The most common definition, provided by the WHO, for an adverse drug reaction (ADR) is "A response to a drug which is noxious and unintended and which occurs at doses normally used in man for prophylaxis, diagnosis, or therapy of disease, or the modification of physiological function or noxious and unintended responses to drugs, when they are administered in their normal recommended dosages." This study focuses on the prediction of ADRs caused by the drug-drug interaction (DDI) of two-drug combinations. Apart from contributing to various productive drug design strategies such as drug repurposing (Zhou et al., 2015), coadministered drugs can exhibit synergistic DDIs (Liu et al., 2017), which comprises a new ADR that may be associated with either of the drugs or the aggravation of an existing ADR. In this study, we have proposed an artificial neural network (ANN) that predicts this specific subclass of ADRs using transcriptomic data, compound chemical fingerprint, and GO ontologies.

AbstractAdverse drug reactions (ADRs) are pharmacological events triggered by drug interactions with various sources of origin including drug-drug interactions. While there are many computational studies that explore models to predict ADRs originating from single drugs, only a few of them explore models that predict ADRs from drug combinations. Further, as far as we know, none of them have developed models using transcriptomic data, specifically the LINCS L1000 drug induced gene expression data to predict ADRs for drug combinations. In this study we use the TWOSIDES database as a source of ADRs originating from two-drug combinations. 34,549 common drug pairs between these two databases were used to train an artificial neural network (ANN), to predict 243 ADRs that were induced by at least 10% of the drug pairs. Our model predicts the occurrence of these ADRs with an average accuracy of 82% across a multi fold cross validation.Source Code and input dataset used in this study can be found at: https://bitbucket.org/ishita98/prediction-of-adr/src/master/