Computational drug repositioning based on the relationships between substructure–indication

2020 ◽  
Author(s):  
Jingbo Yang ◽  
Denan Zhang ◽  
Lei Liu ◽  
Guoqi Li ◽  
Yiyang Cai ◽  
...  
Keyword(s):  
Drug Target ◽  
Drug Repositioning ◽  
Structural Similarity ◽  
Antiviral Effect ◽  
Therapeutic Effects ◽  
Apoptosis Pathway ◽  
Local Structures ◽  
Verification Methods ◽  
Trisphosphate Receptor ◽  
First Time

Abstract At present, computational methods for drug repositioning are mainly based on the whole structures of drugs, which limits the discovery of new functions due to the similarities between local structures of drugs. In this article, we, for the first time, integrated the features of chemical-genomics (substructure–domain) and pharmaco-genomics (domain–indication) based on the assumption that drug–target interactions are mediated by the substructures of drugs and the domains of proteins to identify the relationships between substructure–indication and establish a drug–substructure–indication network for predicting all therapeutic effects of tested drugs through only information on the substructures of drugs. In total, 83 205 drug–indication relationships with different correlation scores were obtained. We used three different verification methods to indicate the accuracy of the method and the reliability of the scoring system. We predicted all indications of olaparib using our method, including the known antitumor effect and unknown antiviral effect verified by literature, and we also discovered the inhibitory mechanism of olaparib toward DNA repair through its specific sub494 (o = C–C: C), as it participates in the low synthesis of the poly subfunction of the apoptosis pathway (hsa04210) by inhibiting the Inositol 1,4,5-trisphosphate receptor(s) (ITPRs) and hydrolyzing poly (ADP ribose) polymerases. ElectroCardioGrams of four drugs (quinidine, amiodarone, milrinone and fosinopril) demonstrated the effect of anti-arrhythmia. Unlike previous studies focusing on the overall structures of drugs, our research has great potential in the search for more therapeutic effects of drugs and in predicting all potential effects and mechanisms of a drug from the local structural similarity.

scholarly journals PROMISCUOUS 2.0: a resource for drug-repositioning

2020 ◽  
Vol 49 (D1) ◽  
pp. D1373-D1380
Author(s):  
Kathleen Gallo ◽  
Andrean Goede ◽  
Andreas Eckert ◽  
Barbara Moahamed ◽  
Robert Preissner ◽  
...  
Keyword(s):  
Side Effects ◽  
Small Molecules ◽  
User Experience ◽  
Drug Target ◽  
Drug Repositioning ◽  
Dynamic Network ◽  
Structural Similarity ◽  
New Drugs ◽  
Entry Level ◽  
Development Costs

Abstract The development of new drugs for diseases is a time-consuming, costly and risky process. In recent years, many drugs could be approved for other indications. This repurposing process allows to effectively reduce development costs, time and, ultimately, save patients’ lives. During the ongoing COVID-19 pandemic, drug repositioning has gained widespread attention as a fast opportunity to find potential treatments against the newly emerging disease. In order to expand this field to researchers with varying levels of experience, we made an effort to open it to all users (meaning novices as well as experts in cheminformatics) by significantly improving the entry-level user experience. The browsing functionality can be used as a global entry point to collect further information with regards to small molecules (∼1 million), side-effects (∼110 000) or drug-target interactions (∼3 million). The drug-repositioning tab for small molecules will also suggest possible drug-repositioning opportunities to the user by using structural similarity measurements for small molecules using two different approaches. Additionally, using information from the Promiscuous 2.0 Database, lists of candidate drugs for given indications were precomputed, including a section dedicated to potential treatments for COVID-19. All the information is interconnected by a dynamic network-based visualization to identify new indications for available compounds. Promiscuous 2.0 is unique in its functionality and is publicly available at http://bioinformatics.charite.de/promiscuous2.

scholarly journals Apomorphine induces mitochondrial-dysfunction-dependent apoptosis in choriocarcinoma

Reproduction ◽  
2020 ◽  
Vol 160 (3) ◽  
pp. 367-377
Author(s):  
Jin-Young Lee ◽  
Jiyeon Ham ◽  
Whasun Lim ◽  
Gwonhwa Song
Keyword(s):  
Molecular Mechanisms ◽  
Drug Repositioning ◽  
Synergistic Effects ◽  
Therapeutic Effects ◽  
Antitumor Effects ◽  
Apoptosis Pathway ◽  
Atp Production ◽  
Intrinsic Apoptosis Pathway ◽  
Choriocarcinoma Cells ◽  
Energy Deprivation

