scholarly journals Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds against Escherichia coli

2020 ◽  
Vol 13 (12) ◽  
pp. 431
Author(s):  
Beatriz Suay-Garcia ◽  
Antonio Falcó ◽  
J. Ignacio Bueso-Bordils ◽  
Gerardo M. Anton-Fos ◽  
M. Teresa Pérez-Gracia ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Antibacterial Activity ◽  
Prediction Models ◽  
Treatment Options ◽  
Drug Repurposing ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Linear Discriminant ◽  
Drug Candidates ◽  
Close Relationship

Drug repurposing appears as an increasing popular tool in the search of new treatment options against bacteria. In this paper, a tree-based classification method using Linear Discriminant Analysis (LDA) and discrete indexes was used to create a QSAR (Quantitative Structure-Activity Relationship) model to predict antibacterial activity against Escherichia coli. The model consists on a hierarchical decision tree in which a discrete index is used to divide compounds into groups according to their values for said index in order to construct probability spaces. The second step consists in the calculation of a discriminant function which determines the prediction of the model. The model was used to screen the DrugBank database, identifying 134 drugs as possible antibacterial candidates. Out of these 134 drugs, 8 were antibacterial drugs, 67 were drugs approved for different pathologies and 55 were drugs in experimental stages. This methodology has proven to be a viable alternative to the traditional methods used to obtain prediction models based on LDA and its application provides interesting new drug candidates to be studied as repurposed antibacterial treatments. Furthermore, the topological indexes Nclass and Numhba have proven to have the ability to group active compounds effectively, which suggests a close relationship between them and the antibacterial activity of compounds against E. coli.

scholarly journals An In Silico Model for Predicting Drug-Induced Hepatotoxicity

2019 ◽  
Vol 20 (8) ◽  
pp. 1897 ◽  
Author(s):  
Shuaibing He ◽  
Tianyuan Ye ◽  
Ruiying Wang ◽  
Chenyang Zhang ◽  
Xuelian Zhang ◽  
...  
Keyword(s):  
Large Scale ◽  
Characteristic Curve ◽  
External Validation ◽  
Model Development ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
New Drugs ◽  
Drug Induced ◽  
Drug Candidates ◽  
External Test

As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative structure–activity relationship (QSAR) model for predicting DILI with satisfactory performance. In this work, we reported a high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin. In model development, a large-scale and diverse dataset consisting of 1254 compounds for DILI was built through a comprehensive literature retrieval. The optimal model was attained by an ensemble method, averaging the probabilities from eight classifiers, with accuracy (ACC) of 0.783, sensitivity (SE) of 0.818, specificity (SP) of 0.748, and area under the receiver operating characteristic curve (AUC) of 0.859. For further validation, three external test sets and a large negative dataset were utilized. Consequently, both the internal and external validation indicated that our model outperformed prior studies significantly. Data provided by the current study will also be a valuable source for modeling/data mining in the future.

scholarly journals Drug repurposing using transcriptome sequencing and virtual drug screening in a patient with glioblastoma

2020 ◽  
Author(s):  
Mohamed E. M. Saeed ◽  
Onat Kadioglu ◽  
Henry Johannes Greten ◽  
Adem Yildirim ◽  
Katharina Mayr ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Treatment Options ◽  
Drug Repurposing ◽  
Sequencing Data ◽  
Survival Times ◽  
Chemical Library ◽  
Ki 67 ◽  
Drug Candidates ◽  
Egfr Expression ◽  
Approved Drugs

SummaryBackground Precision medicine and drug repurposing are attractive strategies, especially for tumors with worse prognosis. Glioblastoma is a highly malignant brain tumor with limited treatment options and short survival times. We identified novel BRAF (47-438del) and PIK3R1 (G376R) mutations in a glioblastoma patient by RNA-sequencing. Methods The protein expression of BRAF and PIK3R1 as well as the lack of EGFR expression as analyzed by immunohistochemistry corroborated RNA-sequencing data. The expression of additional markers (AKT, SRC, mTOR, NF-κB, Ki-67) emphasized the aggressiveness of the tumor. Then, we screened a chemical library of > 1500 FDA-approved drugs and > 25,000 novel compounds in the ZINC database to find established drugs targeting BRAF47-438del and PIK3R1-G376R mutated proteins. Results Several compounds (including anthracyclines) bound with higher affinities than the control drugs (sorafenib and vemurafenib for BRAF and PI-103 and LY-294,002 for PIK3R1). Subsequent cytotoxicity analyses showed that anthracyclines might be suitable drug candidates. Aclarubicin revealed higher cytotoxicity than both sorafenib and vemurafenib, whereas idarubicin and daunorubicin revealed higher cytotoxicity than LY-294,002. Liposomal formulations of anthracyclines may be suitable to cross the blood brain barrier. Conclusions In conclusion, we identified novel small molecules via a drug repurposing approach that could be effectively used for personalized glioblastoma therapy especially for patients carrying BRAF47-438del and PIK3R1-G376R mutations.

