Qualifying a human Liver-Chip for predictive toxicology: Performance assesment and economic implications
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA799303 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
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| SRR17690885_1.fastq.gz | Fastqsanger.gz | |||
| SRR17690885_2.fastq.gz | Fastqsanger.gz | |||
| SRR17690886_1.fastq.gz | Fastqsanger.gz | |||
| SRR17690886_2.fastq.gz | Fastqsanger.gz | |||
| SRR17690887_1.fastq.gz | Fastqsanger.gz |
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Project description:Qualifying a human Liver-Chip for predictive toxicology: Performance assessment and economic implications
| PRJNA854895 | ENA
Project description:We assess RNA expression in human primary hepatocytes in Vials Day 0 and Liver-Chips on Day 3 and Day 7 for two different donors. The hepatocytes were co-cultured in Liver-Chip model with Kupffer cells and Liver sinusoidal endothelial cells (LSECs) and stellate cells .
2022-10-14 | GSE207339 | GEO
Project description:We assess RNA expression in human primary hepatocytes in Liver-Chips on Day 3 and Day 5. The hepatocytes were co-cultured in Liver-Chip model with Kupffer cells, Liver sinusoidal endothelial cells (LSECs) and stellate cells .
2025-01-21 | GSE194147 | GEO
Project description:BackgroundConventional preclinical models often miss drug toxicities, meaning the harm these drugs pose to humans is only realized in clinical trials or when they make it to market. This has caused the pharmaceutical industry to waste considerable time and resources developing drugs destined to fail. Organ-on-a-Chip technology has the potential improve success in drug development pipelines, as it can recapitulate organ-level pathophysiology and clinical responses; however, systematic and quantitative evaluations of Organ-Chips' predictive value have not yet been reported.Methods870 Liver-Chips were analyzed to determine their ability to predict drug-induced liver injury caused by small molecules identified as benchmarks by the Innovation and Quality consortium, who has published guidelines defining criteria for qualifying preclinical models. An economic analysis was also performed to measure the value Liver-Chips could offer if they were broadly adopted in supporting toxicity-related decisions as part of preclinical development workflows.ResultsHere, we show that the Liver-Chip met the qualification guidelines across a blinded set of 27 known hepatotoxic and non-toxic drugs with a sensitivity of 87% and a specificity of 100%. We also show that this level of performance could generate over $3 billion annually for the pharmaceutical industry through increased small-molecule R&D productivity.ConclusionsThe results of this study show how incorporating predictive Organ-Chips into drug development workflows could substantially improve drug discovery and development, allowing manufacturers to bring safer, more effective medicines to market in less time and at lower costs.
| S-EPMC9727064 | biostudies-literature
Project description:Perform microarray study to select sensitive genes for toxicology analysis on hESC validation system.
2014-08-07 | GSE60154 | GEO
Project description:ComptoxAI is a new data infrastructure for computational and artificial intelligence research in predictive toxicology. Here, we describe and showcase ComptoxAI's graph-structured knowledge base in the context of three real-world use-cases, demonstrating that it can rapidly answer complex questions about toxicology that are infeasible using previous technologies and data resources. These use-cases each demonstrate a tool for information retrieval from the knowledge base being used to solve a specific task: The "shortest path" module is used to identify mechanistic links between perfluorooctanoic acid (PFOA) exposure and nonalcoholic fatty liver disease; the "expand network" module identifies communities that are linked to dioxin toxicity; and the quantitative structure-activity relationship (QSAR) dataset generator predicts pregnane X receptor agonism in a set of 4,021 pesticide ingredients. The contents of ComptoxAI's source data are rigorously aggregated from a diverse array of public third-party databases, and ComptoxAI is designed as a free, public, and open-source toolkit to enable diverse classes of users including biomedical researchers, public health and regulatory officials, and the general public to predict toxicology of unknowns and modes of action.
| S-EPMC9805296 | biostudies-literature
Project description:While current genetic toxicology practices can detect downstream genotoxicity effects, such as gene mutation and double strand breaks, they are unable to detect the underlying Mode of Action (MoA) of a chemical or differentiate between direct and indirect acting genotoxicants without additional modification. The Adverse Outcome Pathway (AOP) framework is a useful tool to critically identify and evaluate MOAs and can enable subsequent quantitative dose response assessments of genotoxicity endpoints. The recently developed AOP, “Oxidative DNA damage leading to chromosomal aberrations and mutations” ( https://aopwiki.org/aops/296), pertains to one common genetic toxicology relevant MOA: oxidative stress. ROS are key to regulating many biological processes, however, when disrupted, an excess of ROS can eventually lead to DNA damage and double-strand breaks. Here, we look at 18 compounds with a complete or mixed oxidative stress MOA and use a combination of genomic tools such as Pathway analysis, Connectivity mapping and Transcriptional benchmark dose modeling to investigate the diversity of mechanisms that contribute to genotoxicity. TK6 cells were treated with the 18 compounds for 4 hours and the resulting genomic data was analyzed in correlation to the downstream genotoxic endpoint, micronucleus formation. We provide both a qualitative and quantitative analysis of the contribution of oxidative stress MoAs towards the overall genotoxicity outcome of each chemical. These methods have the potential to evolve into Next Generation Risk Assessment (NGRA) tools that can be used for determining the contribution of the oxidative stress MoA in a predictive toxicology setting.
2025-04-01 | GSE287194 | GEO
Project description:Patient-Specific Pharmacogenomics demonstrates xCT as Predictive Therapeutic Target in Colon Cancer with Possible Implications in Tumor Connectivity
| PRJNA1305329 | ENA
Project description:OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH-relevant endpoints: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset. Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way.
| S-EPMC2941473 | biostudies-literature
Project description:Comparison of whole blood DNA methylation in 45 year-old humans with low and high childhood and adulthood socio-economic position Background: Disadvantaged socio-economic position (SEP) in childhood is associated with increased adult mortality and morbidity. We aimed to establish whether childhood SEP was associated with differential methylation of adult DNA. Methods: Forty adult males from the 1958 British Birth Cohort Study were selected from SEP extremes in both early childhood and mid-adulthood. We performed genome-wide methylation analysis on blood DNA taken at 45y using MeDIP (methylated DNA immunoprecipitation). We mapped in triplicate the methylation state of promoters of ~20,000 genes and 400 microRNAs. Probe methylation scores were averaged across triplicates and differential methylation between groups of individuals was determined. Differentially methylated promoter sites of selected genes were validated using pyrosequencing of bisulfite-converted DNA. Results: 9,112 variably methylated probes (from N=223,359 on the microarray) corresponded to 6,176 gene promoters with at least one variable probe. Unsupervised hierarchical clustering of probes obtained from the 500 most variable promoters revealed a cluster enriched with high SEP individuals confirming that SEP differences contribute to overall epigenetic variation. Methylation levels for 1252 gene promoters were associated with childhood SEP vs 545 promoters for adulthood SEP. Functionally, associations with childhood SEP appear in promoters of genes enriched in key cell signalling pathways. The differentially methylated promoters associated with SEP cluster in megabase-sized regions of the genome. Conclusions: Adult blood DNA methylation profiles show more associations with childhood SEP than adult SEP. Organization of these associations across the genome suggests a well-defined epigenetic pattern linked to early socio-economic environment.
2016-05-25 | GSE31713 | GEO