Apomorphine is a derivative of morphine that is used for the treatment of Parkinson’s disease because of its effects on the hypothalamus. Therapeutic effects of apomorphine have also been reported for various neurological diseases and cancers. However, the molecular mechanisms of the antitumor effects of apomorphine are not clear, especially with respect to choriocarcinoma. This is the first study to elucidate the anticancer effects of apomorphine on choriocarcinoma. We found that apomorphine suppressed the viability, proliferation, ATP production, and spheroid formation of JEG3 and JAR choriocarcinoma cells. Moreover, apomorphine activated the intrinsic apoptosis pathway by activating caspases and inhibited the production of anti-apoptotic proteins in choriocarcinoma cells. Further, apomorphine caused depolarization of mitochondria, calcium overload, energy deprivation, and endoplasmic reticulum stress in JEG3 and JAR cells. We confirmed synergistic effects of apomorphine with paclitaxel, a traditional chemotherapeutic agent, and propose that apomorphine could be a potential therapeutic agent in choriocarcinoma and an important candidate for drug repositioning that could help overcome resistance to conventional chemotherapy.

scholarly journals Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shingo Tsuji ◽  
Takeshi Hase ◽  
Ayako Yachie-Kinoshita ◽  
Taiko Nishino ◽  
Samik Ghosh ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Drug Target ◽  
Target Genes ◽  
Drug Repositioning ◽  
Therapeutic Targets ◽  
Machine Learning Techniques ◽  
Computational Framework ◽  
Novel Therapeutic

Abstract Background Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. Methods In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. Results We applied our computational framework to prioritize novel putative target genes for Alzheimer’s disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). Conclusions Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

scholarly journals On processes of coloniality and decoloniality of knowledge: notions for analysing the international history of mathematics teaching

ZDM ◽  
2021 ◽  
Author(s):  
Gert Schubring
Keyword(s):  
General Education ◽  
Mathematics Teaching ◽  
History Of Mathematics ◽  
Educational Systems ◽  
Political Processes ◽  
Local Structures ◽  
Novel Approach ◽  
History Of ◽  
Global And Local ◽  
First Time

AbstractThe aspiration of this paper is to develop a novel approach towards investigating the socio-political history of mathematics teaching in educational systems. Traditionally, historical studies are confined to just one country, the author’s country. Broader approaches address international developments by confronting and comparing global and local aspects—revealing general patterns and more specific ‘local’ structures and characteristics. Yet, already in antiquity and medieval times, the specific characteristic of mathematics teaching, namely to operate at the crossroads of general education and vocational training, proved to be intimately tied to the functioning of the particular political system. In pre-modern times, however, a truly international pattern emerged for the first time: European powers conquered, occupied and colonised overseas regions. Given that educational systems were emerging at the same time within these states, they often transmitted elements of these structures to their colonies. This phenomenon included mathematics, and the history of its teaching is analysed here as a part of coloniality. It is shown that this was not a uniform process, and the differences between the various colonial powers are discussed. The involvement of mathematics in the process of decolonisation is addressed, as well as its role in the tension between continued coloniality and movements of decoloniality. Finally, the general framework provided for studying socio-political processes connected with establishing mathematics teaching within public educational systems is applied, in order to analyse recent coloniality practices effected by international achievement studies.

scholarly journals An Application of Fit Quality to Screen MDM2/p53 Protein-Protein Interaction Inhibitors

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3174 ◽  
Author(s):  
Xin Xue ◽  
Gang Bao ◽  
Hai-Qing Zhang ◽  
Ning-Yi Zhao ◽  
Yuan Sun ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Drug Target ◽  
P53 Protein ◽  
Pharmacophore Model ◽  
Cellular Level ◽  
Complex Structures ◽  
Ligand Complex ◽  
Null Cell ◽  
Protein Protein Interaction ◽  
First Time

