JGU WEKA REST Service

Abstract:
Metabolites may well play an important role in adverse effects of parent drug or other xenobiotic compounds. In this case study VU (CS leader), HITeC/HHU (associate partner and implementation challenge winner), JGU, and UU have worked together on making methods and tools available for metabolite and site-of-metabolism (SOM) prediction. For that purpose we integrated and used ligand-based metabolism predictors (e.g. MetPred, enviPath, FAME, SMARTCyp) and we incorporated protein-structure and -dynamics based approaches to predict SOMs by Cytochrome P450 enzymes (P450s). P450s metabolise ~75% of the currently marketed drugs and their active-site shape and plasticity often play an important role in determining the substrate’s SOM. It is expected that this work will be continued after the end of the project to make services available for the prediction of microbial biotransformation pathways by integrating the enviPath data and software developed in part by JGU. During method development, model calibration and validation we used databases such as XMetDB and other open-access databases for drugs, xenobiotics and their respective metabolites. To facilitate the combined use of the metabolite prediction approaches and their outcomes, we benefited of ongoing development in workflow management systems and we made Jupyter Notebooks available to facilitate collection and visualization of results from the different available services. We illustrated the added value of having multiple predictors and our Jupyter notebooks available, in a pilot study on retrospective consensus predictions of known SOMs for drug compounds for which possible metabolite-associated toxicity was previously reported.

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).

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). We will present the WEKA machine learning services within the infrastructure and how they can be used to solve complex prediction tasks: the prediction of (i) half-life of chemicals under given environmental conditions and of (ii) nanoparticle transport behavior from physicochemical properties. For that purpose, we will reconstruct previous efforts using complex workflows and architectures and simplify the models while maintaining their prediction performance. In both cases, the overall problem (predicting the fate of a compound depending on its properties and external conditions) is modeled as a cascaded prediction model, where the prediction of one model is, with particular attention to validity and performance, entering another model as input. The approach performs well on the half-life data, while the nanoparticle data are too noisy and incomplete to warrant more than the most basic models. Overall, the reconstruction of the two applications within OpenRiskNet provides more evidence for the power and versatility of the framework.

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.

Abstract:
OpenRiskNet kicked off at Technology Park in Basel, Switzerland (on 15 and 16 December 2016). All partners were present on this two-day event.