scholarly journals Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge

2021 ◽  
Vol 17 (9) ◽  
pp. e1009302
Author(s):  
Zhaoping Xiong ◽  
Minji Jeon ◽  
Robert J. Allaway ◽  
Jaewoo Kang ◽  
Donghyeon Park ◽  
...  

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets (‘polypharmacology’). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

2021 ◽  
Author(s):  
Zhaoping Xiong ◽  
Minji Jeon ◽  
Robert J Allaway ◽  
Jaewoo Kang ◽  
Donghyeon Park ◽  
...  

AbstractA continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets (‘polypharmacology’). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.Author SummaryMany modern drugs are developed with the goal of modulating a single cellular pathway or target. However, many drugs are, in fact, ‘dirty;’ they target multiple cellular pathways or targets. This phenomenon is known as multi-targeting or polypharmacology. While some strive to develop ‘cleaner’ therapeutics that eliminate secondary targets, recent work has shown that multi-targeting therapeutics have key advantages for a variety of diseases. However, while multi-targeting drugs that affect a precisely-defined profile of targets may be more effective, it is difficult to computationally predict which molecules have desirable target profiles. Here, we report the results of a competitive crowdsourcing project (the Multi-Targeting Drug DREAM Challenge), where we challenged participants to predict chemicals that have desired target profiles for cancer and neurodegenerative disease.


2005 ◽  
Vol 11 (1) ◽  
pp. 48-56 ◽  
Author(s):  
Kurumi Y. Horiuchi ◽  
Yuan Wang ◽  
Scott L. Diamond ◽  
Haiching Ma

A central challenge in chemical biology is profiling the activity of a large number of chemical structures against hundreds of biological targets, such as kinases. Conventional 32P-incorporation or immunoassay of phosphorylated residues produces high-quality signals formonitoring kinase reactions but is difficult to use in high-throughput screening (HTS) because of cost and the need for well-plate washing. The authors report a method for densely archiving compounds in nanodroplets on peptide or protein substrate-coated microarrays for subsequent profiling by aerosol deposition of kinases. Each microarray contains over 6000 reaction centers (1.0 nL each) whose phosphorylation progress can be detected by immunofluorescence. For p60c-src, the microarray produced a signal-to-background ratio of 36.3 and Z' factor of 0.63 for HTS and accurate enzyme kinetic parameters (K m ATP = 3.3 µ M) and IC50 values for staurosporine (210 nM) and PP2 (326 nM) at 10 µ M adenosine triphosphate (ATP). Similarly, B-Raf phosphorylation of MEK-coatedmicroarrayswas inhibited in the nanoliter reactions by GW5074 at the expected IC 50of 9 nM. Common kinase inhibitors were printed onmicroarrays, and their inhibitory activities were systematically profiled against B-Raf (V599E), KDR, Met, Flt-3 (D835Y), Lyn, EGFR, PDGFRβ, and Tie2. All results indicate that this platform is well suited for kinetic analysis, HTS, large-scale IC 50 determinations, and selectivity profiling.


2020 ◽  
Vol 20 (14) ◽  
pp. 1114-1131 ◽  
Author(s):  
Kanisha Shah ◽  
Rakesh M. Rawal

Cancer is a complex disease that has the ability to develop resistance to traditional therapies. The current chemotherapeutic treatment has become increasingly sophisticated, yet it is not 100% effective against disseminated tumours. Anticancer drugs resistance is an intricate process that ascends from modifications in the drug targets suggesting the need for better targeted therapies in the therapeutic arsenal. Advances in the modern techniques such as DNA microarray, proteomics along with the development of newer targeted drug therapies might provide better strategies to overcome drug resistance. This drug resistance in tumours can be attributed to an individual’s genetic differences, especially in tumoral somatic cells but acquired drug resistance is due to different mechanisms, such as cell death inhibition (apoptosis suppression) altered expression of drug transporters, alteration in drug metabolism epigenetic and drug targets, enhancing DNA repair and gene amplification. This review also focusses on the epigenetic modifications and microRNAs, which induce drug resistance and contributes to the formation of tumour progenitor cells that are not destroyed by conventional cancer therapies. Lastly, this review highlights different means to prevent the formation of drug resistant tumours and provides future directions for better treatment of these resistant tumours.


