Abstract
The accurate prediction of new interactions between drugs is important for avoiding unknown (mild or severe) adverse reactions to drug combinations. The development of effective in silico methods for evaluating drug interactions based on gene expression data requires an understanding of how various drugs alter gene expression. Current computational methods for the prediction of drug-drug interactions (DDIs) utilize data for known DDIs to predict unknown interactions. However, these methods are limited in the absence of known predictive DDIs. To improve DDIs interpretation, a recent study has demonstrated strong non-linear (i.e., dose-dependent) effects of DDIs. In this study, we present a new unsupervised learning approach involving tensor decomposition (TD)-based unsupervised feature extraction (FE) in 3D. We utilize our approach to reanalyze available gene expression profiles for Saccharomyces cerevisiae. We found that non-linearity is possible, even for single drugs. Thus, non-linear dose-dependence cannot always be attributed to DDIs. Our analysis provides a basis for the design of effective methods for evaluating DDIs.
Highlights
In silico methods are thought to be effective strategies for improving the long, expensive process of drug discovery, in silico drug discovery is, at best, still under development (Rifaioglu et al, 2018; Vamathevan et al, 2019; Kazmi et al, 2019)
(2) We demonstrate that our tensor decomposition (TD)-based unsupervised feature extraction (FE) can replicate the findings of Lukačišin and Bollenbach (Lukacisin and Bollenbach, 2019) based on a principal component analysis (PCA) (Jolliffe and Cadima, 2016)
Because strong non-linear dependence was observed for both treatments, Non-linear dose dependence our analysis demonstrates that these effects are unlikely to reflect drug-drug interactions (DDIs)
Summary
In silico methods are thought to be effective strategies for improving the long, expensive process of drug discovery, in silico drug discovery is, at best, still under development (Rifaioglu et al, 2018; Vamathevan et al, 2019; Kazmi et al, 2019). In addition to the two main approaches for drug discovery, i.e., ligand-based drug discovery (Bacilieri and Moro, 2006; Pal et al, 2019; Robinson et al, 2020) and structure-based drug discovery (Batool et al, 2019; Taguchi, 2017; Lee et al, 2019), interest in gene expression profile-based drug discovery (Chengalvala et al, 2007) has recently increased. For this process, it is important to understand how drug treatments alter gene expression profiles.
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