Repositioned Drugs for Chagas Disease Unveiled via Structure-Based Drug Repositioning

Chagas disease, caused by the parasite Trypanosoma cruzi, affects millions of people in South America. The current treatments are limited, have severe side effects, and are only partially effective. Drug repositioning, defined as finding new indications for already approved drugs, has the potential to provide new therapeutic options for Chagas. In this work, we conducted a structure-based drug repositioning approach with over 130,000 3D protein structures to identify drugs that bind therapeutic Chagas targets and thus represent potential new Chagas treatments. The screening yielded over 500 molecules as hits, out of which 38 drugs were prioritized following a rigorous filtering process. About half of the latter were already known to have trypanocidal activity, while the others are novel to Chagas disease. Three of the new drug candidates—ciprofloxacin, naproxen, and folic acid—showed a growth inhibitory activity in the micromolar range when tested ex vivo on T. cruzi trypomastigotes, validating the prediction. We show that our drug repositioning approach is able to pinpoint relevant drug candidates at a fraction of the time and cost of a conventional screening. Furthermore, our results demonstrate the power and potential of structure-based drug repositioning in the context of neglected tropical diseases where the pharmaceutical industry has little financial interest in the development of new drugs.

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PS4DR: A multimodal workflow for identification and prioritization of drugs based on pathway signatures

10.21203/rs.2.12791/v4 ◽

2020 ◽

Author(s):

Mhammad Asif Emon ◽

Daniel Domingo-Fernández ◽

Charles Tapley Hoyt ◽

Martin Hofmann-Apitius

Keyword(s):

Gene Expression ◽

Genome Wide Association Study ◽

Drug Repositioning ◽

Heterogeneous Data ◽

New Drugs ◽

Gwas Data ◽

Drug Candidates ◽

Gene Expression Signatures ◽

Approved Drugs ◽

Fda Approved Drugs

Abstract Background: During the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning. Results: Here, we present a customizable workflow ( PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs. Conclusion: We showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr .

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In Silico Chemogenomics Drug Repositioning Strategies for Neglected Tropical Diseases

Current Medicinal Chemistry ◽

10.2174/0929867325666180309114824 ◽

2019 ◽

Vol 26 (23) ◽

pp. 4355-4379 ◽

Cited By ~ 12

Author(s):

Carolina Horta Andrade ◽

Bruno Junior Neves ◽

Cleber Camilo Melo-Filho ◽

Juliana Rodrigues ◽

Diego Cabral Silva ◽

...

Keyword(s):

High Throughput Screening ◽

Drug Targets ◽

Neglected Tropical Diseases ◽

Drug Repositioning ◽

Tropical Diseases ◽

Systematic Screening ◽

Drug Candidates ◽

Lead Compounds ◽

The One ◽

Approved Drugs

Only ~1% of all drug candidates against Neglected Tropical Diseases (NTDs) have reached clinical trials in the last decades, underscoring the need for new, safe and effective treatments. In such context, drug repositioning, which allows finding novel indications for approved drugs whose pharmacokinetic and safety profiles are already known, emerging as a promising strategy for tackling NTDs. Chemogenomics is a direct descendent of the typical drug discovery process that involves the systematic screening of chemical compounds against drug targets in high-throughput screening (HTS) efforts, for the identification of lead compounds. However, different to the one-drug-one-target paradigm, chemogenomics attempts to identify all potential ligands for all possible targets and diseases. In this review, we summarize current methodological development efforts in drug repositioning that use state-of-the-art computational ligand- and structure-based chemogenomics approaches. Furthermore, we highlighted the recent progress in computational drug repositioning for some NTDs, based on curation and modeling of genomic, biological, and chemical data. Additionally, we also present in-house and other successful examples and suggest possible solutions to existing pitfalls.

