Improving the prediction of organism-level toxicity through integration of chemical, protein target and cytotoxicity qHTS data
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Prediction of compound toxicity is essential because covering the vast chemical space requiring safety assessment using traditional experimentally-based, resource-intensive techniques is impossible. However, such prediction is nontrivial due to the complex causal relationship between compound structure and in vivo harm. Protein target annotations and in vitro experimental outcomes encode relevant bioactivity information complementary to chemicals’ structures. This work tests the hypothesis that utilizing three complementary types of data will afford predictive models that outperform traditional models built using fewer data types. A tripartite, heterogeneous descriptor set for 367 compounds was comprised of (a) chemical descriptors, (b) protein target descriptors generated using an algorithm trained on 190 000 ligand–protein interactions from ChEMBL, and (c) descriptors derived from in vitro cell cytotoxicity dose–response data from a panel of human cell lines. 100 random forests classification models for predicting rat LD50 were built using every combination of descriptors. Successive integration of data types improved predictive performance; models built using the full dataset had an average external correct classification rate of 0.82, compared to 0.73–0.80 for models built using two data types and 0.67–0.78 for models built using one. Pairwise comparisons of models trained on the same data showed that including a third data domain on top of chemistry improved average correct classification rate by 1.4–2.4 points, with p-values <0.01. Additionally, the approach enhanced the models’ applicability domains and proved useful for generating novel mechanism hypotheses. The use of tripartite heterogeneous bioactivity datasets is a useful technique for improving toxicity prediction. Both protein target descriptors – which have the practical value of being derived in silico – and cytotoxicity descriptors derived from experiment are suitable contributors to such datasets.
Date Issued
2016-03-03
Date Acceptance
2016-03-01
Citation
Toxicology Research, 2016, 5 (3), pp.883-894
ISSN
2045-4538
Publisher
Royal Society of Chemistry
Start Page
883
End Page
894
Journal / Book Title
Toxicology Research
Volume
5
Issue
3
Copyright Statement
This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.
License URL
Subjects
Science & Technology
Life Sciences & Biomedicine
Toxicology
IN-SILICO
RISK-ASSESSMENT
21ST-CENTURY
PERFORMANCE
TOXICOLOGY
MODELS
ENDOTHELIN-1
DATABASES
SELECTION
SCIENCE
Publication Status
Published