Lazar Toxicity Predictions
Toxicity predictions
Lazar (Lazy Structure-Activity Relationships ) takes a chemical structure as input and provides predictions for a variety of toxic properties. Lazar uses an automated and reproducible read across procedure to calculate predictions. Rationales for predictions, applicability domain estimations and validation results are presented in a clear graphical interface for the critical examination by toxicological experts.
For end-users
For developers
Type:
Application, Helper tool, Trained model, Model, Service
API Type:
REST, OpenAPI, REST under OAS3 specification, OAS3
Categories:
Toxicology, chemical properties and bioassay databases, Processing and analysis, Workflow, visualisation and reporting, Predictive toxicology
Applicability domain:
Toxicology, Predictive toxicology
Topic:
Chemical properties, Risk assessment, Structure-activity relationship (SAR / QSAR)
Biological area:
Mutagenicity, NOAEL/LOAEL, Carcinogenicity, Blood brain barrier, Acute toxicity
Targeted industry:
Drugs, Cosmetics, Food, Other consumer products, Chemicals
Targeted users:
Risk assessors, Researchers, Students, Software Developers, Informed public
Relevant OpenRiskNet case study:
ModelRX - Modelling for Prediction or Read Across
Support contact:
Provided by:
in silico toxicology gmbh, Johannes Gutenberg Univertity
Contact:
Licence:
GNU Lesser General Public License 3 (LGPLv3.0)
Login required:
Yes
Implementation status:
Containerised, Graphical user interface available, Available as web service, OAS v3, Application programming interface available
Technology readiness level:
TRL 8 – system complete and qualified
Integration status:
Integrated application
Service integration operations completed:
Utilises the OpenRiskNet APIs to ensure that each service is accessible to our proposed interoperability layer.
Is annotated according to the semantic interoperability layer concept using defined ontologies.
Is containerised for easy deployment in virtual environments of OpenRiskNet instances.
Has documented scientific and technical background.
Is deployed into the OpenRiskNet reference environment.
Is listed in the OpenRiskNet discovery services.
Is listed in other central repositories like eInfraCentral, bio.tools and TeSS (ELIXIR).
Provides legal and ethical statements on how the service can be used.
Resources & Training
Case Study report - Modelling for Prediction or Read Across [ModelRX]
11 Dec 2019
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Abstract:
The ModelRX case study was designed to cover the important area of generating and applying predictive models, and more specifically QSAR models in hazard assessment endorsed by different regulations, as completely in silico alternatives to animal testing and useful also in early research when no data is available for a compound. The QSAR development process schematically presented in Figure 1 begins by obtaining a training data set from an OpenRiskNet data source. A model can then be trained with OpenRiskNet modelling tools and the resulting models are packaged into a container, documented and ontologically annotated. To assure the quality of the models, they are validated using OECD guidelines (Jennings et al. 2018). Prediction for new compounds can be obtained using a specific model or a consensus of predictions of all models. This case study will present this workflow with the example of blood-brain-barrier (BBB) penetration, for which multiple models were generated using tools from OpenRiskNet consortium and associated partners used individually as well as in a consensus approach using Dempster-Shafer theory (Park et al. 2014; Rathman et al. 2018).
The ModelRX case study was designed to cover the important area of generating and applying predictive models, and more specifically QSAR models in hazard assessment endorsed by different regulations, as completely in silico alternatives to animal testing and useful also in early research when no data is available for a compound. The QSAR development process schematically presented in Figure 1 begins by obtaining a training data set from an OpenRiskNet data source. A model can then be trained with OpenRiskNet modelling tools and the resulting models are packaged into a container, documented and ontologically annotated. To assure the quality of the models, they are validated using OECD guidelines (Jennings et al. 2018). Prediction for new compounds can be obtained using a specific model or a consensus of predictions of all models. This case study will present this workflow with the example of blood-brain-barrier (BBB) penetration, for which multiple models were generated using tools from OpenRiskNet consortium and associated partners used individually as well as in a consensus approach using Dempster-Shafer theory (Park et al. 2014; Rathman et al. 2018).
