scholarly journals Advancing Drug Discovery for Neurological Disorders Using iPSC-Derived Neural Organoids

2021 ◽  
Vol 22 (5) ◽  
pp. 2659
Author(s):  
Gianluca Costamagna ◽  
Giacomo Pietro Comi ◽  
Stefania Corti
Keyword(s):  
Drug Discovery ◽  
Drug Screening ◽  
Neural Development ◽  
Neurological Disorders ◽  
Large Scale ◽  
Three Dimensional ◽  
Induced Pluripotent Stem Cell ◽  
Cellular Level ◽  
Complex Data ◽  
Complex Data Sets

In the last decade, different research groups in the academic setting have developed induced pluripotent stem cell-based protocols to generate three-dimensional, multicellular, neural organoids. Their use to model brain biology, early neural development, and human diseases has provided new insights into the pathophysiology of neuropsychiatric and neurological disorders, including microcephaly, autism, Parkinson’s disease, and Alzheimer’s disease. However, the adoption of organoid technology for large-scale drug screening in the industry has been hampered by challenges with reproducibility, scalability, and translatability to human disease. Potential technical solutions to expand their use in drug discovery pipelines include Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) to create isogenic models, single-cell RNA sequencing to characterize the model at a cellular level, and machine learning to analyze complex data sets. In addition, high-content imaging, automated liquid handling, and standardized assays represent other valuable tools toward this goal. Though several open issues still hamper the full implementation of the organoid technology outside academia, rapid progress in this field will help to prompt its translation toward large-scale drug screening for neurological disorders.

scholarly journals Human iPSC-Based Modeling of Central Nerve System Disorders for Drug Discovery

2021 ◽  
Vol 22 (3) ◽  
pp. 1203
Author(s):  
Lu Qian ◽  
Julia TCW
Keyword(s):  
Drug Discovery ◽  
Disease Modeling ◽  
Three Dimensional ◽  
Structural Complexity ◽  
Induced Pluripotent Stem Cell ◽  
Cell Types ◽  
Central Nerve System ◽  
Human Ipscs ◽  
Nerve System

A high-throughput drug screen identifies potentially promising therapeutics for clinical trials. However, limitations that persist in current disease modeling with limited physiological relevancy of human patients skew drug responses, hamper translation of clinical efficacy, and contribute to high clinical attritions. The emergence of induced pluripotent stem cell (iPSC) technology revolutionizes the paradigm of drug discovery. In particular, iPSC-based three-dimensional (3D) tissue engineering that appears as a promising vehicle of in vitro disease modeling provides more sophisticated tissue architectures and micro-environmental cues than a traditional two-dimensional (2D) culture. Here we discuss 3D based organoids/spheroids that construct the advanced modeling with evolved structural complexity, which propels drug discovery by exhibiting more human specific and diverse pathologies that are not perceived in 2D or animal models. We will then focus on various central nerve system (CNS) disease modeling using human iPSCs, leading to uncovering disease pathogenesis that guides the development of therapeutic strategies. Finally, we will address new opportunities of iPSC-assisted drug discovery with multi-disciplinary approaches from bioengineering to Omics technology. Despite technological challenges, iPSC-derived cytoarchitectures through interactions of diverse cell types mimic patients’ CNS and serve as a platform for therapeutic development and personalized precision medicine.

Structure of the Séchilienne unstable slope from large-scale three-dimensional electrical tomography using a Resistivity Distributed Automated System (R-DAS)

2019 ◽  
Vol 219 (1) ◽  
pp. 129-147 ◽  
Author(s):  
M Lajaunie ◽  
J Gance ◽  
P Nevers ◽  
J-P Malet ◽  
C Bertrand ◽  
...  
Keyword(s):  
Large Scale ◽  
Electrical Potential ◽  
Three Dimensional ◽  
Automated System ◽  
Data Uncertainty ◽  
Complex Data ◽  
Electrical Tomography ◽  
Data Set ◽  
Resistivity Model ◽  
Unstable Slope