2018 ◽  
Vol 18 (5) ◽  
pp. 321-368 ◽  
Author(s):  
Juan A. Bisceglia ◽  
Maria C. Mollo ◽  
Nadia Gruber ◽  
Liliana R. Orelli

Neglected diseases due to the parasitic protozoa Leishmania and Trypanosoma (kinetoplastids) affect millions of people worldwide, and the lack of suitable treatments has promoted an ongoing drug discovery effort to identify novel nontoxic and cost-effective chemotherapies. Polyamines are ubiquitous small organic molecules that play key roles in kinetoplastid parasites metabolism, redox homeostasis and in the normal progression of cell cycles, which differ from those found in the mammalian host. These features make polyamines attractive in terms of antiparasitic drug development. The present work provides a comprehensive insight on the use of polyamine derivatives and related nitrogen compounds in the chemotherapy of kinetoplastid diseases. The amount of literature on this subject is considerable, and a classification considering drug targets and chemical structures were made. Polyamines, aminoalcohols and basic heterocycles designed to target the relevant parasitic enzyme trypanothione reductase are discussed in the first section, followed by compounds directed to less common targets, like parasite SOD and the aminopurine P2 transporter. Finally, the third section comprises nitrogen compounds structurally derived from antimalaric agents. References on the chemical synthesis of the selected compounds are reported together with their in vivo and/or in vitro IC50 values, and structureactivity relationships within each group are analyzed. Some favourable structural features were identified from the SAR analyses comprising protonable sites, hydrophobic groups and optimum distances between them. The importance of certain pharmacophoric groups or amino acid residues in the bioactivity of polyamine derived compounds is also discussed.


Molecules ◽  
2021 ◽  
Vol 26 (10) ◽  
pp. 2914
Author(s):  
Kevin J. H. Lim ◽  
Yan Ping Lim ◽  
Yossa D. Hartono ◽  
Maybelle K. Go ◽  
Hao Fan ◽  
...  

Natural products make up a large proportion of medicine available today. Cannabinoids from the plant Cannabis sativa is one unique class of meroterpenoids that have shown a wide range of bioactivities and recently seen significant developments in their status as therapeutic agents for various indications. Their complex chemical structures make it difficult to chemically synthesize them in efficient yields. Synthetic biology has presented a solution to this through metabolic engineering in heterologous hosts. Through genetic manipulation, rare phytocannabinoids that are produced in low yields in the plant can now be synthesized in larger quantities for therapeutic and commercial use. Additionally, an exciting avenue of exploring new chemical spaces is made available as novel derivatized compounds can be produced and investigated for their bioactivities. In this review, we summarized the biosynthetic pathways of phytocannabinoids and synthetic biology efforts in producing them in heterologous hosts. Detailed mechanistic insights are discussed in each part of the pathway in order to explore strategies for creating novel cannabinoids. Lastly, we discussed studies conducted on biological targets such as CB1, CB2 and orphan receptors along with their affinities to these cannabinoid ligands with a view to inform upstream diversification efforts.


2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Janna Hastings ◽  
Martin Glauer ◽  
Adel Memariani ◽  
Fabian Neuhaus ◽  
Till Mossakowski

AbstractChemical data is increasingly openly available in databases such as PubChem, which contains approximately 110 million compound entries as of February 2021. With the availability of data at such scale, the burden has shifted to organisation, analysis and interpretation. Chemical ontologies provide structured classifications of chemical entities that can be used for navigation and filtering of the large chemical space. ChEBI is a prominent example of a chemical ontology, widely used in life science contexts. However, ChEBI is manually maintained and as such cannot easily scale to the full scope of public chemical data. There is a need for tools that are able to automatically classify chemical data into chemical ontologies, which can be framed as a hierarchical multi-class classification problem. In this paper we evaluate machine learning approaches for this task, comparing different learning frameworks including logistic regression, decision trees and long short-term memory artificial neural networks, and different encoding approaches for the chemical structures, including cheminformatics fingerprints and character-based encoding from chemical line notation representations. We find that classical learning approaches such as logistic regression perform well with sets of relatively specific, disjoint chemical classes, while the neural network is able to handle larger sets of overlapping classes but needs more examples per class to learn from, and is not able to make a class prediction for every molecule. Future work will explore hybrid and ensemble approaches, as well as alternative network architectures including neuro-symbolic approaches.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hu Lei ◽  
Han-Zhang Xu ◽  
Hui-Zhuang Shan ◽  
Meng Liu ◽  
Ying Lu ◽  
...  