scholarly journals QSAR modeling of antibacterial activity of some benzimidazole derivatives

2011 ◽  
Vol 17 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Sanja Podunavac-Kuzmanovic ◽  
Dragoljub Cvetkovic
Keyword(s):  
Antibacterial Activity ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Benzimidazole Derivatives ◽  
Qsar Modeling ◽  
Qsar Study ◽  
Validation Procedure ◽  
Leave One Out

A quantitative structure-activity relationship (QSAR) study has been carried out for training set of 12 benzimidazole derivatives to correlate and predict the antibacterial activity of studied compounds against Gram-negative bacteria Pseudomonas aeruginosa. Multiple linear regression was used to select the descriptors and to generate the best prediction model that relates the structural features to inhibitory activity. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based on the following descriptors: parameter of lipophilicity (logP) and hydration energy (HE). Good agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the generated QSAR model.

scholarly journals QSAR Study of Anthra[1,9-cd]pyrazol-6(2H)-one Derivatives as Potential Anticancer Agents Using Statistical Methods

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
El Ghalia Hadaji ◽  
Abdelkarim Ouammou ◽  
Mohammed Bouachrine
Keyword(s):  
Anticancer Agents ◽  
Density Functional ◽  
Cross Validation ◽  
Prediction Models ◽  
Structural Parameters ◽  
Three Dimensional ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Topological Descriptors ◽  
Qsar Study

In this study, the anticancer activity of a series of 32 molecules based on anthra[1,9-cd]pyrazol-6(2H)-one was studied by three-dimensional quantitative structure-activity relationship (QSAR) analyses: multiple linear regression (MLR), partial least squares (PLS), multiple nonlinear regression (MNLR), cross-validation analyses, and Y-randomization. A theoretical study of series was firstly studied using density functional theory (DFT) calculations at B3LYP/6-31 level of theory for employing to determine the structural parameters and electronic properties. Then the topological descriptors were computed using ACD/ChemSketch and ChemDraw 8.0 programs. The RNLM, given the descriptors obtained from the MLR and PLS, exhibited a correlation coefficient close to 0.91. The prediction models collected were confirmed by two methods of cross-validation and scrambling (or Y-randomization). The strong correlation between experimental and predicted activity values was observed, indicating the validation and good quality of the derived QSAR model.

scholarly journals Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational approaches applied to the discovery of drug candidates

2021 ◽  
Author(s):  
Mateus S.M. Serafim ◽  
Jadson C. Gertrudes ◽  
Débora M. A. Costa ◽  
Patricia R. Oliveira ◽  
Vinicius G. Maltarollo ◽  
...  
Keyword(s):  
Current Knowledge ◽  
Treatment Options ◽  
Drug Repurposing ◽  
Critical Issue ◽  
Economic Systems ◽  
Computational Approaches ◽  
Research Teams ◽  
Drug Candidates ◽  
Current Scenario ◽  
Governmental Institutions

Since the emergence of the new severe acute respiratory syndrome-related coronaviruses 2 (SARS-CoV-2) at the end of December 2019 in China, and with the urge of the coronavirus disease 2019 (COVID-19) pandemic, there have been a huge effort of many research teams and governmental institutions worldwide to mitigate the current scenario. Reaching more than 1,377,000 deaths in the world and still with a growing number of infections, SARS-CoV-2 remains a critical issue for global health and economic systems, with an urgency for available therapeutic options. In this scenario, as drug repurposing and discovery remains a challenge, computer-aided drug design (CADD) approaches, including machine learning (ML) techniques, can be useful tools to the design and discovery of novel potential antiviral inhibitors against SARS-CoV-2. In this work, we describe and review the current knowledge on this virus and the pandemic, the latest strategies and computational approaches applied to search for treatment options, as well as the challenges to overcome COVID-19.

