Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity.

Scientific Reports
Laura-Jayne GardinerRitesh Krishna

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 uncertainti...Continue Reading

References

May 25, 2007·Toxicological Sciences : an Official Journal of the Society of Toxicology·Nadine ZidekPhilip G Hewitt
Oct 11, 2011·Journal of Cheminformatics·Noel M O'BoyleGeoffrey R Hutchison
Dec 18, 2013·Alternatives to Laboratory Animals : ATLA·Jarrod BaileyMichael Balls
Jul 30, 2014·Alternatives to Laboratory Animals : ATLA·Jarrod BaileyMichael Balls
Dec 30, 2014·ILAR Journal·Richard M A Parker, William J Browne
Oct 3, 2015·Genome Research·Cole Trapnell
May 7, 2016·Bioinformatics·Zichen WangAvi Ma'ayan
Jun 7, 2018·Scientific Reports·Sonu KashyapJoseph P Grande
Jul 25, 2018·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Ran SuLeyi Wei
Nov 28, 2018·Nature Methods·Rachelly NormandShai S Shen-Orr
Mar 18, 2019·Microbiome·Will Pm RoweMartyn D Winn
Feb 9, 2020·Archives of Toxicology·Sebastian CanzlerJörg Hackermüller

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Citations

Aug 7, 2021·Proceedings of the National Academy of Sciences of the United States of America·Laura-Jayne GardinerAnthony Hall
Aug 12, 2021·Computational and Structural Biotechnology Journal·Raihan RafiqueJulhash U Kazi
Jun 19, 2021·Current Drug Delivery·Maithri H ShanbhogueHemanth Kumar Somareddy

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Methods Mentioned

BETA
RNA-seq
PCA

Software Mentioned

ExtraTreesRegressor
ShuffleSplit
XGBoost
Open Babel
LightGBM

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