scholarly journals Rational Discovery of Dual-Action Multi-Target Kinase Inhibitor for Precision Anti-Cancer Therapy Using Structural Systems Pharmacology

2018 ◽  
Author(s):  
Hansaim Lim ◽  
Di He ◽  
Yue Qiu ◽  
Patrycja Krawczuk ◽  
Xiaoru Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Drug Resistance ◽  
Drug Repurposing ◽  
Target Drug ◽  
Genome Wide ◽  
Anti Cancer ◽  
Dual Action ◽  
Target Screening ◽  
Genome Scale

AbstractAlthough remarkable progresses have been made in the cancer treatment, existing anti-cancer drugs are associated with increasing risk of heart failure, variable drug response, and acquired drug resistance. To address these challenges, for the first time, we develop a novel genome-scale multi-target screening platform 3D-REMAP that integrates data from structural genomics and chemical genomics as well as synthesize methods from structural bioinformatics, biophysics, and machine learning. 3D-REMAP enables us to discover marked drugs for dual-action agents that can both reduce the risk of heart failure and present anti-cancer activity. 3D-REMAP predicts that levosimendan, a drug for heart failure, inhibits serine/threonine-protein kinase RIOK1 and other kinases. Subsequent experiments confirm this prediction, and suggest that levosimendan is active against multiple cancers, notably lymphoma, through the direct inhibition of RIOK1 and RNA processing pathway. We further develop machine learning models to identify cancer cell-lines and patients that may respond to levosimendan. Our findings suggest that levosimendan can be a promising novel lead compound for the development of safe and effective multi-targeted cancer therapy, and demonstrate the potential of genome-wide multi-target screening in designing polypharmacology and drug repurposing for precision medicine.Author SummaryMulti-target drug design (a.k.a targeted polypharmacology) has emerged as a new strategy for discovering novel therapeutics that can enhance therapeutic efficacy and overcome drug resistance in tackling multi-genic diseases such as cancer. However, it is extremely challenging for conventional computational tools that are either receptor-based or ligand-based to screen compounds for selectively targeting multiple receptors. Existing multi-target drug design mainly focuses on compound screening against receptors within the same gene family but not across different gene families. Here, we develop a new computational tool 3D-REMAP that enables us to identify chemical-protein interactions across fold space on a genome scale. The genome-scale chemical-protein interaction network allows us to discover dual-action drugs that can bind to two types of targets simultaneously, one for mitigating side effect and another for enhancing the therapeutic effect. Using 3D-REMAP, we predict and subsequently experiments validate that levosimendan, a drug for heart failure, is active against multiple cancers, notably, lymphoma. This study demonstrates the potential of genome-wide multi-target screening in designing polypharmacology and drug repurposing for precision medicine.

scholarly journals Statin as a Potential Chemotherapeutic Agent: Current Updates as a Monotherapy, Combination Therapy, and Treatment for Anti-Cancer Drug Resistance

2021 ◽  
Vol 14 (5) ◽  
pp. 470
Author(s):  
Nirmala Tilija Pun ◽  
Chul-Ho Jeong
Keyword(s):  
Cell Proliferation ◽  
Drug Resistance ◽  
Drug Repurposing ◽  
Synergistic Action ◽  
Cancer Drug ◽  
Cancer Cell Proliferation ◽  
Cancer Drug Resistance ◽  
Anti Cancer