scholarly journals Topological index Nclass as a factor determining the antibacterial activity of quinolones against Escherichia coli

2019 ◽  
Vol 11 (17) ◽  
pp. 2255-2262 ◽  
Author(s):  
Beatriz Suay-García ◽  
Pedro Alemán-López ◽  
José I Bueso-Bordils ◽  
Antonio Falcó ◽  
María T Pérez-Gracia ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Topological Index ◽  
Molecular Size ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Linear Discriminant ◽  
Structure Activity ◽  
New Antibiotics

Aim: Due to antibiotic resistance and the lack of investment in antimicrobial R&D, quantitative structure–activity relationship (SAR) methods appear as an ideal approach for the discovery of new antibiotics. Result & methodology: Molecular topology and linear discriminant analysis were used to construct a model to predict activity against Escherichia coli. This model establishes new SARs, of which, molecular size and complexity ( Nclass), stand out for their discriminant power. This model was used for the virtual screening of the Index Merck database, with results showing a high success rate as well as a moderate restriction. Conclusion: The model efficiently finds new active compounds. The topological index Nclass can act as a filter in other quantitative structure–activity relationship models predicting activity against E. coli.

scholarly journals Identification of 37 Heterogeneous Drug Candidates for Treatment of COVID-19 via a Rational Transcriptomics-Based Drug Repurposing Approach

2021 ◽  
Vol 14 (2) ◽  
pp. 87
Author(s):  
Andrea Gelemanović ◽  
Tinka Vidović ◽  
Višnja Stepanić ◽  
Katarina Trajković
Keyword(s):  
Treatment Options ◽  
Drug Repurposing ◽  
Enrichment Analysis ◽  
Current Treatment ◽  
Host Cells ◽  
Biological Knowledge ◽  
Pathway Enrichment Analysis ◽  
Biological Processes ◽  
Pathway Enrichment ◽  
Drug Candidates

A year after the initial outbreak, the COVID-19 pandemic caused by SARS-CoV-2 virus remains a serious threat to global health, while current treatment options are insufficient to bring major improvements. The aim of this study is to identify repurposable drug candidates with a potential to reverse transcriptomic alterations in the host cells infected by SARS-CoV-2. We have developed a rational computational pipeline to filter publicly available transcriptomic datasets of SARS-CoV-2-infected biosamples based on their responsiveness to the virus, to generate a list of relevant differentially expressed genes, and to identify drug candidates for repurposing using LINCS connectivity map. Pathway enrichment analysis was performed to place the results into biological context. We identified 37 structurally heterogeneous drug candidates and revealed several biological processes as druggable pathways. These pathways include metabolic and biosynthetic processes, cellular developmental processes, immune response and signaling pathways, with steroid metabolic process being targeted by half of the drug candidates. The pipeline developed in this study integrates biological knowledge with rational study design and can be adapted for future more comprehensive studies. Our findings support further investigations of some drugs currently in clinical trials, such as itraconazole and imatinib, and suggest 31 previously unexplored drugs as treatment options for COVID-19.

scholarly journals Drugs Repurposing Using QSAR, Docking and Molecular Dynamics for Possible Inhibitors of the SARS-CoV-2 Mpro Protease

Molecules ◽  
2020 ◽  
Vol 25 (21) ◽  
pp. 5172
Author(s):  
Eduardo Tejera ◽  
Cristian R. Munteanu ◽  
Andrés López-Cortés ◽  
Alejandro Cabrera-Andrade ◽  
Yunierkis Pérez-Castillo
Keyword(s):  
Molecular Dynamics ◽  
Learning Strategy ◽  
Drug Repurposing ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Free Energies ◽  
Main Protease ◽  
Potential Inhibitors ◽  
Dynamics Analyses ◽  
Free Energy Of Binding