SUMMARY This work presents a 3-D resistivity model of the Séchilienne unstable slope acquired with a network of portable resistivimeters in summer 2017. The instrumentation consisted in distributed measuring systems (IRIS Instruments FullWaver) to measure the spatial variations of electrical potential. 23 V-FullWaver receivers with two 50 m dipoles have been deployed over an area of circa 2 km2; the current was injected between a fixed remote electrode and a mobile electrode grounded successively at 30 locations. The data uncertainty has been evaluated in relation to the accuracy of electrodes positioning. The software package BERT (Boundless Electrical Resistivity Tomography) is used to invert the apparent resistivity and model the complex data set providing the first 3-D resistivity model of the slope. Stability tests and synthetic tests are realized to assess the interpretability of the inverted models. The 3-D resistivity model is interpreted up to a depth of 500 m; it allows identifying resistive and conductive anomalies related to the main geological and hydrogeological structures shaping the slope. The high fracturation of the rock in the most active zone of the landslide appears as a resistive anomaly where the highest resistivity values are located close to the faults. A major drain formed by a fault in the unaltered micaschist is identified through the discharge of a perched aquifer along the conductive zone producing an important conductive anomaly contrasting with the unaltered micaschist.

scholarly journals Application of hepatocyte-like cells to enhance hepatic safety risk assessment in drug discovery

2018 ◽  
Vol 373 (1750) ◽  
pp. 20170228 ◽  
Author(s):  
Dominic P. Williams
Keyword(s):  
Risk Assessment ◽  
Drug Discovery ◽  
High Throughput Screening ◽  
Three Dimensional ◽  
Induced Pluripotent Stem Cell ◽  
Genetic Characteristics ◽  
Short Period ◽  
Donor Variation ◽  
Discovery Pipeline

Hepatic stress and injury from drugs continues to be a major concern within the pharmaceutical industry, leading to preclinical and clinical attrition precautionary warnings and post-market withdrawal of drugs. There is a requirement for more predictive and mechanistically accurate models to aid risk assessment. Primary human hepatocytes, subject to isolation stress, cryopreservation, donor-to-donor variation and a relatively short period of functional capability in two-dimensional cultures, are not suitable for high-throughput screening procedures. There are two areas within the drug discovery pipeline that the generation of a stable, metabolically functional hepatocyte-like cell with unlimited supply would have major impact. First, in routine, cell health risk-assessment assays where hepatic cell lines are typically deployed. Second, at later stages of the drug discovery pipeline approaching candidate nomination where bespoke/investigational studies refining and understanding the risk to patients use patient-derived induced pluripotent stem cell (iPSC) hepatocytes retaining characteristics from the patient, e.g. HLA susceptibility alleles, iPSC hepatocytes with defined disease phenotypes or genetic characteristics that have the potential to make the hepatocyte more sensitive to a particular stress mechanism. Functionality of patient-centric hepatocyte-like cells is likely to be enhanced when coupled with emerging culture systems, such as three-dimensional spheroids or microphysiological systems. Ultimately, the aspiration to confidently use human-relevant in vitro models to predict human-specific hepatic toxicity depends on the integration of promising emerging technologies. This article is part of the theme issue ‘Designer human tissue: coming to a lab near you’.

The Application of the Tissue Microarray (TMA) Technology to Analyze Cerebral Organoids

2021 ◽  
pp. 002215542110253
Author(s):  
Ida Biunno ◽  
Emanuela Paiola ◽  
Pasquale De Blasio
Keyword(s):  
Tissue Microarray ◽  
Neurological Disorders ◽  
Scale Up ◽  
Disease Model ◽  
Three Dimensional ◽  
Induced Pluripotent Stem Cell ◽  
Cellular Systems ◽  
Induced Pluripotent ◽  
Molecular Circuitry

“Multi-Omics” technologies have contributed greatly to the understanding of various diseases by enabling researchers to accurately and rapidly investigate the molecular circuitry that connects cellular systems. The tissue-engineered, three-dimensional (3D), in vitro disease model “organoid” integrates the “omics” results in a model system, elucidating the complex links between genotype and phenotype. These 3D structures have been used to model cancer, infectious disease, toxicity, and neurological disorders. Here, we describe the advantage of using the tissue microarray (TMA) technology to analyze human-induced pluripotent stem cell–derived cerebral organoids. Compared with the conventional processing of individual samples, sectioning and staining of TMA slides are faster and can be automated, decreasing labor and reagent costs. The TMA technology faithfully captures cell morphology variations and detects specific biomarkers. The use of this technology can scale up organoid research results in at least two ways: (1) in the number of specimens that can be analyzed simultaneously and (2) in the number of consecutive sections that can be produced for analysis with different probes and antibodies.

scholarly journals Development of a drug screening system using three-dimensional cardiac tissues containing multiple cell types

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maki Takeda ◽  
Shigeru Miyagawa ◽  
Emiko Ito ◽  
Akima Harada ◽  
Noriko Mochizuki-Oda ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Ion Channel ◽  
Drug Screening ◽  
Cardiac Fibroblasts ◽  
Three Dimensional ◽  
Induced Pluripotent Stem Cell ◽  
K Channel ◽  
Drug Induced ◽  
Cardiac Tissues ◽  
Screening Systems