Cancer is incurable because progressive phenotypic and genotypic changes in cancer cells lead to resistance and recurrence. This indicates the need for the development of new drugs or alternative therapeutic strategies. The impediments associated with new drug discovery have necessitated drug repurposing (i.e., the use of old drugs for new therapeutic indications), which is an economical, safe, and efficacious approach as it is emerged from clinical drug development or may even be marketed with a well-established safety profile and optimal dosing. Statins are inhibitors of HMG-CoA reductase in cholesterol biosynthesis and are used in the treatment of hypercholesterolemia, atherosclerosis, and obesity. As cholesterol is linked to the initiation and progression of cancer, statins have been extensively used in cancer therapy with a concept of drug repurposing. Many studies including in vitro and in vivo have shown that statin has been used as monotherapy to inhibit cancer cell proliferation and induce apoptosis. Moreover, it has been used as a combination therapy to mediate synergistic action to overcome anti-cancer drug resistance as well. In this review, the recent explorations are done in vitro, in vivo, and clinical trials to address the action of statin either single or in combination with anti-cancer drugs to improve the chemotherapy of the cancers were discussed. Here, we discussed the emergence of statin as a lipid-lowering drug; its use to inhibit cancer cell proliferation and induction of apoptosis as a monotherapy; and its use in combination with anti-cancer drugs for its synergistic action to overcome anti-cancer drug resistance. Furthermore, we discuss the clinical trials of statins and the current possibilities and limitations of preclinical and clinical investigations.

Genome-Wide siRNA Screen for Anti-Cancer Drug Resistance in Adherent Cell Lines

BIO-PROTOCOL ◽  
2015 ◽  
Vol 5 (10) ◽  
Author(s):  
Elza de Bruin ◽  
Ming Jiang ◽  
Michael Howell ◽  
Julian Downward
Keyword(s):  
Drug Resistance ◽  
Cell Lines ◽  
Cancer Drug ◽  
Adherent Cell ◽  
Sirna Screen ◽  
Cancer Drug Resistance ◽  
Genome Wide ◽  
Anti Cancer

Identification of Anti-cancer Peptides Based on Multi-classifier System

2020 ◽  
Vol 22 (10) ◽  
pp. 694-704 ◽  
Author(s):  
Wanben Zhong ◽  
Bineng Zhong ◽  
Hongbo Zhang ◽  
Ziyi Chen ◽  
Yan Chen
Keyword(s):  
Machine Learning ◽  
Side Effect ◽  
Learning Models ◽  
Normal Cells ◽  
Classifier System ◽  
Prediction Rate ◽  
Anti Cancer ◽  
Feature Information ◽  
Machine Learning Models ◽  
Better Than

Aim and Objective: Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side effect. However, the identification and development of new anti Materials and Methods: In our study, a multi-classifier system was used, combined with multiple machine learning models, to predict anti-cancer peptides. These individual learners are composed of different feature information and algorithms, and form a multi-classifier system by voting. Results and Conclusion: The experiments show that the overall prediction rate of each individual learner is above 80% and the overall accuracy of multi-classifier system for anti-cancer peptides prediction can reach 95.93%, which is better than the existing prediction model.

Fanconi Anemia DNA Repair Pathway as a New Mechanism to Exploit Cancer Drug Resistance

2020 ◽  
Vol 20 (9) ◽  
pp. 779-787
Author(s):  
Kajal Ghosal ◽  
Christian Agatemor ◽  
Richard I. Han ◽  
Amy T. Ku ◽  
Sabu Thomas ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Dna Repair ◽  
Fanconi Anemia ◽  
Cancer Drug ◽  
Drug Resistant ◽  
Cancer Drugs ◽  
Repair Pathway ◽  
Cancer Drug Resistance ◽  
Anti Cancer ◽  
Cancerous Cells

Chemotherapy employs anti-cancer drugs to stop the growth of cancerous cells, but one common obstacle to the success is the development of chemoresistance, which leads to failure of the previously effective anti-cancer drugs. Resistance arises from different mechanistic pathways, and in this critical review, we focus on the Fanconi Anemia (FA) pathway in chemoresistance. This pathway has yet to be intensively researched by mainstream cancer researchers. This review aims to inspire a new thrust toward the contribution of the FA pathway to drug resistance in cancer. We believe an indepth understanding of this pathway will open new frontiers to effectively treat drug-resistant cancer.

Does the Oxazolidinone Derivatives Constitute a Functional Approach for Cancer Therapy?