Additional materials:
Report
Report
Related services:
Nano-QSAR to predict cytotoxicity of metal and metal oxide nanoparticles
Lazar Toxicity Predictions
JGU WEKA REST Service
Nano-QSAR to predict cytotoxicity of metal and metal oxide nanoparticles
Lazar Toxicity Predictions
JGU WEKA REST Service
Target audience: Risk assessors, Researchers, Students, OpenRiskNet stakeholders, Data modellers, Bioinformaticians
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC, JGU, NTUA, UU
OpenRiskNet Part III: Modelling Services in Chemical/Nano-safety, Environmental Science and Pharmacokinetics
28 Aug 2019
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Abstract:
The OpenRiskNet project (https://openrisknet.org/) is funded by the H2020-EINFRA-22-2016 Programme and its main objective is the development of an open e-infrastructure providing data and software resources and services to a variety of industries requiring risk assessment (e.g. chemicals, cosmetic ingredients, pharma or nanotechnologies). The concept of case studies was followed in order to test and evaluate proposed solutions and is described in https://openrisknet.org/e-infrastructure/development/case-studies/. Two case studies, namely ModelRX and RevK, focus on modelling within risk assessment. The ModelRX – Modelling for Prediction or Read Across case study provides computational methods for predictive modelling and support of existing data suitability assessment. It supports final risk assessment by providing calculations of theoretical descriptors, gap filling of incomplete datasets. computational modelling (QSAR) and predictions of adverse effects. Services are offered through Jaqpot (UI/API), JGU WEKA (API), Lazar (UI) and Jupyter & Squonk Notebooks. In the RevK – Reverse dosimetry and PBPK prediction case study, physiologically based pharmacokinetic (PBPK) models are made accessible for the purpose of risk assessment-relevant scenarios. The PKSim software, the httk R package and custom-made PBPK models have been integrated. RevK offers services through Jaqpot (UI/API).
The OpenRiskNet project (https://openrisknet.org/) is funded by the H2020-EINFRA-22-2016 Programme and its main objective is the development of an open e-infrastructure providing data and software resources and services to a variety of industries requiring risk assessment (e.g. chemicals, cosmetic ingredients, pharma or nanotechnologies). The concept of case studies was followed in order to test and evaluate proposed solutions and is described in https://openrisknet.org/e-infrastructure/development/case-studies/. Two case studies, namely ModelRX and RevK, focus on modelling within risk assessment. The ModelRX – Modelling for Prediction or Read Across case study provides computational methods for predictive modelling and support of existing data suitability assessment. It supports final risk assessment by providing calculations of theoretical descriptors, gap filling of incomplete datasets. computational modelling (QSAR) and predictions of adverse effects. Services are offered through Jaqpot (UI/API), JGU WEKA (API), Lazar (UI) and Jupyter & Squonk Notebooks. In the RevK – Reverse dosimetry and PBPK prediction case study, physiologically based pharmacokinetic (PBPK) models are made accessible for the purpose of risk assessment-relevant scenarios. The PKSim software, the httk R package and custom-made PBPK models have been integrated. RevK offers services through Jaqpot (UI/API).
Related services:
Nano-QSAR to predict cytotoxicity of metal and metal oxide nanoparticles
Lazar Toxicity Predictions
Nano-QSAR to predict cytotoxicity of metal and metal oxide nanoparticles
Lazar Toxicity Predictions
Target audience: Risk assessors, Researchers, Students, Nanosafety community, Data modellers, Bioinformaticians
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC, JGU, NTUA, UU
Demonstration on OpenRiskNet approach on modelling for prediction or read across (ModelRX case study)
25 Jun 2019
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Related services:
Lazar Toxicity Predictions
Lazar Toxicity Predictions
Target audience: Risk assessors, Researchers, Data modellers, Bioinformaticians
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: NTUA
Deploying Applications to an OpenRiskNet Virtual Environment
25 Jun 2019
←
Related services:
Lazar Toxicity Predictions
Lazar Toxicity Predictions
Target audience: Developers, Software developers
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: IM
Deploying Applications to an OpenRiskNet Virtual Environment
24 Jun 2019
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Additional materials:
Slides
Slides
Related services:
Lazar Toxicity Predictions
Lazar Toxicity Predictions
Publisher: OpenRiskNet
Target audience: Developers, Software developers
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: IM
Demonstration on OpenRiskNet approach on modelling for prediction or read across (ModelRX case study)
24 Jun 2019
←
Additional materials:
Slides
Slides
Related services:
Lazar Toxicity Predictions
Lazar Toxicity Predictions
Publisher: OpenRiskNet
Target audience: Risk assessors, Researchers, Students, Data modellers, Bioinformaticians
