scholarly journals Drug discovery of anticancer drugs targeting methylenetetrahydrofolate dehydrogenase 2

Heliyon ◽  
2018 ◽  
Vol 4 (12) ◽  
pp. e01021 ◽  
Author(s):  
Ayumu Asai ◽  
Jun Koseki ◽  
Masamitsu Konno ◽  
Tatsunori Nishimura ◽  
Noriko Gotoh ◽  
...  
2004 ◽  
Vol 9 (7) ◽  
pp. 557-568 ◽  
Author(s):  
Kenneth A. Giuliano ◽  
Yih-Tai Chen ◽  
D. Lansing Taylor

Deciphering the effects of compounds on molecular events within living cells is becoming an increasingly important component of drug discovery. In a model application of the industrial drug discovery process, the authors profiled a panel of 22 compounds using hierarchical cluster analysis of multiparameter high-content screening measurements from nearly 500,000 cells per microplate. RNAi protein knockdown methodology was used with high-content screening to dissect the effects of 2 anticancer drugs on multiple target activities. Camptothecin activated p53 in A549 lung carcinoma cells pretreated with scrambled siRNA, exhibited concentration-dependent cell cycle blocks, and induced moderate microtubule stabilization. Knockdown of camptothecin-induced p53 protein expression with p53 siRNA inhibited the G1/S blocking activity of the drug and diminished its microtubule-stabilizing activity. Paclitaxel activated p53 protein at low concentrations but exhibited G2/M cell cycle blocking activity at higher concentrations where microtubules were stabilized. In cells treated with p53 siRNA, paclitaxel failed to activate p53 protein, but the knockdown did not have a significant effect on the ability of paclitaxel to stabilize microtubules or induce a G2/M cell cycle block. Thus, this model application of the use of RNAi technology within the context of high-content screening shows the potential to provide massive amounts of combinatorial cell biological information on the temporal and spatial responses that cells mount to treatment by promising therapeutic candidates.


2022 ◽  
Vol 11 ◽  
Author(s):  
Nawale Hajjaji ◽  
Soulaimane Aboulouard ◽  
Tristan Cardon ◽  
Delphine Bertin ◽  
Yves-Marie Robin ◽  
...  

Integrating tumor heterogeneity in the drug discovery process is a key challenge to tackle breast cancer resistance. Identifying protein targets for functionally distinct tumor clones is particularly important to tailor therapy to the heterogeneous tumor subpopulations and achieve clonal theranostics. For this purpose, we performed an unsupervised, label-free, spatially resolved shotgun proteomics guided by MALDI mass spectrometry imaging (MSI) on 124 selected tumor clonal areas from early luminal breast cancers, tumor stroma, and breast cancer metastases. 2868 proteins were identified. The main protein classes found in the clonal proteome dataset were enzymes, cytoskeletal proteins, membrane-traffic, translational or scaffold proteins, or transporters. As a comparison, gene-specific transcriptional regulators, chromatin related proteins or transmembrane signal receptor were more abundant in the TCGA dataset. Moreover, 26 mutated proteins have been identified. Similarly, expanding the search to alternative proteins databases retrieved 126 alternative proteins in the clonal proteome dataset. Most of these alternative proteins were coded mainly from non-coding RNA. To fully understand the molecular information brought by our approach and its relevance to drug target discovery, the clonal proteomic dataset was further compared to the TCGA breast cancer database and two transcriptomic panels, BC360 (nanoString®) and CDx (Foundation One®). We retrieved 139 pathways in the clonal proteome dataset. Only 55% of these pathways were also present in the TCGA dataset, 68% in BC360 and 50% in CDx. Seven of these pathways have been suggested as candidate for drug targeting, 22 have been associated with breast cancer in experimental or clinical reports, the remaining 19 pathways have been understudied in breast cancer. Among the anticancer drugs, 35 drugs matched uniquely with the clonal proteome dataset, with only 7 of them already approved in breast cancer. The number of target and drug interactions with non-anticancer drugs (such as agents targeting the cardiovascular system, metabolism, the musculoskeletal or the nervous systems) was higher in the clonal proteome dataset (540 interactions) compared to TCGA (83 interactions), BC360 (419 interactions), or CDx (172 interactions). Many of the protein targets identified and drugs screened were clinically relevant to breast cancer and are in clinical trials. Thus, we described the non-redundant knowledge brought by this clone-tailored approach compared to TCGA or transcriptomic panels, the targetable proteins identified in the clonal proteome dataset, and the potential of this approach for drug discovery and repurposing through drug interactions with antineoplastic agents and non-anticancer drugs.


2016 ◽  
pp. 1-2
Author(s):  
Hsueh-Wei Chang

Most cancer drugs are effective to kill cancer cells but also harm normal cells. Drugs and natural products with the selective killing effect may be helpful to solve this problem. The side effects of many anticancer drugs are partly derived from its damage to both cancer and normal cells without selection. This problem raises the need of anticancer drug discovery with the selective killing effect.


