Dataset Information

Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology.

ABSTRACT:

Background

Conventional 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.

Methods

870 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.

Results

Here, 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.

Conclusions

The 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.

SUBMITTER: Ewart L

PROVIDER: S-EPMC9727064 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology.

Ewart Lorna L Apostolou Athanasia A Briggs Skyler A SA Carman Christopher V CV Chaff Jake T JT Heng Anthony R AR Jadalannagari Sushma S Janardhanan Jeshina J Jang Kyung-Jin KJ Joshipura Sannidhi R SR Kadam Mahika M MM Kanellias Marianne M Kujala Ville J VJ Kulkarni Gauri G Le Christopher Y CY Lucchesi Carolina C Manatakis Dimitris V DV Maniar Kairav K KK Quinn Meaghan E ME Ravan Joseph S JS Rizos Ann Catherine AC Sauld John F K JFK Sliz Josiah D JD Tien-Street William W Trinidad Dennis Ramos DR Velez James J Wendell Max M Irrechukwu Onyi O Mahalingaiah Prathap Kumar PK Ingber Donald E DE Scannell Jack W JW Levner Daniel D

Communications medicine 20221206 1

<h4>Background</h4>Conventional 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 an ...[more]

PMID: 36473994

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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.

Dataset Information

Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology.

Background

Methods

Results

Conclusions

Publications

Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology.

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