Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms

Boland, Mary Regina; Alexandra, Jacunski; Lorberbaum, Tal; Romano, Joseph D.; Moskovitch, Robert; Tatonetti, Nicholas P.

doi:10.1002/wsbm.1323

Cited by 42 publications

(55 citation statements)

References 111 publications

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“…This risk can be acceptable for treating terminal diseases or chronic life-threatening infections but not for slowly progressive diseases that are not fatal or overly burdensome during a patient's normal lifespan. To minimize such drug side effects, recent approaches in systems biology can be used to characterize the molecular features of disease models used in both the early and later stages of drug development [1, 2]. Moreover, these technologies can be adapted to improve our understanding of the pharmacology of a starting molecule with respect to its possible off-target effects and therapeutic potential.…”

Section: Introductionmentioning

confidence: 99%

Transcriptome profiling of NIH3T3 cell lines expressing opsin and the P23H opsin mutant identifies candidate drugs for the treatment of retinitis pigmentosa

Chen

Brooks

Gieser

et al. 2017

Pharmacological Research

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Mammalian cells are commonly employed in screening assays to identify active compounds that could potentially affect the progression of different human diseases including retinitis pigmentosa (RP), a class of inherited diseases causing retinal degeneration with compromised vision. Using transcriptome analysis, we compared NIH3T3 cells expressing wildtype (WT) rod opsin with a retinal disease-causing single P23H mutation. Surprisingly, heterologous expression of WT opsin in NIH3T3 cells caused more than a 2-fold change in 783 out of 16,888 protein coding transcripts. The perturbed genes encoded extracellular matrix proteins, growth factors, cytoskeleton proteins, glycoproteins and metalloproteases involved in cell adhesion, morphology and migration. A different set of 347 transcripts was either up- or down-regulated when the P23H mutant opsin was expressed suggesting an altered molecular perturbation compared to WT opsin. Transcriptome perturbations elicited by drug candidates aimed at mitigating the effects of the mutant protein revealed that different drugs targeted distinct molecular pathways that resulted in a similar phenotype selected by a cell-based high-throughput screen. Thus, transcriptome profiling can provide essential information about the therapeutic potential of a candidate drug to restore normal gene expression in pathological conditions.

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Section: Introductionmentioning

confidence: 99%

Transcriptome profiling of NIH3T3 cell lines expressing opsin and the P23H opsin mutant identifies candidate drugs for the treatment of retinitis pigmentosa

Chen

Brooks

Gieser

et al. 2017

Pharmacological Research

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“…Several approaches have been proposed for predicting drug side effects (for reviews see [4,5]) and can be roughly divided into two groups. The first group of methods exploits the network structure of the bipartite graph connecting drugs to side effects and networks built from drug or side effect side information.…”

Section: Introductionmentioning

confidence: 99%

The Geometric Sparse Matrix Completion Model for Predicting Drug Side effects

Galeano

Paccanaro

2019

Preprint

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Pair-input associations for drug-side effects are obtained through expensive placebocontrolled experiments in human clinical trials. An important challenge in computational pharmacology is to predict missing associations given a few entries in the drug-side effect matrix, as these predictions can be used to direct further clinical trials. Here we introduce the Geometric Sparse Matrix Completion (GSMC) model for predicting drug side effects. Our high-rank matrix completion model learns non-negative sparse matrices of coefficients for drugs and side effects by imposing smoothness priors that exploit a set of pharmacological side information graphs, including information about drug chemical structures, drug interactions, molecular targets, and disease indications. Our learning algorithm is based on the diagonally rescaled gradient descend principle of non-negative matrix factorization. We prove that it converges to a globally optimal solution with a first-order rate of convergence. Experiments on large-scale side effect data from human clinical trials show that our method achieves better prediction performance than six state-of-the-art methods for side effect prediction while offering biological interpretability and favouring explainable predictions.

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“…Conceptual representation in biomedicine is common for various purposes (Moskovitch et al, 2004; Boland et al, 2015). Representing multivariate temporal data using (conceptual) symbolic time intervals is becoming more common in the data mining literature too.…”

Section: Introductionmentioning

confidence: 99%

Prognosis of Clinical Outcomes with Temporal Patterns and Experiences with One Class Feature Selection

Moskovitch

Choi

Hripcsak

et al. 2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Accurate prognosis of outcome events, such as clinical procedures or disease diagnosis, is central in medicine. The emergence of longitudinal clinical data, like the Electronic Health Records (EHR), represents an opportunity to develop automated methods for predicting patient outcomes. However, these data are highly dimensional and very sparse, complicating the application of predictive modeling techniques. Further, their temporal nature is not fully exploited by current methods, and temporal abstraction was recently used which results with symbolic time intervals represetnation. We present Maitreya, a framework for the prediction of outcome events that leverages these symbolic time intervals. Using Maitreya, learn predictive models based on the temporal patterns in the clinical records that are prognostic markers and use these markers to train predictive models for eight clinical procedures. In order to decrease the number of patterns that are used as features we propose the use of three one class feature selection methdos. We evaluate the performance of Maitreya under several parameter settings, including the one-class feature selection, and compare our results to that of atemporal approaches. In general, we found that the use of temporal patterns outperformed the atemporal methods, when representing the number of patterns occurrences.

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Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms

Cited by 42 publications

References 111 publications

Transcriptome profiling of NIH3T3 cell lines expressing opsin and the P23H opsin mutant identifies candidate drugs for the treatment of retinitis pigmentosa

Transcriptome profiling of NIH3T3 cell lines expressing opsin and the P23H opsin mutant identifies candidate drugs for the treatment of retinitis pigmentosa

The Geometric Sparse Matrix Completion Model for Predicting Drug Side effects

Prognosis of Clinical Outcomes with Temporal Patterns and Experiences with One Class Feature Selection

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