preference and residue-preference of Lys residues. Conclusion: The results suggested that the disorder structure and the preference of flanking residues were the most important attributes to distinguish the three types of PTMs, which were consistent with the results reported in previous studies.

Background: Dysregulated pathway identification is an important task which can gain insight into the underlying biological processes of disease. Current pathway-identification methods focus on a set of co-expression genes and single pathways and ignore the correlation between genes and pathways. Objective: This study takes into account the internal correlations not only between genes but also pathways to explore the underlying dysregulated pathways of Alzheimer's disease (AD), the most common form of dementia. Methods: In order to find the significantly differential genes for AD, mutual information (MI) is used to measure interdependencies between genes other than expression valves. Then, by integrating the topology information from KEGG, the significant pathways involved in the feature genes are identified. Next, the distance correlation (DC) is applied to measure the pairwise pathway crosstalks since DC has the advantage of detecting nonlinear correlations when compared to Pearson correlation. Finally, the pathway pairs with significantly different correlations between normal and AD samples are known as dysregulated pathways. Results: We identified 33 dysregulated pathway pairs related to AD in which the crosstalks score calculated by DC greatly changed from normal to AD samples. The molecular biology analysis demonstrated that many dysregulated pathways related to AD pathogenesis have been discovered successfully by the internal correlation detection. Conclusion: Our studies on the identification of the dysregulated pathways in the development and deterioration of AD will help to find new effective target genes which are closely related to the pathogenesis of AD and provide important theoretical guidance for drug design.