AbstractWe hypothesized that an appropriate ratio of cardiomyocytes, fibroblasts, endothelial cells, and extracellular matrix (ECM) factors would be required for the development of three-dimensional cardiac tissues (3D-CTs) as drug screening systems. To verify this hypothesis, ECM-coated human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), ECM-coated cardiac fibroblasts (CFs), and uncoated cardiac endothelial cells (CEs) were mixed in the following ratios: 10:0:0 (10CT), 7:2:1 (7CT), 5:4:1 (5CT), and 2:7:1 (2CT). The expression of cardiac-, fibroblasts-, and endothelial-specific markers was assessed by FACS, qPCR, and immunostaining while that of ECM-, cell adhesion-, and ion channel-related genes was examined by qPCR. Finally, the contractile properties of the tissues were evaluated in the absence or presence of E-4031 and isoproterenol. The expression of ECM- and adhesion-related genes significantly increased, while that of ion channel-related genes significantly decreased with the CF proportion. Notably, 7CT showed the greatest contractility of all 3D-CTs. When exposed to E-4031 (hERG K channel blocker), 7CT and 5CT showed significantly decreased contractility and increased QT prolongation. Moreover, 10CT and 7CT exhibited a stronger response to isoproterenol than did the other 3D-CTs. Finally, 7CT showed the highest drug sensitivity among all 3D-CTs. In conclusion, 3D-CTs with an appropriate amount of fibroblasts/endothelial cells (7CT in this study) are suitable drug screening systems, e.g. for the detection of drug-induced arrhythmia.

scholarly journals A Machine Learning-Enabled Pipeline for Large-Scale Virtual Drug Screening

2021 ◽  
Author(s):  
Aayush Gupta ◽  
Huan-Xiang Zhou
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Drug Screening ◽  
Drug Target ◽  
Large Scale ◽  
False Positives ◽  
Virtual Drug Screening

Virtual screening is receiving renewed attention in drug discovery, but progress is hampered by challenges on two fronts: handling the ever increasing sizes of libraries of drug-like compounds, and separating true positives from false positives. Here we developed a machine learning-enabled pipeline for large-scale virtual screening that promises breakthroughs on both fronts. By clustering compounds according to molecular properties and limited docking against a drug target, the full library was trimmed by 10-fold; the remaining compounds were then screened individually by docking; and finally a dense neural network was trained to classify the hits into true and false positives. As illustration, we screened for inhibitors against RPN11, the deubiquitinase subunit of the proteasome and a drug target for breast cancer.

Composing And Performing Complex Systems: From The Quantum To The Cosmological

Leonardo ◽  
2021 ◽  
pp. 1-7
Author(s):  
JoAnn Kuchera-Morin
Keyword(s):  
Large Scale ◽  
Orchestral Music ◽  
Data Sets ◽  
Complex Data ◽  
Instrumental Ensemble ◽  
Complex Data Sets ◽  
Temporal Structures ◽  
Multi Media ◽  
Spatio Temporal ◽  
And Performance

Abstract This paper discusses the creation and development of a large distributed immersive multimedia computation system and environment based on the discipline of orchestral music composition, concert hall design, and performance. Just as the orchestra evolved through mechanical engineering to become a large distributed multi-user instrument whose information can be transmitted either by a client-server model as in orchestra-conductor, or a client-to-client model, as in an instrumental ensemble, large-scale distributed multi-media computational platforms can be modeled in the same way, facilitating the users as performers of the system. Multiple researchers can mine large, complex data sets to uncover important relationships in their spatio-temporal structures.

scholarly journals Patient-Derived Induced Pluripotent Stem Cell-Based Models in Parkinson’s Disease for Drug Identification

2020 ◽  
Vol 21 (19) ◽  
pp. 7113
Author(s):  
Georgia Kouroupi ◽  
Nasia Antoniou ◽  
Kanella Prodromidou ◽  
Era Taoufik ◽  
Rebecca Matsas
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Discovery ◽  
Neurodegenerative Disorder ◽  
Induced Pluripotent Stem Cell ◽  
Cellular Systems ◽  
Complex Data ◽  
Disease Modifying Drugs ◽  
Disease Modifying ◽  
Induced Pluripotent