Wuhan, China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 and it is the causative agent of the novel human coronavirus disease 2019 (COVID-19). Almost from the beginning of the COVID-19 outbreak several attempts were made to predict possible drugs capable of inhibiting the virus replication. In the present work a drug repurposing study is performed to identify potential SARS-CoV-2 protease inhibitors. We created a Quantitative Structure–Activity Relationship (QSAR) model based on a machine learning strategy using hundreds of inhibitor molecules of the main protease (Mpro) of the SARS-CoV coronavirus. The QSAR model was used for virtual screening of a large list of drugs from the DrugBank database. The best 20 candidates were then evaluated in-silico against the Mpro of SARS-CoV-2 by using docking and molecular dynamics analyses. Docking was done by using the Gold software, and the free energies of binding were predicted with the MM-PBSA method as implemented in AMBER. Our results indicate that levothyroxine, amobarbital and ABP-700 are the best potential inhibitors of the SARS-CoV-2 virus through their binding to the Mpro enzyme. Five other compounds showed also a negative but small free energy of binding: nikethamide, nifurtimox, rebimastat, apomine and rebastinib.

scholarly journals Drug Repurposing for COVID-19 Treatment by Integrating Network Pharmacology and Transcriptomics

Pharmaceutics ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 545
Author(s):  
Dan-Yang Liu ◽  
Jia-Chen Liu ◽  
Shuang Liang ◽  
Xiang-He Meng ◽  
Jonathan Greenbaum ◽  
...  
Keyword(s):  
Immune Cell ◽  
Treatment Plan ◽  
Treatment Options ◽  
Drug Repurposing ◽  
Gene Set Enrichment Analysis ◽  
Exposure Model ◽  
Network Pharmacology ◽  
Integrated Network ◽  
Health Crisis ◽  
Drug Candidates

Since coronavirus disease 2019 (COVID-19) is a serious new worldwide public health crisis with significant morbidity and mortality, effective therapeutic treatments are urgently needed. Drug repurposing is an efficient and cost-effective strategy with minimum risk for identifying novel potential treatment options by repositioning therapies that were previously approved for other clinical outcomes. Here, we used an integrated network-based pharmacologic and transcriptomic approach to screen drug candidates novel for COVID-19 treatment. Network-based proximity scores were calculated to identify the drug–disease pharmacological effect between drug–target relationship modules and COVID-19 related genes. Gene set enrichment analysis (GSEA) was then performed to determine whether drug candidates influence the expression of COVID-19 related genes and examine the sensitivity of the repurposing drug treatment to peripheral immune cell types. Moreover, we used the complementary exposure model to recommend potential synergistic drug combinations. We identified 18 individual drug candidates including nicardipine, orantinib, tipifarnib and promethazine which have not previously been proposed as possible treatments for COVID-19. Additionally, 30 synergistic drug pairs were ultimately recommended including fostamatinib plus tretinoin and orantinib plus valproic acid. Differential expression genes of most repurposing drugs were enriched significantly in B cells. The findings may potentially accelerate the discovery and establishment of an effective therapeutic treatment plan for COVID-19 patients.

scholarly journals Quantitative structure-activity relationships to predict antibacterial effect of some benzimidazole derivatives

2008 ◽  
pp. 181-191 ◽  
Author(s):  
Sanja Podunavac-Kuzmanovic ◽  
Dijana Barna ◽  
Dragoljub Cvetkovic
Keyword(s):  
Antibacterial Activity ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Biologically Active ◽  
Benzimidazole Derivatives ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Lipophilicity Parameter ◽  
Office Software

The antibacterial activity of some substituted benzimidazole derivatives against Gram negative bacteria Escherichia coli was investigated. The tested compounds displayed in vitro inhibitory activity and their minimum inhibitory concentrations were determined. Quantitative structure-activity relationship has been used to study the relationships between the antibacterial activity and lipophilicity parameter, logP. Lipophilicity parameters were calculated for each molecule by using CS Chem-Office Software version 7.0. Multiple linear regression was used to correlate the logP values and antibacterial activity of benzimidazole derivatives. The results are discussed on the basis of statistical data. The most acceptable QSAR model for prediction of antibacterial activity of the investigated series of benzimidazoles was developed. High agreement between experimental and predicted inhibitory values was obtained. The results of this study indicate that the lipophilicity parameter has a significant effect on antibacterial activity of this class of compounds, thus simplifying design of new biologically active molecules.

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