In Silico Chemogenomics Drug Repositioning Strategies for Neglected Tropical Diseases

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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Computational guided approach for drug repurposing against SARS-CoV-2

Future Virology ◽

10.2217/fvl-2020-0403 ◽

2021 ◽

Author(s):

Jigisha Anand ◽

Tanmay Ghildiyal ◽

Aakanksha Madhwal ◽

Rishabh Bhatt ◽

Devvret Verma ◽

...

Keyword(s):

Drug Targets ◽

De Novo ◽

Drug Repositioning ◽

Drug Repurposing ◽

Computational Docking ◽

Drug Candidates ◽

Docking Tool ◽

Inhibitory Potential ◽

Approved Drugs ◽

Tract Infections

Background: In the current SARS-CoV-2 outbreak, drug repositioning emerges as a promising approach to develop efficient therapeutics in comparison to de novo drug development. The present investigation screened 130 US FDA-approved drugs including hypertension, cardiovascular diseases, respiratory tract infections (RTI), antibiotics and antiviral drugs for their inhibitory potential against SARS-CoV-2. Materials & methods: The molecular drug targets against SARS-CoV-2 proteins were determined by the iGEMDOCK computational docking tool. The protein homology models were generated through SWISS Model workspace. The pharmacokinetics of all the ligands was determined by ADMET analysis. Results: The study identified 15 potent drugs exhibiting significant inhibitory potential against SARS-CoV-2. Conclusion: Our investigation has identified possible repurposed drug candidates to improve the current modus operandi of the treatment given to COVID-19 patients.

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

International Journal of Molecular Sciences ◽

10.3390/ijms21228809 ◽

2020 ◽

Vol 21 (22) ◽

pp. 8809

Author(s):

Melissa F. Adasme ◽

Sarah Naomi Bolz ◽

Lauren Adelmann ◽

Sebastian Salentin ◽

V. Joachim Haupt ◽

...

Keyword(s):

Chagas Disease ◽

Neglected Tropical Diseases ◽

Ex Vivo ◽

Drug Repositioning ◽

Protein Structures ◽

New Drugs ◽

Drug Candidates ◽

Growth Inhibitory ◽

Growth Inhibitory Activity ◽

Approved Drugs

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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Developing novel chemical entities for the treatment of lysosomal storage disorders: an academic perspective

AJP Renal Physiology ◽

10.1152/ajprenal.00393.2015 ◽

2015 ◽

Vol 309 (12) ◽

pp. F996-F999 ◽

Cited By ~ 7

Author(s):

James A. Shayman

Keyword(s):

Drug Targets ◽

Lysosomal Storage ◽

Federal Drug Administration ◽

Drug Candidates ◽

Gaucher Disease Type 1 ◽

Biotechnology Companies ◽

Approved Drugs ◽

Novel Drug ◽

Early Drug

Historically, most Federal Drug Administration-approved drugs were the result of “in-house” efforts within large pharmaceutical companies. Over the last two decades, this paradigm has steadily shifted as the drug industry turned to startups, small biotechnology companies, and academia for the identification of novel drug targets and early drug candidates. This strategic pivot has created new opportunities for groups less traditionally associated with the creation of novel therapeutics, including small academic laboratories, for engagement in the drug discovery process. A recent example of the successful development of a drug that had its origins in academia is eliglustat tartrate, an oral agent for Gaucher disease type 1.

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

Briefings in Bioinformatics ◽

10.1093/bib/bby010 ◽

2018 ◽

Vol 20 (4) ◽

pp. 1465-1474 ◽

Cited By ~ 13

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.

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Histone deacetylase enzymes as potential drug targets of Neglected Tropical Diseases caused by cestodes

International Journal for Parasitology Drugs and Drug Resistance ◽

10.1016/j.ijpddr.2019.02.003 ◽

2019 ◽

Vol 9 ◽

pp. 120-132

Author(s):

Hugo R. Vaca ◽

Ana M. Celentano ◽

Natalia Macchiaroli ◽

Laura Kamenetzky ◽

Federico Camicia ◽

...

