scholarly journals Pattern Recognition Analysis of Endogenous Cell Metabolites for High Throughput Mode of Action Identification: Removing the Postscreening Dilemma Associated with Whole-Organism High Throughput Screening

2000 ◽  
Vol 5 (5) ◽  
pp. 335-342 ◽  
Author(s):  
Stephen J.W. Hole ◽  
Peter W.A. Howe ◽  
Paul D. Stanley ◽  
Stephen T. Hadfield

Although whole-organism HTS can give clear indications of in vivo activity, typically few clues are given as to the mechanism of action (MOA), and determining the MOA for large numbers of active compounds can be costly and complex—an alternative approach is required. This report demonstrates that it is possible to conduct relatively high throughput MOA characterization of HTS hits utilizing a single sample preparation and analytical method. By monitoring a wide range of endogenous cellular metabolites via 1H nuclear magnetic resonance spectroscopy, the MOA of herbicides can be predicted using computational methods to compare the metabolite perturbation patterns. Herbicides that induce a characteristic pattern of metabolic perturbation in maize include inhibitors of acetolactate synthase, acetyl co-enzyme A carboxylase, protoporphyrinogen oxidase, 5-enolpyruvylshikimate-3-phosphate synthase, and phytoene desaturase. In soya, photosystem II inhibitors can also be detected, further demonstrating that this method is not limited to inhibitors of enzymes that directly act upon endogenous metabolites, or a single species. The methods, including data analysis, can be readily automated, enabling relatively high throughput MOA elucidation of whole-organism screen hits. Additionally, for compounds with a novel MOA, this approach may lead to MOA identification faster than traditional metods. It is envisaged that application of these data analysis methods to other data types—for example, transcription (mRNA) or translation (protein) profilesis likely to permit higher throughput with smaller sample requirements, along with ability to discriminate MOAs that are not adequately discriminated based upon endogenous metabolite profiles.

2020 ◽  
Vol 26 (1) ◽  
pp. 140-150
Author(s):  
Ann M. Decker ◽  
Kelly M. Mathews ◽  
Bruce E. Blough ◽  
Brian P. Gilmour

The human trace amine-associated receptor 1 (hTAAR1) is a G protein-coupled receptor (GPCR) that is widely expressed in monoaminergic nuclei in the central nervous system and has therapeutic potential for multiple diseases, including drug addiction and schizophrenia. Thus, identification of novel hTAAR1 ligands is critical to advancing our knowledge of hTAAR1 function and to the development of therapeutics for a wide range of diseases. Herein we describe the development of a robust, 3-addition high-throughput screening (HTS) calcium mobilization assay using stable CHO-Gαq16-hTAAR1 cells, which functionally couple hTAAR1 to the promiscuous Gαq16 protein and thus allow signal transduction to occur through mobilization of internal calcium. Our previously established 96-well hTAAR1 assay was first miniaturized to the 384-well format and optimized to provide an assay with a Z′ factor of 0.84, which is indicative of a robust HTS assay. Using the 3-addition protocol, 22,000 compounds were screened and yielded a ~1% agonist hit rate and a ~0.2% antagonist hit rate. Of the antagonist hits, two confirmed hits are the most potent hTAAR1 antagonists identified to date (IC50 = 206 and 281 nM). While scientists have been studying hTAAR1 for years, the lack of suitable hTAAR1 antagonists has been a major roadblock for studying the basic pharmacology of hTAAR1. Thus, these new ligands will serve as valuable tools to study hTAAR1-mediated signaling mechanisms, therapeutic potential, and in vivo functions.


2019 ◽  
Author(s):  
Daniel Reker ◽  
Yulia Rybakova ◽  
Ameya R. Kirtane ◽  
Ruonan Cao ◽  
Jee Won Yang ◽  
...  

AbstractNanoformulations are transforming our capacity to effectively deliver and treat a myriad of conditions. However, many nanoformulation approaches still suffer from high production complexity and low drug loading. One potential solution relies on harnessing co-assembly of drugs and small molecular excipients to facilitate nanoparticle formation through solvent exchange without the need for chemical synthesis, generating nanoparticles with up to 95% drug loading. However, there is currently no understanding which of the millions of possible combinations of small molecules can result in the formation of these nanoparticles. Here we report the development of a high-throughput screening platform coupled to machine learning to enable the rapid evaluation of such nanoformulations. Our platform identified 101 novel self-assembling drug nanoparticles from 2.1 million pairings derived from 788 candidate drugs with one of 2686 excipients, spanning treatments for multiple diseases and often harnessing well-known food additives, vitamins, or approved drugs as carrier materials – with potential for accelerated approval and translation. Given their long-term stability and potential for clinical impact, we further characterize novel sorafenib-glycyrrhizin and terbinafine-taurocholic acid nanoparticles ex vivo and in vivo. We anticipate that this platform could accelerate the development of safer and more efficacious nanoformulations with high drug loadings for a wide range of therapeutics.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhou Fang ◽  
Junjian Chen ◽  
Ye Zhu ◽  
Guansong Hu ◽  
Haoqian Xin ◽  
...  