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: NTUA
How to describe OpenRiskNet services and their functionality by semantic annotation
15 May 2019
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Related services:
Lazar Toxicity Predictions
Lazar Toxicity Predictions
Publisher: OpenRiskNet
Target audience: Developers, Software developers
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC
How to describe OpenRiskNet services and their functionality by semantic annotation
13 May 2019
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Additional materials:
Slides
Slides
Related services:
Lazar Toxicity Predictions
Lazar Toxicity Predictions
Target audience: Developers, Software developers, Data providers
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC
WP4 Service Integration
13 Dec 2018
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Additional materials:
WP4 presentation
WP4 presentation
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC, JGU, CRG, UM, UoB, NTUA, Fraunhofer, UU, VU, IM, INERIS
Report of the Service Integration with OpenRiskNet - Initial Deployment (Deliverable 4.1)
12 Nov 2018
→ doi: 10.5281/zenodo.1484309
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Abstract:
Tis report describes the status of selection of services of high priority for the OpenRiskNet infrastructure and their integration including active services provided by the consortium, associated partners and other third parties.
Tis report describes the status of selection of services of high priority for the OpenRiskNet infrastructure and their integration including active services provided by the consortium, associated partners and other third parties.
Related services:
Lazar Toxicity Predictions
Lazar Toxicity Predictions
Publisher: OpenRiskNet
Target audience: Risk assessors, Developers, Data modellers
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC, JGU, UM, NTUA, IM
Initial API version provided to providers of services (Deliverable 2.2)
7 Nov 2018
→ doi: 10.5281/zenodo.1479444
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Abstract:
This document reports the work towards the first version of the OpenRiskNet application programming interfaces (APIs) to be released to all partners of the consortium and associated partners for feedback and usage. Based on the diversity of the requirement foreseeable when developing the case studies to validate the infrastructure with real-world applications across all areas of predictive toxicology and risk assessment, a bottom-up approach to start with existing APIs and then harmonize them and bring them collectively to higher levels by integrating richer scientific annotation (semantic interoperability layer) was adopted in contrast to a top-down approach, where the API specification is defined by the consortium first and then all services have to be changed to comply to this specification.
This document reports the work towards the first version of the OpenRiskNet application programming interfaces (APIs) to be released to all partners of the consortium and associated partners for feedback and usage. Based on the diversity of the requirement foreseeable when developing the case studies to validate the infrastructure with real-world applications across all areas of predictive toxicology and risk assessment, a bottom-up approach to start with existing APIs and then harmonize them and bring them collectively to higher levels by integrating richer scientific annotation (semantic interoperability layer) was adopted in contrast to a top-down approach, where the API specification is defined by the consortium first and then all services have to be changed to comply to this specification.
Publisher: OpenRiskNet
Target audience: Developers, Software developers
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC, JGU, UM, NTUA, UU, IM, INERIS
Report on deployment of virtual infrastructures with service discovery and container orchestration (Deliverable 2.3)
7 Nov 2018
→ doi: 10.5281/zenodo.1479475
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Abstract:
This report documents the Demonstrator for the Deliverable 2.3, describing the deployment of virtual infrastructure and applications making up the OpenRiskNet Virtual Research Environment (VRE). It outlines the system analysis, deployment fundamentals, service discovery, and a list of the currently available services. The production reference instance is deployed on the Swedish Science Cloud (SSC), and end user access is available at https://home.prod.openrisknet.org.
This report documents the Demonstrator for the Deliverable 2.3, describing the deployment of virtual infrastructure and applications making up the OpenRiskNet Virtual Research Environment (VRE). It outlines the system analysis, deployment fundamentals, service discovery, and a list of the currently available services. The production reference instance is deployed on the Swedish Science Cloud (SSC), and end user access is available at https://home.prod.openrisknet.org.
Related services:
Lazar Toxicity Predictions
Lazar Toxicity Predictions
Publisher: OpenRiskNet
Target audience: Developers, Software developers
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC, JGU, UM, NTUA, UU, IM