Parkinson’s disease (PD) is a common progressive neurodegenerative disorder characterized by loss of striatal-projecting dopaminergic neurons of the ventral forebrain, resulting in motor and cognitive deficits. Despite extensive efforts in understanding PD pathogenesis, no disease-modifying drugs exist. Recent advances in cell reprogramming technologies have facilitated the generation of patient-derived models for sporadic or familial PD and the identification of early, potentially triggering, pathological phenotypes while they provide amenable systems for drug discovery. Emerging developments highlight the enhanced potential of using more sophisticated cellular systems, including neuronal and glial co-cultures as well as three-dimensional systems that better simulate the human pathophysiology. In combination with high-throughput high-content screening technologies, these approaches open new perspectives for the identification of disease-modifying compounds. In this review, we discuss current advances and the challenges ahead in the use of patient-derived induced pluripotent stem cells for drug discovery in PD. We address new concepts implicating non-neuronal cells in disease pathogenesis and highlight the necessity for functional assays, such as calcium imaging and multi-electrode array recordings, to predict drug efficacy. Finally, we argue that artificial intelligence technologies will be pivotal for analysis of the large and complex data sets obtained, becoming game-changers in the process of drug discovery.

scholarly journals Uncovering Proximity of Chromosome Territories using Classical Algebraic Statistics

2015 ◽  
Vol 6 (2) ◽  
Author(s):  
Javier Arsuaga ◽  
Ido Heskia ◽  
Serkan Hosten ◽  
Tatiana Maskalevich
Keyword(s):  
Statistical Significance ◽  
Three Dimensional ◽  
Spatial Proximity ◽  
Published Data ◽  
Algebraic Statistics ◽  
Data Sets ◽  
Complex Data ◽  
Data Set ◽  
Complex Data Sets ◽  
Human Blood Lymphocyte

Exchange type chromosome aberrations (ETCAs) are rearrangements of the genome that occur when chromosomes break and the resulting fragments rejoin with fragments from other chromosomes or from other regions within the same chromosome. ETCAs are commonly observed in cancer cells and in cells exposed to radiation. The frequency of these chromosome rearrangements is correlated with their spatial proximity, therefore it can be used to infer the three dimensional organization of the genome. Extracting statistical significance of spatial proximity from cancer and radiation data has remained somewhat elusive because of the sparsity of the data. We here propose a new approach to study the three dimensional organization of the genome using algebraic statistics. We test our method on a published data set of irradiated human blood lymphocyte cells. We provide a rigorous method for testing the overall organization of the genome, and in agreement with previous results we find a random relative positioning of chromosomes with the exception of the chromosome pairs {1,22} and {13,14} that have a significantly larger number of ETCAs than the rest of the chromosome pairs suggesting their spatial proximity. We conclude that algebraic methods can successfully be used to analyze genetic data and have potential applications to larger and more complex data sets. 

Organ-on-Chip Devices Toward Applications in Drug Development and Screening

2018 ◽  
Vol 12 (4) ◽  
Author(s):  
Christopher Uhl ◽  
Wentao Shi ◽  
Yaling Liu
Keyword(s):  
Drug Development ◽  
High Throughput ◽  
Drug Screening ◽  
Large Scale ◽  
Three Dimensional ◽  
3D Cell Culture ◽  
Throughput Capacity ◽  
Comprehensive Overview ◽  
The Past ◽  
High Throughput Drug Screening

As a necessary pathway to man-made organs, organ-on-chips (OOC), which simulate the activities, mechanics, and physiological responses of real organs, have attracted plenty of attention over the past decade. As the maturity of three-dimensional (3D) cell-culture models and microfluidics advances, the study of OOCs has made significant progress. This review article provides a comprehensive overview and classification of OOC microfluidics. Specifically, the review focuses on OOC systems capable of being used in preclinical drug screening and development. Additionally, the review highlights the strengths and weaknesses of each OOC system toward the goal of improved drug development and screening. The various OOC systems investigated throughout the review include, blood vessel, lung, liver, and tumor systems and the potential benefits, which each provides to the growing challenge of high-throughput drug screening. Published OOC systems have been reviewed over the past decade (2007–2018) with focus given mainly to more recent advances and improvements within each organ system. Each OOC system has been reviewed on how closely and realistically it is able to mimic its physiological counterpart, the degree of information provided by the system toward the ultimate goal of drug development and screening, how easily each system would be able to transition to large scale high-throughput drug screening, and what further improvements to each system would help to improve the functionality, realistic nature of the platform, and throughput capacity. Finally, a summary is provided of where the broad field of OOCs appears to be headed in the near future along with suggestions on where future efforts should be focused for optimized performance of OOC systems in general.

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