2020 ◽  
Vol 7 (2) ◽  
pp. 95-106
Author(s):  
Eduardo Augusto Vasconcelos de Freitas Ramalho ◽  
Marina Galdino da Rocha Pitta ◽  
Hernando de Barros Siqueira Neto ◽  
Ivan da Rocha Pitta
Keyword(s):  
Cell Lines ◽  
Antitumor Agents ◽  
Partial Agonist ◽  
Drug Repurposing ◽  
Antitumor Agent ◽  
Short Review ◽  
Sphingolipid Metabolism ◽  
Solid Cancer ◽  
Ppar Γ ◽  
Anti Cancer

In the last four decades, the emphasis was laid on the research of small organic molecules with potential anti-cancer activity. Linezolid was the first oxazolidinone derivative approved by FDA for MRSA treatment. Despite its major role in antimicrobial activity, these molecules display other properties, also serving as an antitumor agent. The importance of drug repurposing could be highlighted by the use of Oxazolidinone derivatives in pre-clinical studies, which are able to act through different pathways, such as partial agonist of transcription factor PPAR-γ, an inhibitor of key enzymes related to hormone-dependent disorders and even on sphingolipid metabolism as well. The purpose of this short review is to discuss the application of oxazolidinone derivatives as an antitumor agent by highlighting the most promising molecules studied by many research groups worldwide. Main biological activity against several tumor cell lines, including hematopoietic and solid cancer cell lines have been discussed. In addition, this study intends to report how different types of oxazolidinone derivatives can act as antitumor agents describing their distinct mechanisms of action based on their targets.

scholarly journals Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joshua E. Lewis ◽  
Melissa L. Kemp
Keyword(s):  
Machine Learning ◽  
Metabolic Flux ◽  
Metabolic Modeling ◽  
The Cancer Genome Atlas ◽  
Radiation Response ◽  
Metabolomics Data ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Metabolic Biomarkers ◽  
Genome Scale

AbstractResistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

Phenomapping Heart Failure with Preserved Ejection Fraction Using Machine Learning Cluster Analysis

2021 ◽  
Vol 17 (3) ◽  
pp. 499-518
Author(s):  
Elena Galli ◽  
Corentin Bourg ◽  
Wojciech Kosmala ◽  
Emmanuel Oger ◽  
Erwan Donal
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Cluster Analysis ◽  
Ejection Fraction ◽  
Preserved Ejection Fraction

scholarly journals An approach using ddRADseq and machine learning for understanding speciation in Antarctic Antarctophilinidae gastropods

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Juan Moles ◽  
Shahan Derkarabetian ◽  
Stefano Schiaparelli ◽  
Michael Schrödl ◽  
Jesús S. Troncoso ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Sea ◽  
Weddell Sea ◽  
Nucleotide Polymorphisms ◽  
Sea Levels ◽  
Suitable Material ◽  
Genetic Lineages ◽  
Genome Wide ◽  
Gastropod Mollusks ◽  
Double Digestion

AbstractSampling impediments and paucity of suitable material for molecular analyses have precluded the study of speciation and radiation of deep-sea species in Antarctica. We analyzed barcodes together with genome-wide single nucleotide polymorphisms obtained from double digestion restriction site-associated DNA sequencing (ddRADseq) for species in the family Antarctophilinidae. We also reevaluated the fossil record associated with this taxon to provide further insights into the origin of the group. Novel approaches to identify distinctive genetic lineages, including unsupervised machine learning variational autoencoder plots, were used to establish species hypothesis frameworks. In this sense, three undescribed species and a complex of cryptic species were identified, suggesting allopatric speciation connected to geographic or bathymetric isolation. We further observed that the shallow waters around the Scotia Arc and on the continental shelf in the Weddell Sea present high endemism and diversity. In contrast, likely due to the glacial pressure during the Cenozoic, a deep-sea group with fewer species emerged expanding over great areas in the South-Atlantic Antarctic Ridge. Our study agrees on how diachronic paleoclimatic and current environmental factors shaped Antarctic communities both at the shallow and deep-sea levels, promoting Antarctica as the center of origin for numerous taxa such as gastropod mollusks.

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