AbstractPeptides are widely used for surface modification to develop improved implants, such as cell adhesion RGD peptide and antimicrobial peptide (AMP). However, it is a daunting challenge to identify an optimized condition with the two peptides showing their intended activities and the parameters for reaching such a condition. Herein, we develop a high-throughput strategy, preparing titanium (Ti) surfaces with a gradient in peptide density by click reaction as a platform, to screen the positions with desired functions. Such positions are corresponding to optimized molecular parameters (peptide densities/ratios) and associated preparation parameters (reaction times/reactant concentrations). These parameters are then extracted to prepare nongradient mono- and dual-peptide functionalized Ti surfaces with desired biocompatibility or/and antimicrobial activity in vitro and in vivo. We also demonstrate this strategy could be extended to other materials. Here, we show that the high-throughput versatile strategy holds great promise for rational design and preparation of functional biomaterial surfaces.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Martin Gajdošík ◽  
Karl Landheer ◽  
Kelley M. Swanberg ◽  
Christoph Juchem

AbstractIn vivo magnetic resonance spectroscopy (MRS) is a powerful tool for biomedical research and clinical diagnostics, allowing for non-invasive measurement and analysis of small molecules from living tissues. However, currently available MRS processing and analytical software tools are limited in their potential for in-depth quality management, access to details of the processing stream, and user friendliness. Moreover, available MRS software focuses on selected aspects of MRS such as simulation, signal processing or analysis, necessitating the use of multiple packages and interfacing among them for biomedical applications. The freeware INSPECTOR comprises enhanced MRS data processing, simulation and analytical capabilities in a one-stop-shop solution for a wide range of biomedical research and diagnostic applications. Extensive data handling, quality management and visualization options are built in, enabling the assessment of every step of the processing chain with maximum transparency. The parameters of the processing can be flexibly chosen and tailored for the specific research problem, and extended confidence information is provided with the analysis. The INSPECTOR software stands out in its user-friendly workflow and potential for automation. In addition to convenience, the functionalities of INSPECTOR ensure rigorous and consistent data processing throughout multi-experiment and multi-center studies.


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.


F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 146 ◽  
Author(s):  
Guanming Wu ◽  
Eric Dawson ◽  
Adrian Duong ◽  
Robin Haw ◽  
Lincoln Stein

High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network and human curated pathways from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.


2021 ◽  
Author(s):  
Diana Wu ◽  
Chelsea Gordon ◽  
John Shin ◽  
Michael Eisenstein ◽  
Hyongsok Tom Soh

Although antibodies are a powerful tool for molecular biology and clinical diagnostics, there are many emerging applications for which nucleic acid-based aptamers can be advantageous. However, generating high-quality aptamers with sufficient affinity and specificity for biomedical applications is a challenging feat for most research laboratories. In this Account, we describe four techniques developed in our lab to accelerate the discovery of high quality aptamer reagents that can achieve robust binding even for challenging molecular targets. The first method is particle display, in which we convert solution-phase aptamers into aptamer particles that can be screened via fluorescence-activated cell sorting (FACS) to quantitatively isolate individual aptamer particles based on their affinity. This enables the efficient isolation of high-affinity aptamers in fewer selection rounds than conventional methods, thereby minimizing selection biases and reducing the emergence of artifacts in the final aptamer pool. We subsequently developed the multi-parametric particle display (MPPD) method, which employs two-color FACS to isolate aptamer particles based on both affinity and specificity, yielding aptamers that exhibit excellent target binding even in complex matrices like serum. The third method is a click chemistry-based particle display (click-PD) that enables the generation and high-throughput screening of non-nattural aptamers with a wide range of base modifications. We have shown that these base-modified aptamers can achieve robust affinity and specificity for targets that have proven challenging or inaccessible with natural nucleotide-based aptamer libraries. Lastly, we describe the non-natural aptamer array (N2A2) platform, in which a modified benchtop sequencing instrument is used to characterize base-modified aptamers in a massively parallel fashion, enabling the efficient identification of molecules with excellent affinity and specificity for their targets. This system first generates aptamer clusters on the flow-cell surface that incorporate alkyne-modified nucleobases, and then performs a click reaction to couple those nucleobases to an azide-modified chemical moiety. This yields a sequence-defined array of tens of millions of base-modified sequences, which can then be characterized in a high-throughput fashion. Collectively, we believe that these advancements are helping to make aptamer technology more accessible, efficient, and robust, thereby enabling the use of these affinity reagents for a wider range of molecular recognition and detection-based applications.


Lab on a Chip ◽  
2021 ◽  
Author(s):  
Xing Zhao ◽  
Gaozhi Ou ◽  
Mengcheng Lei ◽  
Yang Zhang ◽  
Lina Li ◽  
...  

Cells in native microenvironment are subjected to varying combinations of biochemical cues and mechanical cues in a wide range. Despite many signaling pathways have been found to be responsive for...


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