Keyword(s):

Histone Deacetylase ◽

Drug Targets ◽

Neglected Tropical Diseases ◽

Tropical Diseases ◽

Potential Drug ◽

Potential Drug Targets

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Machine Learning Methods in Drug Discovery

Molecules ◽

10.3390/molecules25225277 ◽

2020 ◽

Vol 25 (22) ◽

pp. 5277

Author(s):

Lauv Patel ◽

Tripti Shukla ◽

Xiuzhen Huang ◽

David W. Ussery ◽

Shanzhi Wang

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Drug Discovery ◽

High Throughput Screening ◽

Drug Targets ◽

Learning Algorithms ◽

Machine Learning Techniques ◽

Online Information ◽

Drug Candidates ◽

Novel Drug

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

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High-Throughput Screening Platform for Natural Product–Based Drug Discovery Against 3 Neglected Tropical Diseases

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057114555846 ◽

2014 ◽

Vol 20 (1) ◽

pp. 82-91 ◽

Cited By ~ 41

Author(s):

F. Annang ◽

G. Pérez-Moreno ◽

R. García-Hernández ◽

C. Cordon-Obras ◽

J. Martín ◽

...

Keyword(s):

Natural Products ◽

Drug Discovery ◽

Natural Product ◽

High Throughput ◽

High Throughput Screening ◽

Neglected Tropical Diseases ◽

Chemical Diversity ◽

Tropical Diseases ◽

Isolation And Characterization ◽

Microbial Extracts

African trypanosomiasis, leishmaniasis, and Chagas disease are 3 neglected tropical diseases for which current therapeutic interventions are inadequate or toxic. There is an urgent need to find new lead compounds against these diseases. Most drug discovery strategies rely on high-throughput screening (HTS) of synthetic chemical libraries using phenotypic and target-based approaches. Combinatorial chemistry libraries contain hundreds of thousands of compounds; however, they lack the structural diversity required to find entirely novel chemotypes. Natural products, in contrast, are a highly underexplored pool of unique chemical diversity that can serve as excellent templates for the synthesis of novel, biologically active molecules. We report here a validated HTS platform for the screening of microbial extracts against the 3 diseases. We have used this platform in a pilot project to screen a subset (5976) of microbial extracts from the MEDINA Natural Products library. Tandem liquid chromatography–mass spectrometry showed that 48 extracts contain potentially new compounds that are currently undergoing de-replication for future isolation and characterization. Known active components included actinomycin D, bafilomycin B1, chromomycin A3, echinomycin, hygrolidin, and nonactins, among others. The report here is, to our knowledge, the first HTS of microbial natural product extracts against the above-mentioned kinetoplastid parasites.

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Searching for Chemokine Receptor Binding Antagonists by High Throughput Screening

CrossRef Listing of Deleted DOIs ◽

10.1177/108705719700200305 ◽

1997 ◽

Vol 2 (3) ◽

pp. 153-157 ◽

Cited By ~ 8

Author(s):

Geoffrey W. Mellor ◽

Simon J. Fogarty ◽

M. Shane O'Brien ◽

Miles Congreve ◽

Martyn N. Banks ◽

...

Keyword(s):

Receptor Binding ◽

High Throughput ◽

Chemokine Receptor ◽

Combinatorial Chemistry ◽

High Throughput Screening ◽

Biological Targets ◽

Drug Candidates ◽

Lead Compounds ◽

Selective Antagonists ◽

Biological Sources

Identification of putative drug candidates by high throughput screening is assuming enormous importance within the pharmaceutical industry, driven by increasing numbers of valid therapeutic targets from both classical and molecular biological sources. Screening is an applied discipline that requires equipment and, more importantly, thinking that is fundamentally different from more traditional, lower throughput assay methodology. This article describes the process as applied to the discovery of selective antagonists of three chemokine receptor binding systems, from the original biological targets to chemically prosecutable lead compounds, which are currently being investigated using traditional medicinal and combinatorial chemistry methods.

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One Health for neglected tropical diseases

Transactions of the Royal Society of Tropical Medicine and Hygiene ◽

10.1093/trstmh/traa117 ◽

2020 ◽

Author(s):

Gabrielle Laing ◽

Marco Antonio Natal Vigilato ◽

Sarah Cleaveland ◽

S M Thumbi ◽

Lucille Blumberg ◽

...

Keyword(s):

One Health ◽

Neglected Tropical Diseases ◽

Animal Health ◽

World Health ◽

Shared Environment ◽

Tropical Diseases ◽

Road Map ◽

The One ◽

Health Organization ◽

And Control

Abstract The forthcoming World Health Organization road map for neglected tropical diseases (NTDs) 2021–2030 recognises the complexity surrounding control and elimination of these 20 diseases of poverty. It emphasises the need for a paradigm shift from disease-specific interventions to holistic cross-cutting approaches coordinating with adjacent disciplines. The One Health approach exemplifies this shift, extending beyond a conventional model of zoonotic disease control to consider the interactions of human and animal health systems within their shared environment and the wider social and economic context. This approach can also promote sustainability and resilience within these systems. To achieve the global ambition on NTD elimination and control, political will, along with contextualised innovative scientific strategies, is required.

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