#11thICCS Chad Allen on the Analysis of ToxCast & Tox21 cmpd set using GHS toxicity annotations and in-silico derived protein-target descriptors
Motivation: the need for tox data outstrips the output of traditional toxicology. In-silico methods can help.
Including heterogenous data can improve performance of tox models. Wanted to repeat approach of Alex Tropsha on a larger dataset.
Problem: generating the dataset with sufficient overlap of data domains.
Introducing GHS pictograms, which are derived from quantitative data. Categories are international standards. Rich source of data for tox annotations. Several public regulatory GHS data sources, e.g. ECHA.
Dataset of 3,336 cmpds. For each we had qHTS data, (plus 3 other things that I missed)
CAS numbers are required for looking up GHS data.
Used in-house target prediction tool: PIDGIN. Available on GitHub.
Used in-house target prediction tool: PIDGIN. Available on GitHub.
A lot of details on how the dataset was put together and cleaned up. In the end, was binned into toxic/non-toxic.
Shows nice plot that GHS classifications correlate with one another across sources. But different administration routes have low correlation, so focussed on oral toxicities after this point.
Compared to ToxAlerts. Number of alerts did not correlate with GHS prediction. But presence/absence of reactive/unstable/toxic ToxAlerts had modest correlation. Conclusion is that ToxAlerts probably shouldn't be used as a filter.
Classes appeared to have different intra and interclass similarity, and may be separable in chemical space. Similarly when distinguished in terms of protein target. Used LDA (linear discrim analysis) to try to separate.
Models are about as good as existing chemical descriptor based models, so why use? More interpretable. This is the subject of my current work.
