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Published on: May 6, 2019
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Computational prediction of immune cell cytotoxicity
Anna K Schrey1, Janette Nickel-Seeber1, Malgorzata N Drwal1
1Charité - University Medicine Berlin, Institute for Physiology and ECRC, Berlin, Germany.
Summary
This study presents a computational method for screening chemical immunotoxicity. The approach uses machine learning to predict adverse immune system effects, offering a faster, in-vitro alternative to traditional testing.
Area of Science:
- Toxicology
- Computational Chemistry
- Immunology
Background:
- Immunotoxicity assessment is crucial for drug and chemical approval.
- Current in-vivo and ex-vivo methods are time-consuming and resource-intensive.
- There is a need for efficient in-vitro and in-silico alternatives.
Purpose of the Study:
- To develop and validate a computational approach for initial immunotoxicity screening.
- To provide an easy-to-use tool for assessing new chemical entities.
- To reduce reliance on traditional animal testing methods.
Main Methods:
- Utilized molecular fingerprints to describe chemical structures.
- Employed a machine-learning model based on the Naïve-Bayes algorithm.
- Trained the model on NCI database blood-cell growth inhibition data and validated with external datasets.
Main Results:
- Achieved areas under the receiver operator curves (ROC/AUC) of 75% or higher in cross-validations and external validations.
- Demonstrated excellent specificities and fair to excellent selectivities in classifying immunotoxicity.
- The model accurately identifies compounds with a high probability of immunotoxicity.
Conclusions:
- The developed computational approach serves as a reliable initial immunotoxicity screen.
- Compounds predicted as immunotoxic can proceed to further testing with high confidence.
- Non-immunotoxic predictions require further investigation in a multistep screening process.
