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2022 ◽  
Vol 15 (1) ◽  
pp. 94
Author(s):  
Maria Galvez-Llompart ◽  
Riccardo Zanni ◽  
Ramon Garcia-Domenech ◽  
Jorge Galvez

Even if amyotrophic lateral sclerosis is still considered an orphan disease to date, its prevalence among the population is growing fast. Despite the efforts made by researchers and pharmaceutical companies, the cryptic information related to the biological and physiological onset mechanisms, as well as the complexity in identifying specific pharmacological targets, make it almost impossible to find effective treatments. Furthermore, because of complex ethical and economic aspects, it is usually hard to find all the necessary resources when searching for drugs for new orphan diseases. In this context, computational methods, based either on receptors or ligands, share the capability to improve the success rate when searching and selecting potential candidates for further experimentation and, consequently, reduce the number of resources and time taken when delivering a new drug to the market. In the present work, a computational strategy based on Molecular Topology, a mathematical paradigm capable of relating the chemical structure of a molecule to a specific biological or pharmacological property by means of numbers, is presented. The result was the creation of a reliable and accessible tool to help during the early in silico stages in the identification and repositioning of potential hits for ALS treatment, which can also apply to other orphan diseases. Considering that further computational and experimental results will be required for the final identification of viable hits, three linear discriminant equations combined with molecular docking simulations on specific proteins involved in ALS are reported, along with virtual screening of the Drugbank database as a practical example. In this particular case, as reported, a clinical trial has been already started for one of the drugs proposed in the present study.


Pharmaceutics ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 62
Author(s):  
Georgiana Nitulescu ◽  
George Mihai Nitulescu ◽  
Anca Zanfirescu ◽  
Dragos Paul Mihai ◽  
Daniela Gradinaru

The pharmacological inhibition of the bacterial collagenases (BC) enzymes is considered a promising strategy to block the virulence of the bacteria without targeting the selection mechanism leading to drug resistance. The chemical structures of the Clostridium perfringens collagenase A (ColA) inhibitors were analyzed using Bemis–Murcko skeletons, Murcko frameworks, the type of plain rings, and docking studies. The inhibitors were classified based on their structural architecture and various scoring methods were implemented to predict the probability of new compounds to inhibit ColA and other BC. The analyses indicated that all compounds contain at least one aromatic ring, which is often a nitrobenzene fragment. 2-Nitrobenzene based compounds are, on average, more potent BC inhibitors compared to those derived from 4-nitrobenzene. The molecular descriptors MDEO-11, AATS0s, ASP-0, and MAXDN were determined as filters to identify new BC inhibitors and highlighted the necessity for a compound to contain at least three primary oxygen atoms. The DrugBank database was virtually screened using the developed methods. A total of 100 compounds were identified as potential BC inhibitors, of which, 10 are human approved drugs. Benzthiazide, entacapone, and lodoxamide were chosen as the best candidates for in vitro testing based on their pharmaco-toxicological profile.


2021 ◽  
Author(s):  
Milan Sencanski ◽  
Vladimir Perovic ◽  
Jelena Milicevic ◽  
Tamara Todorovic ◽  
Radivoje Prodanovic ◽  
...  

In the current pandemic finding an effective drug to prevent or treat the infection is the highest priority. A rapid and safe approach to counteract COVID-19 is in silico drug repurposing. The SARS-CoV-2 PLpro promotes viral replication and modulates the host immune system, resulting in inhibition of the host antiviral innate immune response, and therefore is an attractive drug target. In this study, we used a combined in silico virtual screening for candidates for SARS-CoV-2 PLpro protease inhibitors. We used the Informational spectrum method applied for Small Molecules for searching the Drugbank database followed by molecular docking. After in silico screening of drug space, we identified 44 drugs as potential SARS-CoV-2 PLpro inhibitors that we propose for further experimental testing.


BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hong-Bin Li ◽  
Jian-Li Wang ◽  
Xiao-Dong Jin ◽  
Lei Zhao ◽  
Hui-Li Ye ◽  
...  

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) remains a treatment-refractory malignancy with poor prognosis. It is urgent to identify novel and valid biomarkers to predict the progress and prognosis of PDAC. The S100A family have been identified as being involved in cell proliferation, migration and differentiation progression of various cancer types. However, the expression patterns and prognostic values of S100As in PDAC remain to be analyzed. Methods We investigated the transcriptional expressions, methylation level and prognostic value of S100As in PDAC patients from the Oncomine, GEPIA2, Linkedomics and cBioPortal databases. Real-time PCR was used to detect the expressions of S100A2/4/6/10/14/16 in four pancreatic cancer cell lines and pancreatic cancer tissues from PDAC patients undergoing surgery. To verify the results further, immunohistochemistry was used to measure the expression of S100A2/4/6/10/14/16 in 43 PDAC patients’ tissue samples. The drug relations of S100As were analyzed by using the Drugbank database. Results The results suggested that, the expression levels of S100A2/4/6/10/14/16 were elevated to PDAC tissues than in normal pancreatic tissues, and the promoter methylation levels of S100A S100A2/4/6/10/14/16 in PDAC (n = 10) were lower compared with normal tissue (n = 184) (P < 0.05). In addition, their expressions were negatively correlated with PDAC patient survival. Conclusions Taken together, these results suggest that S100A2/4/6/10/14/16 might be served as prognostic biomarkers for survivals of PDAC patients.