2019 ◽  
Vol 25 (40) ◽  
pp. 5613-5630 ◽  
Author(s):  
Jack Ho Wong ◽  
Stephen Cho Wing Sze ◽  
Tzi Bun Ng ◽  
Randy Chi Fai Cheung ◽  
Chit Tam ◽  
...  

The purpose of this account is to review the compounds capable of eliciting mitochondria-mediated apoptosis in cancer cells produced by medicinal fungi and plants. The medicinal fungi discussed encompass Cordyceps, Ganoderma species, Coriolus versicolor and Hypsizygus marmoreus. The medicinal plants discussed comprise Astragalus complanatus, Dendrobium spp, Dioscorea spp, Glycyrrhiza spp, Panax notoginseng, Panax ginseng, and Momordica charantia. These compounds have the potential of development into anticancer drugs.


2020 ◽  
Vol 26 ◽  
Author(s):  
Tadesse Bekele Tafesse ◽  
Mohammed Hussen Bule ◽  
Fazlullah Khan ◽  
Mohammad Abdollahi ◽  
Mohsen Amini

Background: Due to higher failure rates, lengthy time and high cost of the traditional de novo drug discovery and development process; the rate of opportunity to get new, safe and efficacious drugs for the targeted population including pediatric patients with cancer becomes sluggish. Objectives: This paper discusses the development of novel anticancer drugs focusing on the identification and selection of target anticancer drug development for the targeted population. Methods: Information presented in this review was obtained from different databases including PUBMED, SCOPUS, Web of Science, and EMBASE. Various keywords were used as search terms. Results: The pharmaceutical companies currently are executing drug repurposing as an alternative means to accelerate the drug development process that reduces the risk of failure, time and cost, which takes 3-12 years with almost 25% overall probability of success as compared to de novo drug discovery and development process (10-17 years) which has less than 10% probability of success. An alternative strategy to the traditional de novo drug discovery and development process, called drug repurposing, is also presented. Conclusion: Therefore, to continue with the progress of developing novel anticancer drugs towards the targeted population, identification and selection of the target to the specific disease type is important considering the aspects of the age of the patient and the disease stages such as each cancer types are different when we consider the disease at a molecular level. Drug repurposing technique becomes an influential alternative strategy to discover and develop novel anticancer drug candidates.


2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Jin-Jian Lu ◽  
Jiao-Lin Bao ◽  
Xiu-Ping Chen ◽  
Min Huang ◽  
Yi-Tao Wang

Alkaloids are important chemical compounds that serve as a rich reservoir for drug discovery. Several alkaloids isolated from natural herbs exhibit antiproliferation and antimetastasis effects on various types of cancers bothin vitroandin vivo. Alkaloids, such as camptothecin and vinblastine, have already been successfully developed into anticancer drugs. This paper focuses on the naturally derived alkaloids with prospective anticancer properties, such as berberine, evodiamine, matrine, piperine, sanguinarine, and tetrandrine, and summarizes the mechanisms of action of these compounds. Based on the information in the literature that is summarized in this paper, the use of alkaloids as anticancer agents is very promising, but more research and clinical trials are necessary before final recommendations on specific alkaloids can be made.


2019 ◽  
Vol 19 (5) ◽  
pp. 592-598 ◽  
Author(s):  
Rabia Hameed ◽  
Afsar Khan ◽  
Sehroon Khan ◽  
Shagufta Perveen

Background: One of the major goals of computational chemists is to determine and develop the pathways for anticancer drug discovery and development. In recent past, high performance computing systems elicited the desired results with little or no side effects. The aim of the current review is to evaluate the role of computational chemistry in ascertaining kinases as attractive targets for anticancer drug discovery and development. Methods: Research related to computational studies in the field of anticancer drug development is reviewed. Extensive literature on achievements of theorists in this regard has been compiled and presented with special emphasis on kinases being the attractive anticancer drug targets. Results: Different approaches to facilitate anticancer drug discovery include determination of actual targets, multi-targeted drug discovery, ligand-protein inverse docking, virtual screening of drug like compounds, formation of di-nuclear analogs of drugs, drug specific nano-carrier design, kinetic and trapping studies in drug design, multi-target QSAR (Quantitative Structure Activity Relationship) model, targeted co-delivery of anticancer drug and siRNA, formation of stable inclusion complex, determination of mechanism of drug resistance, and designing drug like libraries for the prediction of drug-like compounds. Protein kinases have gained enough popularity as attractive targets for anticancer drugs. These kinases are responsible for uncontrolled and deregulated differentiation, proliferation, and cell signaling of the malignant cells which result in cancer. Conclusion: Interest in developing drugs through computational methods is a growing trend, which saves equally the cost and time. Kinases are the most popular targets among the other for anticancer drugs which demand attention. 3D-QSAR modelling, molecular docking, and other computational approaches have not only identified the target-inhibitor binding interactions for better anticancer drug discovery but are also designing and predicting new inhibitors, which serve as lead for the synthetic preparation of drugs. In light of computational studies made so far in this field, the current review highlights the importance of kinases as attractive targets for anticancer drug discovery and development.


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