Pharmacogenomic and clinical data link non-pharmacokinetic metabolic dysregulation to drug side effect pathogenesis

By Daniel C. Zielinski, F. Filipp, Aarash Bordbar, Kasper Jensen, Jeffrey W. Smith, Markus J. Herrgård, Monica L. Mo, Bernhard O. Palsson. 2015

46 citations recorded by the source corpus.

Drug side effects cause a significant clinical and economic burden. However, mechanisms of drug action underlying side effect pathogenesis remain largely unknown. Here, we integrate pharmacogenomic and clinical data with a human metabolic network and find that non-pharmacokinetic metabolic pathways dysregulated by drugs are linked to the development of side effects. We show such dysregulated metabolic pathways contain genes with sequence variants affecting side effect incidence, play established roles in pathophysiology, have significantly altered activity in corresponding diseases, are susceptible to metabolic inhibitors and are effective targets for therapeutic nutrient supplementation. Our results indicate that metabolic dysregulation represents a common mechanism underlying side effect pathogenesis that is distinct from the role of metabolism in drug clearance. We suggest that elucidating the relationships between the cellular response to drugs, genetic variation of patients and cell metabolism may help managing side effects by personalizing drug prescriptions and nutritional intervention strategies. Adverse drug reactions are an important clinical problem. Here the authors combine information about drug-induced gene expression changes and genetic variability of patients with a genome-scale metabolic model to identify drug-induced changes in cellular metabolism that may be linked to drug side effects.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

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Citations in the corpus

The SIDER database of drugs and side effects (2015, methodology): “Thanks to the availability of downloadable files, SIDER has since been used in many studies, for example to identify metabolic dysregulation as a cause for ADRs (8), to investigate the effect of essential proteins on ADRs (9) and to predict drug indications (10).”
Recon3D: A Resource Enabling A Three-Dimensional View of Gene Variation in Human Metabolism (2018, unknown): “This data was used to guide and inform genome-scale constraint-based modeling analyses 42,43 to identify the metabolic pathways most perturbed in a given condition (Supplementary Figure 15).”