Drug Screening with Genetically Encoded Fluorescent Sensors: Today and Tomorrow

Potekhina, Ekaterina; Bass, Dina Y; Kelmanson, Ilya V.; Ivanenko, Alexander V.; Belousov, Vsevolod V.; Bilan, Dmitry S.

doi:10.3390/ijms22010148

Cited by 19 publications

(12 citation statements)

References 219 publications

(226 reference statements)

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“…By enabling the simultaneous monitoring of proteostasis and hyperexcitability over the time scale of neurodegeneration, synthetic circuits can be used to identify compounds that selectively affect these processes in the diseaseaffected neuronal subtype. As the number of genetically encoded sensors of neurological processes continues to expand [90][91][92], we envision that circuits may bridge these short time scale, dynamic phenotypes with longer time scale assays. Continued development of circuits that decode dynamic signals will enable simple readouts and/or recording of cellular events across longer time scales to facilitate drug screens that span fast molecular events to longer-term phenotypic outcomes.…”

Section: A Vision For Circuit-based Screening In Neurodegenerative Diseasesmentioning

confidence: 99%

Synthetic gene circuits as tools for drug discovery

Beitz

Oakes

Galloway

2022

Trends in Biotechnology

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Within mammalian systems, there exists enormous opportunity to use synthetic gene circuits to enhance phenotype-based drug discovery, to map the molecular origins of disease, and to validate therapeutics in complex cellular systems. While drug discovery has relied on marker staining and high-content imaging in cell-based assays, synthetic gene circuits expand the potential for precision and speed. Here we present a vision of how circuits can improve the speed and accuracy of drug discovery by enhancing the efficiency of hit triage, capturing disease-relevant dynamics in cell-based assays, and simplifying validation and readouts from organoids and microphysiological systems (MPS). By tracking events and cellular states across multiple length and time scales, circuits will transform how we decipher the causal link between molecular events and phenotypes to improve the selectivity and sensitivity of cell-based assays. Enhancing phenotypic drug discovery with synthetic biologyDevelopment of new drugs requires identification and optimization of functional molecules that often proceeds sequentially through high-throughput discovery screening, hit validation, hit expansion and optimization, and preclinical disease modeling (see Glossary) (Figure 1, Key figure)[1]. Target-based drug discovery (TDD) programs aim to identify drug candidates that modulate a defined biological target that is hypothesized to play a causal role in initiating or sustaining a disease. TDD programs offer precision by directly screening for drug candidate efficacy using purified molecular targets [2]. By contrast, phenotypic drug discovery (PDD) programs aim to identify drug candidates that modulate a physiologically-relevant biological system or cellular signaling pathway and screen for drug efficacy using cell-based assays [2]. Cell-based assays are target-agnostic and provide the opportunity to identify therapeutics that resolve diseaseassociated phenotypic deficits even when the disease etiology remains obscure and targets are undefined. Thus, PDD has recently received renewed interest for its potential to identify first-in-class drugs that act via a novel mechanism of action (MoA) as well as identify drugs for disease with complex or unknown etiology [1][2][3][4]. In order to identify drug candidates that translate to effective therapeutics in the clinic, PDD programs require cell-based assays that faithfully recapitulate and report on disease-relevant processes. To efficiently and effectively screen in phenotypic models, assay development for initial discovery screens must balance throughput with the ability to capture these disease processes [1]. As PDD probes a larger biological space than TDD to identify potential therapeutics, the number of hits from an initial discovery screen in a PDD program may be very large [2]. Following initial discovery screens, hit validation and triage are required to produce a small number of high-confidence candidates to proceed to the resource-intensive stages of hit expansion, compound optimization, and hig...

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Section: A Vision For Circuit-based Screening In Neurodegenerative Diseasesmentioning

confidence: 99%

Synthetic gene circuits as tools for drug discovery

Beitz

Oakes

Galloway

2022

Trends in Biotechnology

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“…Genome‐editing technology is a powerful tool used for plant breeding (Ku & Ha, 2020), gene therapy (Li et al, 2020), and drug development (Potekhina et al, 2020). More specifically, the clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR‐associated protein 9 (Cas9) system has been recently applied for improving the traits of various crops, including rice, maize, and tomato (Ku & Ha, 2020; Nonaka et al, 2017; Y.…”

Section: Introductionmentioning

confidence: 99%

Unbiased prediction of off‐target sites in genome‐edited rice using SITE‐Seq analysis on a web‐based platform

et al. 2022

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Genome-editing using the CRISPR-Cas9 system has the potential to substantially accelerate crop breeding. Since off-target editing is one of problems, a reliable method for comprehensively detecting off-target sites is needed. A number of in silico methods based on homology to on-target sequence have been developed, however the prediction without false negative is still under discussion. In this study, we performed a SITE-Seq analysis to predict potential off-target sites. SITE-Seq analysis is a comprehensive method that can detect double-strand breaks in vitro. Furthermore, we developed a systematic method using SITE-Seq in combination with web-based Galaxy system (Galaxy for Cut Site Detection), which can perform reproducible analyses without command line operations. We conducted a SITE-Seq analysis of a rice genome targeted by OsFH15 gRNA-Cas9 as a model, and found 41 candidate off-target sites in the annotated regions. Detailed amplicon-sequencing revealed mutations at one off-target site in actual genome-edited rice. Since this off-target site has an uncommon protospacer adjacent motif, it is difficult to predict using in silico methods alone. Therefore, we propose a novel off-target assessment scheme for genome-edited crops that combines the prediction of off-target candidates by SITE-Seq and in silico programs and the validation of off-target sites by amplicon-sequencing.

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“…In despite of high degree of genomic correlation between the original tumor and the derived cancer cell line, in vitro experiments of high-throughput drug screening still cannot accurately capture the mode of action of drug molecules in vivo (Ferreira et al, 2013). Microcalorimetry screening (Kragh et al, 2021) and genetically encoded fluorescent sensors (Potekhina et al, 2021) have been developed to screen effective antimicrobial combinations for in vivo disease treatment. However, these techniques require skilled operations and complicated experimental procedures.…”

Section: Introductionmentioning

confidence: 99%

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations

Wang

Zhang

Shen

et al. 2021

Preprint

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Motivation: Drug combination therapy becomes promising method in the treatment of cancer. However, the number of possible drug combinations toward cancer cell lines is too large, and it is challenging to screen synergistic drug combinations through wet-lab experiments. Therefore, the computational screening has become an important way to prioritize drug combinations. Graph attention network has recently shown strong performance in screening of compound-protein interactions, but it has not been applied to the screening of drug combinations. Results: In this paper, we proposed a deep learning model (DeepDDS) based on graph neural networks and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cell line. The graph representation of drug molecule structure and gene expression profiles is taken as input to predict the synergistic effects of drug combinations. We compare DeepDDS with traditional machine learning methods (random forest, support vector machine) and other deep learning methods (DeepSynergy, DTF) on the same data set. Our experimental results show that DeepDDS achieved best performance by the AUC value 0.93. Also, on an independent test set released by AstraZeneca, DeepDDS is superior to other comparative methods by 12.2% higher than the suboptimal method. We believe that DeepDDS is a effective tool that can prioritize synergistic drug combinations.

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Drug Screening with Genetically Encoded Fluorescent Sensors: Today and Tomorrow

Cited by 19 publications

References 219 publications

Synthetic gene circuits as tools for drug discovery

Synthetic gene circuits as tools for drug discovery

Unbiased prediction of off‐target sites in genome‐edited rice using SITE‐Seq analysis on a web‐based platform

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations

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