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Signal Transduction Pathways as Therapeutic Target for Chagas Disease

Current Medicinal Chemistry ◽

10.2174/0929867326666190620093029 ◽

2019 ◽

Vol 26 (36) ◽

pp. 6572-6589 ◽

Cited By ~ 5

Author(s):

Alejandra Cecilia Schoijet ◽

Tamara Sternlieb ◽

Guillermo Daniel Alonso

Keyword(s):

Chagas Disease ◽

New Technologies ◽

Neglected Tropical Diseases ◽

Drug Repositioning ◽

Basic Research ◽

Effector Proteins ◽

Adenylyl Cyclases ◽

Clinical Phase ◽

Leishmania Spp ◽

Approved Drugs

Trypanosomatids are a group of flagellated unicellular eukaryotes, causing serious human diseases including Chagas disease (Trypanosoma cruzi), sleeping sickness (Trypanosoma brucei spp.) and Leishmaniasis (Leishmania spp.). The second messenger cAMP is involved in numerous and fundamental processes in these parasites including differentiation between stages, proliferation, osmoregulation, oxidative stress and quorum sensing. Interestingly, its signaling pathway is quite different from that of mammals, including structurally different adenylyl cyclases, the shortage of orthologous effector proteins and the absence of G-protein-coupled-receptors, among others. These characteristics make the proteins involved in these transduction pathways good candidates for therapeutic targets. However, the identification of new unknown druggable targets involves extensive research time and is economically very expensive, making difficult the transition from basic research to the clinical phase. Trypanosomatid PDEs have characteristic binding pockets that allow for a differential inhibition from their human orthologs. Modification in the approved drugs for human to convert them into trypanocidal treatments could lead to more effective therapies, shorter lab time and lower costs. In view of the fact that kinetoplastid PDEs are highly conserved with their mammalian counterparts, and since there are already numerous drugs on the market against human PDEs, the drug repositioning approach is highly promising. The development of new technologies, higher government and industrial involvement and more scientists committed to basic investigation, are the key to ultimately find an effective treatment and cure for the neglected tropical diseases.

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PS4DR: A multimodal workflow for identification and prioritization of drugs based on pathway signatures

10.21203/rs.2.12791/v6 ◽

2020 ◽

Author(s):

Mhammad Asif Emon ◽

Daniel Domingo-Fernández ◽

Charles Tapley Hoyt ◽

Martin Hofmann-Apitius

Keyword(s):

Gene Expression ◽

Genome Wide Association Study ◽

Drug Repositioning ◽

Heterogeneous Data ◽

New Drugs ◽

Gwas Data ◽

Drug Candidates ◽

Gene Expression Signatures ◽

Approved Drugs ◽

Fda Approved Drugs

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PS4DR: A multimodal workflow for identification and prioritization of drugs based on pathway signatures

10.21203/rs.2.12791/v3 ◽

2020 ◽

Author(s):

Mhammad Asif Emon ◽

Daniel Domingo-Fernández ◽

Charles Tapley Hoyt ◽

Martin Hofmann-Apitius

Keyword(s):

Gene Expression ◽

Genome Wide Association Study ◽

Drug Repositioning ◽

Heterogeneous Data ◽

New Drugs ◽

Gwas Data ◽

Drug Candidates ◽

Gene Expression Signatures ◽

Approved Drugs ◽

Fda Approved Drugs

Abstract Background: During the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning.Results: Here, we present a customizable workflow (PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs. Conclusion: We showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr.

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PS4DR: A multimodal workflow for identification and prioritization of drugs based on pathway signatures

10.21203/rs.2.12791/v5 ◽

2020 ◽

Author(s):

Mhammad Asif Emon ◽

Daniel Domingo-Fernández ◽

Charles Tapley Hoyt ◽

Martin Hofmann-Apitius

Keyword(s):

Gene Expression ◽

Genome Wide Association Study ◽

Drug Repositioning ◽

Heterogeneous Data ◽

New Drugs ◽

Gwas Data ◽

Drug Candidates ◽

Gene Expression Signatures ◽

Approved Drugs ◽

Fda Approved Drugs

Abstract Background: During the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning.Results: Here, we present a customizable workflow (PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs. Conclusion: We showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr.

