Three-Dimensional Tissue Models for Drug Discovery and Toxicology

2009 ◽  
Vol 3 (2) ◽  
pp. 103-117 ◽  
Author(s):  
Francesco Pampaloni ◽  
Ernst Stelzer ◽  
Andrea Masotti
Keyword(s):  
Drug Discovery ◽  
Three Dimensional ◽  
Tissue Models

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.

Easy and affordable method for rapid prototyping of tissue models in vitro using three-dimensional bioprinting

2018 ◽  
Vol 38 (1) ◽  
pp. 158-169 ◽  
Author(s):  
Ashutosh Bandyopadhyay ◽  
Vimal Kumar Dewangan ◽  
Kiran Yellappa Vajanthri ◽  
Suruchi Poddar ◽  
Sanjeev Kumar Mahto
Keyword(s):  
Rapid Prototyping ◽  
Three Dimensional ◽  
Tissue Models

scholarly journals Clarifying intact 3D tissues on a microfluidic chip for high-throughput structural analysis

2016 ◽  
Vol 113 (52) ◽  
pp. 14915-14920 ◽  
Author(s):  
Yih Yang Chen ◽  
Pamuditha N. Silva ◽  
Abdullah Muhammad Syed ◽  
Shrey Sindhwani ◽  
Jonathan V. Rocheleau ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Three Dimensional ◽  
Microfluidic System ◽  
High Throughput Drug Screening ◽  
Image Analysis Algorithm ◽  
Screening Assays ◽  
On Chip ◽  
Tissue Models

On-chip imaging of intact three-dimensional tissues within microfluidic devices is fundamentally hindered by intratissue optical scattering, which impedes their use as tissue models for high-throughput screening assays. Here, we engineered a microfluidic system that preserves and converts tissues into optically transparent structures in less than 1 d, which is 20× faster than current passive clearing approaches. Accelerated clearing was achieved because the microfluidic system enhanced the exchange of interstitial fluids by 567-fold, which increased the rate of removal of optically scattering lipid molecules from the cross-linked tissue. Our enhanced clearing process allowed us to fluorescently image and map the segregation and compartmentalization of different cells during the formation of tumor spheroids, and to track the degradation of vasculature over time within extracted murine pancreatic islets in static culture, which may have implications on the efficacy of beta-cell transplantation treatments for type 1 diabetes. We further developed an image analysis algorithm that automates the analysis of the vasculature connectivity, volume, and cellular spatial distribution of the intact tissue. Our technique allows whole tissue analysis in microfluidic systems, and has implications in the development of organ-on-a-chip systems, high-throughput drug screening devices, and in regenerative medicine.

Smart, Innovative and Intelligent Technologies Used in Drug Designing

2014 ◽  
pp. 285-301
Author(s):  
S. Deshpande ◽  
S. K. Basu ◽  
X. Li ◽  
X. Chen
Keyword(s):  
Drug Discovery ◽  
Protein Structures ◽  
Three Dimensional ◽  
New Drugs ◽  
Smart Manufacturing ◽  
Data Sets ◽  
Mathematical Methods ◽  
Structure Based Drug Design ◽  
Recent Developments ◽  
Therapeutic Compounds

Smart and intelligent computational methods are essential nowadays for designing, manufacturing and optimizing new drugs. New and innovative computational tools and algorithms are consistently developed and applied for the development of novel therapeutic compounds in many research projects. Rapid developments in the architecture of computers have also provided complex calculations to be performed in a smart, intelligent and timely manner for desired quality outputs. Research groups worldwide are developing drug discovery platforms and innovative tools following smart manufacturing ideas using highly advanced biophysical, statistical and mathematical methods for accelerated discovery and analysis of smaller molecules. This chapter discusses novel innovative applications in drug discovery involving use of structure-based drug design which utilizes geometrical knowledge of the three-dimensional protein structures. It discusses statistical and physics based methods such as quantum mechanics and classical molecular dynamics which can also play a major role in improving the performance and in prediction of computational drug discovery. Lastly, the authors provide insights on recent developments in cloud computing with significant increase in smart and intelligent computational power thus allowing larger data sets to be analyzed simultaneously on multi processor cloud systems. Future directions for the research are outlined.

scholarly journals Artificial intelligence and big data facilitated targeted drug discovery

2019 ◽  
Vol 4 (4) ◽  
pp. 206-213 ◽  
Author(s):  
Benquan Liu ◽  
Huiqin He ◽  
Hongyi Luo ◽  
Tingting Zhang ◽  
Jingwei Jiang
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Drug Discovery ◽  
Three Dimensional ◽  
Data Bank ◽  
The Cancer Genome Atlas ◽  
Biological Databases ◽  
Cancer Genome Atlas ◽  
Targeted Drug ◽  
Targeted Drug Discovery

Different kinds of biological databases publicly available nowadays provide us a goldmine of multidiscipline big data. The Cancer Genome Atlas is a cancer database including detailed information of many patients with cancer. DrugBank is a database including detailed information of approved, investigational and withdrawn drugs, as well as other nutraceutical and metabolite structures. PubChem is a chemical compound database including all commercially available compounds as well as other synthesisable compounds. Protein Data Bank is a crystal structure database including X-ray, cryo-EM and nuclear magnetic resonance protein three-dimensional structures as well as their ligands. On the other hand, artificial intelligence (AI) is playing an important role in the drug discovery progress. The integration of such big data and AI is making a great difference in the discovery of novel targeted drug. In this review, we focus on the currently available advanced methods for the discovery of highly effective lead compounds with great absorption, distribution, metabolism, excretion and toxicity properties.

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