: The judicious application of ligand or binding efficiency (LE) metrics, which quantify the molecular properties required to obtain binding affinity for a drug target, is gaining traction in the selection and optimization of fragments, hits and leads. Here we report for the first time the use of LE based metric, fit quality (FQ), in virtual screening (VS) of MDM2/p53 protein-protein interaction inhibitors (PPIIs). Firstly, a Receptor-Ligand pharmacophore model was constructed on multiple MDM2/ligand complex structures to screen the library. The enrichment factor (EF) for screening was calculated based on a decoy set to define the screening threshold. Finally, 1% of the library, 335 compounds, were screened and re-filtered with the FQ metric. According to the statistical results of FQ vs activity of 156 MDM2/p53 PPIIs extracted from literatures, the cut-off was defined as FQ = 0.8. After the second round of VS, six compounds with the FQ > 0.8 were picked out for assessing their antitumor activity. At the cellular level, the six hits exhibited a good selectivity (larger than 3) against HepG2 (wt-p53) vs Hep3B (p53 null) cell lines. On the further study, the six hits exhibited an acceptable affinity (range of Ki from 102 to 103 nM) to MDM2 when comparing to Nutlin-3a. Based on our work, FQ based VS strategy could be applied to discover other PPIIs.

scholarly journals Antiviral Effects of Gelatin Stabilized Ferrous Sulfide Nanoparticles

2021 ◽  
Author(s):  
Ting Tong ◽  
Shuangfei Deng ◽  
Xiaotong Zhang ◽  
Liurong Fang ◽  
Jiangong Liang ◽  
...  
Keyword(s):  
Environmental Remediation ◽  
Low Cost ◽  
Antiviral Effect ◽  
High Reactivity ◽  
Host Cells ◽  
Antiviral Effects ◽  
Ferrous Sulfide ◽  
Effective Inhibition ◽  
Co Precipitation ◽  
First Time

Abstract Ferrous sulfide nanoparticles (FeS NPs) are widely applied to environmental remediation, catalysis, energy storage and medicine because of their high reactivity, large specific surface area and low cost, arousing great interest of researchers. However, there is no literature reported on its application in the antiviral field. In the study, gelatin stabilized FeS nanoparticles (Gel-FeS NPs) were synthesized by co-precipitation of Fe2+ and S2‒ in the aqueous phase with continuous stirring under anaerobic conditions. The as-prepared Gel-FeS NPs were good stabilization and dispersibility with the size distribution of 77.7 ± 16.4 nm, as determined by UV-Vis spectrometer, TEM, FTIR, XRD and XPS. We reported for the first time the virucidic and antiviral activity of Gel-FeS NPs. The Gel-FeS NPs with good dispersibility and biocompatibility were synthesized, and they exhibited effective inhibition on the proliferation of PRRSV by blocking the PRRSV outside the host cells. Moreover, the Fe2+ from degraded ferrous sulfide still displayed an antiviral effect, demonstrating the advantage as an antiviral nanomaterial of Gel-FeS NPs compared to other nanomaterials. This work highlighted the antiviral effect of Gel-FeS NPs, broaden the applications of iron-based nanoparticles for combating the virus.

AN ANALYSIS OF THE THZ FREQUENCY SIGNATURES IN THE CELLULAR COMPONENTS OF BIOLOGICAL AGENTS

2007 ◽  
Vol 17 (02) ◽  
pp. 225-237 ◽  
Author(s):  
ALEXEI BYKHOVSKI ◽  
TATIANA GLOBUS ◽  
TATYANA KHROMOVA ◽  
BORIS GELMONT ◽  
DWIGHT WOOLARD
Keyword(s):  
Molecular Structure ◽  
Structural Similarity ◽  
Md Simulations ◽  
Detection Capability ◽  
Dna Structures ◽  
E Coli ◽  
Cellular Components ◽  
First Time ◽  
Insight Into ◽  
Specific Contribution

The development of an effective biological (bio) agent detection capability based upon terahertz (THz) frequency absorption spectra will require insight into how the constituent cellular components contribute to the overall THz signature. In this work, the specific contribution of ribonucleic acid (RNA) to THz spectra is analyzed in detail. Previously, it has only been possible to simulate partial fragments of the RNA (or DNA) structures due to the excessive computational demands. For the first time, the molecular structure of the entire transfer RNA (tRNA) molecule of E. coli was simulated and the associated THz signature was derived theoretically. The tRNA that binds amino acid tyrosine (tRNAtyr) was studied. Here, the molecular structure was optimized using the potential energy minimization and molecular dynamical (MD) simulations. Solvation effects (water molecules) were also included explicitly in the MD simulations. To verify that realistic molecular signatures were simulated, a parallel experimental study of tRNAs of E. coli was also conducted. Two very similar molecules, valine and tyrosine tRNA were investigated experimentally. Samples were prepared in the form of water solutions with the concentrations in the range 0.01-1 mg/ml. A strong correlation of the measured THz signatures associated with valine tRNA and tyrosine tRNA was observed. These findings are consistent with the structural similarity of the two tRNAs. The calculated THz signature of the tyrosine tRNA of E. coli reproduces many features of our measured spectra, and, therefore, provides valuable new insights into bio-agent detection.