2021 ◽  
Author(s):  
Milan Sencanski ◽  
Vladimir Perovic ◽  
Jelena Milicevic ◽  
Tamara Todorovic ◽  
Radivoje Prodanovic ◽  
...  

The need for an effective drug against COVID-19, is, after almost 18 months since the global pandemics outburst, still very high. A very quick and safe approach to counteract COVID-19 is in silico drug repurposing. The SARS-CoV-2 PLpro promotes vi-ral replication and modulates the host immune system, resulting in inhibition of the host antiviral innate immune response, and there-fore is an attractive drug target. In this study, we used a combined in silico virtual screening candidates for SARS-CoV-2 PLpro protease inhibitors. We used the Informational spectrum method applied for Small Molecules for searching the Drugbank database and further followed by molecular docking. After in silico screening of drug space, we identified 44 drugs as potential SARS-CoV-2 PLpro inhibitors that we propose for further experimental testing.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Qichao Luo ◽  
Shenglong Mo ◽  
Yunfei Xue ◽  
Xiangzhou Zhang ◽  
Yuliang Gu ◽  
...  

Abstract Background Drug-drug interaction (DDI) is a serious public health issue. The L1000 database of the LINCS project has collected millions of genome-wide expressions induced by 20,000 small molecular compounds on 72 cell lines. Whether this unified and comprehensive transcriptome data resource can be used to build a better DDI prediction model is still unclear. Therefore, we developed and validated a novel deep learning model for predicting DDI using 89,970 known DDIs extracted from the DrugBank database (version 5.1.4). Results The proposed model consists of a graph convolutional autoencoder network (GCAN) for embedding drug-induced transcriptome data from the L1000 database of the LINCS project; and a long short-term memory (LSTM) for DDI prediction. Comparative evaluation of various machine learning methods demonstrated the superior performance of our proposed model for DDI prediction. Many of our predicted DDIs were revealed in the latest DrugBank database (version 5.1.7). In the case study, we predicted drugs interacting with sulfonylureas to cause hypoglycemia and drugs interacting with metformin to cause lactic acidosis, and showed both to induce effects on the proteins involved in the metabolic mechanism in vivo. Conclusions The proposed deep learning model can accelerate the discovery of new DDIs. It can support future clinical research for safer and more effective drug co-prescription.


Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1100
Author(s):  
Sebastián A. Cuesta ◽  
José R. Mora ◽  
Edgar A. Márquez

Coronavirus desease 2019 (COVID-19) is responsible for more than 1.80 M deaths worldwide. A Quantitative Structure-Activity Relationships (QSAR) model is developed based on experimental pIC50 values reported for a structurally diverse dataset. A robust model with only five descriptors is found, with values of R2 = 0.897, Q2LOO = 0.854, and Q2ext = 0.876 and complying with all the parameters established in the validation Tropsha’s test. The analysis of the applicability domain (AD) reveals coverage of about 90% for the external test set. Docking and molecular dynamic analysis are performed on the three most relevant biological targets for SARS-CoV-2: main protease, papain-like protease, and RNA-dependent RNA polymerase. A screening of the DrugBank database is executed, predicting the pIC50 value of 6664 drugs, which are IN the AD of the model (coverage = 79%). Fifty-seven possible potent anti-COVID-19 candidates with pIC50 values > 6.6 are identified, and based on a pharmacophore modelling analysis, four compounds of this set can be suggested as potent candidates to be potential inhibitors of SARS-CoV-2. Finally, the biological activity of the compounds was related to the frontier molecular orbitals shapes.


2021 ◽  
Vol 12 ◽  
Author(s):  
Victor Kaytser ◽  
Pengfei Zhang

Background: Polypharmacy in abortive medications is often inevitable for patients with refractory headaches.Objective: We seek to enumerate an exhaustive list of headaches abortive medications that are without drug-drug interactions.Methods: We updated a list of acute medications based on the widely used Jefferson Headache Manual with novel abortive medications including ubrogepant, lasmiditan, and rimegepant. Opioids and barbiturate-containing products are excluded. From this resultant list of medications, we then conducted an exhaustive search of all pair-wise interactions via DrugBank's API. Using this interaction list, we filtered all possible two, three, and four drug combinations of abortive medications. The list of medications was then reapplied to DrugBank to verify the lack of known drug-drug interactions.Results: There are 192 medication combinations that do not contain any drug-drug interactions. Most common elements in these combinations are ubrogepant, prochlorperazine, followed by tizanidine. There are 67 three-drug combinations that do not contain interactions. Only two of the four-drug combinations do not yield some form of drug-drug interactions.Conclusion: This list of headaches abortive medications without drug-drug interactions is a useful tool for clinicians seeking to more effectively manage refractory headaches by implementing a rational polypharmacy.


2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Abdellah El Aissouq ◽  
Oussama Chedadi ◽  
Mohammed Bouachrine ◽  
Abdelkrim Ouammou

The recent outbreak of the coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) in the last few months raised global health concern. Previous research described that remdesivir and ritonavir can be used as effective drugs against COVID-19. In this study, we applied the structure-based virtual screening (SBVS) on the high similar remdesivir- and ritonavir-approved drugs, selected from the DrugBank database as well as on a series of ritonavir derivatives, selected from the literature. The aim was to provide new potent SARS-CoV-2 main protease (Mpro) inhibitors with high stability. The analysis was performed using AutoDock VINA implicated in the PyRx 0.8 tool. Based on the ligand binding energy, 20 compounds were selected and then analyzed by AutoDock tools. Among the 20 compounds, 3 compounds were selected as high-potent anti-COVID-19.


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