Keywords

machine learning, classification, cell lines, drug development, precision medicine

Abstract

Background

Drug-induced liver injury (DILI) is a serious concern during drug development and the treatment of human disease. The ability to accurately predict DILI risk could yield significant improvements in drug attrition rates during drug development, in drug withdrawal rates, and in treatment outcomes. In this paper, we outline our approach to predicting DILI risk using gene-expression data from Build 02 of the Connectivity Map (CMap) as part of the 2018 Critical Assessment of Massive Data Analysis CMap Drug Safety Challenge.

Results

First, we used seven classification algorithms independently to predict DILI based on gene-expression values for two cell lines. Similar to what other challenge participants observed, none of these algorithms predicted liver injury on a consistent basis with high accuracy. In an attempt to improve accuracy, we aggregated predictions for six of the algorithms (excluding one that had performed exceptionally poorly) using a soft-voting method. This approach also failed to generalize well to the test set. We investigated alternative approaches—including a multi-sample normalization method, dimensionality-reduction techniques, a class-weighting scheme, and expanding the number of hyperparameter combinations used as inputs to the soft-voting method. We met limited success with each of these solutions.

Conclusions

We conclude that alternative methods and/or datasets will be necessary to effectively predict DILI in patients based on RNA expression levels in cell lines.

Original Publication Citation

Sumsion GR†, Bradshaw III MS†, Beales JT†, Ford E†, Caryotakis GRG†, Garrett DJ†, LeBaron ED†, Nwosu IO‡, Piccolo SR*. Diverse approaches to predicting drug-induced liver injury using gene-expression profiles. Biology Direct, 2020, 15:1

Document Type

Peer-Reviewed Article

Publication Date

2020-01-15

Publisher

BioMed Central

Language

English

College

Life Sciences

Department

Biology

University Standing at Time of Publication

Associate Professor

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