Predictability of drug-induced liver injury by machine learning

• Published in: Biology direct Vol. 15; no. 1; pp. 3 - 10
• Main Authors: Chierici, Marco, Francescatto, Margherita, Bussola, Nicole, Jurman, Giuseppe, Furlanello, Cesare
• Format: Journal Article
• Language: English
• Published: London BioMed Central 13.02.2020; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Accuracy; Adverse drug reactions; Algorithms; Bioinformatics; Biomedical and Life Sciences; Biotechnology; Cancer; Classification; CMap; Correlation coefficient; Correlation coefficients; Data analysis; Deep learning; Diagnosis; DILI; Drug development; Experiments; Gene expression; Health risk assessment; Hepatotoxicity; Information management; Learning algorithms; Life Sciences; Liver; Liver diseases; Machine learning; Microarray; Multilayers; Pharmacovigilance; Proceedings of the Critical Assessment of Massive Data Analysis (CAMDA) Satellite Meeting to ISMB 2018; Product safety; Risk factors; Tumor cell lines; Italy; United States > US; Deep learning; DILI; Microarray; CMap; Classification
• ISSN: 1745-6150; 1745-6150
• DOI: 10.1186/s13062-020-0259-4

Background Drug-induced liver injury (DILI) is a major concern in drug development, as hepatotoxicity may not be apparent at early stages but can lead to life threatening consequences. The ability to predict DILI from in vitro data would be a crucial advantage. In 2018, the Critical Assessment Massive Data Analysis group proposed the CMap Drug Safety challenge focusing on DILI prediction. Methods and results The challenge data included Affymetrix GeneChip expression profiles for the two cancer cell lines MCF7 and PC3 treated with 276 drug compounds and empty vehicles. Binary DILI labeling and a recommended train/test split for the development of predictive classification approaches were also provided. We devised three deep learning architectures for DILI prediction on the challenge data and compared them to random forest and multi-layer perceptron classifiers. On a subset of the data and for some of the models we additionally tested several strategies for balancing the two DILI classes and to identify alternative informative train/test splits. All the models were trained with the MAQC data analysis protocol (DAP), i.e. , 10x5 cross-validation over the training set. In all the experiments, the classification performance in both cross-validation and external validation gave Matthews correlation coefficient (MCC) values below 0.2. We observed minimal differences between the two cell lines. Notably, deep learning approaches did not give an advantage on the classification performance. Discussion We extensively tested multiple machine learning approaches for the DILI classification task obtaining poor to mediocre performance. The results suggest that the CMap expression data on the two cell lines MCF7 and PC3 are not sufficient for accurate DILI label prediction. Reviewers This article was reviewed by Maciej Kandula and Paweł P. Labaj.