Towards Nano-Risk Assessment With High Throughput Screening and High Content Analysis: An Intelligent Testing Strategy

Author(s):  
Deepti Mittal ◽  
Gautam Kaul
2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Bernard Michael Corfe ◽  
Joanna Chowdry ◽  
Gareth J. Griffiths ◽  
Rod P. Benson

2014 ◽  
Vol 19 (10) ◽  
pp. 1402-1408 ◽  
Author(s):  
Stephanie D. Cole ◽  
Janna S. Madren-Whalley ◽  
Albert P. Li ◽  
Russell Dorsey ◽  
Harry Salem

In vitro models that accurately and rapidly assess hepatotoxicity and the effects of hepatic metabolism on nonliver cell types are needed by the U.S. Department of Defense and the pharmaceutical industry to screen compound libraries. Here, we report the first use of high content analysis on the Integrated Discrete Multiple Organ Co-Culture (IdMOC) system, a high-throughput method for such studies. We cultured 3T3-L1 cells in the presence and absence of primary human hepatocytes, and exposed the cultures to 4-aminophenol and cyclophosphamide, model toxicants that are respectively detoxified and activated by the liver. Following staining with calcein-AM, ethidium homodimer-1, and Hoechst 33342, high content analysis of the cultures revealed four cytotoxic endpoints: fluorescence intensities of calcein-AM and ethidium homodimer-1, nuclear area, and cell density. Using these endpoints, we observed that the cytotoxicity of 4-aminophenol in 3T3-L1 cells in co-culture was less than that observed for 3T3-L1 monocultures, consistent with the known detoxification of 4-aminophenol by hepatocytes. Conversely, cyclophosphamide cytotoxicity for 3T3-L1 cells was enhanced by co-culturing with hepatocytes, consistent with the known metabolic activation of this toxicant. The use of IdMOC plates combined with high content analysis is therefore a multi-endpoint, high-throughput capability for measuring the effects of metabolism on toxicity.


2018 ◽  
Vol 38 (1) ◽  
pp. 12-26 ◽  
Author(s):  
Daniel L. Villeneuve ◽  
Katie Coady ◽  
Beate I. Escher ◽  
Ellen Mihaich ◽  
Cheryl A. Murphy ◽  
...  

2008 ◽  
Vol 27 (6) ◽  
pp. 405-405
Author(s):  
David J. Dix

The U.S. Environmental Protection Agency (EPA), National Toxicology Program (NTP), and National Institutes of Health (NIH) Chemical Genomics Center (NCGC) have complementary research programs designed to improve chemical toxicity evaluations by developing high throughput screening (HTS) methods that evaluate the impact of environmental chemicals on key toxicity pathways. These federal partners are coordinating an extension of the EPA’s ToxCast program, the NTP’s HTS initiative, and the NCGC’s Molecular Libraries Initiative into a collaborative research program focused on identifying toxicity pathways and developing in vitro assays to characterize the ability of chemicals to perturb those pathways. The goal is to develop new paradigm for high throughput toxicity testing that collects mechanistic and quantitative data from in vitro assays measuring chemical modulation of biological processes involved in the progression to toxicity. As toxicity pathways are identified, the in vitro assays can be optimized for comparison to in vivo animal studies, and for predicting effects in humans. Subsequent computational modeling of toxicity pathway responses and appropriate chemical dosimetry will need to be developed to make these predictions relevant for human health risk assessment. This work was reviewed by EPA and approved for publication but does not necessarily reflect official Agency policy. Index Terms: Toxicogenomics, High Throughput Screening/Testing, EPA ToxCast, Chemical Risk Assessment


2017 ◽  
Vol 125 (4) ◽  
pp. 623-633 ◽  
Author(s):  
Salomon Sand ◽  
Fred Parham ◽  
Christopher J. Portier ◽  
Raymond R. Tice ◽  
Daniel Krewski

2020 ◽  
Vol 25 (8) ◽  
pp. 939-949
Author(s):  
Bruce Nmezi ◽  
Laura L. Vollmer ◽  
Tong Ying Shun ◽  
Albert Gough ◽  
Harshvardhan Rolyan ◽  
...  

Autosomal dominant leukodystrophy (ADLD) is a fatal, progressive adult-onset disease characterized by widespread central nervous system (CNS) demyelination and significant morbidity. The late age of onset together with the relatively slow disease progression provides a large therapeutic window for the disorder. However, no treatment exists for ADLD, representing an urgent and unmet clinical need. We have previously shown that ADLD is caused by duplications of the lamin B1 gene causing increased expression of the lamin B1 protein, a major constituent of the nuclear lamina, and demonstrated that transgenic mice with oligodendrocyte-specific overexpression of lamin B1 exhibit temporal and histopathological features reminiscent of the human disease. As increased levels of lamin B1 are the causative event triggering ADLD, approaches aimed at reducing lamin B1 levels and associated functional consequences represent a promising strategy for discovery of small-molecule ADLD therapeutics. To this end, we have created an inducible cell culture model of lamin B1 overexpression and developed high-content analysis in connection with multivariate analysis to define, analyze, and quantify lamin B1 expression and its associated abnormal nuclear phenotype in mouse embryonic fibroblasts (MEFs). The assay has been optimized to meet high-throughput screening (HTS) criteria in multiday variability studies. To control for batch-to-batch variation in the primary MEFs, we have implemented a screening strategy that employs sentinel cells to avoid costly losses during HTS. We posit the assay will identify bona fide suppressors of lamin B1 pathophysiology as candidates for development into potential therapies for ADLD.


2019 ◽  
Vol 24 (6) ◽  
pp. 615-627 ◽  
Author(s):  
Tijmen H. Booij ◽  
Leo S. Price ◽  
Erik H. J. Danen

The introduction of more relevant cell models in early preclinical drug discovery, combined with high-content imaging and automated analysis, is expected to increase the quality of compounds progressing to preclinical stages in the drug development pipeline. In this review we discuss the current switch to more relevant 3D cell culture models and associated challenges for high-throughput screening and high-content analysis. We propose that overcoming these challenges will enable front-loading the drug discovery pipeline with better biology, extracting the most from that biology, and, in general, improving translation between in vitro and in vivo models. This is expected to reduce the proportion of compounds that fail in vivo testing due to a lack of efficacy or to toxicity.


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