Research projects at LJMU including the PBK modelling dataset
At LJMU our research includes curating in silico resources that may be beneficial to researchers who use in silico tools such as (quantitative) structure activity relationships, read-across or PBK modelling, to make predictions regarding the activity, toxicity, ADME properties or kinetics of chemicals of interest.
In 2021 we published a PBK Modelling Dataset (PMD); this comprises an Excel spreadsheet, using a controlled vocabulary, to catalogue PBK models published prior to 2021. 7,541 models for 1,150 unique chemicals are listed, along with summary information for the models.
Information includes: species, sex, life-stage, route of administration, software platform employed, availability of model equations in the corresponding publication, chemical identifiers and physico-chemical properties.
The PMD is described in the publication, 'A Systematic Review of Published Physiologically-based Kinetic Models and an Assessment of their Chemical Space Coverage' by Thompson et al in 2021. Alternatives to Laboratory Animals (ATLA).
PBK Modelling dataset
The full PBK Modelling Dataset (PMD) is available as supplementary information from the publisher, as provided with the above article. The LJMU PBK Modelling Dataset is adapted from the data provided in the ATLA publication.
View or download a copy of the LJMU PBK Modelling Dataset (Excel, 976KB)
Future developments for the PMD
Ongoing research at LJMU (funded by The Humane Research Trust CIO) is developing an update to this dataset with PBK models published after 2021. Information on newer models will be made available as soon as possible.
If you have a published PBK model that you would like to be added to the LJMU PBK Modelling Dataset, please email details of the publication to Judith Madden.
Using the PBK modelling dataset
One application of the PBK Modelling Dataset, is to use data from an existing model (source) to inform the development of a PBK model for another chemical (target) in a read across approach.
To aid the process, researchers at LJMU developed a KNIME workflow to help identify similar chemicals in the PMD. The KNIME workflow, and an exemplary case study are available from the following publications:
- Using read-across to build physiologically-based kinetic models: Part 1. Development of a KNIME workflow to assist analogue selection for PBK modelling - ScienceDirect, by Thompson et al (2024) Computational Toxicology 29 100292.
- Using Read-Across to build Physiologically-Based Kinetic models: Part 2. Case studies for atenolol and flumioxazin - ScienceDirect by Thompson et al (2024). Computational Toxicology 29 100293.
These articles form part of the Virtual Special Issue on PBK Modelling in Computational Toxicology. Other articles are also available from the journal website.
Additional information on 'In Silico' resources
An introduction to 'in silico' methods for predicting chemical activity, toxicity, ADME and kinetic properties of chemicals is given in the following:
- 'A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications', Madden JC et al (2020). Alternatives to Laboratory Animamls48: 4, 146-172.
A summary of over 900 in silico toxicology databases to support read-across and (quantitative) structure activity relationships is available here:
- 'In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR', Pawar et al (2019). Front. Pharmacol.