Predicting adverse drug reactions of two‐drug combinations using structural and transcriptomic drug representations to train an artificial neural network. (16th October 2020)

• Record Type: Journal Article
• Authors: Shankar, Susmitha; Bhandari, Ishita; Okou, David T.; Srinivasa, Gowri; Athri, Prashanth
• Is Part Of: Chemical biology & drug design. Volume 97:Number 3(2021)
• Page Start: 665
• Page End: 673
• Publication Date: 2020-10-16
• Subjects: ADR -- adverse drug reaction -- artificial intelligence -- artificial neural network -- drug–drug interaction -- LINCS L1000 -- transcriptomic data -- TWOSIDES dataset; Drugs -- Design -- Periodicals; Pharmaceutical chemistry -- Periodicals; Biochemistry -- Periodicals; 615.19005
• DOI: 10.1111/cbdd.13802
• Languages: English
• ISSNs: 1747-0277

Abstract

Abstract: Adverse 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 multifold cross‐validation. Abstract : An artificial neural network that predicts adverse drug reactions originating from drug–drug interaction.