Faculty Opinions recommendation of Host-Microbe Co-metabolism Dictates Cancer Drug Efficacy in C. elegans.

Author(s):  
Alla Grishok ◽  
Ruben Esse
Keyword(s):  
Cell ◽  
2017 ◽  
Vol 169 (3) ◽  
pp. 442-456.e18 ◽  
Author(s):  
Timothy A. Scott ◽  
Leonor M. Quintaneiro ◽  
Povilas Norvaisas ◽  
Prudence P. Lui ◽  
Matthew P. Wilson ◽  
...  
Keyword(s):  

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3355 ◽  
Author(s):  
Wanyoung Lim ◽  
Sungsu Park

Three-dimensional (3D) cell culture is considered more clinically relevant in mimicking the structural and physiological conditions of tumors in vivo compared to two-dimensional cell cultures. In recent years, high-throughput screening (HTS) in 3D cell arrays has been extensively used for drug discovery because of its usability and applicability. Herein, we developed a microfluidic spheroid culture device (μFSCD) with a concentration gradient generator (CGG) that enabled cells to form spheroids and grow in the presence of cancer drug gradients. The device is composed of concave microwells with several serpentine micro-channels which generate a concentration gradient. Once the colon cancer cells (HCT116) formed a single spheroid (approximately 120 μm in diameter) in each microwell, spheroids were perfused in the presence of the cancer drug gradient irinotecan for three days. The number of spheroids, roundness, and cell viability, were inversely proportional to the drug concentration. These results suggest that the μFSCD with a CGG has the potential to become an HTS platform for screening the efficacy of cancer drugs.


Author(s):  
Wanyoung Lim ◽  
Sungsu Park

Three-dimensional (3D) cell culture is considered more clinically relevant in mimicking the structural and physiological conditions of tumors in vivo compared to two-dimensional cell cultures. In recent years, high-throughput screening (HTS) in 3D cell arrays has been extensively used for drug discovery because of its usability and applicability. Herein, we developed a microfluidic spheroid culture device (μFSCD) with a concentration gradient generator (CGG) that enabled cells to form spheroids and grow in the presence of cancer drug gradients. The device is composed of concave microwells with several serpentine micro-channels which generate a concentration gradient. Once the colon cancer cells (HCT116) formed a single spheroid (approximately 120 μm in diameter) in each microwell, spheroids were perfused in the presence of the cancer drug gradient irinotecan for 3 days. The number of spheroids, roundness, and cell viability, were inversely proportional to the drug concentration. These results suggest that the μFSCD with a CGG has the potential to become an HTS platform for screening the efficacy of cancer drugs.


2021 ◽  
Author(s):  
Samuel Sofela ◽  
Sarah Sahloul ◽  
Yong-Ak Song

AbstractCaenorhabditis elegans has emerged as a powerful model organism for drug screening due to its cellular simplicity, genetic amenability and homology to humans combined with its small size and low cost. Currently, high-throughput drug screening assays are mostly based on image-based phenotyping not exploiting key locomotory parameters of this multicellular model with muscles such as its thrashing force, a critical parameter when screening drugs for muscle-related diseases. In this study, we demonstrated the use of a micropillar-based force assay chip in combination with an imaging assay to evaluate the efficacy of various drugs currently used in treatment of neuromuscular diseases. Using this two-dimensional approach, we showed that the force assay was generally more sensitive in measuring efficacy of drug treatment in Duchenne Muscular Dystrophy and Parkinson’s Disease mutant worms as well as partly in Amyotrophic Lateral Sclerosis model. These results underline the potential of our force assay chip in screening of potential drug candidates for the treatment of neuromuscular diseases when combined with an imaging assay in a two-dimensional analysis approach.


2020 ◽  
Vol 11 (1) ◽  
Author(s):  
JungHo Kong ◽  
Heetak Lee ◽  
Donghyo Kim ◽  
Seong Kyu Han ◽  
Doyeon Ha ◽  
...  

Abstract Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models. The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin. We further confirm our biomarkers using external transcriptomic datasets of drug-sensitive and -resistant isogenic cancer cell lines. Finally, concordance analysis between the transcriptomic biomarkers and independent somatic mutation-based biomarkers further validate our method. This work presents a method to predict cancer patient drug responses using pharmacogenomic data derived from organoid models by combining the application of gene modules and network-based approaches.


2020 ◽  
Vol 7 (8) ◽  
pp. 1996-2010 ◽  
Author(s):  
Stephanie J. Franks ◽  
Kate Firipis ◽  
Rita Ferreira ◽  
Katherine M. Hannan ◽  
Richard J. Williams ◽  
...  

Self-assembling peptide hydrogels can effectively transport, hold and release therapeutic molecules in a spatially and temporally controlled manner and, in doing so, improve anti-cancer drug efficacy while reducing non-specific toxicity.


Author(s):  
Jianping Hua ◽  
Chao Sima ◽  
Milana Cypert ◽  
Edward R. Dougherty ◽  
Jeffery M. Trent ◽  
...  

To the development of effective cancer drug, it is necessary to, first, identify drugs and their possible combinations that could exert desired control over the type of cancer being considered; second, have a drug testing method that allows one to assess the variety of responses that can be provoked by drugs. To facilitate such an experiment-modeling-experiment cycle for drug development, a method based on the dynamical systems of pathways is presented. It involves a three-state experimental design: (1) formulate an oncologic pathway model of relevant cancer; (2) perturb the pathways with the drugs of known effects on components of the pathways of interest; and (3) measure process activity indicators at various points on cell populations. To evaluate the drug response in a high-throughput manner, a green fluorescent protein reporter-based technology has been developed. The authors apply the dynamical approach to several issues in the context of colon cancer cell lines.


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