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The Role of nitro (NO2-), chloro (Cl), and fluoro (F) Substitution in the Design of Antileishmanial and Antichagasic Compounds

Current Drug Targets ◽

10.2174/1389450121666201228122239 ◽

2020 ◽

Vol 21 ◽

Author(s):

Boniface Pone ◽

Ferreira Igne Elizabeth

Keyword(s):

Chagas Disease ◽

Mammalian Cells ◽

Neglected Tropical Diseases ◽

New Drugs ◽

Pharmacokinetic Studies ◽

Scientific Publications ◽

Antitrypanosomal Activity ◽

Chemical Groups

: Neglected tropical diseases (NTDs) are responsible for over 500,000 deaths annually and are characterized by multiple disabilities. Leishmaniasis and Chagas disease are among the most severe NTDs, and are caused by the Leishmania sp, and Trypanosoma cruzi, respectively. Glucantime, pentamidine and miltefosine are commonly used to treat leishmaniasis, whereas nifurtimox, benznidazole are current treatments for Chagas disease. However, these treatments are associated with drug resistance, and severe side effects. Hence, the development of synthetic products, especially those containing N02, F, or Cl, which chemical groups are known to improve the biological activity. The present work summarizes the information on the antileishmanial and antitrypanosomal activity of nitro-, chloro-, and fluoro-synthetic derivatives. Scientific publications referring to halogenated derivatives in relation to antileishmanial and antitrypanosomal activities were hand searched in databases such as SciFinder, Wiley, Science Direct, PubMed, ACS, Springer, Scielo, and so on. According to the literature information, more than 90 compounds were predicted as lead molecules with reference to their IC50/EC50 values in in vitro studies. It is worth to mention that only active compounds with known cytotoxic effects against mammalian cells were considered in the present study. The observed activity was attributed to the presence of nitro-, fluoro- and chloro-groups in the compound backbone. All in all, nitro and h0alogenated derivatives are active antileishmanial and antitrypanosomal compounds and can serve as baseline for the development of new drugs against leishmaniasis and Chagas disease. However, efforts on in vitro and in vivo toxicity studies of the active synthetic compounds is still needed. Pharmacokinetic studies, and the mechanism of action of the promising compounds need to be explored. The use of new catalysts and chemical transformation can afford unexplored halogenated compounds with improved antileishmanial and antitrypanosomal activity.

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Screening of FDA Approved Drugs Against COVID-19 Main Protease: Coronavirus Disease

10.20944/preprints202004.0062.v1 ◽

2020 ◽

Cited By ~ 4

Author(s):

Vijayakumar Balakrishnan ◽

Karthik Lakshminarayanan

Keyword(s):

Vaccine Development ◽

New Drugs ◽

World Health ◽

Wuhan City ◽

Drug Candidates ◽

Human Contact ◽

Main Protease ◽

Potent Drug ◽

Approved Drugs ◽

Fda Approved Drugs

In the end of December 2019, a new strain of coronavirus was identified in the Wuhan city of Hubei province in China. Within a shorter period of time, an unprecedented outbreak of this strain was witnessed over the entire Wuhan city. This novel coronavirus strain was later officially renamed as COVID-19 (Coronavirus disease 2019) by the World Health Organization. The mode of transmission had been found to be human-to-human contact and hence resulted in a rapid surge across the globe where more than 1,100,000 people have been infected with COVID-19. In the current scenario, finding potent drug candidates for the treatment of COVID-19 has emerged as the most challenging task for clinicians and researchers worldwide. Identification of new drugs and vaccine development may take from a few months to years based on the clinical trial processes. To overcome the several limitations involved in identifying and bringing out potent drug candidates for treating COVID-19, in the present study attempts were made to screen the FDA approved drugs using High Throughput Virtual Screening (HTVS). The COVID-19 main protease (COVID-19 Mpro) was chosen as the drug target for which the FDA approved drugs were initially screened with HTVS. The drug candidates that exhibited favorable docking score, energy and emodel calculations were further taken for performing Induced Fit Docking (IFD) using Schrodinger’s GLIDE. From the flexible docking results, the following four FDA approved drugs Sincalide, Pentagastrin, Ritonavir and Phytonadione were identified. In particular, Sincalide and Pentagastrin can be considered potential key players for the treatment of COVID-19 disease.

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RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

Journal of Translational Medicine ◽

10.1186/s12967-020-02541-3 ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Ghazale Fahimian ◽

Javad Zahiri ◽

Seyed Shahriar Arab ◽

Reza H. Sajedi

Keyword(s):

Breast Cancer ◽

Biological Networks ◽

Drug Repositioning ◽

Machine Learning Algorithms ◽

New Drugs ◽

Stage Ii ◽

Cancer Stage ◽

New Drug ◽

Approved Drugs ◽

Fda Approved Drugs

Abstract Background It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time. Methods In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease. Results The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II. Conclusion Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.

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