scholarly journals Open-source chemogenomic data-driven algorithms for predicting drug–target interactions

2018 ◽  
Vol 20 (4) ◽  
pp. 1465-1474 ◽  
Author(s):  
Ming Hao ◽  
Stephen H Bryant ◽  
Yanli Wang
Keyword(s):  
Drug Discovery ◽  
Human Genome ◽  
High Throughput Screening ◽  
Drug Target ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Research Field ◽  
Data Driven ◽  
Source Codes ◽  
Novel Technologies

AbstractWhile novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug–target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

PRESENCE OF OBSTACLES TO SERVING A SENTENCE AS A CRIMINAL LAW FEATURE IN THE CONTEXT OF RELEASE FROM PUNISHMENT FOR ILLNESS

2017 ◽  
Author(s):  
Nazarii Polishchuk ◽  
Keyword(s):  
Medical Care ◽  
Criminal Law ◽  
Informed Decision ◽  
Medical Examination ◽  
Correctional Facilities ◽  
Therapeutic Effects ◽  
Serious Illness ◽  
First Time

This article analyzes and investigates the grounds, conditions and circumstances which can be considered by the court as a mandatory criminal law feature one of which is the existence of obstacles to serving a sentence of imprisonment when considering release from punishment in connection with another serious illness. It is proved that in case of impossibility to conduct a medical examination or provide medical care, such a circumstance should be taken into account by the court as a factor preventing the serving of a sentence of imprisonment. Exemption from punishment due to illness is a certain activity, the result of which is a decision of the court to release or refuse to release a particular convict. In order for the court to make an informed decision, scientists and the legislator have identified several mandatory criminal law features, including circumstances that prevent the serving of sentences, which include such an obstacle to serving a sentence in case of a serious illness of the convict. This article for the first time analyzes the previously unselected grounds, conditions and circumstances that must be considered by the court as a mandatory criminal legal sign of release from punishment in connection with another serious illness, including an obstacle to serving a sentence in case of serious illness. Analysis of the conditions and procedure for keeping convicts in ordinary (nonmedical) correctional facilities allows us to conclude that a serious illness prevents the implementation of punitive measures in institutions not intended for the use of medical measures (therapeutic effects). This conclusion generally applies to punishment in the form of arrest, during which the convict is also in conditions of severe isolation from society.

scholarly journals SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Cheng Yan ◽  
Jianxin Wang ◽  
Wei Lan ◽  
Fang-Xiang Wu ◽  
Yi Pan
Keyword(s):  
Computational Methods ◽  
Drug Target ◽  
Large Scale ◽  
High Efficiency ◽  
Drug Repositioning ◽  
External Validation ◽  
Complex Diseases ◽  
Biological Databases ◽  
Biomolecular Networks ◽  
New Chemical Entities

It is well known that drug discovery for complex diseases via biological experiments is a time-consuming and expensive process. Alternatively, the computational methods provide a low-cost and high-efficiency way for predicting drug-target interactions (DTIs) from biomolecular networks. However, the current computational methods mainly deal with DTI predictions of known drugs; there are few methods for large-scale prediction of failed drugs and new chemical entities that are currently stored in some biological databases may be effective for other diseases compared with their originally targeted diseases. In this study, we propose a method (called SDTRLS) which predicts DTIs through RLS-Kron model with chemical substructure similarity fusion and Gaussian Interaction Profile (GIP) kernels. SDTRLS can be an effective predictor for targets of old drugs, failed drugs, and new chemical entities from large-scale biomolecular network databases. Our computational experiments show that SDTRLS outperforms the state-of-the-art SDTNBI method; specifically, in the G protein-coupled receptors (GPCRs) external validation, the maximum and the average AUC values of SDTRLS are 0.842 and 0.826, respectively, which are superior to those of SDTNBI, which are 0.797 and 0.766, respectively. This study provides an important basis for new drug development and drug repositioning based on biomolecular networks.

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