Development of an adverse drug event network to predict drug toxicity

Qier Wu; Olivier Taboureau; Karine Audouze

doi:10.1016/j.crtox.2020.06.001

Development of an adverse drug event network to predict drug toxicity

Curr Res Toxicol. 2020 Jun 11:1:48-55. doi: 10.1016/j.crtox.2020.06.001. eCollection 2020 Jun 10.

Authors

Qier Wu¹, Olivier Taboureau², Karine Audouze¹

Affiliations

¹ Université de Paris, T3S, Inserm UMR S-1124, F-75006 Paris, France.
² Université de Paris, BFA, CNRS UMR 8251, ERL Inserm U1133, CNRS UMR 8251, F-75013 Paris, France.

Abstract

Despite of their therapeutic effects, drug's exposure may have negative effects on human health such as adverse drug reaction (ADR) and side effects (SE). Adverse drug events (ADEs), that correspond to an event occurring during the drug treatment (i.e. ADR and SE), is not necessarily caused by the drug itself, as this is the case with medical errors and social factors. Due to the complexity of the biological systems, not all ADEs are known for marketed drugs. Therefore, new and effective methods are needed to determine potential risks, including the development of computational strategies. We present an ADE association network based on 90,827 drug-ADE associations between 930 unique drug and 6221 unique ADE, on which we implemented a scoring system based on a pull-down approach for prediction of drug-ADE combination. Based on our network, ADEs proposed for three drugs, safinamide, sonidegib, rufinamide are further discussed. The model was able to identify, already known drug-ADE associations that are supported by the literature and FDA reports, and also to predict uncharacterized associations such as dopamine dysregulation syndrome, or nicotinic acid deficiency for the drugs safinamide and sonidegib respectively, illustrating the power of such integrative toxicological approach.

Keywords: ADE, adverse drug event; ADR, adverse drug reaction; AOP, adverse outcome pathway; Adverse event network; Computational toxicology; FAERS, FDA Adverse Event Reporting System; FDA, Food and Drug Administration; HMS-PCI, high-throughput mass spectrometric protein complex identification; LRT, Likelihood Ratio Test; MedDRA, Medical Dictionary for Regulatory Activities; Network science; PPAN, protein-protein association network; PT, Preferred Term; Predictive toxicity; QSAR, Quantitative structure-activity relationships; SE, side effect; SOC, System Organ Class; System toxicology; TAP–MS, tandem-affinity-purification method coupled to mass spectrometry; pullS, pull-down score; wS, weighted score.