Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity.
Sci Rep
; 10(1): 9522, 2020 06 12.
Article
in En
| MEDLINE
| ID: mdl-32533004
ABSTRACT
During the development of new drugs or compounds there is a requirement for preclinical trials, commonly involving animal tests, to ascertain the safety of the compound prior to human trials. Machine learning techniques could provide an in-silico alternative to animal models for assessing drug toxicity, thus reducing expensive and invasive animal testing during clinical trials, for drugs that are most likely to fail safety tests. Here we present a machine learning model to predict kidney dysfunction, as a proxy for drug induced renal toxicity, in rats. To achieve this, we use inexpensive transcriptomic profiles derived from human cell lines after chemical compound treatment to train our models combined with compound chemical structure information. Genomics data due to its sparse, high-dimensional and noisy nature presents significant challenges in building trustworthy and transparent machine learning models. Here we address these issues by judiciously building feature sets from heterogenous sources and coupling them with measures of model uncertainty achieved through Gaussian Process based Bayesian models. We combine the use of insight into the feature-wise contributions to our predictions with the use of predictive uncertainties recovered from the Gaussian Process to improve the transparency and trustworthiness of the model.
Full text:
1
Database:
MEDLINE
Main subject:
Gene Expression Profiling
/
Drug-Related Side Effects and Adverse Reactions
/
Machine Learning
/
Models, Theoretical
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Animals
/
Humans
Language:
En
Year:
2020
Type:
Article