AbstractIdentifying novel drug targets to overcome resistance to tyrosine kinase inhibitors (TKIs) and eradicating leukemia stem/progenitor cells are required for the treatment of chronic myelogenous leukemia (CML). Here, we show that ubiquitin-specific peptidase 47 (USP47) is a potential target to overcome TKI resistance. Functional analysis shows that USP47 knockdown represses proliferation of CML cells sensitive or resistant to imatinib in vitro and in vivo. The knockout of Usp47 significantly inhibits BCR-ABL and BCR-ABLT315I-induced CML in mice with the reduction of Lin−Sca1+c-Kit+ CML stem/progenitor cells. Mechanistic studies show that stabilizing Y-box binding protein 1 contributes to USP47-mediated DNA damage repair in CML cells. Inhibiting USP47 by P22077 exerts cytotoxicity to CML cells with or without TKI resistance in vitro and in vivo. Moreover, P22077 eliminates leukemia stem/progenitor cells in CML mice. Together, targeting USP47 is a promising strategy to overcome TKI resistance and eradicate leukemia stem/progenitor cells in CML.


2018 ◽  
Vol 8 (1) ◽  
pp. 22-33 ◽  
Author(s):  
Da-Yong Lu ◽  
Jin-Yu Che ◽  
Nagendra Sastry Yarla ◽  
Hong-Ying Wu ◽  
Ting-Ren Lu ◽  
...  

The causality and etio-pathologic risks for patients with Type 2 Diabetes (T2DM) are important areas in modern medicine. Disease complications are largely unpredictable in patients with T2DM. In the future, we welcome therapeutics of both cutting-edge and traditional for anti-diabetic treatments and management with higher efficiency and less cost. Expanding medical knowledge, behavior/life-style notification in healthcare, modern genetic/bioinformatics diagnostic promotion, clinical developments (Traditional Chinese Medicine and personalized medicine) and new drug developments - including candidate drug targets should be implemented in the future. These efforts might be useful avenues for updating anti-diabetic therapeutics globally. This article aims at introducing this information for T2DM treatment boosts.


2020 ◽  
Author(s):  
Liya Thurakkal ◽  
Satyam Singh ◽  
Sushabhan Sadhukhan ◽  
Mintu Porel

The emerging paradigm shift from ‘one molecule, one target, for one disease’ towards ‘multi-targeted small molecules’ has paved an ingenious pathway in drug discovery in recent years. This idea has been extracted for the investigation of competent drug molecules for the unprecedented COVID-19 pandemic which became the greatest global health crisis now. Perceiving the importance of organosulfur compounds against SARS-CoV-2 from the drugs under clinical trials, a class of organosulfur compounds effective against SARS-CoV were selected and studied the interaction with multiple proteins of the SARS-CoV-2. One compound displayed inhibition against five proteins (both structural and non-structural) of the virus namely, main protease, papain-like protease, spike protein, helicase and RNA dependent RNA polymerase. Consequently, this compound emanates as a potential candidate for treating the virulent disease. The pharmacokinetics, ADMET properties and target prediction studies carried out in this work further inflamed the versatility of the compound and urge to execute <i>in-vitro</i> and <i>in-vivo</i> analysis on SARS-CoV-2 in the future.<br>


2016 ◽  
Author(s):  
Zheng Zhao ◽  
Lei Xie ◽  
Philip E. Bourne

AbstractProtein kinases are critical drug targets for treating a large variety of human diseases. Type-I and type-II kinase inhibitors frequently exhibit off-target toxicity or lead to mutation acquired resistance. Two highly specific allosteric type-III MEK-targeted drugs, Trametinib and Cobimetinib, offer a new approach. Thus, understanding the binding mechanism of existing type-III kinase inhibitors will provide insights for designing new type-III kinase inhibitors. In this work we have systematically studied the binding mode of MEK-targeted type-III inhibitors using structural systems pharmacology and molecular dynamics simulation. Our studies provide detailed sequence, structure, interaction-fingerprint, pharmacophore and binding-site information on the binding characteristics of MEK type-III kinase inhibitors. We propose that the helix-folding activation loop is a hallmark allosteric binding site for type-III inhibitors. Subsequently we screened and predicted allosteric binding sites across the human kinome, suggesting other kinases as potential targets suitable for type-III inhibitors. Our findings will provide new insights into the design of potent and selective kinases inhibitors.Author SummaryHuman protein kinases represent a large protein family relevant to many diseases, especially cancers, and have become important drug targets. However, developing the desired selective kinase-targeted inhibitors remain challenging. Kinase allosteric inhibitors provide that opportunity, but, to date, few have been designed other than MEK inhibitors. To more efficiently develop kinase allosteric inhibitors, we systematically studied the binding mode of the MEK type-III allosteric kinase inhibitors using structural system pharmacology and molecular dynamics approaches. New insights into the binding mode and mechanism of type-III inhibitors were revealed that may facilitate the design of new prospective type-III kinase inhibitors.


Sign in / Sign up

Export Citation Format

Share Document